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Reply to the reviewers
Manuscript number: RC-2024-02824
Corresponding author(s): Rita tewari
1. General Statements [optional]
We wish to thank the reviewers and the Editor for their constructive comments and valuable suggestions to improve our manuscript. We have addressed as far as possible all comments and concerns and we hope that this revised manuscript, with additional new data, will be acceptable for publication. Please find below detailed responses (red text) to all specific points raised by the reviewers
2. Point-by-point description of the revisions
This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *
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We would like to thank all the reviewers for using their valuable time to review our manuscript and to provide constructive comments and suggestions. We have now revised the manuscript taking their comments into consideration; our responses to these comments are detailed below (in red).
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Minor comments: In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?
We observed free kinetochores in the ALLAN-KO parasites with no associated spindle microtubules (see Fig. 6Gh), while kinetochores are attached to spindle microtubules in WT-GFP cells (see Fig. 6Gc). To provide further evidence we analysed additional images and found that ALLAN-KO cells have free kinetochores in the centre of nucleus, unattached to spindle microtubules. We provide some more images clearly showing free kinetochores in these cells (new supplementary Fig. S11).
However, in the ALLAN mutant, this difference is not absolute: in a search of over 50 cells, one example of a cell with a "normal" nuclear spindle and attached kinetochores was observed.
The use of serial sectioning has limitations for examining small structures like kinetochores in whole cells. The limitations of the various techniques (for example, SBF-SEM vs tomography) are highlighted in our previous study (Hair et al 2022; PMID: 38092766), and we consider that examining a population of randomly sectioned cells provides a better understanding of the overall incidence of specific features.
Discussion Section:
Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis?
We observed no phenotype in asexual blood stage parasites associated with the sun1 and allan gene deletions. Several other Plasmodium berghei gene knockout parasites with a phenotype in sexual stages, for example CDPK4 (PMID: 15137943), SRPK (PMID: 20951971), PPKL (PMID: 23028336) and kinesin-5 (PMID: 33154955) have no phenotype in blood stages, so perhaps this is not surprising. One explanation may be the substantial differences in the mode of cell division between these two stages. Asexual blood stages produce new progeny (merozoites) over 24 hours with closed mitosis and asynchronous karyokinesis during schizogony, while male gametogenesis is a rapid process, completed within 15 min to produce eight flagellated gametes. During male gametogenesis the nuclear envelope must expand to accommodate the increased DNA content (from 1N to 8N) before cytokinesis. Furthermore, male gametogenesis is the only stage of the life cycle to make flagella, and axonemes must be assembled in the cytoplasm to produce the flagellated motile male gametes at the end of the process. Thus, these two stages of parasite development have some very different and specific features.
Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement?
We observed the loops/folds in the nuclear envelope (NE) as revealed by SUN1-GFP and 3D TEM images during male gametogenesis. These folds/loops occur mainly in the vicinity of the nuclear MTOC where the spindles are assembled (as visualised by EB1 fluorescence) and attached to kinetochores (as visualised by NDC80 fluorescence). These loops/folds may form due to the contraction of the spindle pole back to the nuclear periphery, inducing distortion of the NE. Since there is no physical segregation of chromosomes during the three rounds of mitosis (DNA increasing from 1N to 8N), we suggest that these folds provide additional space for spindle and kinetochore dynamics within an intact NE to maintain separation from the cytoplasm (as shown by location of kinesin-8B).
In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodelling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?
ALLAN-GFP is expressed throughout the parasite life cycle but as the reviewer points out, a functional role is more pronounced during male gametogenesis. This does not mean that it has no role at other stages of the life cycle even if there is no obvious phenotype following deletion of the gene during the asexual blood stage. The fact that ALLAN is not essential during the asexual blood stage is noted in lines 628-29.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Introduction Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.
We have modified the sentence in which we discuss current opinions about 'open' and 'closed' mitosis. It is believed that there is no complete disassembly of the NE during open mitosis and no completely intact NE during closed mitosis, respectively. In fact, the NE plays a critical role in the different modes of mitosis during MTOC organisation and spindle dynamics. Please see the modified lines 64-71.
Results
Fig 7 is the final figure; but would be more useful upfront.
We have provided a new introductory figure (Fig 1) showing a schematic of conventional /canonical LINC complexes and evidence of SUN protein functions in model eukaryotes and compare them to what is known in apicomplexans.
Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.
We agree with the reviewer. Fixation with paraformaldehyde (PFA) results in a coalescence of the SUN1-GFP signal. We have also tried methanol fixation (see below, new Fig. S2), but a similar problem was encountered.
Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support.
These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.
We agree with the reviewer that whilst the live cell imaging showed an even distribution of SUN1-GFP signal, after fixation with either PFA or methanol, then SUN1-GFP puncta are observed in addition to the peripheral location around the stained DNA (Hoechst) (See the above figure; puncta are indicated by arrows). These SUN1-GFP labelled puncta were observed at the junction of the nuclear MTOC and the basal body (Fig. 2F). Quantification of the distribution showed that these SUN1-GFP puncta are associated with nuclear MTOC in more than 90 % of cells (18 cells examined). Live cell imaging of the dual labelled parasites; SUN1xkinesin-8B (Fig. 2H) and SUN1x EB1 (Fig. 2I) provides further support for the association of SUN1-GFP puncta with BB (kinesin-8B) /nuclear MTOC (EB1).
The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.
The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.
We have modified the sentence on line 213 to accommodate this suggestion.
Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.
We now provide the overlap at a pixel-by-pixel level for representative images, and we have quantified more cells (n>30), as documented in the new Fig. S4A, which is displayed below. We have also modified the sentence on line 219 to reflect these additions.
The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal.
We agree with the reviewer that the overlap between EB1 and Sun1 signals is expected to be minimal. We have quantified the data and included it in Supplementary Fig. S4A.
Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.
We agree with the reviewer's analysis that EB1 and NDC80 occupy overlapping locations on the spindle, although the length of NDC80 is less at the ends of spindles (see below Fig A) as shown in our previous study where we compared the locations of two spindle proteins, ARK2 and EB1, with that of NDC80 (Zeeshan et al, 2022; PMID: 37704606). In the present study we observed that Sun1-GFP partially overlaps with EB1 at the ends of the spindle, but not with NDC80. Please see Fig. B, below.
I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.
We appreciate the reviewer's correction; we have now modified the sentence to indicate that the protein is largely localized in the space between the INM and the ONM on line 617.
Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.
We agree with the reviewer's suggestion that the data indicate that contraction of spindle poles back to the nuclear periphery may induce distortion of the NE.
The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.
We thank the reviewer for this suggestion. The study by Sayers et al, (2024) was published while our manuscript was under preparation. It was interesting to see that these complementary studies have similar findings about the role of SUN1 and the novel complex of SUN1-ALLAN. Our study contains a more detailed, in-depth analysis both by Expansion and TEM of SUN1. We include additional studies on the role of ALLAN. We discuss the overlap in the findings of the two studies in lines 590-605.
While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".
We have toned down the wording of this conclusion in lines 665-677.
In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.
We agree with reviewer, and future studies will need to focus on identifying the KASH replacement that links SUN1 to the ONM.
It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?
We have deleted the sentence that raised this suggestion.
While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.
The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.
Following the reviewer's suggestion we have moved these data to the supplementary information (Fig. S5D-I) and toned down their discussion in the results and discussion sections.
The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."
In agreement with the reviewer's comments we have moved these data to supplementary information (Fig. S6) and substantially toned down the conclusions based on these findings.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Major comments: My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.
In more detail:
- The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.
As detailed above we have moved the lipidomics data to supplementary information (Fig. S6) and substantially toned down the discussion of these data in the results and discussion sections.
- I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.
As suggested by the reviewer we have removed the 30 min post activation data from the manuscript. We have also moved the rest of the transcriptomics data to supplementary information (Fig. S5) and toned down the presentation of this aspect of the work in the results and discussion sections.
- Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging.
We have now added videos to the manuscript as suggested by the reviewer. Please see the supplementary Videos S1 and S2.
In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.
It was difficult to provide SBF-SEM images as that work is beyond the scope of this manuscript. We will consider this approach in our future work. We re-examined many of our TEM images of SUN1-KO and ALLAN-KO parasites and did find some micrographs showing aberrant nuclear membrane folding ( - I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify
1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO,
DNA replication is not affected in the SUN1-KO and ALLAN-KO mutants: DNA content increases to 8N (data added in Fig. 3J and Fig. S10F).
2) the proportion of anucleated gametes in WT and the KO lines
We have added these data in Fig. 3K and Fig. S10G
3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript.
We have reanalysed the BB clustering phenotype and added the quantitative data in Fig. 4E and Fig. S7.
Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-Kos
We have examined many microgametes (100+ sections). In WT parasites a small proportion of gametes can appear to lack a nucleus if it does not extend all the way to the apical and basal ends (Hair et al. 2022). However, the proportion of microgametes that appear to lack a nucleus (no nucleus seen in any section) was much higher in the SUN1 mutant. In contrast, this difference was not as clear cut in the ALLAN mutant with a small proportion of intact (with axoneme and nucleus) microgametes being observed.
We have done additional analysis of male gametes, looking for the presence of the nucleus by live cell imaging after DNA staining with Hoechst. Please see the figure below. These data are added in Fig. 3K (for Sun1-KO) and S10G (for Allan-KO).
- The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.
Our observation and quantification of the data indicated that 100% of kinetochores were attached to spindle microtubules and that 0% were unattached kinetochores in the WT parasites. However, the exact opposite was found for the SUN1 mutant with 100% unattached kinetochores and 0% attached. The result was not quite as clear cut in the ALLAN mutant, with 98% unattached and 2% attached. An important observation was the lack of separation of the nuclear poles and any spindle formation. Spindle formation was never or very rarely observed in the mutants.
- Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?
We agree with the reviewer. Overall, the ALLAN-KO has a less prominent phenotype than that of the Sun1-KO. The main difference is that in the ALLAN-KO mutant some basal body segregation can occur, leading to the production of some fertile microgametocytes, and ookinetes, and oocyst formation (Fig. 8). Approximately 5% of oocysts sporulated to release infective sporozoites that could infect mice in bite back experiments and complete the life cycle. In contrast the Sun1-KO mutant made no healthy oocysts, or infective sporozoites, and could not complete the life cycle in bite back experiments. We have analysed the phenotype in detail and provide quantitative data for gametocyte stages by EM and ExM in Figs. 4 and S8 (SUN1) and Figs. 7 and S11 (ALLAN). We have also performed detailed analysis of oocyst and sporozoite stages and included the data in Fig. 3 (SUN1) and S10 (ALLAN).
Based on the location, and functional and interactome data, we think that SUN1 plays a central role in coordinating nucleoplasm and cytoplasmic events as a key component of the nuclear membrane lumen, whereas ALLAN is located in the nucleoplasm. Deleting the SUN1 gene may disrupt the connection between INM and ONM whereas the deletion of ALLAN may affect only the INM.
. Some additional points where the data is not entirely sound yet or could be improved:
- Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)?
High resolution SIM and expansion microscopy showed that the SUN1-GFP molecules coalesce to form puncta, in contrast to the more uniform distribution observed by live cell imaging. This apparent difference may be due to a better resolution that could not be achieved by live cell imaging. We agree with the reviewer that the two green foci are outside of the cell. As a negative control we have used WT-ANKA cells (which contain no GFP) and the anti-GFP antibody, which gave no signal. This confirms the specificity of the antibody (please see the new Fig. S3).
- The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation?
There was no difference in the fixation process for samples examined by SIM and ExM, but we used an anti-GFP antibody in ExM to visualise the SUN1-GFP, while in SIM the images of GFP signal were collected directly after fixation. We used both PFA and methanol as fixative, and both methods showed a coalescing of the SUN1-GFP signal (please see the new Fig. S2 and S3).
Can the authors trace their NE in ExM according to the NHS-Ester signal?
We could trace the NE in the ExM by the NHS-ester signal and observed that the SUN1-GFP signal was largely coincident with the NE (Please see the new Fig. S3B below).
- Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?
We have measured oocyst size (data added in new Fig. 3) and added the sporozoite quantification data in Fig. 3D.
- Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.
We have added the oocyst size data in Fig. S10. We mention the changes in sporozoite numbers (now shown in Fig. 7D, E), and refer to the bite back data shown in current Fig. 7E.
- Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?
We have provided the original blot showing the bands of ALLAN-GFP and SUN1-GFP. As a positive control, we used an RNA associated protein (RAP-GFP) that is highly expressed in Plasmodium and regularly used in our lab for this purpose.
- Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.
We have now performed the experiment of crossing the Sun1-KO parasite line with a male deficient line (Hap2-KO) and added the data in Fig. 3I. We have added images showing sporozoites in oocysts.
- In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?
We did generate a SUN1-mCherry line, with the intent to cross ALLAN-GFP and SUN1-mCherry lines and observe the co-location of the proteins. Despite multiple attempts this cross was unsuccessful. This may have been due to their close proximity such that the addition of both GFP and mCherry was difficult to facilitate a proper protein-protein interaction between either of the proteins.
- Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?
Approximately 67% of cells showed the clumping of BBs. We have now added the numbers in Figs. 6H and S11I.
- The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.
We have extended this discussion now in lines 590-605.
Suggestions how to improve the writing and data presentation.
- General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels).
Whilst we agree with the reviewer, sometimes it is difficult to see the features in the merged images. Therefore, we would like to request to be allowed to retain the colours, which can be easily followed in both individual and merged images.
Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so.
We apologise for this lack of consistency, and we have now added arrows wherever they are missing to harmonise in the presentations.
Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).
- Are the scale bars of all expansion microscopy images adjusted for the expansion factor?
Yes, the scale bars are adjusted accordingly.
- The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)
The figure legends have been streamlined, with removal of the description of results.
- Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?
They are expressed before activation, but the signal intensifies after activation. Images from before and after activation of gametocytes have been added in Fig. S1F.
- Line 267: Reference to the original nek4-KO paper missing
This reference is now included.
- Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.
We have moved these data to supplementary information and modified the text.
- Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.
We have corrected the text to the start of exflagellation.
- Line 588: Spelling mistake in SUN1-domain
Corrected.
- Line 726/731: i missing in anti-GFP
Corrected.
- Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.
Moved to right place
- Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"
Changed.
- Line 974, 976: please correct to WT-GFP and dsun1
Corrected.
- Line 1041, 1044: WT-GFP instead of WTGFP.
Corrected to WT-GFP.
- Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?
We have added the time points to the images in these figures.
- Fig 1D/Line 727: the scale of the scale bar on the inset is missing.
We have added the scale bar.
- Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.
We indicate now the number of cells analysed in individual figures and also in Fig. S5C and S8C, respectively.
- Fig 5B: What is NP
Nuclear Pole (NP), also known as the nuclear/acentriolar MTOC (Zeeshan et al 2022; PMID: 35550346).
- Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.
We have added the arrow.
- Fig S2C: Is the scale bar really the same for the zygote and the ookinete?
We have checked this and used the same for both zygote and ookinete.
- Fig S3C, S7C: which stages was qRT-PCR done on?
Gametocytes activated for 8 min.
- Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?
The bite back experiment was performed using 15-20 mosquitoes infected with WT-GFP and gene knockout lines to feed on one naïve mouse each, in three different experiments. We have now included the data points in the bar diagrams.
- Fig S3: Panel letters E and G are missing
We have updated the lettering in current Fig. S5
- Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?
We mention the boxes in the figure legends. It is rare to find unbound kinetochores in WT parasite. We provide large scale and zoomed-in images of free kinetochores in Fig. S8.
- Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins
We now describe the insets in the figure legends and have added scale bars to the zoomed-in images.
- Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?
We have now included the full image showing the inset (new Fig. S8).
- Fig S5C and S8C: Change "DNA" to "Nucleus".
We have changed "DNA" to "Nucleus". Now they are Fig. S8K and S11I.
Reviewer #3 (Significance (Required)):
Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.
We have moved the lipidomics and transcriptomics data to supplementary information and toned down the emphasis on these data to make the manuscript more focused on the cell biology and analysis of the genetic KO data.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In this manuscript, the authors investigate the function of the protein SUN1, a proposed nuclear envelope protein linking nuclear and cytoplasmic cytoskeleton, during the rapid male gametogenesis of the rodent malaria parasite Plasmodium berghei. They reveal that SUN1 localises to the nuclear envelope (NE) in male and female gametes, and show that the male NE has unexpectedly high dynamics during the rapid process of gametogenesis. Using expansion microscopy, the authors find that SUN1 is enriched at the neck of the bipartite MTOC that links the intranuclear spindle to the basal bodies of the cytoplasmic axonemes. They further show that upon deletion of SUN1, the basal bodies of the eight axonemes fail to segregate, no spindle is formed, and emerging gametes are anucleated, leading to a complete block in transmission. By interactomics they identify a divergent allantoicase-like protein, ALLAN, as a main interaction partner of SUN1 and further show that ALLAN deletion largely phenocopies the effect of SUN1. Overall, the work here reveals a new protein complex important for maintaining the structural integrity of the bipartite MTOC during the rapid rounds of endomitosis in male gametogenesis. In addition, the authors use transcriptomics and lipidomics to further characterise the effects of SUN1 deletion on gametogenesis and conclude that SUN1 is also required for lipid homeostasis and NE dynamics.
Major comments:
My main concern with this manuscript is that the authors do conclude not only that SUN1 is important for spindle formation and basal body segregation, but also that it influences for lipid metabolism and NE dynamics. I don't think the data supports this conclusion, for several reasons listed below. I would suggest to remove this claim from the manuscript or at least tone it down unless more supporting data are provided, in particular showing any change in NE dynamics in the SUN1-KO. Instead I would recommend to focus on the more interesting role of SUN1-ALLAN in bipartite MTOC organisation, which likely explains all observed phenotypes (including those in later stages of the parasite life cycle). In addition, some aspects of the knockout phenotype should be quantified to a bit deeper level.
In more detail:
- The lipidomics analysis is clearly the weakest point of the manuscript: The authors state that there are significant changes in some lipid populations between WT and sun1-KO, and between activated and non-activated cells, yet no statistical analysis is shown and the error bars are quite high compared to only minor changes in the means. For some discussed lipids, the result text does not match the graphs, e.g. PA, where the increase upon activation is more pronounced in the SUN1-KO vs WT (contrary to the text), or MAG, which is reduced in the SUN1-KO vs WT (contrary to the text). I don't see the discussed changes in arachidonic acid levels and myristic acid levels in the data either. Even if the authors find after analysis some statistically significant differences between some groups, they should carefully discuss the biological significance of these differences. As it is, I do not think the presented data warrants the conclusion that deletion of SUN1 changes lipid homeostasis, but rather shows that overall lipid homeostasis is not majorly affected by gametogenesis or SUN1 deletion. As a minor comment, if you decide to keep the lipidomics analysis in the manuscript, please state how many replicates were done.
- I can't quite follow the logic why the authors performed transcriptomic analysis of the SUN1 and how they chose their time points. Their data up to this point indicate that SUN1 has a structural or coordinating role in the bipartite MTOC during male gametogenesis. Based on that it is rather unlikely that SUN1 KO directly leads to transcriptional changes within the 8 min of exflagellation. Isn't it more likely that transcriptional differences are purely a downstream effect of incomplete/failed gametogenesis? This is particularly true for the comparison at 30 min, which compares a mixture of exflagellated/emerged gametes and zygotes in WT to a mixture of aberrant, arrested gametes in the knockout, which will likely not give any meaningful insight. The by far most significant GO-term is then also nuclear-transcribed mRNA catabolic process, which is likely not related at all to SUN1 function (and the authors do not even comment on this in the main text). I would therefore suggest removing the 30 min data set from this manuscript. As a minor point, I would suggest highlighting some of the top de-regulated gene IDs in the volcano plots and stating their function. Also, please state how you prepared the cells for the transcriptomes and in how many replicates this was done.
- Live-cell imaging of SUN1-GFP does nicely visualise the NE during gametogenesis, showing a highly dynamic NE forming loops and folds, which is very exciting to see. It would be beneficial to also show a video from the life-cell imaging. In their discussion, the authors state multiple times that NE dynamics are changed upon SUN1 KO. Yet, they do not provide data supporting this claim, i.e. that the extended loops and folds found in the nuclear envelope during gametogenesis are affected in any way by the knockout of SUN1 or ALLAN. What happens to the NE in absence of SUN1? Are there less loops and folds? In absence of a reliable NE marker this may not be entirely easy to address, but at least some SBF-SEM images of the sun1-KO gametocytes could provide insight.
- I think the exciting part of the manuscript is the cell biological role of SUN1 on male gametogenesis, which could be carved out a bit more by a more detailed phenotyping. Specifically it would be good to quantify 1) if DNA replication to an octoploid state still occurs in SUN1-KO and ALLAN-KO, 2) the proportion of anucleated gametes in WT and the KO lines and 3) a quantification of the BB clustering phenotype (in which proportion of cells do the authors see this phenotype). This could be addressed by simple fixed immunofluorescence images of the respective WT/KO lines at various time points after activation (or possibly by reanalysis of the already obtained images) and would really improve the manuscript. Especially the claim that emerged SUN1-KO gametes lack a nucleus is currently only based on single slices of few TEM cells and would benefit from a more thorough quantification in both SUN1- and ALLAN-KOs
- The TEM suggests that in the SUN1-KO, kinetochores are free in the nucleus. Are all kinetochores free or do some still associate to a (minor/incorrectly formed) spindle? The authors could address this by tagging NDC80 in the KO lines.
- Finally, I think it is curious that in contrast to SUN1, ALLAN seems to be less important, with some KO parasite completing the life cycle. Maybe a more detailed phenotyping as above gives some more hints to where the phenotypic difference between the two proteins lies. I would assume some ALLAN-KO cells can still segregate the basal body. Can the authors speculate/discuss in more detail why these two proteins seems to have slightly different phenotypes?
Minor comments:
Some additional points where the data is not entirely sound yet or could be improved:
- Localisation of SUN1: There seems to be a discrepancy between SUN1-GFP location as observed by live cell microscopy, and by Expansion Microscopy (ExM), similar for ALLAN-GFP. By live-cell microscopy, the SUN1 localisation is much more evenly distributed around the NE, while the localisation in ExM is much more punctuated, and e.g. in Figure 1E seems to be within the nucleus. Do the authors have an explanation for this? Also, in Fig. 1D there are two GFP foci at the cell periphery (bottom left of the image), which I would think are not SUN1-Foci, as they seem to be outside of the cell. Is the antibody specific? Was there a negative control done for the antibody (WT cells stained with GFP antibodies after ExM)? - The authors argue that SIM gave unexpected results due to PFA fixation leading to collapse of the NE loops. However, they also fix their ExM cells and their EM cells with PFA and do not observe a collapse, at least from what I see in the two presented images and in the 3D reconstruction. Is there something else different in the sample preparation? Can the authors trace their NE in ExM according to the NHS-Ester signal?
- Fig 2D: It would be good to not just show images of oocysts but actually quantify their size from images. Also, have the authors determined the sporozoite numbers in SUN1-KO?
- Line 481-483: the authors state that oocyst size is reduced in ALLAN-KO but do not show the data. Please quantify oocyst size or at least show representative images. Also the drastic decrease in sporozoite numbers (Fig. 6D, E) is not mentioned in the text. Please add reference to Fig S7D when talking about the bite back data.
- Fig S1C, 6C: Both WB images are stitched, but this is not clearly indicated e.g. by leaving a small gap between the lanes. Also please show a loading control along with the western blots. Also there seems to be a (unspecific?) band in the control, running at the same height as Allan-GFP WB. What exactly is the control?
- Regarding the crossing experiment: The authors conclude from this cross that SUN1 is only needed in males, yet for this conclusion they would need to also show that a cross with a female line does not rescue the phenotype. The authors should repeat the cross with a male-deficient line to really test if the phenotype is an exclusively male phenotype. In addition, line 270-272 states that no oocysts/sporozoites were detected in sun1-ko and nek4-ko parasites. However, the figure 2E shows only oocysts, not sporozoites, and shows also that sun1-ko does form oocysts, albeit dead ones.
- In Fig S1 the authors show that they also generated a SUN1-mCherry line, yet they do not use it in any of the presented experiments (unless I missed it). Would it be beneficial to cross the SUN1-mCherry line with the Allan1-GFP line to test colocalisation (possibly also by expansion microscopy)?
- Line 498: "In a significant proportion of cells" - What was the proportion of cells, and what does significant mean in this context?
- The authors should discuss a bit more how their work relates to the work of Sayers et al. 2024, which also identified the SUN1-ALLAN complex. The paper is cited, but only very briefly commented on.
Suggestions how to improve the writing and data presentation.
- General presentation of microscopy images: Considering that large parts of the manuscript are based on microscopy data, their presentation could be improved. Single-channel microscopy images would benefit from being depicted in gray scale instead of color, which would make it easier to see the structures and intensities (especially for blue channels). Also, it would be good to harmonize in which panels arrows are shown (e.g. Fig 1G, where some white arrows are in the SUN1-GFP panel, while others are in the merge panel, but they presumably indicate the same thing.). At the same time, Fig 1H doesn't have any with arrows, even though the figure legend states so. Fig 3A and S4 show the same experiment but are coloured in different colours (NHS-Eester in green vs grey scale).
- Are the scale bars of all expansion microscopy images adjusted for the expansion factor?
- The figure legends would benefit from streamlining, as they have very different style between figures (eg Fig. 6 which has a concise figure legend vs microscopy figures where figure legends are very long and describe not only the figure but the results)
- Line 155-156: The text makes it sound like the expression only happens after activation. is that the case? Are these images activated or non-activated gametocytes?
- Line 267: Reference to the original nek4-KO paper missing
- Line 301: The reference to Figure 2J seems to be a bit arbitrarily placed. Also, this schematic of lipid metabolism is never discussed in relation to the transcriptomic or lipidomic data.
- Line 347-349 states that gametes emerged, but the referenced figure shows activated gametocytes before exflagellation.
- Line 588: Spelling mistake in SUN1-domain
- Line 726/731: i missing in anti-GFP
- Line 787-789: statement of scale bar and number of cells imaged is not at the right position in the figure legend.
- Line 779, 783: "shades of green" should be just "green". Same goes for line 986, 989 with "shades of grey"
- Line 974, 976: please correct to WT-GFP and dsun1
- Line 1041, 1044: WT-GFP instead of WTGFP.
- Fig 1B, D, E, Fig S1G, H: What are the time points of imaging?
- Fig 1D/Line 727: the scale of the scale bar on the inset is missing.
- Fig 3 E-G and 6H-J: Please indicate total number of cells/images analysed per quantification, either in the graphs themselves or in the figure legend.
- Fig 5B: What is NP?
- Fig S1B/D: The legend states that there is an arrow indicating the band, but there is none.
- Fig S2C: Is the scale bar really the same for the zygote and the ookinete?
- Fig S3C, S7C: which stages was qRT-PCR done on?
- Fig. S3D, S7D: According to the figure legend, three independent experiments were performed. How many mice were used per experiment? It would be good to depict the individual data points instead of the bar graph. For S7D, 3 data points are depicted (one in WT, two in allan-KO), what do they mean?
- Fig S3: Panel letters E and G are missing
- Fig 3D: Please indicate what those boxes are. I presume that these are the insets show in b, e and j, but it is never mentioned. J is not even larger than i. Also, f is quite cropped, it would be good to see the large-scale image it comes from to see where in the nucleus these kinetochores are placed. Were there unbound kinetochores found in WT?
- Fig S4: Insets are not mentioned in the figure legend. Please add scale bar to zoom-ins
- Fig S5A, B: Please indicate which inset belongs to which sub-panel. Where does Ac stem from?
- Fig S5C and S8C: Change "DNA" to "Nucleus".
Significance
This study uses extensive microscopy and genetics to characterise an unusual SUN1-ALLAN complex and provides new insights into the molecular events during Plasmodium male gametogenesis, especially how the intranuclear events (spindle formation and mitosis) are linked to the extranuclear, cytoplasmic formation of the axonemes. While it could be more extensive, the phenotypic characterisation of the mutants reveals an interesting phenotype, showing that SUN1 and ALLAN are localised to and maintain the neck region of the bipartite MTOC. The authors here confirm and expand the previous knowledge about SUN1 in P. berghei (as published by Sayers et al., 2024), adding more detail to its localisation and dynamics, and further characterise the interaction partner ALLAN. Yet, the statement that SUN1 is also important for lipid homoeostasis and NE dynamics is currently not backed up by sufficient data. I believe that the manuscript would benefit from removing the less convincing transcriptomic and lipidomic datasets and rather focus on more deeply characterising the cell biology of the knockouts. This way, the results would be interesting not only for parasitologists, but also for more general cell biologists.
My expertise lies within the cell biology of malaria parasites, especially during early transmission stages.
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Referee #2
Evidence, reproducibility and clarity
This manuscript by Zeeshan et al describes the organisation of SUN1 during the rapid closed mitosis of male Plasmodium gametocytes and the consequences of knockout of the SUN1 gene for male gamete formation and oocyst development.
SUN (Sad1, UNC84-domain) proteins have been shown, in other studies, in other organisms, to be part of a bridging complex (LINC) that links cytoplasm-located structural elements with nuclear structures. They are anchored in the inner nuclear envelope and present a C-terminal SUN domain into the space between nuclear envelope (NE) inner and outer membranes. In humans, the SUN domain interacts with the outer NE-embedded KASH (Klarsicht, ANC-1, Syne Homology)-protein, which in turn binds to the cytoskeletal components, including the centrosome.
Introduction
Line 63: The authors stat: "NE is integral to mitosis, supporting spindle formation, kinetochore attachment, and chromosome segregation..". Seemingly at odds, they also say (Line 69) that 'open' "mitosis is "characterized by complete NE disassembly". The authors could explain better the ideas presented in their quoted review from Dey and Baum, which points out that truly 'open' and 'closed' topologies may not exist and that even in 'open' mitosis, remnants of the NE may help support the mitotic spindle.
Results
Fig 7 is the final figure; but would be more useful upfront. The authors compared the sequence of SUN1, ALLAN, KASH proteins and lamins across apicomplexans, and Arabidopsis and humans. They note that plasmodium has two SUN domain proteins. Plasmodium SUN1 has the same orientation as in human SUN1 with the C-terminal SUN domain into the space between nuclear envelope (NE) inner and outer membranes. In agreement with previous reports, no KASH-like or lamin proteins were identified.
Fig 1D. The authors generated a C-terminal GFP-tagged SUN1 transfectants and used ultrastructure expansion microscopy (U-ExM) and structured illumination microscopy (SIM) to examine SUN1-GFP in male gametocytes post-activation. The immuno-labelling of SUN1-GFP in these fixed cells appears very different to the live cell images of SUN1-GFP. The labelling profile comprises distinct punctate structures (particularly in the U-ExM images), suggesting that paraformaldehyde fixation process, followed by the addition of the primary and secondary antibodies has caused coalescing of the SUN1-GFP signal into particular regions within the NE.
Given these fixation issues, the suggestion that the SUN1-GFP signal is concentrated at the BB/ nuclear MTOC and "enriched near spindle poles" needs further support. These statements seem at odd with the data for live cell imaging where the SUN1-GFP seems evenly distributed around the nuclear periphery. Can the observation be quantitated by calculating the percentage of BB/ nuclear MTOC structures with associated SUN1-GFP puncta? If not, I am not convinced these data help understand the molecular events.
The authors then generated dual transfectants and examined the relative locations of different markers in live cells. These data are more informative.
The authors state; " ..SUN1-GFP marked the NE with strong signals located near the nuclear MTOCs situated between the BB tetrads". The nuclear MTOCs are not labelled in this experiment. The SUN1-GFP signal between the kinesin-8B puncta is evident as small puncta on regions of NE distortion. I would prefer to not describe this signal as "strong". The signal is stronger in other regions of the NE.
Line 219. The authors state; "..SUN1-GFP is partially colocalized with spindle poles as indicated by EB1,.. it shows no overlap with kinetochores (NDC80)." The authors should provide an analysis of the level of overlap at a pixel by pixel level to support this statement.
The SUN1 construct is C-terminally GFP-tagged. By analogy with human SUN1, the C-terminal SUN domain is expected to be in the NE lumen. That is in a different compartment to EB1, which is located in the nuclear lumen (on the spindle). Thus, the overlap of signal is expected to be minimal. Similarly, given that EB1 and NDC80 are known to occupy overlapping locations on the spindle, it seems unlikely that SUN1 can overlap with one and not the other.
I note on Line 609, the authors state "Our study demonstrates that SUN1 is primarily localized to the nuclear side of the NE.." As per Fig 7D, and as discussed above, the bulk of the protein, including the SUN1 domain, is located in the space between the INM and the ONM.
Interestingly, as the authors point out, nuclear membrane loops are evident around EB1 and NDC80 focal regions. The data suggests that the contraction of the spindle pole back to the nuclear periphery induces distortion of the NE.
The authors generate Δsun1 parasites and showed that a functional sun1 gene is required for male gamete formation and subsequent oocyst development.
In a very impressive set of micrographs (Fig 3), the authors used U-ExM and TEM to show that spindle formation is severely disrupted, and BB fail to segregate in Δsun1 gametocytes. Axoneme elongation occurs but the axenomes are inconnected to BBs and nuclear spindles.
The authors undertook immunoprecipitation (IP) experiment using a nanobody that recognises SUN1-GFP in lysates of purified activated gametocytes.
They identified several nuclear pore proteins, as well as the allantoicase-like protein (ALCC1/ ALLAN). They reverse-immunoprecipitated ALLAN-GFP from lysates of activated gametocytes and identified SUN1 and its interactors, DDRGK-domain containing protein and kinesin-15. This is an important finding.
The authors used AlphaFold to predict potential complexes of SUN1 and ALLAN. A complex is predicted between the plasmodium-specific N-terminal domain of SUN1. The authors conclude that ALLAN is located in the nuclear lumen and is involved in linking SUN1 to nuclear components.
The authors generated a line expressing ALLAN-GFP. In activated male gametocytes, ALLAN-GFP rapidly relocates to focal points at the nuclear periphery that correlated with the nuclear MTOCs (spindle poles). This is another important finding.
Δallan mutants exhibit a very similar phenotype to the Δsun1 parasites. Activated male gametocyte exhibited clustered BB, with incomplete segregation and misalignment relative to the nuclear MTOCs. TEM data is consistent with the author's conclusion that "ALLAN is critical for the alignment of spindle microtubules with kinetochores and BB segregation."
Taken together these results are consistent with the suggestion that SUN1 and ALLN proteins play an important structural role in linking the nuclear spindle of P. berghei male gametocytes to the BB and axonemes.
These are important findings. The author should discuss further the overlap of findings of this study with that from a recent manuscript (https://doi.org/10.1016/j.cels.2024.10.008). That Sayers et al. study identified a complex of SUN1 and ALLC1 as essential for male fertility in P. berghei. Sayers et al. also provide evidence that this complex particulate in the linkage of the MTOC to the NE and is needed for correct mitotic spindle formation during male gametogenesis.
While the work is interesting, the conclusions may need to be tempered. The authors suggestion that in the absence of KASH-domain proteins, the SUN1-ALLAN complex forms a non-canonical LINC complex (that is, a connection across the NE), that "achieves precise nuclear and cytoskeletal coordination".
In other organisms, KASH interacts with the C-terminal domain on SUN1, which as mentioned above is located between the INM and ONM. By contrast, ALLAN interacts with the N-terminal domain of SUN1, which is located in the nuclear lumen. The SUN1-ALLAN interaction is clearly of interest, and ALLAN might replace some of the roles of lamins. However, the protein that functionally replaces KASH (i.e. links SUN1 to the ONM) remains unidentified.
It may also be premature to suggest that the SUN1-ALLAN complex is promising target for blocking malaria transmission. How would it be targeted?
While the above datasets are interesting and internally consistent, there are two other aspects of the manuscript that need further development before they can usefully contribute to the molecular story.
The authors undertook a transcriptomic analysis of Δsun1 and WT gametocytes, at 8 and 30 min post-activation, revealing moderate changes (~2-fold change) in different genes. GO-based analysis suggested up-regulation of genes involved in lipid metabolism. Given the modest changes, it may not be correct to conclude that "lipid metabolism and microtubule function may be critical functions for gametogenesis that can be perturbed by sun1 deletion." These changes may simply be a consequence of the stalled male gametocyte development.
The authors have then undertaken a detailed lipid analysis of the Δsun1 and WT gametocytes, before and after activation. Substantial changes in lipid metabolites might not be expected in such a short period of time. And indeed, the changes appear minimal. Similarly, there are only minor changes in a few lipid sub-classes between Δsun1 and WT gametocytes. In my opinion, the data are not sufficient to support the authors conclusion that "SUN1 plays a crucial role, linking lipid metabolism to NE remodelling and gamete formation."
Significance
While the work is interesting, the conclusions may need to be tempered. Datasets are interesting and internally consistent. The aspects of manuscript and conclusion derived from transcriptomic and the lipidomic analysis, however, need further development before they can usefully contribute to the molecular story.
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Referee #1
Evidence, reproducibility and clarity
Summary: The study explores the role of the SUN1-ALLAN complex in Plasmodium berghei, identifying it as a unique mediator of nuclear envelope (NE) remodeling and microtubule-organizing center (MTOC) coordination during the rapid closed mitosis of male gametogenesis. The authors demonstrate that SUN1, a nuclear envelope protein, and ALLAN, a novel allantoicase-like protein, form a non-canonical complex. This complex bridges chromatin and cytoskeletal interactions, compensating for the lack of canonical LINC components like KASH-domain proteins and lamins in Plasmodium. Using lipidomics, mass spectrometry, RNA-seq, and advanced imaging methods like ultrastructure expansion microscopy (U-ExM), they reveal that disruption of this complex results in impaired spindle assembly, basal body segregation, and kinetochore attachment. This leads to defective, anuclear flagellated gametes incapable of fertilization. Furthermore, SUN1 deletion affects lipid metabolism, emphasizing its role in maintaining NE homeostasis. The study sheds light on a highly specialized adaptation for rapid mitotic division in Plasmodium, providing insights into NE and MTOC evolution and identifying potential targets for malaria transmission-blocking strategies.
The authors have utilized an impressive array of techniques, including lipidomics, mass spectrometry, RNA sequencing, and diverse microscopy approaches, to characterize the role of SUN1 deletion during male gametogenesis in Plasmodium.
Minor comments:
In the results section (lines 498-499), the authors describe free kinetochores in many cells without associated spindle microtubules. However, some nuclei appear to have kinetochores, as presented in Figure 6. Could the authors clarify how this conclusion was derived using transmission electron microscopy (TEM) without serial sectioning, as this is not explicitly mentioned in the materials and methods?
Discussion Section:
Could the authors expand on why SUN1 and ALLAN are not required during asexual replication, even though they play essential roles during male gametogenesis? Lines 611-613 states: "These loops serve as structural hubs for spindle assembly and kinetochore attachment at the nuclear MTOC, separating nuclear and cytoplasmic compartments." Could the authors elaborate on the evidence supporting this statement? In lines 621-622, the authors suggest that ALLAN may have a broader role in NE remodeling across the parasite's lifecycle. Could they reflect on or remind readers of the finding that ALLAN is not essential during the asexual stage?
Significance
General assessment:
The introduction is well-constructed, providing a clear and comprehensive overview of the current understanding of closed mitosis in protozoa and how it differs in Plasmodium parasites. The results are presented clearly and without overstatement, allowing readers to follow the logical progression of the study.
The knockout (KO) and rescue experiment for Neck4 was particularly innovative, effectively demonstrating the absence of male gametocytes in the SUN1 KO line.
Impact: This study uncovers how malaria parasites orchestrate one of the fastest cell division processes in biology during male gametogenesis, a critical step for disease transmission. By identifying a novel protein complex, the SUN1-ALLAN axis, that links the nuclear envelope to the machinery organizing cell division, we reveal a unique solution evolved by the parasite to achieve rapid and precise chromosome segregation. This discovery sheds light on how these parasites overcome the lack of proteins commonly found in other organisms, using an entirely distinct strategy to sustain their lifecycle. The findings not only deepen our understanding of the cellular innovations in malaria parasites but also open new avenues for interventions targeting the processes essential for parasite survival and transmission. These insights could contribute to the development of next-generation strategies to combat a disease that continues to impact millions worldwide.
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Reply to the reviewers
Reviewer #1
Evidence, reproducibility and clarity
Summary:
- In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.*
- To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background.*
- Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features.*
- Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.*
- Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo.*
- However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.*
Major comments:
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It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice? The previous studies referred to in the manuscript (ref 21 and 23) are both using wt mice of younger age, 3.5 and 3 weeks respectively. It is known that age influences immune status, and some of the experiments in these previous studies are performed in even younger animals (3 to 8 days suckling mice) likely for this specific reason. The different mice strains in these studies may also influence their susceptibility to infection.
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*While LGTV and LGTVT:prME fails to result in symptomatic infection in wt mice in our study, a certain level of localized infection is likely taking place and the outcome will depend on the immune status of the animals (age/immune deficiencies). What we tried to highlight in the manuscript was that the relative pathogenicity (TBEV/LGTV The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)
Thank you for pointing out the lack of clarity. All the tropism studies, figure 4 and 5, were done in adult WT mice infected i.c. to allow the virus to surpass the initial barrier of peripheral immune response and establish infection in the brain. We have now stressed this in the result section and in the relevant figure legends.
Minor comments:
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Figures need some re-working:*
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Figure 1 :
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1D : only the difference between TBEV and LGTVT:prME is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.* Thank you for this suggestion. However, as there is no statistical difference between LGTV and ChLGTV in Fig 1D we have maintained the figure as originally made.
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Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected. Thank you, this has been corrected in the figure.
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Figure 3 : Route of infection could be added to figure labels for more clarity. Thank you, we have added this to the figure.
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Figure 4A : Labelling the Mock panel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy. We agree that adding these labels improves the clarity and accessibility of the figure and have added this to 4A.
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Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend. Thank you, we have added the explanation that microglia were stained by Iba1 and increased the size of the images in Figure 4. Additionally, co-staining of viral antigens and the neuronal marker UCHL-1 has been added as the new Figure 4E and Iba-1 staining moved to 4F.
Significance
Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. * The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain. * However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.
We agree that studying tropism in MAVS-/- mice might be misleading and that is why the whole tropism study was performed in adult WT mice, we have clarified in the text that these data are from WT mice. In addition to the significance of this study in highlighting the respective contribution of structural proteins and the immune response in shaping tropism, this study also provides a __well-characterized chimeric virus __with a safety profile comparable to LGTV while retaining key structural and antigenic features of TBEV, model that has already helped advance studies on flavivirus receptor interactions and structural dynamics.
Reviewer #2
Evidence, reproducibility and clarity
In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, * pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralization using serum of TBEV vaccinated individuals.*
General comment: * In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.*
Thank you for these very relevant comments and suggestions. In line with these, we have now added a later timepoint (8 days) for LGTV:prME in IPS1-/- mice to better understand the kinetics of the chimeric virus at later time points (Figure 3). Additionally, we have added a neuronal marker in figure 4. The explanation of quantification of the fluorescence data is described in detail in the material and method, where the concept of this arbitrary unit (AU) used for quantification is described.
Specific points: * • Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....*
We agree here and have specified this accordingly.
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Line 70 - Low pathogenicity in which species? All? Humans? The sentence refers to mice as there has not been any human clinical case with LGTV. We have added that to the text.
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Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient Corrected accordingly.
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Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs? We have clarified in the text that this is referring to similarity to the pathogenicity of LGTV.
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Line 95: Good to mention in the text the cell type in which the foci are seen We agree, this information has been added to the main text in addition to the figure legend.
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Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before) We compare the structure of our chimeric virus with the previously published Kuutsalo-14 strain (ref 25). The use of 93/783 in this study is to ensure the same strain of TBEV is used as was used to generate LGTV:prME and to compare the chimeric virus to infectious clones of the parental viruses rescued and passaged in the same way as the chimeric virus itself to ensure differences observed is indeed due to the genetic factors.
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Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation. Thank you for bringing this to our attention. The figure 3E is displaying data for MAVS-/- mice infected with 10^5 FFU, where the some animals meet end-point criteria already around day 7-9. To address this comment, we have added an additional timepoint at day 8 (seven animals) to explore the trend in viral loads in the brain. However, we refrain from analyzing later time points as this would require a high number of starting animals to ensure adequate numbers surviving to e.g. the suggested day 14, which is not in line with RRR.
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Interestingly, there is not significant increase in viral loads of LGTV:prME infected animals between day 6 and 8. In line with this, IF imaging analysis of brains from later end-point animals (day 10-14) has shown limited staining of viral antigen in the brain (data not included in manuscript but could be provided to reviewers if requested). This suggests that inflammation is driving the pathology in these animals rather than uncontrolled viral replication. This has also been noted in the text. The tropism and imaging is done in WT mice infected i.c.. and the time/infectious dose has been adjusted to ensure similar clinical manifestation as presented in supplemental Figure 2A. These mice are then euthanized around day 5-6 and processed for brain imaging, line 189.
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Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2). Viral serum titers have been analyzed previously in MAVS-/- mice in Kurhade et al 2016, and they are high at day 2 and go down to almost detection limit day 4, meaning earlier drop than in peripheral organ and was not included in these experiments. For neutralization, the included time points for the experiments in Figure 3E-H the time points are too short for robust detection of IgG antibody responses.
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Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV? We appreciate the comment and we have clarified that WT mice are i.c. inoculated. Since we wanted to compare the three viruses, we needed to use an inoculation route that is working for all three viruses. While the tropism after peripheral infection of TBEV is a very interesting question, it remains outside the scope of this study as this cannot be compared with LGTV in WT mice.
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Figure 4B - What could account for the large variation seen in the TBEV group? This is a very good question that is difficult to answer. Although these are inbred mice, we have previously seen that there are differences in infection rate between different mice using whole brain imaging (Chotiwan et al 2023).
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Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above. Thank you for this suggestion we have added a neuronal marker. We have also clarified in the text that we confirm the infection pattern in rhinal cortex with confocal microscopy. Microglia activation has been added to the discussion.
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Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses? Quantification of microglial activation is sensitive to imaging quality and area of imaging and requires large sample sets to ensure validity in the conclusions. Here we do not observe any clear differences nor claim that the microglia activation is different between the different viral strains.
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Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F? Thank you for pointing this out the Figure 4D should be Figure 4E, this has been corrected.
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Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A. Thank you we have now removed the mock, LGTV and LGTVT:prME from fig 5C to more clearly communicate the message of difference in infection between GM and WM for TBEV specifically.
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Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5? The quantification of OPT in figure 5C is not at the level of single cell resolution but rather virus signal over mock. We agree the cells in the cerebellum has different sizes but we do not claim that the Purkinje layer is more infected compared to the granular cell layer, only that Purkinje cells are infected which is relevant in human TBE.
NS5 antibody is raised against a peptide in the TBEV NS5 protein which is highly conserved. The aa identity between TBEV and LGTV is 93%, we have not seen a difference in the staining between the different viruses using this antibody.
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Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal? We have added "as measured by stronger fluorescent signal" to better explain what we mean with higher infection.
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Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus. We have changed the wording to infected cells.
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Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here? We were unsure about the specific concern raised by the reviewer in this comment and, therefore, have not made any changes. If the reviewer could clarify their request, we would be happy to address it accordingly.
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Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x Thank you for this suggestion we have added this point into the discussion.
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Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis? We have now added some information about cerebellum symptoms in human TBE infection to the discussion.
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Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination" Thank you for these suggestions we have now added these aspects in the conclusions.
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Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing. Thank you for this suggestion. We have added a section describing the statistics in the methods.
Significance
Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.
- Expertise: arboviruses, imaging, neurovirulence, animal models*
- Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.*
Reviewer #3 (Evidence, reproducibility and clarity (Required)): * SUMMARY: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism. *
MAJOR COMMENTS: * The conclusions are supported by the data.*
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However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition. We thank the reviewer for this suggestion. In line with this we have now added Ifnb1 and Rsad2 RNA levels in different peripheral organs and we see that early on in infection most mice infected with LGTVT:prME show higher upregulation of these genes. These data have been added as a new panel F and G in figure 3.
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Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E. The rational for not using the C protein of TBEV is that we did not want to reduce the RNA to C interaction which, could affect the packaging or encapsidation. In line with this, previous research on chimeric flaviviruses has shown that exchanging the prM-E proteins are usually well tolerated while exchanging the C-protein may lead to attenuation or even failure to rescue the virus.
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Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment. A549 cells were used in the spread and kinetic study because it is an IFN competent cell type which TBEV and LGTV grows well in. The structural studies were performed in A549 MAVS cells because the lack of MAVS results in higher virus titers. The ability of these cells to produce large amount of virus while grown without serum greatly facilitated the purification protocols for cry-EM and mass spectrometry analysis. This has been highlighted in the text of both the material and method and very briefly in the result.
The pathogenicity with peripheral infection can only be done with MAVS-/- mice as they are more sensitive to LGTV and it is a lethal model. Adult WT mice are resistant to LGTV infection i.p.. As the immune response is important in shaping the tropism, a direct comparison of the viruses is best analyzed in a WT mouse model.
MINOR COMMENTS:
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- Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.* We have removed recombinant.
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Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I All experiments in figure 2 are performed in A549 MAVS-/- as highlighted in the material and methods.
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Line 158 - to be change "Fig 2I" with "fig 2J" Corrected
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Line 159 - as above: fig 2J to be change with figure 2k Corrected
*Significance: *
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The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses. *
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My field of expertise: virus-host interaction, at molecular level.*
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Referee #3
Evidence, reproducibility and clarity
Summary: The authors generated an LGTV chimeric virus harboring the prM and ectodomain of E from TBEV. Aim of the study is to understand how the virals structural proteins influence the distribution and tropism of the virus in the brain. They solved the atomic structures of LGTV and the chimeric virus demonstrating that the chimeric virus is structurally and antigenically similar to TBEV. In vivo experiments demonstrate that the chimeric virus is less pathogenic than LGTV. Finally using 3D whole brain OPT imaging techniques the authors demonstrate that the three viruses show a similar viral distribution in cerebral cortex with the rhnial cortex being the primary site of cortical infection for all viruses. In general TBEV exhibit higher infection rates and is more widespread in the brain, particularly in cerebellum, compared to LGTV and the chimeric virus. The authors concluded that the distribution and tropism of LGTV and TBEV are not solely dependent on receptor tropism.
Major Comments: The conclusions are supported by the data.
However, I think the work can be improved if the authors investigate the differences in the antiviral response induced by the chimeric virus compared to LGTV. The authors speculate that the non-structural proteins may play a role in shaping tropism, likely through their immunomodulating role. These data become especially important if you consider that in the experiments of fig 1 the chimeric virus behave similar to the LGTV wt with even an advantage in cell-to-cell spread but in the in vivo experiments with MAVS-/- mice the chimeric virus behave differently, being less pathogenic than LGTV suggesting that the chimeric virus could not escape the antiviral response even in MAVS-/- condition.
Moreover, in the discussion, line 270 the authors speculate that the observed attenuation could also be due to sub-optial interactions between TBEV prM and C and transmembrane domain of LGTV E. I think it is important to explain and justify why they decided to do not include C protein of TBEV in the chimeric virus, as well as the transmembrane domain of E.
Finally, the authors first used A549 cells for studying the kinetics and viral spread of the chimeric virus in vitro. Than they switch to A549-/- cells for studying structure and antigenicity. The different pathogenicity was assessed in Mavs-/- mice but lastly they used mice WT for the 3D whole brain OPT imaging. I found this discrepancy confusing. The authors should justify, including the explanation in the text, why they switch from WT to A549-/- from experiment to experiment.
Minor comments:
Line 96 - "recombinant parental LGTV" and "recombinant TBEV", the word recombinant is misused in the sentence.
Line 143-144-145 - I believe the authors are referring to Fig 2I and not 2H as written. Moreover, the authors should clarify if all the experiemtns of fig 2 have been performed in A549-/- cells or only the one of fig 2I
Line 158 - to be change "Fig 2I" with "fig 2J"
Line 159 - as above: fig 2J to be change with figure 2k
Significance
The authors designed a chimeric low pathogenic model virus to study the importance of the structural proteins in determing viral tropism and pathogenicity. The strengths of this work is that they combined the use of the chimeric virus with in vivo experiments and 3D whole brain OPT imaging. Integrating together these tools and assays the authors provided an example of complete investigation method for studying neuroinvasive viruses.
My field of expertise: virus-host interaction, at molecular level.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript entitled "The influence of the pre-membrane and envelope proteins on structure, pathogenicity and tropism of tick-borne encephalitis virus" Ebba Rosendal and colleagues present a wealth of data regarding generation and characterisation of a chimeric LGTV virus with TBEV structural proteins, comparing this virus to both LGTV and TBEV across a number of different basic and advanced readouts. They present interesting data regarding the ability of the LGTV-TBEV chimera to spread cell-cell, and the prolonged survival of immunocompromised mice compared with LGTV, which the authors associate with reduced replication in the periphery. As well as an overall increased ability of TBEV to replicate in vitro, and lead to mortality in WT mice in vivo, TBEV was found to be able to infect the cerebellum, whilst this region was rarely infected by LGTV and the chimera. The authors also demonstrate the cross-reactivity of these three viruses via neutralisation using serum of TBEV vaccinated individuals.
General comment:
In general, I am impressed by the amount of work and breadth of techniques included in this manuscript, which I think speaks to the benefit of multidisciplinary collaboration. However, in my opinion, some points are lacking. My primary concerns lie with the in vivo experiments. The comparison of LGTV and the chimera at the same timepoints isn't ideal as the shift in mortality means these animals are at a different stage of disease at different time points. Whilst this is interesting in itself, it leaves questions about viral titres and tropism of i.p. inoculated animals at end points, in addition to the exclusion of serum titre analysis, the strength of discussion regarding peripheral replication and its potential impact on neuroinvasion/virulence is weakened. Further, claims of neuronal infection are made in figure 4 in total absence of a neuron marker. If the authors wish to claim cell-specific tropism, the cell-specific markers must be included. For figures dependent upon fluorescent imaging, further clarification as to what the AU axes indicate would aid in better interpretation of the data, especially regarding comparison of cerebellar layers for TBEV infection (described in more detail in my specific comments). Finally, In general, I think some opportunities are missed to describe the big picture of potential applicability/impact/translatability of the results obtained, especially the conclusions can be expanded to better highlight this.
Specific points:
- Line 67: "It" is a bit of a shaky antecedent - assumedly the authors are referring to tropism, but would be good to state this, as they could also be referring to the underlying mechanisms of pathology. i.e. Tropism is determined by....
- Line 70 - Low pathogenicity in which species? All? Humans?
- Line 79 - Strange wording - "and which viral factors influence tropism" is sufficient
- Line 82 - What does "low pathogenic" mean in this context? Good survivability? No clinical signs?
- Line 95: Good to mention in the text the cell type in which the foci are seen
- Line 133 - What is the rationale for the different TBEV strains used? (Kuutsalo-14 here but 93/783 before)
- Line 175/Figure 3 - Why these time points and not later ones for the LGTV chimera? I understand the early time points for replication in the periphery, but would also be good to see brain titres around day 14 when the survival of the chimera inoculated mice decreases quite rapidly. Further, imaging at timepoints at which mortality is somewhat comparable (meaning that virus is likely in the brain) would enable additional readouts to characterise neurovirulence such as cell death markers etc. and allow for a more solid comparative characterisation.
- Line 174-182/Figure 3 - Why were serum titres not included in these experiments? These would help to strengthen your argument. (also nice to look at neutralisation in this context, though maybe not essential thanks to your data in figure 2)
- Line 183 - Good to overtly state that this is via i.c. inoculation and the justification for use of this route, and that the mice are assumedly WT. I understand LGTV struggles to get to the brain in mice, but is this representative of how neurotropism looks in animals inoculated via a more "natural" route for TBEV?
- Figure 4B - What could account for the large variation seen in the TBEV group?
- Line 200-201 - This image doesn't answer the question of tropism, but contributes to that of microglial activation. A neuronal marker should be included to surmise the cell type infected, rather than using staining for a viral protein to indicate cell morphology/type. Also, the justification for use of the microglial marker over neuronal is lacking, especially as microglia are not mentioned anywhere in the discussion. Also, see suggestion regarding cell death markers above.
- Line 203/Figure 4E - Are these images quantifiable? Are any differences observed between the viruses?
- Line 210 - Bit strange to mention figure 4D again after figure 4E, and I also couldn't spot reference to figure 4F?
- Are both figures 5A and 5C required for the message you wish to get across? I would suggest either only use 5C or only include the white matter/grey matter comparison for TBEV, in combination with 5A.
- Figure 5D: does the method of quantification you use/the conclusions you arrive at account for cell size/number? The Purkinje cell bodies are very large and the virus signal in these cells looks saturated - however within the granular layer the nuclei are much smaller but have what seem like large foci of NS5 positivity. Though the overall signal is likely much lower, how does relative distribution look when you account for cell size/number or a binary positive/negative quantification? Relatedly, does the primary anti-NS5 antibody have the same affinity for both LGTV and TBEV NS5?
- Line 242: Please clarify what you mean by "higher infection" - higher titres? Higher fluorescent signal?
- Line 242: Can you really say anything about replication here? Infection, yes, but the AU readout and lack of multiple time points doesn't allow for much of an insight into replication, especially when TBEV was left out of the comparison in figure 3F, though even this did not look at live virus.
- Line 269-271: Exactly what I was wondering and maybe worth discussing a bit more - is there appropriate literature that you could cite here?
- Line 274-275: Also mosquito borne viruses. See nice paper related to impact of TBEV vaccination on ADE for mosquito borne flaviviruses. Very interesting and would increase the impact of this point. https://doi.org/10.1038/s41467-024-45806-x
- Line 290-291: Are clinical signs associated with cerebellar injury common for TBEV patients? i.e. does this have translatability to human disease and diagnosis?
- Line 308 conclusions; Your point about the potential use of the chimera for vaccine research/to understand cross-reactivity is worth reiterating here, and potentially something about "highlighting the role of non-structural proteins on tropism determination"
- Methods: whilst I realise the statistics are described in the figure legends, it is usually customary to include a short statistics section in the methods to indicate which program was used and why certain statistical tests were chosen, e.g. in figure 1 you use both parametric and non-parametric testing.
Significance
Broad ranging characterisation of a novel chimera which has potential applications for vaccine/cross-reactivity research and highlights a key role of non-structural proteins in the determination of viral fitness and tropism. Some limitations regarding cell-specific tropism and kinetics of neuroinvasion and neurovirulence. Likely of interest for basic researchers from range of disciplines within arbovirology.
Expertise: arboviruses, imaging, neurovirulence, animal models
Limited expertise: in-depth structural biology, therefore my comments on figure 2 are limited.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this study, authors investigate the impact of pre-membane (prM) and envelope (E) proteins of tick-borne encephalitis virus (TBEV) on viral distribution and tropism, mostly in the brain.
To do so, authors use high resolution imaging of whole mouse brain after infection by either LGTV, a low pathogenic orthoflavivirus also transmitted by ticks, TBEV, or TBEV/LGTV chimeric virus where prM and E of TBEV are inserted in a LGTV background. Structural and antigenic characterization of the chimeric virus reveal that it remains a low pathogenic virus exhibiting TBEV structural and antigenic features. Those viruses are then used to infect wt or mavs -/- mice and viral propagation / tropism is explored, revealing that LGTV and LGTVT:prM predominantly infect cerebral cortex while TBEV infects cerebellum.<br /> Authors work at characterizing their viruses is nicely done and convincing, showing that LGTVT:prM replicated just like LGTV, and exhibited increased viral spread in cellulo. However LGTVT:prM appear to be less pathogenic in vivo and its brain tropism in mavs -/- mice seems to be similar to wt LGTV virus, stressing the fact that the role of structural proteins prM/E is only modest in TBEV specific tropism to cerebellum.
Major comments:
- It is stated in the introduction that prior work on LGTV/TBEV chimera have already been done, and that both LGTV and LGTV/TBEV are neuroinvasive and neurovirulent in animal models. In this study, both LGTV and LGTVT:prM fails to establish infection in wt mouse model. Were previous published data on LGTV and derivatives also only performed in mavs, or ifnar deficient mice?
The fact that the whole "tropism" part of the study is performed in mavs -/- mice limits the impact of the study as escape from innate immune response is central in shaping viral tropism. Authors should advertise more this fact (absent from the abstract) and discuss more the links between LGTV / TBEV and innate immune response (escape mechanisms and NS proteins, implication of prM in controlling MDA5, MAVS)
Minor comments:
Figures need some re-working :
Figure 1 :
1D : only the difference between TBEV and LGTVT:prM is shown. Plotting the difference LGTV / LGTVT:prM would be a nice upgrade.
Figure 2 : Numbering in the panels is wrong (2j in the text is 2K, 2H is 2I, ...) and should be corrected.
Figure 3 : Route of infection could be added to figure labels for more clarity.
Figure 4A : Labelling the Mock pannel with areas of concern in the brain(Cerebrum, Cerebellum, ...) would help a lot readers not familiar with brain anatomy.
Figure 4 E : images are too small to be convincing. What is staining Iba-1 is not mentioned in the figure legend.
Significance
Prior studies already described the generation and characterization of TBEV/LGTV chimeric viruses. The main addition of this paper to the field is the use of impressive high-resolution imaging of whole mouse brains, to explore viral infection and tropism in the brain.
However, presented data remain mostly descriptive, and experiments are performed in a model that may not be optimal to study tropism. As the ability of the virus to escape type I interferon participates to tropism, the fact that infections are only performed in mavs -/- mice limits the relevance of those findings.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
The authors describe a genome-wide CRISPR screen in mouse ES cells to identify factors and genes that regulate positively and negatively FGF/ERK signaling during differentiation. Out of known and potentially novel regulating signals, Mediator subunit Med12 was a strong hit in the screen and it was clearly and extensively shown by that the loss of Med12 results in impaired FGF/ERK signal responsiveness, modulation of mRNA levels and disturbed cell differentiation leading to reduced stem cell plasticity.<br /> This is a very concise and well written manuscript that demonstrates for the first time the important role of Med12 in ES cells and during early cell differentiation. The results support data that had been previously observed in Med12 mouse models and in addition show that Med12 cooperates with various signaling systems to control gene expression during early lineage decision.
We thank the reviewer for their positive evaluation of our work.
Fig. 3 Supp1A-B:<br /> The loci of all three independent Med12 mutant clones and the absence of Med12 should be included. Are all three Med12 loss-of-function mutants?
In the revised version of the manuscript, we have updated the scheme in Fig. 3 Supp 1A to represent both deletions that were obtained with the CRISPR guides used. Both the more common 97 bp deletion as well as the 105 bp deletion that occurred in one clonal line result in a complete loss of the protein on the western blot (Fig. 3 Supp. 1B), suggesting that all mutant clones used for further experiments are loss-of-function mutants.
Minor:<br /> Line 466: Should be Fig. 6F, not 6E.
We have removed this figure panel and the corresponding text in response to the other reviewers' comments.
Reviewer #1 (Significance):
The CRISPR screen identified list of some novel interesting factors that regulate FGF/ERK signaling in ES cells. Med12 was then analyzed in very detail on various levels and under various differentiation conditions, resulting in a complex picture how Med12 controls stem cell plasticity. These data support results observed in mouse models and identified novel regulating mechanisms of Med12.
Reviewer #2 (Evidence, reproducibility and clarity):
In the manuscript "Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity" Ferkorn and Schröter report on the role of Med12 in mouse embryonic stem cells. The perform an elegant genetic screen to identify regulators of Spry4 in mouse ESCs, screening for mutations that increase and decrease Spr4-reporter expression in serum/LIF conditions. They find that Med12 deletion results in defects in the exit from naïve pluripotency and in PrE-formation upon Gata-TF overexpression. Using scRNAseq experiments they report a reduction in biological noise in Med12 KO cells differentiating towards PrE upon Gata6 OE.
Major points:<br /> 1) The title might not exactly reflect the scientific findings of the manuscript. There is little direct evidence for a decrease in plasticity upon Med12 depletion.
We have changed the title to "Med12 cooperates with multiple differentiation signals to facilitate efficient lineage transitions in embryonic stem cells". In addition, we have toned down claims that Med12 regulates plasticity throughout the manuscript.
2) Fig 1G: From the data provided it is not entirely clear how well screen results can be validated. Did some of the mutants identified in the screen also produce no detectable phenotypes? What would be the phenotype of knocking out an unrelated gene? In other words, are some of the weak phenotypes really showing Spry4 downregulation or are they withing the range of biological variance?
Fluorescence levels in Fig. 1G have been normalized to control wild-type cells (dashed red line). Absence of a detectable phenotype would have resulted in normalized fluorescence values around 1. Fluorescence values of all tested mutants were significantly different from 1, as indicated in the statistical analysis given in the figure legend. Furthermore, H2B-Venus fluorescence of cells transfected with a non-targeting control vector are shown in Fig. 1F, and are not different from that of untransfected control wild-type cells. We have now added an explicit explanation how we normalized the data to the figure legend of Fig. 1G, and hope that this addresses the reviewer's concern.
3) Rescue experiments by re-expressing Med12 in Med12 KO ESCs are missing. Can the differentiation and transcriptional phenotypes be rescued?
We agree with the reviewer that a rescue experiment re-expressing Med12 would be ideal to ensure that the observed phenotypes are specifically due to loss of Med12. However, we could not identify commercially available full-length Med12 cDNA clones. Even though we managed to amplify full-length Med12 cDNA after reverse transcription, we were unable to clone it into expression vectors. These observations suggest that specific properties of the Med12 cds make the construction of expression vectors by conventional means difficult, and solving these issues is beyond the scope of this study.
Throughout the study we used multiple independent clonal lines in multiple experimental readouts and obtained congruent results. The reduced expression of pluripotency genes for example was observed in bulk sequencing of the lines introduced in Fig. 3, and by single-cell sequencing of independently generated _Med12-_mutant GATA6-mCherry inducible lines (Fig. 5 Supp. 1B). We argue that this congruence makes it unlikely that the results are dominated by off-target effects.
4) L365: The subheading "Transitions between embryonic... buffered against loss of Med12" is confusing. The data simply shows that Med12 KOs can still, albeit less efficiently generate PrE upon Gata TF OE. Is there evidence for some active buffering? I think the authors could simply report the data as is, stating that the phenotypes are not a complete block but an impairment of differentiation.
Prompted by the reviewer's comment as well as remarks along similar lines by reviewer #4, we have completely reorganized this section and now present all the analysis pertaining to PrE differentiation in a new figure 4. In the revised text (lines 316 - 378), we refrain from any speculations about possible buffering and simply report the data as is, as suggested by the reviewer.
5) L386: Would it not make more sense to reduce dox concentrations in control cells to equalize Gata6 OE to equalize levels between Med12 KO and controls? A shorter pulse of Gata6 does not really directly address unequal expression levels due to loss of Med12. Different pulse length of OE might have consequences that the authors do not control for. This also impacts scRNAseq experiments which suffer from the same, in my opinion, suboptimal experimental setup. This is a point that needs to be addressed.
We agree with the reviewer that it would have been desirable to equalize GATA6 overexpression levels between wild-type and Med12-mutant cells while keeping induction time the same. In our experience however, reducing the dox concentration is not suitable to achieve this: Rather than reducing transgene expression levels across the board, lower dox concentrations tend to increase the variability within the population - see Fig. 2 in PMID: 16400644 for an example. Since we agree with the reviewer that the setup of the scRNAseq experiment limits our ability to draw conclusion regarding the separation of cell states, we have decided remove these analyses in the revised manuscript. In doing so, we have reorganized the previous figures 5 and 6 into a new single figure 4. This has made the manuscript more concise and allowed us to focus on the main phenotype of the Med12 mutant cells, namely their delayed exit from pluripotency.
6) The reduced transcript number in Med12 KOs is interesting, but how does it come about. Is there indeed less transcriptional activity or is reduced transcript numbers a side effect of slower growth or the different cell states between WT and Med12 mutants. Appropriate experiments to address this should be performed.
To address this point, we have performed EU labeling experiments, to compare RNA synthesis rates between wild-type and Med12-mutant during the exit from pluripotency. These experiments confirmed an increase in the mRNA production upon differentiation for both wild-type and Med12 mutant cells, but the method was not sensitive enough to detect any differences between wild-type and Med12 mutant cells within the same condition. The EU labeling thus supports the notion that overall transcriptional rate increases during differentiation, but leaves open the possibility that reduced mRNA levels in Med12 mutant cells arise from effects other than reduced transcriptional output. These new analyses areshown in Fig. 4 Supp. 3 and described in the main text in lines 373 - 378.
7) I the proposed reduction of biological noise a feature of the PrE differentiation experiments or can it also be observed in epiblast differentiation.
To address this question, we have carried out single-cell measurements of Spry4 and Nanog mRNA numbers to compare transcriptional variability between wild-type and Med12-_mutant cells during epiblast differentiation (new Fig. 3 Supp. 1G, H). These measurements confirmed the differences between genotypes in mean expression levels detected by RNA sequencing. However, this analysis did not reveal strong differences in mRNA number distributions. Furthermore, as discussed in point 6 above, our interpretations of noise levels in the PrE differentiation paradigm could have been influenced by the unequal GATA6 induction times. Finally, reviewer #4 pointed out that 10x genomics scRNAseq is not ideal to compare noise levels when total mRNA content differ between samples, as is the case in our dataset. We therefore decided to tone down our conclusions regarding altered noise levels in _Med12-mutant cells.
8) I cannot follow the authors logic that Med12 loss results in enhanced separation between lineages. How is this experimentally supported.
As discussed in point 6 above, this result could have been influenced by the unequal induction times between wild type and Med12-mutant cells. We have therefore decided to remove this analysis in the revised version of the manuscript.
Minor points:<br /> Fig 3, Supp1 A: What exactly are the black and blue highlighted letters?
The black and blue highlighted letters indicate whether bases are part of an intron or an exon. Exon 7 is now explicitly labelled in the figure, and the meaning of the highlighting is explained in the figure legend.
Reviewer #2 (Significance):
Overall, this is an interesting study. The screen has been performed to a high technical standard and differentiation defects were appropriately analyzed. The manuscript has some weaknesses in investigating the molecular mode of action of Med12 which could be improved to provide more significant insights.
Reviewer #3 (Evidence, reproducibility and clarity):
The authors sought to identify genes important for the transcriptional changes needed during mouse ES cell differentiation. They identified a number of genes and focussed on Med12, as it was the strongest hit from a cluster of Mediator components.
Using knockout ES cells, differentiation assays, bulk and scRNAseq, they clearly show that Med12 is important for transgene activation and for gene activation generally during exit from self-renewal, but it is not specifically influencing differentiation efficacy per se. Rather, cells lacking Med12 display "a reduced ability to react to changing culture conditions" and, by inference, to environmental changes. They conclude that Med12 "contributes to the maintenance of cellular plasticity during differentiation and lineage transitions."
Med12 is a structural component of the kinase module of Mediator, but it is not clear what this study tells us about Mediator function. The authors state that their results contrast with those obtained using a Cdk8 inhibitor, which resulted in increased self-renewal (lines 577-580). I'm not sure where their results show "...that loss of Med12 leads to reduced pluripotency." (lines 579-580). They do not test potency of these cells. There is reduced expression of some pluripotency-associated markers and fewer colonies formed in a plating assay, but these assays to not test cellular potency.
We agree with the reviewer that our RNA sequencing and colony formation assays do not exhaustively test cellular potency. We have therefore changed the wording in the paragraphs that describe these assays and now talk about "reduced pluripotency gene expression" (e.g. lines 20, 228, 461, 512).
While their phenotype certainly appears different from that reported in cells treated with Cdk8 inhibitor, it's not clear to me what to make of it, or what it might tell us about the function of the Mediator Kinase module or of Mediator. That a co-activator is important for gene expression in general, or even for gene activation upon receipt of some signal, is not really surprising.
We believe that reporting differences in the phenotypes obtained with Cdk8 inhibition versus knock-out of Med12 is relevant, because it yields new insight into the different functions that the components of the Mediator kinase module have in pluripotent cells. We have previously discussed possible reasons for these functional differences (discussion line 519 - 528), and further expand on them in the revised manuscript.
Minor points:
It is surprising they don't relate their work to that of Hamilton et al (https://doi.org/10.1038/s41586-019-1732-z) who conclude that differentiation from the ES cell state towards primitive endoderm is compromised without Med24.
Thank you for pointing out this omission. We now cite the work of Hamilton et al., in line 317 (related to new Fig. 4) and 537 - 538 in the discussion.
Stylistic point: please make the separation between paragraphs more obvious. With no indentation or extra spacing between paragraphs it looks like one solid mass of words.
Reviewer #3 (Significance):
There is a lot of careful work here, but I'm not getting a big conclusion here. Perhaps the authors could argue their main points somewhat more stridently and what we've learned beyond this current system.
Prompted by the reviewer's comment, we have re-organized the functional analyses of Med12 function in the manuscript by condensing the previous figures 5 and 6 into a new single figure 4. We have removed all discussions of transcriptional noise and plasticity, and now focus more strongly on the slowed pluripotency transitions as the main phenotype of the Med12 mutant cells. These changes make the manuscript more concise, and we hope that they help to deliver a single, clear message to the reader.
Reviewer #4 (Evidence, reproducibility and clarity):
Fernkorn and Schröter report the results of a screen in mESCs based on modulation of the fluorescent intensity of the Spry4:H2B-Venus reporter. They identify candidate genes that both positively and negatively modulate the expression of the reporter. Amongst those, are several known regulators of the FGF pathway (transcriptional activator of Spry4) that serve as a positive control for the screen. The manuscript focuses on characterisation of Med12, and the authors conclude that Med12 does not specifically affect FGF-targets. Paradoxically, the authors show that based on the expression of key naïve markers Med12 cells show delayed differentiation. Functionally, however, Med12 mutant cells at 48hrs can form less colonies when plated back in naïve conditions (that would normally indicate accelerated differentiation ). The authors conclude that Med12 mutants have "a reduced ability to react to changing culture conditions". Next, they examine the Med12 mutation affects embryonic/extraembryonic differentiation using an inducible Gata6 expression system. They show that transgene induction is slower and dampened in mutant cells and that overall the balance of fates is skewed towards embryonic cells. Finally, they use single cell RNA sequencing and observe differences in the number of mRNAs detected, as well as the separation between clusters in the mutant cells. They conclude that the mutants have reduce transcriptional noise levels.
Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.
Mayor points:
- Med12, transcription levels and noise (Figure 6G, J-L). This is an intriguing observation. The labelling and multiplexing helped resolve many of the issue typically associated with comparing 10x dataset. I have two observations about this analysis:<br /> 1) Clarify how number of mRNA counts per cell is calculated (figure 6F) - the methods only described a value normalised by the total number of counts per cell.
The mRNA counts shown in the figure correspond to the raw number of UMIs detected per cell. We now explicitly state this in the figure legend. Please note that after re-organizing the manuscript, former Fig. 6F has become Fig. 4 Supp. 3A.
I feel this observation is key and has repercussions for the interpretation of the data (see point below) and should be independently validated (although I recognise it's difficult!). Since the authors observed differences in a randomly integrated transgene (iGata experiments), it's possible/likely that the dysregulation of transcription output is more generic. A possible suggestion is measuring global mRNA synthesis and degradation rates, either using inhibitors or by adding modified nucleotides and measuring incorporation rate and loss through pulse/chase labelling.
We have performed an EU labeling experiment to address this point, which is shown in Fig. 4 Supp. 3 and described in the main text in lines 373 - 378 of the revised manuscript. Please refer to our response to reviewer #2, point 6 for a short description of the results.
2) 10x is not the ideal for looking at heterogeneity/noise since it has a low capture efficiency and there are a lot of gaps/zeros in the lower expression range. Therefore, it's simply possible that mutant cells have dampened transcriptional output, meaning lowly expressed genes which in the WT contribute to the apparent heterogeneity (because there is a higher chance of not being captured), are below the 10x detection range in the mutant. This can be seen by plotting the cumulative sum of the mean gene count across each sample - the 50% mark (=mean gene count at 50% detection) reflects a measure of the "capture efficiency" (either because of technical reasons or lower mRNA input). Generally (e.g. also seen across technical repeats), the mean coefficient of variation, entropy and other measures of population heterogeneity directly scale with this "mean gene count at 50% detection", while the cell-cell correlation inversely scales with the "mean gene count at 50% detection". If this scaling relationships are observed for the WT and mutant, then it is impossible to say from the single cell RNA-seq whether the differences in heterogeneity are due to biological or technical reasons. Unfortunately, down-sampling the reads does not generally correct or normalise for this type of technical noise since the technical errors accumulate at every step of sample prep. Of course, it's possible that the technical noise in the RNAseq obfuscates real differences in the level of noise. The failure of mutant cells to re-establish the naïve network certainly suggest there is something going on. Therefore, I suggest performing the analysis of capture efficiency vs CV2 mentioned above and adjusting the discussion accordingly, and potentially perform single molecule FISH of key variable genes at the interface of the two clusters to validate the difference in heterogeneity.
As suggested by the reviewer, we have performed single molecule FISH measurements of variable genes (Fig. 3 Supp. 1 G, H), but these did not provide independent evidence for increased noise levels in Med12 mutant cells. In light of the caveats raised by reviewer #4 when estimating noise levels from 10x scRNAseq data, and the suggestion of reviewer #3 to sharpen the focus of the manuscript, we have decided to remove any strong conclusions about different noise levels between the genotypes. Instead, we focus on the slowed pluripotency transitions as the main phenotype of the Med12 mutant cells to make the manuscript more concise, to deliver a single, clear message.
- Are Oct4 levels affected? Reduction of Oct4 is sufficient to block differentiation (Radzisheuskaya et al. 2013 - PMID: 23629142).
We thank the reviewer for this idea. We measured OCT4 expression levels in single cells via quantitative immunostaining and found that that there is no difference between wild-type and Med12-mutant cells. It is therefore unlikely that lowered OCT4 levels block differentiation in the mutant. These new results are shown in Fig. 5, Supp. 1 D, E.
- Med12 mutants showing transcriptionally delayed differentiation (related to figure 4C). Is this delay also reflected in the expression of formative genes? If I understand correctly, Figure 4C is made from a panel of naïve markers. It would be good to determine if the formative network is equally affected (and in the same direction - suggesting a delay), or if the transcriptional changes speak to a global dysregulation/dampened expression.
Prompted by the reviewer's suggestion, we have extended our analysis of the differentiation delays to genes that are upregulated during differentiation, such as formative genes. Rather than trying to come up with an new set of formative markers to produce a variation of the original Fig. 4C (Fig. 5C in the revised manuscript), we have taken an unbiased approach and extended Fig. 5E with a panel showing the distribution of expression slopes of the 100 most upregulated genes determined as in Fig. 5D. This analysis demonstrates a lower upregulation slope in Med12-mutant cells. This result confirms that both the upregulation and downregulation of genes is less efficient upon the loss of MED12, in line with our conclusion of delayed differentiation.
- Control for the re-plating experiments in 2i/LIF (Figure 4B). Replating in 2iLIF + FBS can have a large selective effect in certain mutant backgrounds (e.g. Nodal mutants) which don't accurately reflect the differentiation status. To exclude such effects, it would be good to repeat the replating assays in serum-free conditions (laminin coating can help with attachment) and include undifferentiated controls to ensure that the mutant doesn't have a clonal disadvantage.
The reason we have included FBS in the re-plating assays is that in our experience, Fgf4-_mutant cells show strongly impaired growth standard in 2i+LIF medium. We anticipate that using laminin coating to help with attachment would not overcome this requirement. We have therefore decided against repeating the re-plating assays. Instead, we state the reason why we used FBS in the main text, and also explicitly acknowledge the reviewers' concern of the risk of selective effects of the FBS and the possible clonal disadvantages of the _Med12 mutant line.
Minor points:<br /> - I found figure 3D and the corresponding text and caption difficult to understand. It is unclear what a "footprint", "relative pathway activity" or "spearman correlation of footprint" mean. Were all the genes listed below Med12 knocked out and sequenced in this study? I suggest re-working and maybe simplifying the text and figure.
We re-worked the description about the pathway analysis and stated more clearly that:
- The footprint is a quantitative measure of the differences in gene expression change of a defined list of target genes between wild-type and perturbation.
- Only the Med12 mutant data is new data produced in this manuscript and all examples below are from Lackner et al., 2021.
We think that a more extensive explanation of the terms "relative pathway activity" and "spearman correlation of footprint" would disturb the flow of the manuscript too much. Therefore, we now cite the original paper just next to the sentence these terms are mentioned.
In figure S1 Sup1 the authors report the dose response of targets to FGF - are those affected in the mutant?
In this manuscript we have not tested if the dose response of FGF target genes changes upon perturbation of Med12. We argue that such an experiment would be beyond the scope of the current manuscript, since - as acknowledged by the reviewer - "Med12 does not specifically affect FGF-targets".
- Similarly, it would be helpful to guide the reader through figure 5H-I and the corresponding text and caption since it's not immediately obvious how the analysis/graphs lead to the conclusion stated.
As a consequence of our reorganization of the manuscript, the original figure 5H-I has been moved to Fig. 4, Supp. 1 in the revised version. The analysis strategy has been described in more detail in one of our previous publications (PMID: 26511924). In keeping with our general decision to make the manuscript more focused and concise, we have decided against further expanding on these data, but instead refer the reader to the original publication.
- Role of Med12 in regulating FGF signalling. There are two observations that seems a bit at odds with the text description and it would be helpful to clarify: "ppERK levels were indistinguishable between wild-type and Med12-mutant lines" (line 222) - 5/6 datapoints show an increase. "[...] overall these results argue against a strong and specific role of Med12 in regulation of FGF target genes." (line 274). If I understood correctly, ~50% of genes are differentially transcribed because of Med12 KO.
To address the reviewers' first question, we have performed a statistical test on the quantifications of the western blots. This test indicates that there is no significant change of ppERK levels upon loss-of MED12, which now stated clearly in the text (line 217).
Second, to clarify why our data argues against a strong and specific role of Med12 in regulation of FGF target genes, we now formulate an expectation (lines 276 - 277): If MED12 specifically regulated FGF target genes, the number of differentially expressed genes would be higher in the wild-type than in the Med12-mutant upon stimulation with FGF. This however is not the case.
- "[...] as well as transitions between different pluripotent states" (line 41) - references missing.
We have added a reference to PMID: 28174249 (line 39).
- Line 447: "differentiation conditions" - it's unclear what it's mean by differentiation and how it relates to the diagram in figure 6A. Are those the 20hr cells? Do the -8h, -4hr and 0hr cells (if I understand the meaning of the diagram) cluster all together?
We now specify in the text that pluripotency conditions refer to cells maintained in 2i + LIF medium, whereas differentiation refers to cells switched to N2B27 after the doxycycline pulse (lines 341 - 342).
- The difference in dynamics of mCherry activation as a consequence of Med12 KO are not apparent from figure 5E. It might be easier to visualise this observation if x-axis was normalised to the starting point plotting "time from start of induction".
We agree with the reviewer that the current alignment has not been optimized to compare GATA6 induction dynamics between wild-type and Med12-mutant cells. If we changed the alignment however, it would not be clear any longer that both genotypes were in N2B27 for the same amount of time before analyzing Epi and PrE differentiation. Since our focus is on the differentiation of the two lineages rather than GATA6-mCherry induction dynamics, we decided to keep the original alignment.
- Figure 3H/I - what does "gene expression changes" and "fold change ratio" mean?
In Fig. 3H, we plot the the fold change of gene expression upon FGF4 stimulation in _Med12-_mutant versus that in wild-type cells; in Fig. 3I we plot the distribution of the ratio of these two fold changes across all genes. To make this strategy clearer, we have changed the axis label in Fig. 3H to "expression fold change upon FGF", to make it consistent with the axis label "fold-change ratio" in Fig. 3I.
- Line 579-580 - please clarify what is meant by "reduced pluripotency".
Prompted by a similar concern raised by reviewer #3, we have changed the wording throughout this paragraph and now talk of "reduced pluripotency gene expression". See also our response to reviewer #3 above.
- Title: "enhance ESC plasticity". not sure enhance is the right word? There is no evidence that the plasticity of cells is affected.
We have changed the title; see also our response to reviewer #2, point 1.
Reviewer #4 (Significance):
Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.
-
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Referee #4
Evidence, reproducibility and clarity
Fernkorn and Schröter report the results of a screen in mESCs based on modulation of the fluorescent intensity of the Spry4:H2B-Venus reporter. They identify candidate genes that both positively and negatively modulate the expression of the reporter. Amongst those, are several known regulators of the FGF pathway (transcriptional activator of Spry4) that serve as a positive control for the screen. The manuscript focuses on characterisation of Med12, and the authors conclude that Med12 does not specifically affect FGF-targets. Paradoxically, the authors show that based on the expression of key naïve markers Med12 cells show delayed differentiation. Functionally, however, Med12 mutant cells at 48hrs can form less colonies when plated back in naïve conditions (that would normally indicate accelerated differentiation ). The authors conclude that Med12 mutants have "a reduced ability to react to changing culture conditions". Next, they examine the Med12 mutation affects embryonic/extraembryonic differentiation using an inducible Gata6 expression system. They show that transgene induction is slower and dampened in mutant cells and that overall the balance of fates is skewed towards embryonic cells. Finally, they use single cell RNA sequencing and observe differences in the number of mRNAs detected, as well as the separation between clusters in the mutant cells. They conclude that the mutants have reduce transcriptional noise levels.
Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.
Major points:
- Med12, transcription levels and noise (Figure 6G, J-L). This is an intriguing observation. The labelling and multiplexing helped resolve many of the issue typically associated with comparing 10x dataset. I have two observations about this analysis:
- Clarify how number of mRNA counts per cell is calculated (figure 6F) - the methods only described a value normalised by the total number of counts per cell. I feel this observation is key and has repercussions for the interpretation of the data (see point below) and should be independently validated (although I recognise it's difficult!). Since the authors observed differences in a randomly integrated transgene (iGata experiments), it's possible/likely that the dysregulation of transcription output is more generic. A possible suggestion is measuring global mRNA synthesis and degradation rates, either using inhibitors or by adding modified nucleotides and measuring incorporation rate and loss through pulse/chase labelling.
- 10x is not the ideal for looking at heterogeneity/noise since it has a low capture efficiency and there are a lot of gaps/zeros in the lower expression range. Therefore, it's simply possible that mutant cells have dampened transcriptional output, meaning lowly expressed genes which in the WT contribute to the apparent heterogeneity (because there is a higher chance of not being captured), are below the 10x detection range in the mutant. This can be seen by plotting the cumulative sum of the mean gene count across each sample - the 50% mark (=mean gene count at 50% detection) reflects a measure of the "capture efficiency" (either because of technical reasons or lower mRNA input). Generally (e.g. also seen across technical repeats), the mean coefficient of variation, entropy and other measures of population heterogeneity directly scale with this "mean gene count at 50% detection", while the cell-cell correlation inversely scales with the "mean gene count at 50% detection". If this scaling relationships are observed for the WT and mutant, then it is impossible to say from the single cell RNA-seq whether the differences in heterogeneity are due to biological or technical reasons. Unfortunately, down-sampling the reads does not generally correct or normalise for this type of technical noise since the technical errors accumulate at every step of sample prep. Of course, it's possible that the technical noise in the RNAseq obfuscates real differences in the level of noise. The failure of mutant cells to re-establish the naïve network certainly suggest there is something going on. Therefore, I suggest performing the analysis of capture efficiency vs CV2 mentioned above and adjusting the discussion accordingly, and potentially perform single molecule FISH of key variable genes at the interface of the two clusters to validate the difference in heterogeneity.
- Are Oct4 levels affected? Reduction of Oct4 is sufficient to block differentiation (Radzisheuskaya et al. 2013 - PMID: 23629142).
- Med12 mutants showing transcriptionally delayed differentiation (related to figure 4C). Is this delay also reflected in the expression of formative genes? If I understand correctly, Figure 4C is made from a panel of naïve markers. It would be good to determine if the formative network is equally affected (and in the same direction - suggesting a delay), or if the transcriptional changes speak to a global dysregulation/dampened expression.
- Control for the re-plating experiments in 2i/LIF (Figure 4B). Replating in 2iLIF + FBS can have a large selective effect in certain mutant backgrounds (e.g. Nodal mutants) which don't accurately reflect the differentiation status. To exclude such effects, it would be good to repeat the replating assays in serum-free conditions (laminin coating can help with attachment) and include undifferentiated controls to ensure that the mutant doesn't have a clonal disadvantage.
Minor points:
- I found figure 3D and the corresponding text and caption difficult to understand. It is unclear what a "footprint", "relative pathway activity" or "spearman correlation of footprint" mean. Were all the genes listed below Med12 knocked out and sequenced in this study? I suggest re-working and maybe simplifying the text and figure. In figure S1 Sup1 the authors report the dose response of targets to FGF - are those affected in the mutant?
- Similarly, it would be helpful to guide the reader through figure 5H-I and the corresponding text and caption since it's not immediately obvious how the analysis/graphs lead to the conclusion stated.
- Role of Med12 in regulating FGF signalling. There are two observations that seems a bit at odds with the text description and it would be helpful to clarify: "ppERK levels were indistinguishable between wild-type and Med12-mutant lines" (line 222) - 5/6 datapoints show an increase. "[...] overall these results argue against a strong and specific role of Med12 in regulation of FGF target genes." (line 274). If I understood correctly, ~50% of genes are differentially transcribed because of Med12 KO.
- "[...] as well as transitions between different pluripotent states" (line 41) - references missing .
- Line 447: "differentiation conditions" - it's unclear what it's mean by differentiation and how it relates to the diagram in figure 6A. Are those the 20hr cells? Do the -8h, -4hr and 0hr cells (if I understand the meaning of the diagram) cluster all together?
- The difference in dynamics of mCherry activation as a consequence of Med12 KO are not apparent from figure 5E. It might be easier to visualise this observation if x-axis was normalised to the starting point plotting "time from start of induction".
- Figure 3H/I - what does "gene expression changes" and "fold change ratio" mean?
- Line 579-580 - please clarify what is meant by "reduced pluripotency".
- Title: "enhance ESC plasticity". not sure enhance is the right word? There is no evidence that the plasticity of cells is affected.
Significance
Overall, it was an interesting article exploring the molecular consequences of knocking out a subunit of the mediator complex. The characterisation focuses primarily on the description of the screen and the more functional consequences of the KO, rather than delving onto the molecular aspects (e.g. whether mediator complex assembly is affected, or it's binding etc). The analysis of the transcriptional noise will be of particular interest to the community, although I have some suggestions to exclude the possibility that the analysis simply reflects changes in global transcription levels. I have a small number of concerns and requests for clarification on the data but all of them should be relatively easy to address.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #3
Evidence, reproducibility and clarity
The authors sought to identify genes important for the transcriptional changes needed during mouse ES cell differentiation. They identified a number of genes and focussed on Med12, as it was the strongest hit from a cluster of Mediator components.
Using knockout ES cells, differentiation assays, bulk and scRNAseq, they clearly show that Med12 is important for transgene activation and for gene activation generally during exit from self-renewal, but it is not specifically influencing differentiation efficacy per se. Rather, cells lacking Med12 display "a reduced ability to react to changing culture conditions" and, by inference, to environmental changes. They conclude that Med12 "contributes to the maintenance of cellular plasticity during differentiation and lineage transitions."
Med12 is a structural component of the kinase module of Mediator, but it is not clear what this study tells us about Mediator function. The authors state that their results contrast with those obtained using a Cdk8 inhibitor, which resulted in increased self-renewal (lines 577-580). I'm not sure where their results show "...that loss of Med12 leads to reduced pluripotency." (lines 579-580). They do not test potency of these cells. There is reduced expression of some pluripotency-associated markers and fewer colonies formed in a plating assay, but these assays to not test cellular potency. While their phenotype certainly appears different from that reported in cells treated with Cdk8 inhibitor, it's not clear to me what to make of it, or what it might tell us about the function of the Mediator Kinase module or of Mediator. That a co-activator is important for gene expression in general, or even for gene activation upon receipt of some signal, is not really surprising.
Minor points:
It is surprising they don't relate their work to that of Hamilton et al (https://doi.org/10.1038/s41586-019-1732-z) who conclude that differentiation from the ES cell state towards primitive endoderm is compromised without Med24.
Stylistic point: please make the separation between paragraphs more obvious. With no indentation or extra spacing between paragraphs it looks like one solid mass of words.
Significance
There is a lot of careful work here, but I'm not getting a big conclusion here. Perhaps the authors could argue their main points somewhat more stridently and what we've learned beyond this current system.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript "Med12 cooperates with multiple differentiation signals to enhance embryonic stem cell plasticity" Ferkorn and Schröter report on the role of Med12 in mouse embryonic stem cells. The perform an elegant genetic screen to identify regulators of Spry4 in mouse ESCs, screening for mutations that increase and decrease Spr4-reporter expression in serum/LIF conditions. They find that Med12 deletion results in defects in the exit from naïve pluripotency and in PrE-formation upon Gata-TF overexpression. Using scRNAseq experiments they report a reduction in biological noise in Med12 KO cells differentiating towards PrE upon Gata6 OE.
Major points:
- The title might not exactly reflect the scientific findings of the manuscript. There is little direct evidence for a decrease in plasticity upon Med12 depletion.
- Fig 1G: From the data provided it is not entirely clear how well screen results can be validated. Did some of the mutants identified in the screen also produce no detectable phenotypes? What would be the phenotype of knocking out an unrelated gene? In other words, are some of the weak phenotypes really showing Spry4 downregulation or are they withing the range of biological variance?
- Rescue experiments by re-expressing Med12 in Med12 KO ESCs are missing. Can the differentiation and transcriptional phenotypes be rescued?
- L365: The subheading "Transitions between embryonic... buffered against loss of Med12" is confusing. The data simply shows that Med12 KOs can still, albeit less efficiently generate PrE upon Gata TF OE. Is there evidence for some active buffering? I think the authors could simply report the data as is, stating that the phenotypes are not a complete block but an impairment of differentiation.
- L386: Would it not make more sense to reduce dox concentrations in control cells to equalize Gata6 OE to equalize levels between Med12 KO and controls? A shorter pulse of Gata6 does not really directly address unequal expression levels due to loss of Med12. Different pulse length of OE might have consequences that the authors do not control for. This also impacts scRNAseq experiments which suffer from the same, in my opinion, suboptimal experimental setup. This is a point that needs to be addressed.
- The reduced transcript number in Med12 KOs is interesting, but how does it come about. Is there indeed less transcriptional activity or is reduced transcript numbers a side effect of slower growth or the different cell states between WT and Med12 mutants. Appropriate experiments to address this should be performed.
- I the proposed reduction of biological noise a feature of the PrE differentiation experiments or can it also be observed in epiblast differentiation.
- I cannot follow the authors logic that Med12 loss results in enhanced separation between lineages. How is this experimentally supported.
Minor points:
Fig 3, Supp1 A: What exactly are the black and blue highlighted letters?
Significance
Overall, this is an interesting study. The screen has been performed to a high technical standard and differentiation defects were appropriately analyzed. The manuscript has some weaknesses in investigating the molecular mode of action of Med12 which could be improved to provide more significant insights.
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Referee #1
Evidence, reproducibility and clarity
The authors describe a genome-wide CRISPR screen in mouse ES cells to identify factors and genes that regulate positively and negatively FGF/ERK signaling during differentiation. Out of known and potentially novel regulating signals, Mediator subunit Med12 was a strong hit in the screen and it was clearly and extensively shown by that the loss of Med12 results in impaired FGF/ERK signal responsiveness, modulation of mRNA levels and disturbed cell differentiation leading to reduced stem cell plasticity.<br /> This is a very concise and well written manuscript that demonstrates for the first time the important role of Med12 in ES cells and during early cell differentiation. The results support data that had been previously observed in Med12 mouse models and in addition show that Med12 cooperates with various signaling systems to control gene expression during early lineage decision.
Fig. 3 Supp1A-B:<br /> The loci of all three independent Med12 mutant clones and the absence of Med12 should be included. Are all three Med12 loss-of-function mutants?
Minor:
Line 466: Should be Fig. 6F, not 6E.
Significance
The CRISPR screen identified list of some novel interesting factors that regulate FGF/ERK signaling in ES cells. Med12 was then analyzed in very detail on various levels and under various differentiation conditions, resulting in a complex picture how Med12 controls stem cell plasticity. These data support results observed in mouse models and identified novel regulating mechanisms of Med12.
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Referee #3
Evidence, reproducibility and clarity
This manuscript addresses the important topic of cell-cell junction maturation and mechanical stability, with a specific focus on how mechanotransduction through the Piezo1 channel regulates these processes. The authors present compelling in vivo evidence demonstrating that Piezo1 plays a role in junction stability and barrier function, particularly in aged tissue. The work makes a valuable contribution to our understanding of mechanotransduction in epithelial biology. However, several aspects of the mechanistic model and in vitro experiments require additional development to fully support the authors' conclusions.
Major Strengths:
- The in vivo experiments are well-designed and provide convincing evidence for Piezo1's role in barrier function
- The study identifies an important connection between mechanical sensing and junction maturation
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The age-dependent phenotype provides interesting insights into tissue mechanics
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Areas Requiring Additional Development:
a. Mechanistic Model Definition A major issue is that the central concept of Piezo1 "balancing membrane and cortical tension" requires more precise definition and experimental support. The authors need to clearly explain what this balance means mechanistically and how it is achieved.
b. Localization-Function Discrepancy There is an important inconsistency between the authors' claims about Piezo1's role and its localization: while they conclude that Piezo1 is crucial for mechanical stability, they also show that Piezo1 is not localized at mature junctions. This apparent contradiction needs to be addressed with a clear mechanistic explanation.
c. Quantification and Statistical Analysis Several key conclusions would benefit from more rigorous quantification: - The quantitation of junction maturation in Fig. 1a and 2a should include independent analysis of each experiment rather than pooling cells from multiple experiments - Actin morphology and pMLC2 levels at junctions in Fig. 1 need systematic quantification - Cytoskeletal dynamics and morphological changes in Piezo1-eKO cells (Fig. 2a) require quantification
d. Methodological and Timeline Clarity The analysis methods and temporal aspects of several experiments need better documentation: Analysis Methods:
The quantification method for mature adhesions (used in Figs. 1a, 1e, 1f, 2a) needs clarification. The Methods section states that "The transition from zipper-like adhesions to mature continuous intercellular junctions were quantified manually," but crucial details are missing: - What specific criteria defined a "continuous junction"? - Was this based on complete visibility of the cell perimeter as one junction? - How were cells classified as having continuous versus zipper-like adhesions?
e. The protein intensity quantification at junctions requires methodological clarification. The Methods state "For quantifying intensities at junctions, max projection images were generated, and region of interests (ROIs) were restricted to ZO1-positive junctions." However: - Were ROIs drawn empirically by the user? Or was the ZO-1 signal used to make a mask? - Was there an automated step to determine junctional areas (e.g., intensity threshold)? - Was the analysis blinded? If subjective methods were used, this should be clearly stated and potential variability addressed. 2. Timeline Documentation:
For blebbistatin experiments (Fig. 1e), specify observation timeframes and quantify the extent of accelerated maturation
The hypotonic shock experiment (Fig. 3e) timeline needs clarification: - When were measurements taken relative to Ca2+ switch? - Duration of hypotonic media exposure? - Were there time-dependent effects in cell response? 3. Data Support and Interpretation
a. Several conclusions require additional support or clarification: - The claim about "more dynamic cytoskeletal motion and irregularly shaped" cells (Fig. 2a) is not supported by the provided data. Quantification of dynamics and cell shape are needed to support this conclusion. Cytoskeletal imaging data would also be useful.
b. The interpretation of junctional tension requires revision: - Current conclusions about increased junctional tension are inferred indirectly from vinculin (Fig. 1c) and a18-catenin (Fig. S1a) immunostaining images. - Consider either:
a) Adding direct junctional tension measurements (e.g., optical measurements, PMID 31964776) b) Limiting claims to well-supported morphological differences and moving tension-related interpretations to the Discussion as speculative elements
c. The description "Analysis of vinculin translocation to intercellular junctions showed reduced levels of vinculin at cell-cell contacts, but abundant vinculin at cell-matrix adhesions (Supplementary Fig. S2a), indicating abnormal build-up of stresses at intercellular junctions of Piezo1-eKO cells" needs revision: - "Build-up" suggests higher tensions in Piezo1-eKO cells, which contradicts impaired adhesion maturation findings. Suggest replacing with "distribution" or "organization" "Intercellular" is used ambiguously to include both cell-cell and cell-matrix adhesions 4. Literature Context:
The discussion should incorporate recent relevant literature on Piezo1's role in tight junction regulation (e.g., PMID 37005489, PMID 33636174, PMID 31409093) 5. Technical Considerations - For localization studies (Fig. 2), using keratinocytes from Piezo1-tdTomato mouse (JAX #029214) would be preferable to heterologously-expressed Piezo1-FLAG, as it would avoid potential artifacts from non-physiological expression levels - Supp Fig. 1b requires additional replicates - The Fig. 3A legend states "Note increase in FLIPPER-TR lifetime indicative of elevated membrane tension in Piezo1-eKO" when the data actually shows the opposite - a decrease in Flipper-TR lifetime indicating lower membrane tension 6. Conceptual and Experimental Clarity Needed Several statements require clearer explanation or additional supporting evidence:
a. Regarding junction maturation mechanisms:
The authors state: "This indicated that formation of belt-like adhesions was associated with initial contractility build-up by actomyosin stress fibers linked to junctions, followed by a switch to parallel actomyosin bundles and reduced contractility at adhesions, while the junctions themselves were stabilized in a stressed state indicated by a strengthened actin-junction link." Each part of this claim needs experimental support: - The "initial contractility build-up by actomyosin stress fibers linked to junctions" needs to be demonstrated - The "switch to parallel actomyosin bundles and reduced contractility at adhesions" requires quantification - The claim about "junctions themselves were stabilized in a stressed state" needs stronger evidence
b. The statement "contact expansion from zippers to a belt requires collaborative regulation of adhesion tension and actomyosin cytoskeleton to lower interfacial tension at the contact" is unclear and needs clarification
c. The claim "Concomitant with emergence of continuous junctions (8h), the stress fibers were replaced by thick actin bundles positioned perpendicularly to junctions (Fig. 1b)" is not clearly supported by the data 7. Regarding experimental interpretation: - In Fig. 1e, the authors claim that 5µM blebbistatin accelerates junction maturation, but this conclusion is not supported by the statistics (p = 0.0784). Additionally, the timeframe of observation and the quantification of maturation speed should be specified - The results section describing Fig. 3 presents seemingly disconnected observations without clear mechanistic links between them, making it difficult to follow the authors' logic and support their conclusions - The mechanism by which both reduced contractility (blebbistatin) and increased membrane tension can accelerate maturation (Fig. 1e, f; and also in Piezo1-eKO Fig. 3d, e) needs explanation. The fact that these interventions also accelerate maturation also in Piezo1-eKO suggests a mechanism independent of Piezo1 which is at odds with their broad conclusion that Piezo1 balances membrane tension and cortical contractility in the maturation process. The precise mechanism of Piezo1's role in sensing membrane and cortex tension requires clarification. - How Piezo1 maintains mechanical stability of mature junctions despite not being localized there needs to be explained 8. Suggested Additional Experiments:
a. Optional: Given the age-dependent tissue stiffness effects proposed by the authors, examining keratinocyte behavior in vitro on substrates of varying stiffness would provide valuable insights
b. Optional: Direct measurements of tension at cell-cell junctions where Piezo1 localizes would help validate the proposed mechanical model 9. Minor Points: - The cell biology sections, particularly descriptions of in vitro experiments, would benefit from a thorough revision to improve precision and clarity. For instance, the Results section describes "Analysis of vinculin translocation to intercellular junctions" when no translocation is actually being studied - Figure legends should clearly indicate what individual data points represent - Several conclusions are overstated. For example, the authors conclude that "Piezo1 controls the maturation process" and that "Piezo1 is required for cell junction maturation into junctional belts" based on Fig. 2. These are exaggerated claims since maturation still progresses in Piezo1's absence, just more slowly. "Regulates" or "modulates" would be more appropriate terminology
In conclusion, while this manuscript presents important findings regarding Piezo1's role in junction maturation and stability, addressing the mechanistic and quantification issues outlined above is essential for supporting the authors' conclusions. The authors have laid groundwork for understanding an important biological process, and addressing these points would help readers better appreciate the significance of their findings.
Significance
General Assessment: This study investigates the critical role of mechanosensing in epithelial barrier formation and maintenance, with a particular focus on Piezo1's contribution to junction maturation and stability. The work's primary strengths lie in its compelling in vivo demonstrations of Piezo1's importance for barrier function, particularly in aged tissue, and its identification of a novel connection between mechanical sensing and junction maturation. The age-dependent phenotype provides valuable insights into tissue mechanics and barrier maintenance. However, the mechanistic understanding of how Piezo1 coordinates these processes requires further development, particularly regarding the proposed balance between membrane and cortical tension.
Advance: This work provides several important advances:
- First demonstration of Piezo1's role in regulating the maturation of cell-cell junctions from zipper-like to belt-like structures
- Novel insights into how mechanical forces influence junction maturation through mechanosensitive ion channels
- Important connection between aging, tissue mechanics, and barrier function
- Integration of mechanical sensing with junction assembly and stability
The findings extend our understanding of epithelial barrier formation beyond traditional molecular pathways to include mechanotransduction, suggesting new therapeutic possibilities for barrier dysfunction. The age-dependent phenotype is particularly significant as it reveals how mechanical properties of tissue influence barrier maintenance over time.
Audience: This research will be of broad interest to multiple communities:
- Cell biologists studying junction assembly and epithelial organization
- Mechanobiologists interested in force transmission and sensing
- Ion channel researchers interested in the physiological roles of channels
- Aging researchers investigating tissue barrier function
- Bioengineers developing therapeutic strategies for epithelial barriers
The findings have both basic research and translational implications, particularly for understanding and treating age-related barrier dysfunction in epithelia.
Reviewer Expertise: Cell biology, mechanobiology, live cell imaging, quantitative image analysis, ion channels I have sufficient expertise to evaluate all aspects of the manuscript except for the specific age-related physiological changes in mouse skin, which falls outside my area of expertise.
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Referee #2
Evidence, reproducibility and clarity
This manuscript describes the role of the mechanosensitive ion channel Piezo1 in epithelial junction assembly, using Piezo-1-KO primary epidermal keratinocytes in vitro and mouse skin in vivo. The authors conclude that Piezo1 allows balancing of membrane versus cortical tension to stabilize junctions and promote tight juntion (TJ) barrier integrity assembly. The conclusion that Piezo1 has an important function in the formation and maintenance of apical junctions of keratinocytes both in vitro and in vivo is well documented by experiments in WT, KO and rescue cells/tissues where different parameters are carefully measured: protein localization, quantification of mature junctions, membrane tension using the flipper probe, use of the myosin inhibitor blebbistatin, analysis of cortical stiffness by AFM, etc. Although, the physiological relevance and the mechanism through which Piezo operates in young skin are not clear, the authors make reasonable claims, that are not too speculative.
Major comments:
- The Supplementary Figure 4d (panel d) that is described in the Results section is missing. It supposedly shows that 1 year-old Piezo1-eKO mice diplay an increase in transepidermal water loss, inducating that TJ barrier function is compromised. The Figure legend for the panel is also missing. Please provide the Figure panel and the legend.
- TJ barrier function depends on claudins, and the loss of claudin-1 leads to transepidermal water loss (please cite the relevant paper from the Tsukita lab). Considering that altered TJ barrier function is observed only in 1-yr old mice (Supplementary Figure to be shown, see point n.1) and not in young mice (Suppl. Fig. 3f-h), the expression pattern of the main claudin isoforms, and especially claudin-1, in the different cell populations (see Suppl. Fig. 3b, or by IF analysis) in young vs old and WT vs KO mice must provided, to provide a mechanistic basis for the observed TJ barrier phenotype. This would help to determine if the phenotype is linked to altered claudin expression or to altered (increased) perijunctional tension.
- Mechanistically, the authors mention in the Discussion that Piezo1 might act through RhoA signaling. In Rübsam et al 2017 the authors showed that the uppermost viable layer of the skin has increased apical junctional tension, due to anisotropy of AJ distribution which correlates with EGFR activation and localization. In this context, it is important to know if KO of Piezo-1 affects EGFR localization and signaling, and to probe the RhoA pathway using for example the ROCK inhibitor, instead of blebbistatin.
Minor comments:
- The Methods sections should be improved with additional details. For example, the description of quantification of junctional labeling is vague, and there is often no or little indication in the Legends that specifies number of experiments and junctional segments. In addition, quantification of junctional stainings for specific proteins should be done using a junctional reference marker and not as "absolute" values, because there can be variability of staining between samples and experiments. This is especially important when measuring ZO-1, which is a dual AJ-TJ protein (for example at zipper-like junctions ZO-1 colocalizes with AJ markers). Double labelling with a true TJ marker (occludin or cingulin) and/or a true AJ marker (PLEKHA7, afadin, Ecadherin or a catenin) and quantifying junctional labeling by ratio is highly recommended. This is particularly important when evaluating tension-sensitive epitopes/antigens (alpha-catenin, vinculin, etc)
- Please use ZO-1 (and ZO-2) consistently, instead of ZO1 (or ZO2), which is completely inaccurate.
- Plase cite Furuse et al 2002 JCB (see above).
- Please include statistical data in Figure Legends, specifying the number of separate experiments and number of samples. At least three experiments is recommended.
- At the end of the introduction the authors mention "putative" occludin-containing TJs. I would delete putative. Epithelial junctions that contain a continuous circumferential linear distribution of occludin/ZO-1/cingulin and form a barrier comply with the definition of a TJs (Citi et al JCS 2024) .
- Please insert page numbers in the manuscript.
Significance
The notion that mechanosensitive calcium channels contribute to the formation of continuous apical junctions (repair and assembly) was introduced by the Miller lab, using Xenopus oocytes. This manuscript provides a significant conceptual advance, not only by using in vitro and in vivo mouse (mammalian) epidermal keratinocytes as model system, but especially by using Piezo1-KO and rescue experiments, which was not done in the Xenopus model.
This research would be of great interest to cell biologists interested in epithelial differentiation, polarization and junction assembly, and to clinicians that are interested in the molecular basis of skin pathophysiology.
My expertise is in the biochemistry, cell biology and mechanobiology of epithelial junctions. I have used Xenopus embryos, cultured epithelial cells, primary keratinocytes and keratinocyte cell lines and KO mice as model systems. The research of my group focuses on how specific cytoskeletal proteins are organized to transmit forces and are recruited to junctions, and how junctional proteins respond to mechanical force. I have experience in all of the methods described in this paper, except for transepidermal water loss measurement, in situ RNA hybridization and mechanical stretching experiments.
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Referee #1
Evidence, reproducibility and clarity
The studies described in this manuscript investigated the mechanical regulation of tight junction (TJ) maturation in the epidermis using a combination of in vitro and in vivo analysis. The findings indicate that during calcium-induced cell-cell adhesion in keratinocytes, there is an initial build up cortical tension in the actin cytoskeleton, followed by an increase in membrane tension, which is required for formation of mature TJs. The studies also demonstrate that loss of Piezo1 delays TJ maturation via defects in membrane tension. Loss of Piezo1 also impaired epidermal homeostasis and barrier function in aged mice. The authors propose that the balance in forces between the cortex and membrane is essential for TJ assembly and is mediated by Piezo1.
Overall, the studies are carefully designed and executed and provide a clear role for membrane tension and Piezo1 in TJ development, making use of molecular forces sensors, imaging, and chemical and genetic perturbations. However, not all of the conclusions are fully supported by the data, and some key findings require additional quantitative and statistical analysis.
- The statement at the end of page 5 ("This indicated that formation of belt-like...) is somewhat overinterpreted from the data shown. To draw conclusions about a switch to reduced contractility at adhesions requires more careful spatio-temporal quantification of F-actin and pmyosin beyond the example single cells shown in 1b. It would also help to see the localization of Ecadherin during this process.
- To avoid confusion, the authors should pay careful attention to terminology and be specific when referring to adherens junctions or TJs, rather than just junctions generally.
- The labelling of Figure 2b could be clearer. Were the CNL cells also transfected with Piezo1 or mock transfected to control for general effects of transfection? This was not clear from the figure captions.
- In Figure 2c-g it is not specified which timepoints the images represent, and the qualitative description of changes in localisation require quantification.
- The importance of Piezo1 in junction maturation is somewhat overstated throughout. While Piezo KO clearly delays TJ maturation, the process can still be completed. In the absence of Piezo1 what triggers the rise in membrane tension? Could there be any compensation from Piezo2?
- Some of the differences noted are subtle and not strongly significant, such as K6expression, Ca++ induced Piezo1 expression, and F4/80 staining. The conclusions related to these responses should be tempered or qualified.
- Analysis of the immune infiltration and the suggested inflammatory response in aged mice is fairly preliminary and not well supported by the data. A second marker of macrophages and addition of T cell markers would help clarify the type of immune response. It would also help to describe the localisation of specific immune cells in more detail and include a direct marker of inflammation (e.g. inflammatory cytokines).
- OPTIONAL: Although not essential for the conclusions of the study, the impact and insight could be improved by providing more analysis of the mechanism for the role of Piezo1. For example, does the build up of cortical tension trigger changes in ion channel signalling, and how does this then regulate membrane tension? Is RhoA or aPKC involved?
Significance
The process by which epithelia assemble and maintain effective barriers is complex and requires precise spatio-temporal regulation. This study provides some new insight into the mechanical regulation of TJ assembly within the epidermis. It builds upon previous work that identified essential biomechanical cross-talk between adherens junctions and TJs and adds some new information on the timings and specific roles of membrane tension and Piezo1. The interplay between cortical and membrane tension is noteworthy, and this mechanism may have important implications in other barrier tissues. A limitation of the study is a lack of mechanistic detail in how the mechanical switch occurs during TJ maturation, including the specific molecules, structures, and interactions with Piezo1.
The study also describes the functional implications, whereby loss of Piezo1 in the mouse disrupts barrier integrity. However, these effects were quite subtle. Barrier homeostasis was only disrupted in aged mice, and in vitro, loss of Piezo1 delayed but did not prevent junction maturation. It is therefore interesting to speculate what other mechanisms may be involved in TJ maturation. A potential limitation here is also a lack of detail in the analysis of the inflammatory and immune response in Piezo KO skin.
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Reply to the reviewers
Response to Reviewers
We thank the reviewers for their comments and suggestions, which we think are helpful and will improve the manuscript, and intend to address with the changes and planned revisions below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.
We apologise for the mistake in the figure legend that made this confusing, which we have now revised.
The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.
We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, and have substantially reworded the manuscript accordingly.
Whilst the effects in the eQTL analysis are significant, it is worth noting that this is likely due to the much larger number of donors (133-217) giving greater power to detect the subtle changes in expression (~1.1 to 2 fold in eQTL). This change is of a similar magnitude in our SNP edited lines (~1.2 fold in SNP edited lines) as would be expected of most common regulatory variants so we believe that it could be the primary causal variant. However, we cannot exclude that other variants in the haplotype could contribute to the effect, so have also reworded accordingly to make this clear.
Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.
We apologise for the mistake in the figure legend that made this confusing, which we have now revised along with clarification in the revised text. It is difficult to be sure whether changes in chromatin accessibility are a cause or consequence of CEBPb binding, but the fact that the binding of CEBPb is increased in the CC allele (Fig 2a, Fig 2c), that the C allele better matches the consensus sequence (Fig 2b) and there is increased chromatin accessibility (Fig 2a, Supp Fig 3b) suggests that CEBPb binding is causing the formation of the region of chromatin accessibility.
In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?
We agree that the downstream effects of the SNP are much stronger than the effects on PTK2B expression, and we have substantially reworded the manuscript to make it clear that we are unsure that the effects of the SNP are all mediated via PTK2B.
However, we note that there is evidence in the literature of a loss in CCL4 and CCL5 expression upon PTK2B knockout in macrophages (https://www.nature.com/articles/s41467-021-27038-5) and inhibition of PTK2B in monocytes results in a reduction in CCL5 and CXCL1 (https://www.nature.com/articles/s41598-019-44098-2) consistent with our observations.
Experiments to manipulate PTK2B expression in microglia and readout changes at the RNA level would take a few months to complete, but we would be willing to do this if the reviewer felt this was necessary.
Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.
We apologise for the error in this figure which we have corrected in the revised version. You are correct that the CC lines have a lower chemokine level in both unstimulated and stimulated cells, and we have now discussed further how this may be linked to increased disease risk.
"Even though overexpression of these chemokines is characteristic of neuroinflammation, correlated with disease progression and found in late stages of AD, knockout of chemokines, such as CCL2, and chemokine receptors, such as CCR2 and CCR5, in mice is associated with increased Aβ deposition and accumulation [47,50-52,107]. It has also been found that patients carrying CCR5Δ32 mutation, which prevents CCR5 surface expression, develop AD at a younger age[108]. Therefore, we hypothesize that in individuals carrying the C/C allele of rs28834970 downregulation of these chemokines in macrophages and microglia harbouring the C/C allele of rs28834970 affects Aβ-induced microglia chemotaxis, leukocytes recruitment and clearance of Aβ, and may increase the risk of developing symptomatic AD"
Reviewer #1 (Significance (Required)):
Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
SUMMARY: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/Cas9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.
MAJOR COMMENTS
1- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet.
Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".
We agree with the reviewer that given the effect of the SNP on PTK2B expression in the edited lines is small and only significant in macrophages, we should not interpret the effects to be mediated solely through PTK2B expression, or that they cause AD. We have substantially reworded the manuscript throughout to account for this.
2- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change.
We have performed preliminary analyses of PTK2B expression by Western blot in these cell lines after differentiation, but were unable to observe a significant change in abundance in the edited cell lines. This is not unexpected given the results at the RNA level, since the effect size of this common regulatory variant is likely very small (estimated to be ~1.2 fold from the eQTL analysis), and likely within the variability of this assay.
As mentioned above, we have reworded the manuscript to avoid interpreting that the effects of rs28834970 are mediated solely through effects on PTK2B expression. We think that an experiment to manipulate PTK2B levels (see next point) may be a better way to demonstrate whether these effects are mediated through PTK2B expression.
An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.
We agree that this would be a valuable experiment, and are planning additional experiments to investigate the effect of manipulating PTK2B levels (through knockout) on microglia.
3- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.
We apologise for the errors in these figures that were due to a mistake during uploading where the incorrect versions were used. The legends for figure 2 and panels in figure 4 have now been corrected, and show the effects of rs28834970 on microglial migration and chemokine release in the presence or absence of IFNg.
4- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?
We thank the reviewer for this comment. We acknowledge that the t-test may lead to inflated false discovery rates. However, it has been shown that for small sample sizes parametric tests have a power advantage compared to non-parametric ones that may outweigh the possibly exaggerated false positives. See https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4#Sec3 which states:
"In conclusion, when the per-condition sample size is less than 8, parametric methods may be used because their power advantage may outweigh their possibly exaggerated false positives."
We have also modified the legend of supplementary figure 4E to clarify the number of replicates used.
5- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.
We now show individual replicates on box plots (Figure 2D, 2E and supp figure 4E).
MINOR COMMENTS:
a- Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?
We have now referenced the original papers and commented on the markers that we see differentially expressed, notably P2RY12 which is a key homeostatic microglia marker that distinguishes these cells from macrophages.
b- In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.
Whilst there may be small changes in CEBPb binding at the second intronic PTK2B chromatin peak, this is not statistically significant given the variability between repeats. In fact, the only significant change we see in CEBPb binding genome-wide is at the locus overlapping the SNP (Fig 2c).
c- Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.
You are correct that CHRNA2 and EPHX2 are not expressed in our macrophages or microglia, and we have now explicitly stated this in the revised text.
d- In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:
. Please increase font size whenever possible.
. Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).
. Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.
. Please label the Venn's diagrams comparisons in Sup. Fig. 4b.
. In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.
We have improved the resolution of the images in the pdf and Fig 1d has been revised to include the position of the SNP. The colour code for T/T and C/C is correct in fig 3a and 3b, but since the PCA plots are independently created, we would not always expect the position of the T/T and C/C alleles to be the same. The Venn diagrams in Sup Fig 4b have been updated, and the letters for the figure panels made consistently upper case throughout.
e- In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.
We have altered this accordingly.
f- In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.
We have now discussed the conflicting evidence in the revised manuscript.
Reviewer #2 (Significance (Required)):
ADVANCE: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.
An important point is to determine whether TàC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.
The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.
AUDIENCE: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.
REVIEWER EXPERTISE: Basic science close to the field.
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Referee #2
Evidence, reproducibility and clarity
Summary: In this manuscript the authors explore the biological effects of an intronic SNP in the PTK2B gene, previously shown to be associated with late onset Alzheimer's disease (AD) risk. Based on the likely effect of the SNP locus on PTK2B expression in the macrophage lineage, the authors explore the consequences of introducing with the Crispr/CAS9 technique the biallelic SNP base change (C/C vs T/T) in a human IPSC line that is then differentiated into macrophages or microglia. They observe that C/C increases chromatin accessibility and CEBPb binding in comparison to T/T, with a slight decrease in PTK2B expression, significant in macrophages but not in microglia. The authors then investigate the transcriptome changes induced by the C/C mutation and find alteration in many genes, including a decreased expression of a number of cytokine or receptor proteins involved in inflammatory responses. The authors also mention a decreased effect on IFNg-induced reduced mobility but the data are missing (see Figure errors below). Overall the authors propose that the risk SNP is associated with a decreased PTK2B expression and hypothesize a link between this change and a decreased function of macrophages/microglia that may contribute to AD pathology.
Major comments:
- The authors claim that their results show that the investigated SNP has a causal effects in "microglial function" (Title) and in Alzheimer's disease (AD) (Abstract 2nd sentence "Here we validate a causal single nucleotide polymorphism (SNP) associated with an increased risk of Alzheimer's disease". The word "causal" is repeated many times. However the authors should qualify their claim with respect to AD. Their results do show that the SNP has an effect on chromatin accessibility, CEBP binding, PTK2B expression and transcriptome, but the link between these changes is not formally demonstrated and their potential role in AD-like phenotype is not explored. The "causal" role is not formally and logically demonstrated. It remains an interesting, plausible hypothesis and the results provide strong arguments in support of that hypothesis but do not prove it, yet. Concerning the title, "causal effects on microglial function" is awkward, anything that has effects is logically "causal" in these effects. The title should be "... has effects on microglial functions" or "... alters microglial function".
- One major difficulty in the results is to link the slight decrease in PTK2B transcript, which is only significant in macrophages, with the rest of the phenotype. Because what matters to make this link is not the mRNA but the protein, and because mRNA levels are often not strictly correlated with the protein levels, the authors should measure the PTK2B/PYK2 protein levels in their differentiated cell lines in basal conditions and following activation (as they do for other readouts) using immunoblotting. A robust and significant diminution in PYK2 protein would strongly support its role in linking PTK2B expression and transcriptome change. An optional additional key experiment would be to reverse the transcriptome phenotype by increasing the expression of PTK2B (e.g. by cDNA transfection). Note that these points are important because an alternative hypothesis to explain the effects of C/C mutation on macrophage function would be that the C/C mutation has a long distance effect on other chromatin regions with key role in regulating these cells.
- The manuscript contains several errors in the figures and figure legends. In Fig. 2 the legends for the figure items are shuffled. Figure 4 and Supplementary Figure 5 are duplicates of the same one. Consequently important data are not presented.
- When the number of replicates is small (e.g. n = 3) it is preferable to use non parametric tests (rank analysis, e.g. Mann Whitney's test) rather than t test. This applies to Figures 2D (current legend 2A), 2E (current legend 2B), Figure 4A-C, Supplementary Figures 2A, 2B. In Supplementary Fig 4E (MARCO) the number of replicates (presumably 3 because based on RNAseq) and the used test are not indicated. Is it the RNAseq statistical analysis?
- In addition to the above comment on tests, when the number of replicates is small it is not appropriate (and misleading) to show box plots or bars with SEM. In the indicated figures the individual data points should be shown.
Minor comments:
- a. Macrophages and microglia are very similar cell types. Could the authors comment more on the differences they observe and how they are related to those previously described?
- b. In Fig. 2A CEBPb cut and run plot, the differences are not limited to the SNP immediate vicinity, there are also visible differences between T/T and C/C plots in at least a 40-kb range. Is it due to multiple interactions of CEBPb? How can the point difference have broad consequences? Please explain this potentially interesting and relevant finding.
- c. Potentially cis-altered genes near the SNP include CHRNA2 and EPHX2 (see Sup. Fig. 3a). Their expression may not be detected in macrophage lineage. If this is the case please indicate in the text, otherwise please include the corresponding data in Sup. Fig. 3b to show the presence or absence of SNP-induced change.
- d. In general the Figures are not of very high quality and are difficult to read or understand without constantly going back and forth to the legends (which are mislabeled in some instances). To improve:
- Please increase font size whenever possible.
- Please improve Fig. 1d by indicating the position of the SNP, numbering the exons (an intermediate scale plot may be necessary and lines on bottom trace are hardly visible).
- Please indicate the correct color code for T/T and C/C in Fig 3a and b, left panels, which currently doesn't match.
- Please label the Venn's diagrams comparisons in Sup. Fig. 4b.
- In the text and legends the Figure items are identified with letters in upper case, in the figures they are in lower case. Please be consistent.
- e. In Fig. 2D and 2E, the Y axes should start at zero to avoid artificially increasing the visual differences. If there is a strong reason not to do so (I don't see any here), the Y axis should be clearly interrupted to avoid confusion.
- f. In the introduction the authors provide some background about previous work about the potential role of PTK2B/PYK2 in AD pathophysiology. The cited preclinical results suggest that PTK2B activity could have a deleterious effect (references in the manuscript). In contrast, some other reports (PMID: 29803828, 33718872) suggest a protective effect of PTK2B/PYK2. Because the evidence in the current manuscript suggests that the risk-associated SNP results in a decreased function of PTK2B/PYK2 (through decreased levels), at least in cells of the macrophage lineage, the authors could broaden their discussion to include these results.
Significance
Advance: Late onset Alzheimer's disease is a major medical issue. It has a complex genetic risk component with many associated loci identified in GWAS. Most of these have only a small individual impact on the risk. One of the SNPs associated with increased risk (rs28834970) is located in an intron of the PTK2B gene. Although various reports have investigated the role of the PTK2B gene product, the tyrosine kinase PYK2, in several AD models, the possible link with rs28834970, is unclear.
An important point is to determine whether TC SNP corresponding to rs28834970 alters PTK2B expression and how it does so. An alternative hypothesis could be that the SNP has a strong linkage disequilibrium with an unidentified allele in human populations that could be responsible for AD risk. The current manuscript is a significant step forward in addressing that question. By generating a biallelic C/C SNP mutation in a human IPSC line the current study allows to eliminate such linked contribution.
The strength of the manuscript is to show an effect on chromatin accessibility, CEBP binding and possibly PTK2B transcripts. It also provides interesting evidence of a broad effect of the C/C mutation on the transcriptome of macrophage lineage cells. In its current form the manuscript presents weaknesses that could be improved. These flaws include issues with the presentation discussed above and the uncomplete demonstration that it is the decrease in PTK2B expression that causes the macrophage/microglia phenotype. If these flaws were overcome the paper would represent a significant advance.
Audience: The expected audience is specialized in AD with a possible broader range if all weaknesses are addressed.
Reviewer Expertise: Basic science close to the field.
-
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Referee #1
Evidence, reproducibility and clarity
Bello et al look at the SNP rs28834970 associated with Alzheimer's disease (AD), with C being the risk allele, on chromatin accessibility and expression of a nearby gene, PTK2B, in microglia. Their contention is that the single SNP affects chromatin accessibility and binding of the transcription factor CEBP[beta] in an intronic region of PTK2B and thereby affects PTKB expression. I had a few questions that I think are critical to be addressed. Please note that my numbering of panels is based on the figures, not the legends, which do not seem to quite agree with each other. There are also some figure legends that say "IFNg" while the figures say "LPS", which should be fixed.
The abstract says that editing a line that is homozygous for protective alleles to homozygous for risk results in "subtle downregulation of PTK2B expression". It isn't clear to me that the presented data fully supports this contention, which is central to the argument of the paper. In figure 2e, the authors show in both RNAseq and ddPCR that there is numerically lower PTK2B expression but this is not indicated to be statistically significant by one-way paired ANOVA. If there is no nominally significant difference in the edited lines, compared to the proposed significant differences in lines carrying the full risk haplotype (figure 1), then it would not seem sensible to ascribe the effects to the single edited base pair.
Given this uncertainty about the overall strength of effect of the single base pair change it would seem important to evaluate the proposed mechanism of CEBPb binding. It wasn't clear whether the ATAC-seq data summarized in the volcano plot in 2C is proposed to be a cause or a consequence of the CEBPb binding change. I am assuming that the 'fold change' estimate here is CC compared to TT, which would be consistent with direction of effect in figure 1, but please clarify.
In contrast to the subtle effects at PTK2B, the global transcriptional effects in figure 3 look quite strong. Are any of these changes dependent on PTK2B, that is to say, are they mimicked by partial suppression of PTK2B expression or activity?
Finally, in figure 4, it should be clarified as to why lower expression of PTK2B would be expected to have a detrimental effect on Alzheimer's risk. If understood correctly, and again fixing the figure legends would be helpful, the CC edited lines (risk) have lower chemokine induction than the unedited TT lines.
Significance
Going from GWAS hits, which represent blocks of high LD inherited variants, to single functional variants is a difficult problem in human genetics. The current paper attempts to isolate the effect of a single variant within an LD block on IPSC derived macrophages and microglia. This idea might be useful in nominating PTK2B as a therapeutic target for AD, although there is some question in my mind as to direction of effect.
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Reply to the reviewers
Reply to the Reviewers
I would like to thank the reviewers for their comments and interest in the manuscript and the study.
Referee #1
- I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.
Response: The directional positioning of CTCF-binding sites at chromatin interaction sites was analyzed by CRISPR experiment (Guo Y et al. Cell 2015). We found that the machine learning and statistical analysis showed the same directional bias of the CTCF-binding motif sequence at chromatin interaction sites as the experimental analysis of Guo Y et al. (lines 229-245, Figure 3b, c, d and Table 1). Since CTCF is involved in different biological functions (Braccioli L et al. Essays Biochem. 2019 ResearchGate webpage), the directional bias of binding sites may be reduced in all binding sites including those at chromatin interaction sites (lines 68-73). In our study, we investigated the DNA-binding sites of proteins using the ChIP-seq data of DNA-binding proteins and DNase-seq data. We also confirmed that the DNA-binding sites of SMC3 and RAD21, which tend to be found in chromatin loops with CTCF, also showed the same directional bias as CTCF by the computational analysis.
- Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.
Response: Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.
- Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.
Response: As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality of insulator-associated DNA-binding sites is their overall tendency, and it may be difficult to notice the directionality from each binding site because the directionality may be weaker than that of CTCF, RAD21, and SMC3 as shown in Table 1 and Supplementary Table 2.
I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.
- The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
Response: According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value 5. Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
Response: I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.
- Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.
Response: Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2's comments.
Referee #2
- Introduction, line 95: CTCF appears two times, it seems redundant.
Response: On lines 91-93, I deleted the latter CTCF from the sentence "and examined the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".
- Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?
Response: Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.
I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.
- Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.
Response: On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".
- Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.
Response: On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines". On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".
- Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.
Response: The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.
- Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?
Response: As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).
In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.
FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.
- In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?
Response: Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.
The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.
As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The 'loop extrusion' hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.
Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.
- Do the authors think that the identified DBPs could work in that way as well?
Response: The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).
Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.
- Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?
Response: Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.
- Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?
Response: Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 - 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops. To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).
Referee #3
- Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.
Response: When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 20 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.
- I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.
Response: As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 - 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.
- I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.
Response: Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 - 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 - 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).
- I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.
Response: The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.
Minor comments:
- PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.
Response: The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.
As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.
- DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.
Response: In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.
Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.
Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Osato and Hamada propose a systematic approach to identify DNA binding proteins that display directional binding. They used a modified Deep Learning method (DEcode) to investigate binding profiles of 1356 DBP from GTRD database at promoters (30 of 100bp bins around TSS) and enhancers (200 bins of 10Kb around eSNPs) and use this to predict expression of 25,071 genes in Fibroblasts, Monocytes, HMEC and NPC. This method achieves a good prediction power (Spearman correlation between predicted and actual expression of 0.74). They then use PIQ, and overlap predicted binding sites with actual ChIP-seq data to investigate the motifs of TFs that are controlling gene expression. They find 99 insulator proteins showing either a specific directional bias or minor non-directional bias, corresponding to 23 DBP previously reported to have insulator function. Of the 23 proteins they identify as regulating enhancer promoter interactions, 13 are associated with CTCF. They also show that there are significantly more insulator proteins binding sites at borders of polycomb domains, transcriptionally active or boundary regions based on chromatin interactions than other proteins.
Major Comments:
- Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.
- I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.
- I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.
- I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.
Minor comments:
- PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g., https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.
- DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.
Referee Cross-Commenting
I would like to mention that I agree with the comments of reviewers 1 and 2.
Significance
General assessment:
This is the first study to my knowledge that attempts to use Deep Learning to identify insulators and directional biases in binding. One of the limitations is that no additional methods were used to show that these DBP have directional binding bias. It is not necessarily to employ additional methods, but it would definitely strengthen the paper.
Advancements:
This is a useful catalogue of potential DNA binding proteins of interest, beyond just CTCF. Some known TFs are there, but also new ones are found.
Audience:
Basic research mainly, with particular focus on chromatin conformation and TF binding fields.
My expertise:
ML/AI methods in genomics, TF binding models, epigenetics and 3D chromatin interactions.
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Referee #2
Evidence, reproducibility and clarity
In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.
In general, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see my points below). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the following list.
Also, I encourage the authors to integrate the current presentation of the data with other (published) data about chromatin architecture, to make more robust the claims and go deeper into the biological implications of the current work. Se my list below.
It follows a specific list of relevant points to be addressed:
Specific points:
- Introduction, line 95: CTCF appears two times, it seems redundant;
- Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?
- Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS;
- Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details;
- Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.
- Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?
- In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?
- Do the authors think that the identified DBPs could work in that way as well?
- Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?
- Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?
Significance
In this work, the authors describe a deep learning computational tool to identity binding motifs of DNA binding proteins associated to insulators that led to the discovery of 99 motifs related to insulation. This is in turn related to chromatin architecture and highlight the importance of directional bias in order to form chromatin loops.
In general, chromatin organization is an important topic in the context of a constantly expanding research field. Therefore, the work is timely and could be useful for the community. The paper appears overall well written and the figures look clear and of good quality. Nevertheless, there are some aspects to be clarified and better explored to make stronger conclusions. In particular, there are some aspects to clarify in the text about the Machine Learning procedure (see list of specific points). In addition, I have some general questions about the biological implications of the discussed findings, listed in detail in the above reported points.
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Referee #1
Evidence, reproducibility and clarity
The study by Osato and Hamada aims at computationally identifying a set of novel putative insulator-associated DNA binding proteins (DBPs) via estimation of their contribution to the expression of genes in the same chromosome region of their binding sites (+- 1Mbp from TSS). To achieve this, the authors leverage a deep learning architecture already published via which ChIP-seq peaks of DBPs in the TSS of a given gene are used to predict its expression level in four human cell lines.
Building on this, the authors used another tool called DeepLIFT to evaluate the weight of each DBP binding site on the final gene expression value. Hence they made the assumption that if a given DBP had an insulator function they could restrict the prediction of the gene's expression to the region included between pairs of that DBP binding sites, and evaluate the pair's motif directionality bias in the distribution of weights. They exemplify their approach's validity by the fact that they can predict the known directionality bias of CTCF/cohesin-bound sites as the highest of the lot, with the F-R orientation of the pairs the most enriched, recapitulating what already known in literature: i.e., that F-R chromatin interaction peaks are the most enriched. In addition, they find several new DBPs showing significant directionality bias; hence they could be candidates for insulation activity. They then provide correlation between these putative insulator binding sites and sites of transition between euchromatin and heterochromatin by independently using histone mark and gene expression datasets. This, of course, is not surprising because (a) there is insulation between regions with heterotypic chromatin identities, and (b) it was already known from the first papers describing insulated chromatin domains that their boundaries were well-enriched for active transcription and transcriptional regulators (e.g., Dixon et al, Nature 2012).
Finally, they use chromatin interaction (looping) sites to check the overlap between CTCF and all other DBPs and define a subset of putative insulator DBPs not overlapping CTCF peaks, suggesting potentially new insulatory mechanisms. These factors were all known transcriptional activators, but this part of the findings carry most of the novelty in the work and have the potential of opening up new directions for research in chromatin organization.
Overall, the methodology applied here is adequate, clear, and reproducible. The major issue, in our view, is that the entire manuscript's findings relies on the usage of deepLIFT, a tool which was not benchmarked previously or by the current study. In fact, deepLIFT is public as regards its code, and also appears as a preprint from 2017 on biorXiv and published in the Proceedings of Machine Learning Research conference. Also, this key tool was developed by the Kundaje lab (who produce high quality alogrithms), and not by the authors. Therefore, the manuscript is predominantly based on the execution of existing workflows to publicly-available data. This does not take anything away from the interesting question posed here, but at the same time does not provide the community with any new algorithm/workflow.
Finally, although I appreciate that the authors are purely computational and have likely no capacity for experimental validation of their claims of new DBPs having insulator roles, I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning. Using this kind of data, effects on gene expression can at least be tested in regard to the authors' predictions. Moreover, in terms of validation, Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure. Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.
As secondary issues, we would point out that:
- The suggested alternative transcripts function, also highlighted in the manuscript;s abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
- Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
- Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.
Significance
The scientific novelty of the work lies primarily in the identification of a set of DBPs that are proposed to confer insulator activity genome-wide. This has been long sought after in human data (whilst it is well understood and defined in Drosophila). The authors produce a quantitative ranking of the putative insulation effect of these DBPs and, most importantly, go on to identify a smaller subset that are apparently non-overlapping with anchors of CTCF-cohesin loop anchors; the presence of strong motif orientation biases in many DBPs can also be of broad interest, especially those that cannot be trivially ascribable to the loop extrusion process.
However, although these findings open the way for speculation on multiple insulation mechanisms via proteins with multiple regulatory functions, the manuscript provide no experimental or computational means to test the proposed roles of these DBPs - and, as such, this limits the potential impact of the work and mostly targets researchers in the field of genome organization that can test these findings. Having said this, if validated, this work can significantly broaden our understanding of how chromatin is organized in 3D nuclear space.
I typically identify myself to the authors: A. Papantonis, expertise in 3D genome architecture, chromatin biology, and genomics/bioinformatics.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.
In the study, performed experiments are well described.
My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.
Major comments:
- What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms. Response: Thank you very much for your insightful comments. 1) To address "what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment1. We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Figure R1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with anti-DO-1 (mouse) antibody (Figure R1). The latter detects both endogenous wild-type p53 and the V5-tagged FLp53 since the antibody epitope is within the N-terminus (aa 20-25). This result supports the reviewer's comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments. (Figure R1 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__
In summary, in line with the reviewer's comment that 'under normal physiological conditions p53 expression is usually low,' we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Figures R1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.
2) We agree with the reviewer that 'It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario'. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.
Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.
Response: Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Figure R1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.
3. On similar lines, authors described:
"To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."
Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.
Response: Thank you for raising this point regarding the physiological relevance of the ratios used in our study. 1) In the revised manuscript (lines 193-195), we added in this direction that "The elevated Δ133p53 protein modulates p53 target genes such as miR‑34a and p21, facilitating cancer development2, 3. To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10." This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.
2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p534, 5. Additionally, ∆133TP53 is a p53 target gene6, 7 and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing8. Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp538. These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies3, 4, 9, 10.
3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Figure R1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.
4. Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.
Response: Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). "The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.
One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death11. To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis12. The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope."
**Referees cross-commenting**
I think the comments from the other reviewers are very much reasonable and logical.
Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.
Response: Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53. To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation.
Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure R3). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions.
Reviewer #1 (Significance (Required)):
The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.
Response: Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.
This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:
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- Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.* Response: Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): "Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer13, 14, 15. Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines16. Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells17. Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions."
2. Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.
Response: Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure R3, lower panel), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation__.__ However, no FLp53 aggregates were observed when it was expressed alone (Figure R2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation.
(Figure R2 is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__
3. Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).
Response: We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa). Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation18, 19. Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)20, 21, 22, potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.
Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.
Reviewer #2 (Significance (Required)):
The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.
Response: We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.
First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).
Response: Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): "For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript."
One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:
- Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599.
- Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.
- Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10.
- Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.
- Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.
- Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.
- Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90. Response: Thank you very much for your comment and for highlighting these important studies.
We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. 'pro-oncogenic activity' with 'dominant-negative effect' in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.
After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases. Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:
- Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.
- Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.
- Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.
- Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.
- Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.
- Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed non-cancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. "Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative inhibition of a subset of p53 target genes."
- Gong, 2016: Suggested that Δ133p53 promotes cell survival under low-level oxidative stress, but its role under different stress conditions remains uncertain. We have revised the Introduction to provide a more balanced discussion of Δ133p53's dule role (lines 62-73):
"The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations23, 24, where it promotes cell survival in a non-oncogenic manner25, 26, especially under low oxidative stress27. Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 4, 6. The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis28 and in melanoma cells' aggressiveness10. Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a2, 29 by dominant-negative effect, the exact mechanism is not known."
On the figures presented in this manuscript, I have three major concerns:
*1- Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. *
Response: Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.
First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.
To further rule out any tag-related artifacts, we conducted a co-immunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5-tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAG-tagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone.
In the revised paper, we added the following sentences (Lines 146-152): "To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAG-tagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other."
Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We've added this result in the revised manuscript (lines 236-245): "To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation."
We've also discussed this in the Discussion section (lines 349-356): "In our study, we primarily utilized an overexpression strategy involving FLAG/V5-tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitope-containing proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins."
In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.
2- The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.
Response: Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions3, 4, 9. For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue4. Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells3. Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:19. These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.
Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.
3-The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.
Response: Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)4, 29, 30, 31. Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p534. Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure below. The discrepancy may be caused by a potentially confusing statement in that paper4
(The Figure from Bourdon JC et al. (2005) is included in the file "RC-2024-02608 Figures of Response to Reviewer.)__
The localization of p53 is governed by multiple factors, including its nuclear import and export32. The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)4 . However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import.
Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence33. We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy34. This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.
In the revised manuscript, we discussed this in Discussion (lines 328-335): "Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B33. High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy34. Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems."
Minor concerns:
- Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"
Response: Thank you! The revised Figure 1A has been created in the revised paper.
- Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands
Response: Thank you for this suggestion. We've changed the image and the new Figure 2 has been shown in the revised paper.
- Figure 3C: what ratio of FLp53/Delta isoform was used?
Response: We have added the ratio in the figure legend of Figure 3C (lines 845-846) "Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio."
- Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.
Response: Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53.
These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53's anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominant-negative effects.
We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.
- Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.
Response: Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated. Changes have been made in lines 214-215: "The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B)."
**Referees cross-commenting**
I agree that the system needs to be improved to be more physiological.
Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.
Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).
To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.
Response: Thank you for these comments. We've addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not 'overwhelmingly high'.
First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development2, 3, 4, 9. Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We've discussed this in the Results section (lines 254-269).
Reviewer #3 (Significance (Required)):
Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.
The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.
The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.
This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.
My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases
Response: Thank you very much for your positive and critical comments. We've included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73).
References
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Fujita K, et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nature cell biology 11, 1135-1142 (2009).
Fragou A, et al. Increased Δ133p53 mRNA in lung carcinoma corresponds with reduction of p21 expression. Molecular medicine reports 15, 1455-1460 (2017).
Bourdon JC, et al. p53 isoforms can regulate p53 transcriptional activity. Genes & development 19, 2122-2137 (2005).
Ghosh A, Stewart D, Matlashewski G. Regulation of human p53 activity and cell localization by alternative splicing. Molecular and cellular biology 24, 7987-7997 (2004).
Aoubala M, et al. p53 directly transactivates Δ133p53α, regulating cell fate outcome in response to DNA damage. Cell death and differentiation 18, 248-258 (2011).
Marcel V, et al. p53 regulates the transcription of its Delta133p53 isoform through specific response elements contained within the TP53 P2 internal promoter. Oncogene 29, 2691-2700 (2010).
Zhao L, Sanyal S. p53 Isoforms as Cancer Biomarkers and Therapeutic Targets. Cancers 14, (2022).
Nutthasirikul N, Limpaiboon T, Leelayuwat C, Patrakitkomjorn S, Jearanaikoon P. Ratio disruption of the ∆133p53 and TAp53 isoform equilibrium correlates with poor clinical outcome in intrahepatic cholangiocarcinoma. International journal of oncology 42, 1181-1188 (2013).
Tadijan A, et al. Altered Expression of Shorter p53 Family Isoforms Can Impact Melanoma Aggressiveness. Cancers 13, (2021).
Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour suppression? Cell death and differentiation 25, 104-113 (2018).
Ghorbani N, Yaghubi R, Davoodi J, Pahlavan S. How does caspases regulation play role in cell decisions? apoptosis and beyond. Molecular and cellular biochemistry 479, 1599-1613 (2024).
Petronilho EC, et al. Oncogenic p53 triggers amyloid aggregation of p63 and p73 liquid droplets. Communications chemistry 7, 207 (2024).
Forget KJ, Tremblay G, Roucou X. p53 Aggregates penetrate cells and induce the co-aggregation of intracellular p53. PloS one 8, e69242 (2013).
Farmer KM, Ghag G, Puangmalai N, Montalbano M, Bhatt N, Kayed R. P53 aggregation, interactions with tau, and impaired DNA damage response in Alzheimer's disease. Acta neuropathologica communications 8, 132 (2020).
Arsic N, et al. Δ133p53β isoform pro-invasive activity is regulated through an aggregation-dependent mechanism in cancer cells. Nature communications 12, 5463 (2021).
Melo Dos Santos N, et al. Loss of the p53 transactivation domain results in high amyloid aggregation of the Δ40p53 isoform in endometrial carcinoma cells. The Journal of biological chemistry 294, 9430-9439 (2019).
Mestrom L, et al. Artificial Fusion of mCherry Enhances Trehalose Transferase Solubility and Stability. Applied and environmental microbiology 85, (2019).
Kaba SA, Nene V, Musoke AJ, Vlak JM, van Oers MM. Fusion to green fluorescent protein improves expression levels of Theileria parva sporozoite surface antigen p67 in insect cells. Parasitology 125, 497-505 (2002).
Snapp EL, et al. Formation of stacked ER cisternae by low affinity protein interactions. The Journal of cell biology 163, 257-269 (2003).
Jain RK, Joyce PB, Molinete M, Halban PA, Gorr SU. Oligomerization of green fluorescent protein in the secretory pathway of endocrine cells. The Biochemical journal 360, 645-649 (2001).
Campbell RE, et al. A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America 99, 7877-7882 (2002).
Hofstetter G, et al. Δ133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. British journal of cancer 105, 1593-1599 (2011).
Bischof K, et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Scientific reports 9, 5244 (2019).
Gong L, et al. p53 isoform Δ113p53/Δ133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell research 25, 351-369 (2015).
Gong L, et al. p53 isoform Δ133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Scientific reports 6, 37281 (2016).
Gong L, Pan X, Yuan ZM, Peng J, Chen J. p53 coordinates with Δ133p53 isoform to promote cell survival under low-level oxidative stress. Journal of molecular cell biology 8, 88-90 (2016).
Candeias MM, Hagiwara M, Matsuda M. Cancer-specific mutations in p53 induce the translation of Δ160p53 promoting tumorigenesis. EMBO reports 17, 1542-1551 (2016).
Horikawa I, et al. Δ133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell death and differentiation 24, 1017-1028 (2017).
Mondal AM, et al. Δ133p53α, a natural p53 isoform, contributes to conditional reprogramming and long-term proliferation of primary epithelial cells. Cell death & disease 9, 750 (2018).
Joruiz SM, Von Muhlinen N, Horikawa I, Gilbert MR, Harris CC. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell death & disease 15, 454 (2024).
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Horikawa I, et al. Autophagic degradation of the inhibitory p53 isoform Δ133p53α as a regulatory mechanism for p53-mediated senescence. Nature communications 5, 4706 (2014).
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Referee #3
Evidence, reproducibility and clarity
In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by co-aggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominant-negative effect over it.
First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).
One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples: Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 1593-1599. Bischof, K. et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244. Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1-578.e10. Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369. Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281. Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028. Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90.
On the figures presented in this manuscript, I have three major concerns:
- Most results in the manuscript rely on the overexpression of the FLAG-tagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rule[s] out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added.
- The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.
- Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation. The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.
Minor concerns:
- Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"
- Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands
- Figure 3C: what ratio of FLp53/Delta isoform was used?
- Figure 3C suggests that the "dominant-negative" effect is mostly senescence-specific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.
- Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.
Referees cross-commenting
I agree that the system needs to be improved to be more physiological.
Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.
Using overexpression always raises concerns, but in this case I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).
To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.
Significance
Overall, the paper is interesting particularly considering the range of techniques used which is the main strength. The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.
The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.
This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.
My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.
This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:
- Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.
- Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.
- Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).
Significance
The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the co-aggregation of FLp53 with Δ133p53 and Δ160p53.
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Referee #1
Evidence, reproducibility and clarity
Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.
In the study, performed experiments are well described.
My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.
Major comments:
- What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.
- Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.
- On similar lines, authors described: "To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)." Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.
- Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.
Referees cross-commenting
I think the comments from the other reviewers are very much reasonable and logical. Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.
Significance
The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.
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- Feb 2025
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
I would like to thank the reviewers for their comments and interest in the manuscript and the study.
Reviewer #1
1) I would assume that there are RNA-seq and/or ChIP-seq data out there produced after knockdown of one or more of these DBPs that show directional positioning.
As the reviewer pointed out, a wet experimental validation of the results of this study would give an opportunity for more biological researchers to have an interest in the study. I plan to promote the wet experimental analysis in collaboration with biological experimental researchers as a next step of this study. The same analysis in this study can be performed in immortalized cells for CRISPR experiment (e.g. Guo Y et al. Cell 2015).
2) Figure 6 should be expanded to incorporate analysis of DBPs not overlapping CTCF/cohesin in chromatin interaction data that is important and potentially more interesting than the simple DBPs enrichment reported in the present form of the figure.
Following the reviewer's advice, I performed the same analysis with the DNA-binding sites that do no overlap with the DNA-binding sites of CTCF and cohesin (RAD21 and SMC3) (Fig. 6 and Supplementary Fig. 4). The result showed the same tendency in the distribution of DNA-binding sites. The height of a peak on the graph became lower for some DNA-binding proteins after removing the DNA-binding sites that overlapped with those of CTCF and cohesin. I have added the following sentence on lines 427 and 817: For the insulator-associated DBPs other than CTCF, RAD21, and SMC3, the DNA-binding sites that do not overlap with those of CTCF, RND21, and SMC3 were used to examine their distribution around interaction sites.
3) Critically, I would like to see use of Micro-C/Hi-C data and ChIP-seq from these factors, where insulation scores around their directionally-bound sites show some sort of an effect like that presumed by the authors - and many such datasets are publicly-available and can be put to good use here.
As suggested by the reviewer, I have added the insulator scores and boundary sites from the 4D nucleome data portal as tracks in the UCSC genome browser. The insulator scores seem to correspond to some extent to the H3K27me3 histone marks from ChIP-seq (Fig. 4a and Supplementary Fig. 3). The direction of DNA-binding sites on the genome can be shown with different colors (e.g. red and green), but the directionality is their overall tendency, and it may be difficult to notice the directionality from each binding site.
I found that the CTCF binding sites examined by a wet experiment in the previous study may not always overlap with the boundary sites of chromatin interactions from Micro-C assay (Guo Y et al. Cell 2015). The chromatin interaction data do not include all interactions due to the high sequencing cost of the assay. The number of the boundary sites may be smaller than that of CTCF binding sites acting as insulators and/or some of the CTCF binding sites may not be locate in the boundary sites. It may be difficult for the boundary location algorithm to identify a short boundary location. Due to the limitations of the chromatin interaction data, I planned to search for insulator-associated DNA-binding proteins without using chromatin interaction data in this study. I have added the statistical summary of the analysis in lines 364-387 as follows: Overall, among 20,837 DNA-binding sites of the 97 insulator-associated proteins found at insulator sites identified by H3K27me3 histone modification marks (type 1 insulator sites), 1,315 (6%) overlapped with 264 of 17,126 5kb long boundary sites, and 6,137 (29%) overlapped with 784 of 17,126 25kb long boundary sites in HFF cells. Among 5,205 DNA-binding sites of the 97 insulator-associated DNA-binding proteins found at insulator sites identified by H3K27me3 histone modification marks and transcribed regions (type 2 insulator sites), 383 (7%) overlapped with 74 of 17,126 5-kb long boundary sites, 1,901 (37%) overlapped with 306 of 17,126 25-kb long boundary sites. Although CTCF-binding sites separate active and repressive domains, the limited number of DNA-binding sites of insulator-associated proteins found at type 1 and 2 insulator sites overlapped boundary sites identified by chromatin interaction data. Furthermore, by analyzing the regulatory regions of genes, the DNA-binding sites of the 97 insulator-associated DNA-binding proteins were found (1) at the type 1 insulator sites (based on H3K27me3 marks) in the regulatory regions of 3,170 genes, (2) at the type 2 insulator sites (based on H3K27me3 marks and gene expression levels) in the regulatory regions of 1,044 genes, and (3) at insulator sites as boundary sites identified by chromatin interaction data in the regulatory regions of 6,275 genes. The boundary sites showed the highest number of overlaps with the DNA-binding sites. Comparing the insulator sites identified by (1) and (3), 1,212 (38%) genes have both types of insulator sites. Comparing the insulator sites between (2) and (3), 389 (37%) genes have both types of insulator sites. From the comparison of insulator and boundary sites, we found that (1) or (2) types of insulator sites overlapped or were close to boundary sites identified by chromatin interaction data.
4) The suggested alternative transcripts function, also highlighted in the manuscripts abstract, is only supported by visual inspection of a few cases for several putative DBPs. I believe this is insufficient to support what looks like one of the major claims of the paper when reading the abstract, and a more quantitative and genome-wide analysis must be adopted, although the authors mention it as just an 'observation'.
According to the reviewer's comment, I performed the genome-wide analysis of alternative transcripts where the DNA-binding sites of insulator-associated proteins are located near splicing sites. The DNA-binding sites of insulator-associated DNA-binding proteins were found within 200 bp centered on splice sites more significantly than the other DNA-binding proteins (Fig. 4e and Table 2). I have added the following sentences on lines 397 - 404: We performed the statistical test to estimate the enrichment of insulator-associated DNA-binding sites compared to the other DNA-binding proteins, and found that the insulator-associated DNA-binding sites were significantly more abundant at splice sites than the DNA-binding sites of the other proteins (Fig 4e and Table 2; Mann‒Whitney U test, p value < 0.05). The comparison between the splice sites of both ends of first and last introns and those of other introns showed the similar statistical significance of enrichment and number of splice sites with the insulator-associated DNA-binding proteins (Table 2 and Table S9).
5) Figure 1 serves no purpose in my opinion and can be removed, while figures can generally be improved (e.g., the browser screenshots in Figs 4 and 5) for interpretability from readers outside the immediate research field.
I believe that the Figure 1 would help researchers in other fields who are not familiar with biological phenomena and functions to understand the study. More explanation has been included in the Figures and legends of Figs. 4 and 5 to help readers outside the immediate research field understand the figures.
6) Similarly, the text is rather convoluted at places and should be re-approached with more clarity for less specialized readers in mind.
Reviewer #2's comments would be related to this comment. I have introduced a more detailed explanation of the method in the Results section, as shown in the responses to Reviewer #2’s comments.
Reviewer #2
1) Introduction, line 95: CTCF appears two times, it seems redundant.
On lines 91-93, I deleted the latter CTCF from the sentence "We examine the directional bias of DNA-binding sites of CTCF and insulator-associated DBPs, including those of known DBPs such as RAD21 and SMC3".
2) Introduction, lines 99-103: Please stress better the novelty of the work. What is the main focus? The new identified DPBs or their binding sites? What are the "novel structural and functional roles of DBPs" mentioned?
Although CTCF is known to be the main insulator protein in vertebrates, we found that 97 DNA-binding proteins including CTCF and cohesin are associated with insulator sites by modifying and developing a machine learning method to search for insulator-associated DNA-binding proteins. Most of the insulator-associated DNA-binding proteins showed the directional bias of DNA-binding motifs, suggesting that the directional bias is associated with the insulator.
I have added the sentence in lines 96-99 as follows: Furthermore, statistical testing the contribution scores between the directional and non-directional DNA-binding sites of insulator-associated DBPs revealed that the directional sites contributed more significantly to the prediction of gene expression levels than the non-directional sites. I have revised the statement in lines 101-110 as follows: To validate these findings, we demonstrate that the DNA-binding sites of the identified insulator-associated DBPs are located within potential insulator sites, and some of the DNA-binding sites in the insulator site are found without the nearby DNA-binding sites of CTCF and cohesin. Homologous and heterologous insulator-insulator pairing interactions are orientation-dependent, as suggested by the insulator-pairing model based on experimental analysis in flies. Our method and analyses contribute to the identification of insulator- and chromatin-associated DNA-binding sites that influence EPIs and reveal novel functional roles and molecular mechanisms of DBPs associated with transcriptional condensation, phase separation and transcriptional regulation.
3) Results, line 111: How do the SNPs come into the procedure? From the figures it seems the input is ChIP-seq peaks of DNBPs around the TSS.
On lines 121-124, to explain the procedure for the SNP of an eQTL, I have added the sentence in the Methods: "If a DNA-binding site was located within a 100-bp region around a single-nucleotide polymorphism (SNP) of an eQTL, we assumed that the DNA-binding proteins regulated the expression of the transcript corresponding to the eQTL".
4) Again, are those SNPs coming from the different cell lines? Or are they from individuals w.r.t some reference genome? I suggest a general restructuring of this part to let the reader understand more easily. One option could be simplifying the details here or alternatively including all the necessary details.
On line 119, I have included the explanation of the eQTL dataset of GTEx v8 as follows: " The eQTL data were derived from the GTEx v8 dataset, after quality control, consisting of 838 donors and 17,382 samples from 52 tissues and two cell lines”. On lines 681 and 865, I have added the filename of the eQTL data "(GTEx_Analysis_v8_eQTL.tar)".
5) Figure 1: panel a and b are misleading. Is the matrix in panel a equivalent to the matrix in panel b? If not please clarify why. Maybe in b it is included the info about the SNPs? And if yes, again, what is then difference with a.
The reviewer would mention Figure 2, not Figure 1. If so, the matrices in panels a and b in Figure 2 are equivalent. I have shown it in the figure: The same figure in panel a is rotated 90 degrees to the right. The green boxes in the matrix show the regions with the ChIP-seq peak of a DNA-binding protein overlapping with a SNP of an eQTL. I used eQTL data to associate a gene with a ChIP-seq peak that was more than 2 kb upstream and 1 kb downstream of a transcriptional start site of a gene. For each gene, the matrix was produced and the gene expression levels in cells were learned and predicted using the deep learning method. I have added the following sentences to explain the method in lines 133 - 139: Through the training, the tool learned to select the binding sites of DNA-binding proteins from ChIP-seq assays that were suitable for predicting gene expression levels in the cell types. The binding sites of a DNA-binding protein tend to be observed in common across multiple cell and tissue types. Therefore, ChIP-seq data and eQTL data in different cell and tissue types were used as input data for learning, and then the tool selected the data suitable for predicting gene expression levels in the cell types, even if the data were not obtained from the same cell types.
6) Line 386-388: could the author investigate in more detail this observation? Does it mean that loops driven by other DBPs independent of the known CTCF/Cohesin? Could the author provide examples of chromatin structural data e.g. MicroC?
As suggested by the reviewer, to help readers understand the observation, I have added Supplementary Fig. S4c to show the distribution of DNA-binding sites of "CTCF, RAD21, and SMC3" and "BACH2, FOS, ATF3, NFE2, and MAFK" around chromatin interaction sites. I have modified the following sentence to indicate the figure on line 493: Although a DNA-binding-site distribution pattern around chromatin interaction sites similar to those of CTCF, RAD21, and SMC3 was observed for DBPs such as BACH2, FOS, ATF3, NFE2, and MAFK, less than 1% of the DNA-binding sites of the latter set of DBPs colocalized with CTCF, RAD21, or SMC3 in a single bin (Fig. S4c).
In Aljahani A et al. Nature Communications 2022, we find that depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Together, our data show that loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression. Goel VY et al. Nature Genetics 2023 mentioned in the abstract: Microcompartments frequently connect enhancers and promoters and though loss of loop extrusion and inhibition of transcription disrupts some microcompartments, most are largely unaffected. These results suggested that chromatin loops can be driven by other DBPs independent of the known CTCF/Cohesin.
I added the following sentence on lines 561-569: The depletion of cohesin causes a subtle reduction in longer-range enhancer-promoter interactions and that CTCF depletion can cause rewiring of regulatory contacts. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. Furthermore, the loop extrusion is not essential for enhancer-promoter interactions, but contributes to their robustness and specificity and to precise regulation of gene expression.
FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates (Ji D et al. Molecular Cell 2024). CTCF have also found to form transcriptional condensate and phase separation (Lee R et al. Nucleic acids research 2022). FOS was found to be an insulator-associated DNA-binding protein in this study and is potentially involved in chromatin remodeling, transcription condensation, and phase separation with the other factors such as BACH2, ATF3, NFE2 and MAFK. I have added the following sentence on line 548: FOXA1 pioneer factor functions as an initial chromatin-binding and chromatin-remodeling factor and has been reported to form biomolecular condensates.
7) In general, how the presented results are related to some models of chromatin architecture, e.g. loop extrusion, in which it is integrated convergent CTCF binding sites?
Goel VY et al. Nature Genetics 2023 identified highly nested and focal interactions through region capture Micro-C, which resemble fine-scale compartmental interactions and are termed microcompartments. In the section titled "Most microcompartments are robust to loss of loop extrusion," the researchers noted that a small proportion of interactions between CTCF and cohesin-bound sites exhibited significant reductions in strength when cohesin was depleted. In contrast, the majority of microcompartmental interactions remained largely unchanged under cohesin depletion. Our findings indicate that most P-P and E-P interactions, aside from a few CTCF and cohesin-bound enhancers and promoters, are likely facilitated by a compartmentalization mechanism that differs from loop extrusion. We suggest that nested, multiway, and focal microcompartments correspond to small, discrete A-compartments that arise through a compartmentalization process, potentially influenced by factors upstream of RNA Pol II initiation, such as transcription factors, co-factors, or active chromatin states. It follows that if active chromatin regions at microcompartment anchors exhibit selective "stickiness" with one another, they will tend to co-segregate, leading to the development of nested, focal interactions. This microphase separation, driven by preferential interactions among active loci within a block copolymer, may account for the striking interaction patterns we observe.
The authors of the paper proposed several mechanisms potentially involved in microcompartments. These mechanisms may be involved in looping with insulator function. Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently (Hsieh TS et al. Nature Genetics 2022). Among the identified insulator-associated DNA-binding proteins, Maz and MyoD1 form loops without CTCF (Xiao T et al. Proc Natl Acad Sci USA 2021 ; Ortabozkoyun H et al. Nature genetics 2022 ; Wang R et al. Nature communications 2022). I have added the following sentences on lines 563-567: Another group reported that enhancer-promoter interactions and transcription are largely maintained upon depletion of CTCF, cohesin, WAPL or YY1. Instead, cohesin depletion decreased transcription factor binding to chromatin. Thus, cohesin may allow transcription factors to find and bind their targets more efficiently. I have included the following explanation on lines 574-576: Maz and MyoD1 among the identified insulator-associated DNA-binding proteins form loops without CTCF.
As for the directionality of CTCF, if chromatin loop anchors have some structural conformation, as shown in the paper entitled "The structural basis for cohesin-CTCF-anchored loops" (Li Y et al. Nature 2020), directional DNA binding would occur similarly to CTCF binding sites. Moreover, cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops (Davidson IF et al. Nature Reviews Molecular Cell Biology 2021). Regarding loop extrusion, the ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions (Guerin TM et al. EMBO Journal 2024). I have added the following sentences on lines 535-539: Cohesin complexes that interact with convergent CTCF sites, that is, the N-terminus of CTCF, might be protected from WAPL, but those that interact with divergent CTCF sites, that is, the C-terminus of CTCF, might not be protected from WAPL, which could release cohesin from chromatin and thus disrupt cohesin-mediated chromatin loops. I have included the following sentences on lines 569-574: The ‘loop extrusion’ hypothesis is motivated by in vitro observations. The experiment in yeast, in which cohesin variants that are unable to extrude DNA loops but retain the ability to topologically entrap DNA, suggested that in vivo chromatin loops are formed independently of loop extrusion. Instead, transcription promotes loop formation and acts as an extrinsic motor that extends these loops and defines their final positions.
Another model for the regulation of gene expression by insulators is the boundary-pairing (insulator-pairing) model (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016). Molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies. Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent. I have summarized the model on lines 551-559: Other types of chromatin regulation are also expected to be related to the structural interactions of molecules. As the boundary-pairing (insulator-pairing) model, molecules bound to insulators physically pair with their partners, either head-to-head or head-to-tail, with different degrees of specificity at the termini of TADs in flies (Fig. 7). Although the experiments do not reveal how partners find each other, the mechanism unlikely requires loop extrusion. Homologous and heterologous insulator-insulator pairing interactions are central to the architectural functions of insulators. The manner of insulator-insulator interactions is orientation-dependent.
8) Do the authors think that the identified DBPs could work in that way as well?
The boundary-pairing (insulator-pairing) model would be applied to the insulator-associated DNA-binding proteins other than CTCF and cohesin that are involved in the loop extrusion mechanism (Bing X et al. Elife 2024) (Ke W et al. Elife 2024) (Fujioka M et al. PLoS Genetics 2016).
Liquid-liquid phase separation was shown to occur through CTCF-mediated chromatin loops and to act as an insulator (Lee, R et al. Nucleic Acids Research 2022). Among the identified insulator-associated DNA-binding proteins, CEBPA has been found to form hubs that colocalize with transcriptional co-activators in a native cell context, which is associated with transcriptional condensate and phase separation (Christou-Kent M et al. Cell Reports 2023). The proposed microcompartment mechanisms are also associated with phase separation. Thus, the same or similar mechanisms are potentially associated with the insulator function of the identified DNA-binding proteins. I have included the following information on line 546: CEBPA in the identified insulator-associated DNA-binding proteins was also reported to be involved in transcriptional condensates and phase separation.
9) Also, can the authors comment about the mechanisms those newly identified DBPs mediate contacts by active processes or equilibrium processes?
Snead WT et al. Molecular Cell 2019 mentioned that protein post-transcriptional modifications (PTMs) facilitate the control of molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin (Tang X et al. Nature Communications 2024). I found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Supplementary Fig. 2d). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation by PTMs. I have added the following explanation on lines 576-582: Furthermore, protein post-transcriptional modifications (PTMs) facilitate control over the molecular valency and strength of protein-protein interactions. O-GlcNAcylation as a PTM inhibits CTCF binding to chromatin. We found that the identified insulator-associated DNA-binding proteins tend to form a cluster at potential insulator sites (Fig. 4f and Supplementary Fig. 3c). These proteins may interact and actively regulate chromatin interactions, transcriptional condensation, and phase separation through PTMs.
10) Can the author provide some real examples along with published structural data (e.g. the mentioned micro-C data) to show the link between protein co-presence, directional bias and contact formation?
Structural molecular model of cohesin-CTCF-anchored loops has been published by Li Y et al. Nature 2020. The structural conformation of CTCF and cohesin in the loops would be the cause of the directional bias of CTCF binding sites, which I mentioned in lines 531 – 535 as follows: These results suggest that the directional bias of DNA-binding sites of insulator-associated DBPs may be involved in insulator function and chromatin regulation through structural interactions among DBPs, other proteins, DNAs, and RNAs. For example, the N-terminal amino acids of CTCF have been shown to interact with RAD21 in chromatin loops.
To investigate the principles underlying the architectural functions of insulator-insulator pairing interactions, two insulators, Homie and Nhomie, flanking the Drosophila even skipped locus were analyzed. Pairing interactions between the transgene Homie and the eve locus are directional. The head-to-head pairing between the transgene and endogenous Homie matches the pattern of activation (Fujioka M et al. PLoS Genetics 2016).
Reviewer #3
1. Some of these TFs do not have specific direct binding to DNA (P300, Cohesin). Since the authors are using binding motifs in their analysis workflow, I would remove those from the analysis.
When a protein complex binds to DNA, one protein of the complex binds to the DNA directory, and the other proteins may not bind to DNA. However, the DNA motif sequence bound by the protein may be registered as the DNA-binding motif of all the proteins in the complex. The molecular structure of the complex of CTCF and Cohesin showed that both CTCF and Cohesin bind to DNA (Li Y et al. Nature 2020). I think there is a possibility that if the molecular structure of a protein complex becomes available, the previous recognition of the DNA-binding ability of a protein may be changed. Therefore, I searched the Pfam database for 99 insulator-associated DNA-binding proteins identified in this study. I found that 97 are registered as DNA-binding proteins and/or have a known DNA-binding domain, and EP300 and SIN3A do not directory bind to DNA, which was also checked by Google search. I have added the following explanation in line 249 to indicate direct and indirect DNA-binding proteins: Among 99 insulator-associated DBPs, EP300 and SIN3A do not directory interact with DNA, and thus 97 insulator-associated DBPs directory bind to DNA. I have updated the sentence in line 22 of the Abstract as follows: We discovered 97 directional and minor nondirectional motifs in human fibroblast cells that corresponded to 23 DBPs related to insulator function, CTCF, and/or other types of chromosomal transcriptional regulation reported in previous studies.
2. I am not sure if I understood correctly, by why do the authors consider enhancers spanning 2Mb (200 bins of 10Kb around eSNPs)? This seems wrong. Enhancers are relatively small regions (100bp to 1Kb) and only a very small subset form super enhancers.
As the reviewer mentioned, I recognize enhancers are relatively small regions. In the paper, I intended to examine further upstream and downstream of promoter regions where enhancers are found. Therefore, I have modified the sentence in lines 917 – 919 of the Fig. 2 legend as follows: Enhancer-gene regulatory interaction regions consist of 200 bins of 10 kbp between -1 Mbp and 1 Mbp region from TSS, not including promoter.
3. I think the H3K27me3 analysis was very good, but I would have liked to see also constitutive heterochromatin as well, so maybe repeat the analysis for H3K9me3.
Following the reviewer's advice, I have added the ChIP-seq data of H3K9me3 as a truck of the UCSC Genome Browser. The distribution of H3K9me3 signal was different from that of H3K27me3 in some regions. I also found the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions and took some screenshots of the UCSC Genome Browser of the regions around the sites in Supplementary Fig. 3b. I have modified the following sentence on lines 962 – 964 in the legend of Fig. 4: a Distribution of histone modification marks H3K27me3 (green color) and H3K9me3 (turquoise color) and transcript levels (pink color) in upstream and downstream regions of a potential insulator site (light orange color). I have also added the following result on lines 348 – 352: The same analysis was performed using H3K9me3 marks, instead of H3K27me3 (Fig. S3b). We found that the distribution of H3K9me3 signal was different from that of H3K27me3 in some regions, and discovered the insulator-associated DNA-binding sites close to the edges of H3K9me3 regions (Fig. S3b).
4. I was not sure I understood the analysis in Figure 6. The binding site is with 500bp of the interaction site, but micro-C interactions are at best at 1Kb resolution. They say they chose the centre of the interaction site, but we don't know exactly where there is the actual interaction. Also, it is not clear what they measure. Is it the number of binding sites of a specific or multiple DBP insulator proteins at a specific distance from this midpoint that they recover in all chromatin loops? Maybe I am missing something. This analysis was not very clear.
The resolution of the Micro-C assay is considered to be 100 bp and above, as the human nucleome core particle contains 145 bp (and 193 bp with linker) of DNA. However, internucleosomal DNA is cleaved by endonuclease into fragments of multiples of 10 nucleotides (Pospelov VA et al. Nucleic Acids Research 1979). Highly nested focal interactions were observed (Goel VY et al. Nature Genetics 2023). Base pair resolution was reported using Micro Capture-C (Hua P et al. Nature 2021). Sub-kilobase (20 bp resolution) chromatin topology was reported using an MNase-based chromosome conformation capture (3C) approach (Aljahani A et al. Nature Communications 2022). On the other hand, Hi-C data was analyzed at 1 kb resolution. (Gu H et al. bioRxiv 2021). If the resolution of Micro-C interactions is at best at 1 kb, the binding sites of a DNA-binding protein will not show a peak around the center of the genomic locations of interaction edges. Each panel shows the number of binding sites of a specific DNA-binding protein at a specific distance from the midpoint of all chromatin interaction edges. I have modified and added the following sentences in lines 585-589: High-resolution chromatin interaction data from a Micro-C assay indicated that most of the predicted insulator-associated DBPs showed DNA-binding-site distribution peaks around chromatin interaction sites, suggesting that these DBPs are involved in chromatin interactions and that the chromatin interaction data has a high degree of resolution. Base pair resolution was reported using Micro Capture-C.
1.PIQ does not consider TF concentration. Other methods do that and show that TF concentration improves predictions (e.g.,https://www.biorxiv.org/content/10.1101/2023.07.15.549134v2 or https://pubmed.ncbi.nlm.nih.gov/37486787/). The authors should discuss how that would impact their results.
The directional bias of CTCF binding sites was identified by ChIA-pet interactions of CTCF binding sites. The analysis of the contribution scores of DNA-binding sites of proteins considering the binding sites of CTCF as an insulator showed the same tendency of directional bias of CTCF binding sites. In the analysis, to remove the false-positive prediction of DNA-binding sites, I used the binding sites that overlapped with a ChIP-seq peak of the DNA-binding protein. This result suggests that the DNA-binding sites of CTCF obtained by the current analysis have sufficient quality. Therefore, if the accuracy of prediction of DNA-binding sites is improved, althought the number of DNA-binding sites may be different, the overall tendency of the directionality of DNA-binding sites will not change and the results of this study will not change significantly.
As for the first reference in the reviewer's comment, chromatin interaction data from Micro-C assay does not include all chromatin interactions in a cell or tissue, because it is expensive to cover all interactions. Therefore, it would be difficult to predict all chromatin interactions based on machine learning. As for the second reference in the reviewer's comment, pioneer factors such as FOXA are known to bind to closed chromatin regions, but transcription factors and DNA-binding proteins involved in chromatin interactions and insulators generally bind to open chromatin regions. The search for the DNA-binding motifs is not required in closed chromatin regions.
2. DeepLIFT is a good approach to interpret complex structures of CNN, but is not truly explainable AI. I think the authors should acknowledge this.
In the DeepLIFT paper, the authors explain that DeepLIFT is a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input (Shrikumar A et al. ICML 2017). DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.
Truly explainable AI would be able to find cause and reason, and to make choices and decisions like humans. DeepLIFT does not perform causal inferences. I did not use the term "Explainable AI" in our manuscript, but I briefly explained it in Discussion. I have added the following explanation in lines 615-620: AI (Artificial Intelligence) is considered as a black box, since the reason and cause of prediction are difficult to know. To solve this issue, tools and methods have been developed to know the reason and cause. These technologies are called Explainable AI. DeepLIFT is considered to be a tool for Explainable AI. However, DeepLIFT does not answer the reason and cause for a prediction. It calculates scores representing the contribution of the input data to the prediction.
Furthermore, to improve the readability of the manuscript, I have included the following explanation in lines 159-165: we computed DeepLIFT scores of the input data (i.e., each binding site of the ChIP-seq data of DNA-binding proteins) in the deep leaning analysis on gene expression levels. DeepLIFT compares the importance of each input for predicting gene expression levels to its 'reference or background level' and assigns contribution scores according to the difference. DeepLIFT calculates a metric to measure the difference between an input and the reference of the input.
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Reply to the reviewers
Manuscript number: RC-2024-02788
Corresponding author(s): Kazuhiro, Aoki and Yuhei, Goto
1. General Statements [optional]
We sincerely thank all reviewers for their insightful comments and constructive suggestions that have substantially improved our manuscript. We provide point-to-point responses to each comment and added detailed explanations in the preliminary revised manuscript. The reviewers' comments are shown in dark blue italics, followed by our responses.
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
Major Concerns
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- Fig. 3G, Cdc2-miRFP670 levels appear to drop after cell division, which is a surprising observation because Cdc2 is generally considered stable. This could be an imaging artifact because the level recovers quickly after division. The authors should substantiate their findings with a western blot analysis of tagged vs untagged proteins. Additionally, the authors should test whether endogenously tagging Cdc2 and Cdc13 causes any cell cycle phenotypes. While Cdc2 protein levels are indeed stable in whole cells as you noted, we specifically measured nuclear Cdc2-miRFP670 levels. A previous study has shown that nuclear Cdc2 levels fluctuate throughout the cell cycle, increasing during interphase and decreasing during mitosis (Curran et al*., 2022). This known behavior of nuclear Cdc2 is consistent with our observation.
To address your concerns about potential artifacts from fluorescent protein tagging to endogenous Cdc2 and Cdc13, we will perform two additional experiments:
- Compare protein expression levels between wild-type and fluorescently tagged strains for Cdc2 and Cdc13 using western blot analysis.
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Examine whether the fluorescent tags affect cell cycle progression by measuring cell cycle duration in tagged versus untagged strains using time-lapse imaging.
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The authors explore a panel of red-fluorescent proteins to identify those with the best photobleaching properties. Conducting a similar review with a panel of green fluorescent proteins would significantly enhance the manuscript. It would be particularly helpful to test the properties of the new StayGold fluorescent protein.*
Thank you for this valuable suggestion. We will expand our photobleaching analysis to include green fluorescent proteins, specifically mEGFP and the recently developed mStayGold as well as mNeonGreen. These measurements will be conducted under identical experimental conditions to our red fluorescent protein analysis, allowing for direct comparison of their photostability properties. This additional data will provide a more comprehensive evaluation of fluorescent protein options for FCCS.
- In both yeast and mammalian experiments, the green fluorophore is consistently fused to the cyclin and the far-red fluorophore to Cdk1. The authors should include an FCCS control reversing the fluorophores in at least one experiment to verify whether comparable Kd values are obtained.*
We plan to conduct FCCS measurements with reversed fluorophore combinations in HeLa cells to validate our experiments. Specifically, we will compare Kd values between:
- cyclin D1-miRFP670 and CDK4-mNG pair versus cyclinD1-mNG and CDK4-miRFP670 pair
- cyclin D3-miRFP670 and CDK6-mNG pair versuscyclin D3-mNG and CDK6-miRFP670 pair.
- We also plan to do it in fission yeast cells comparing Kd values between: Cdc13-miRFP670 and Cdc2-mNG pair versus Cdc13-mNG and Cdc2-miRFP670 pair Reviewer #2
SectionA
Major Comments
(ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 EGFexpression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?
We would like to thank you for raising these important comments and suggestions about cell cycle stage determination. We agree that using Cdc13-mNG levels alone as a cell cycle marker requires more rigorous validation.We will incorporate cell length measurements as an independent cell cycle stage indicator for FCCS measurements. However, it is important to note that traditional cell cycle stage classification is limited in fission yeast cells due to its unique cell cycle characteristics; a brief G1 phase, continuous S phase during cell separation, and an extended G2 phase. Cdc13 expression keeps at the undetectable level during G1 and S phases, and therefore this inevitably restricts our FCCS measurements to G2 and M phases. G2 and M phase cells can be distinguished by the characteristic relocalization of Cdc2 and Cdc13 to the mitotic spindle during the M phase (Sugiyama et al., 2024). In the revised manuscript, we will demonstrate the FCCS data with both quantitative (cell length) and qualitative (G2 and M phase localization pattern) indicators for more precise cell cycle staging.
(iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.
We agree with the reviewer's comment, which suggests validation of the functionality of tagged proteins. We will perform two key control experiments:
- Compare Cdc13 protein expression levels between wild-type cells and cells expressing Cdc13-mNG and Cdc2-miRFP670 using western blot analysis with anti-Cdc13 antibody.
- Measure cell cycle duration in both strains through time-lapse microscopy to assess any potential effect of the fluorescent tags on cell cycle progression. Major points
(ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.
Thank you for highlighting the need for more rigorous statistical analysis. We will calculate and add the confidence intervals for all Kd values of each cyclin-CDK pair.
(iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?
We appreciate the reviewer's valuable comment regarding the determination of low affinity interactions. As mentioned above, we are currently calculating confidence intervals for our curve fitting analyses across all measurements. Based on these statistical analyses, we will carefully evaluate the reliability of the >10 µM designations and revise our descriptions accordingly in the manuscript to ensure accurate representation of the binding parameters.
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3. Description of the revisions that have already been incorporated in the transferred manuscript
Reviewer #1
Major Concerns
- The authors extensively characterize the Kd of cyclin/Cdk pairs using overexpressed proteins. This approach is problematic due to the heterogeneous expression levels associated with transient expression and competition between overexpressed proteins and endogenous proteins. Variable expression levels are a concern because of the limiting rate of T-loop phosphorylation on Cdks (Merrick et al., 2008), which is required to stabilise cyclin/Cdk complexes. While the authors acknowledge the competition between exogenous and endogenous proteins, they do not take into account the cell cycle-dependent fluctuation of cyclin levels. For instance, in cells with low levels of endogenous Cyclin B1 (S-phase), competition with overexpressed Cyclin B1 will have less impact on cross-correlation measurements compared to cells with high endogenous Cyclin B1 (G2-phase).*
These issues severely affect the relevance of this dataset. Indeed, the reported measurements differ by at least an order of magnitude from the Kd values obtained through biochemical methods or FCCS with endogenously tagged proteins. Moreover, the data partially diverge from the literature; for example, Cdk1 is known to form unconventional complexes with Cyclin Ds and Es.
We acknowledge the important issues about the limitations of using overexpressed proteins for Kd measurement. Indeed, several factors affect the reliability of our measurements. At first, competition between overexpressed and endogenous proteins varies throughout the cell cycle due to cell cycle-dependent fluctuations in endogenous cyclin levels. Indeed, we had analyzed the effects of the overexpression on in vivo Kd measurements with FCCS (Sadaie, Mol Cell Biol, 2014), showing that not only endogenous proteins but also competitive binding proteins affect Kd values quantified in living cells. Second, variable expression levels from transient transfection may impact T-loop phosphorylation of CDKs, which is known to be rate-limiting (Merrick et al., 2008). We have expanded our discussion to address these limitations and their implications for interpreting the cyclin-CDK binding affinities (page 25, line 16-18). We also note that our overexpression experiments may not fully capture the formation of previously reported unconventional complexes, such as those between CDK1 and D- or E-type of cyclins (Koff et al. 1992; Zhang et al. 1993) (page 26, line 8-10).
- Fig. S3A, Cyclin E levels are shown to persist into mitosis, whereas endogenous Cyclin E is degraded in late S and G2 phases. This is likely to be caused by over-expression and the authors should comment on this.*
We agree that the observed persistence of Cyclin E into mitosis differs from the known behavior of endogenous Cyclin E, which is typically degraded during late S and G2 phases. This discrepancy is likely due to our overexpression system overwhelming the normal degradation machinery. In the revised manuscript, we have explicitly acknowledged this limitation and discuss how overexpression may alter the typical cell cycle-dependent regulation of cyclin proteins (page 26, line 12-16). This observation further highlights the importance of considering expression levels when interpreting protein-protein interaction data from overexpression systems.
Minor Comments
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- The authors should reference relevant studies from Jan Ellenberg's lab on FCS (e.g., Wachsmuth et al., 2015; Cai et al., 2018).* Thank you for your suggestion. We have cited these two papers in introduction (page 6, line 5-8).
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The statement, "In order to perform FCCS in a reproducible manner, we are trying to find a better fluorescent protein pair that is bright, crosstalk-free, and highly resistant to photobleaching," would be improved by removing the word "better".*
We removed the word "better".
- In Fig. 1C, F, G, and H, the colour codes are difficult to read and should be improved.*
We have changed the color codes to make them easy to distinguish.
- The paragraph discussing Fig. 3 states: "We used a fission yeast strain that expressed SynPCB2.1 under the control of the adh promoter," raising the question of how emiRFP670 was imaged in earlier experiments.*
We apologize for the unclear description. All experiments involving miRFP670 imaging, including those in Figure 1, were performed using fission yeast cells expressing SynPCB2.1 under the control of the adh1 promoter. We have clarified these important experimental details in the revised manuscript under the section "miRFP670, a near-infrared fluorescent protein, is suitable for simultaneous imaging with mNeonGreen."
- The authors estimate the volume of a mammalian cell as approximately 5 pL. This estimate requires a supporting reference or experimental data. Additionally, it would be helpful to specify which cell type was considered and at which cell cycle stage this estimate applies.*
Our cell volume estimate was based on HeLa cells reported by our previous work (Aoki, PNAS, 2011). In our study, total cell volume was determined using differential interference contrast microscopy, while nuclear volume was measured through Höechst 33258 fluorescence imaging. While we reported average volumes from 20 cells, we acknowledge that the cell cycle stage was not specified in our measurement. We have added these experimental details to the revised manuscript (page 15, line 7-9), noting that cell volumes vary with cell cycle stage.
- Including page and/or line numbers would facilitate future revisions.*
We have added page numbers and line numbers throughout the revised manuscript.
Reviewer #2
Section A
Major Comments
(i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"
We apologize for not providing sufficient details about the fitting constraints. In the revised Material and Methods section (page 11, line 20-21) and (page 13, line 8-9), we have specified the initial parameter estimates and their boundary conditions used in our fitting process. These have included explicit numerical values for all parameters and the physical reasoning behind each constraint.
Minor points
*(i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (We have changed the color palette to make them easy to distinguish.
SectionB
Major points
(i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.
We acknowledge this important point about the complexity of cyclin-CDK interactions in cellular context. Our current analysis, which assumes simple binary interactions between overexpressed proteins, has several limitations as the reviewer suggested:
- As demonstrated by Pines laboratory's work with CDK1-cyclin B1 FCCS, dissociation constant can vary throughout the cell cycle, suggesting regulation by additional factors.
- Both cyclins and CDKs interact with multiple binding partners in cells, and therefore the analysis with binary interaction does not account for.
- Overexpression of exogenous proteins may alter the balance of these interactions. While our previous studies (Sadaie, MCB, 2014; Komatsubara, JBC, 2019) cited in the manuscript have addressed similar considerations, we agree that this aspect requires more thorough explanation. We have expanded our explanation in the results section (page 16, line 26-page17, line 8) and discussion part (page 26, line 7-23).
(iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.
We acknowledge the limitations of the current approach that averages dissociation constants across the cell cycle. The Pines laboratory's work revealed cell cycle-dependent variations in the dissociation constant for Cyclin B1-CDK1, suggesting complex regulation beyond simple binary interactions. These variations likely reflect both changes in cyclin expression levels and the involvement of additional regulatory factors throughout the cell cycle. While our comprehensive survey of multiple cyclin-CDK pairs provides a useful overview of relative binding preferences, we agree that a more focused analysis of selected pairs across different cell cycle stages would offer deeper mechanistic insights. We have expanded our discussion to address the significance of cell cycle-dependent changes in binding affinities and the potential role of additional regulatory factors as well as the trade-offs between breadth and depth in studying cyclin-CDK interactions (page 26, line 7-23).
Minor Points
(i) For both Figures 3 and 4 address red/green color pair choice.
We have modified the color codes in Figures 3 and 4.
**Referee cross-commenting**
I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.
We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:
(i) Competition between tagged and endogenous proteins
(ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).
(iii) Cell cycle dependent protein expression
Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.
As mentioned above, we have expanded our discussion to address these limitations and their implications for interpreting the cyclin-CDK binding affinities (page 26, line 7-23).
Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.
We are sorry, but we could not fully understand what the reviewer wanted to ask.
We also have shared concerns about the data presentation in Figure 4.
According to the suggestion, we have modified Figure 4.
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4. Description of analyses that authors prefer not to carry out
Reviewer #2
Major Comments
(iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.
We appreciate the suggestion to analyze cell cycle-dependent changes in dissociation constants using synchronized cells. However, we have deliberately chosen not to use cell synchronization methods in fission yeast for several important reasons. During cell cycle arrest, cells continue to grow and synthesize proteins, leading to cell elongation and abnormal accumulation of Cdc13. These unphysiological perturbations are evidenced by the unusually rapid progression through the subsequent cell cycle following release. Such conditions deviate significantly from normal cellular physiology. One of the key advantages of FCCS is its ability to measure protein-protein interactions in individual, asynchronous cells. While traditional biochemical analyses require cell synchronization to obtain population-averaged measurements, they inherently suffer from the artifacts mentioned above.
Instead, as described in (ii), we will utilize cell length as a natural indicator of cell cycle progression in fission yeast, allowing us to examine the relationship between cell cycle stage and Kd values while maintaining normal cellular physiology.
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Referee #2
Evidence, reproducibility and clarity
Summary
In the first part of the manuscript the authors present a thorough description of the background and theoretical basis to the identification of a fluorescent pair that permits both FCS and FCCS measurements at the single cell level to enable the determination of Kd values between labelled protein pairs (Figures 1 and 2). The generation of the reagents and subsequent experimental details are thorough and would permit the experiments to be repeated. The first two sections are well argued and appropriately controlled.
They then tag the endogenous S. pombe cdk1 and cdc13 genes at their 3' ends with sequences that encode miRFP670 (a near infrared fluorescent protein) and mNG (mNeonGreen) respectively and from measurements collected on 13 cells derive a mean Kd value calculated for each of the 13 cells of 0.31{plus minus}0.22 μM. They note that this value agrees with that reported by the Pines lab following labelling of cyclin B1 and CDK1 with genome editing in RPE-1/hTERT cells.
The final part of the manuscript then extends the technique to a pair-wise analysis of 9 cyclins and 4 CDKs in a human cell line.
Major Comments
(i) Materials and Methods: Page 10 "The fitting process was constrained by initial estimates and bounded by physically reasonable limits." Please define physically reasonable limits"
(ii) For the characterisation of the cell cycle dependent expression of Cdc13 and its association with Cdc2, the level of Cdc13 expression is used to identify cell cycle stage. It would be appropriate to have an independent measure of cell cycle stage (?cell length). In using Cdc13 to identify cell cycle stage, please define the criteria used ie what level of Cdc13-mNG fluorescence intensity was used to define G1 vs S vs G2?
(iii) Include a control experiment to compare the level of Cdc13 expression in untagged wild-type cells vs the Cdc13-mNG, CDK1- miRFP670 expressing cells to confirm that tagging does not affect Cdc13 expression, cell cycle duration or Cdc13 function.
(iv). Could the authors consider exploiting the tractability of yeast cells to block and release and/or genetic means to establish synchronous populations to improve data acquisition? This approach could also be employed to assess whether CDK1-cyclin B1 affinity changes with cell cycle stage (as was shown by Pines et al in RPE-1 cells) and would demonstrate that their approach is as equally suitable to sensitively distinguish CDK-cyclin pairs in yeast cells.
Minor points
(i) Figure 1. Panels C, F, G and H. Please improve color palette to distinguish the overlapping traces. It might be helpful to remove the edge grey and broaden the color spectrum for visual inclusion (eg straw/blue vs green/red). Could the statement "As expected, mNG exhibited tolerance to the photobleaching when excited at low laser power (< 5%) (Fig. 1C)." be supported by additional labelling on the figure panel.
The manuscript then goes on to describe the measurement of Kds for 36 CDK-cyclin pairs in HeLa cells by overexpression of labelled CDKs and cyclins following transient overexpression by plasmid co-transfection. This last section of the manuscript requires significant revision.
Major points
(i) In analysing the data, the model assumes that the monomeric CDK and cyclin subunits are either bound to form a binary complex or not. Can the authors discuss whether this can be presumed to be the case when they present the results. Either the labelled proteins are overexpressed to such a level that it can be presumed in the data handling that they are behaving as monomeric proteins and the resulting derived Kds reflect binary CDK-cyclin interactions. However, within the cell, the situation is more complex, and both CDKs and cyclins will mostly likely (and dependent on identity) be variably associated with multiple alternative protein partners. Can such effects be discounted in the analysis presented here and what would be the experimental grounds to do so. The authors make note of this fact in the discussion when they note that the results presented in this manuscript differ by circa an order of magnitude for the CDK1-cyclin B1 pairing reported by Pines et al using endogenously labelled proteins. They suggest that the discrepancy might result in part from competition from endogenously unlabelled proteins. This discrepancy has to be addressed.
(ii) Please provide the confidence interval for the data fit for each CDK-cyclin pair. In panel Figure 4I, the results are represented as a heat map to define the Kd for each CDK-cyclin pair. This panel suggests that the technique can sensitively distinguish alternative CDK-cyclin complexes where their Kd values differ in 1 uM increments. The heat map is presented with block colours, but the key to the color coding is a graded color scheme and it is not possible to move between the two. This disconnect has to be addressed. The accompanying text on pages 18 and 19 is a qualitative description of the results, a comparative and quantitative analysis of the data (Kd values with accompanying confidence intervals) has to be included to justify the apparent strength of the technique to discriminate different CDK-cyclin pairs that Figure 4 implies.
(iii) For "low affinity" interactions that are determined to be >10 uM. Please define how this value was calculated. Would it be more appropriate to say a value could not be determined as the data could not be fitted?
(iv) Previous work from the Pines lab using FCS and FCCS to measure the binding of CDK1 to cyclin B1 in RPE-1 cells reported not only a higher affinity for the pair but also that their apparent affinity was dependent on cell cycle stage suggesting that their assembly might be multi-stepped. Both affinity and cell cycle dependency of CDK-cyclin pairings are of great interest to scientists working in the cell cycle field. It could be argued that measurements of the affinities of multiple CDK-cyclin pairs each "averaged out" over the cell cycle will have less impact on the field than a few well-chosen CDK-cyclin pairs characterised in greater depth.
Minor Points
(i) For both Figures 3 and 4 address red/green color pair choice.
Referee cross-commenting
I would like to thank the other reviewer for their comments about requirements and possible control experiments for the use of the fluorescent probes.
We agree that the use of tagged proteins overexpressed in cells to measure Kd values has significant limitations:
(i) Competition between tagged and endogenous proteins
(ii) Limiting factors that affect CDK-cyclin complex stability (PTMs and contributions from binding and assembly factors mentioned).
(iii) Cell cycle dependent protein expression
Points (ii) and (iii) are not applicable to all protein-protein pairs but are significant when trying to determine CDK-cyclin affinities.
Ideally it would be demonstrated that this approach can return the established values for a limited subset of CDK-cyclin pairs in mammalian cells and so extrapolate the results from yeast cells where endogenous labelling was carried out.
We also have shared concerns about the data presentation in Figure 4.
Significance
Technology: The paper describes a technical advance in identifying a fluorescent probe pair suitable for FCCS in living cells.
Cell cycle: The ability of CDKs and cyclins to discriminate each other and pair to form complexes that characterise different cell cycle stages and drive progression has long been appreciated. The formation of non-cognate pairings when the cell cycle is perturbed has also been noted and a greater understanding of the in-cell affinities of all possible CDK-cyclin complexes would be a significant advance in our understanding. However, this manuscript currently does not (i) provide statistically validated measures of apparent differences in affinity between different CDK-cyclin pairs and (ii) address whether the measurements are cell cycle dependent. (iii) Interpretation of the results has to take into consideration that both the CDK and cyclin components are transiently over expressed in cells and therefore the values that are measured are difficult to interpret in terms of CDK and cyclin function. These considerations would dampen interest in the findings by cell cycle biologists.
Expertise: CDKs, cyclin, cell cycle biology.
Non-expert in technical aspects of fluorescence microscopy
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Referee #1
Evidence, reproducibility and clarity
Major Concerns
- Fig. 3G, Cdc2-miRFP670 levels appear to drop after cell division, which is a surprising observation because Cdc2 is generally considered stable. This could be an imaging artifact because the level recovers quickly after division. The authors should substantiate their findings with a western blot analysis of tagged vs untagged proteins. Additionally, the authors should test whether endogenously tagging Cdc2 and Cdc13 causes any cell cycle phenotypes.
- The authors explore a panel of red-fluorescent proteins to identify those with the best photobleaching properties. Conducting a similar review with a panel of green fluorescent proteins would significantly enhance the manuscript. It would be particularly helpful to test the properties of the new StayGold fluorescent protein.
- In both yeast and mammalian experiments, the green fluorophore is consistently fused to the cyclin and the far-red fluorophore to Cdk1. The authors should include an FCCS control reversing the fluorophores in at least one experiment to verify whether comparable Kd values are obtained.
- The authors extensively characterize the Kd of cyclin/Cdk pairs using overexpressed proteins. This approach is problematic due to the heterogeneous expression levels associated with transient expression and competition between overexpressed proteins and endogenous proteins. Variable expression levels are are a concern because of the limiting rate of T-loop phosphorylation on Cdks (Merrick et al., 2008), which is required to stabilise cyclin/Cdk complexes. While the authors acknowledge the competition between exogenous and endogenous proteins, they do not take into account the cell cycle-dependent fluctuation of cyclin levels. For instance, in cells with low levels of endogenous Cyclin B1 (S-phase), competition with overexpressed Cyclin B1 will have less impact on cross-correlation measurements compared to cells with high endogenous Cyclin B1 (G2-phase). These issues severely affect the relevance of this dataset. Indeed, the reported measurements differ by at least an order of magnitude from the Kd values obtained through biochemical methods or FCCS with endogenously tagged proteins. Moreover, the data partially diverge from the literature; for example, Cdk1 is known to form unconventional complexes with Cyclin Ds and Es.
- Fig. S3A, Cyclin E levels are shown to persist into mitosis, whereas endogenous Cyclin E is degraded in late S and G2 phases. This is likely to be caused by over-expression and the authors should comment on this.
Minor Comments
- The authors should reference relevant studies from Jan Ellenberg's lab on FCS (e.g., Wachsmuth et al., 2015; Cai et al., 2018).
- The statement, "In order to perform FCCS in a reproducible manner, we are trying to find a better fluorescent protein pair that is bright, crosstalk-free, and highly resistant to photobleaching," would be improved by removing the word "better".
- In Fig. 1C, F, G, and H, the colour codes are difficult to read and should be improved.
- The paragraph discussing Fig. 3 states: "We used a fission yeast strain that expressed SynPCB2.1 under the control of the adh promoter," raising the question of how emiRFP670 was imaged in earlier experiments.
- The authors estimate the volume of a mammalian cell as approximately 5 pL. This estimate requires a supporting reference or experimental data. Additionally, it would be helpful to specify which cell type was considered and at which cell cycle stage this estimate applies.
- Including page and/or line numbers would facilitate future revisions.
- Fig. 4I would benefit from providing actual Kd values alongside the color-coded representation.
Significance
In this study, Toyama and colleagues characterize a novel low-bleaching fluorophore pair to detect protein-protein interactions through FCCS. They demonstrate that while red-fluorescent proteins bleach rapidly, NeonGreen and iRFP670 are relatively stable over time and applicable to both yeast and mammalian cells. Furthermore, they apply their system to cyclin-Cdk pairs and describe a clever approach to enhance the brightness of iRFP670 in mammalian cells. The data are clear and the identification of suitable fluors for FCCS will be of value to the field; however, there are several major concerns that need to be addressed before publication.
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Reply to the reviewers
Reply to the Reviewers
We sincerely appreciate your insightful and constructive comments from the reviewers, which have significantly enhanced the clarity and rigor of our manuscript.
Reviewer #1
Evidence, reproducibility and clarity
The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:
1) Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).
2) Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.
These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:
1) The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.
Response:
Thanks for your suggestion to avoid confusion. We used the phrase "nodal spacing" instead of "nodal distribution" throughout the revised manuscript.
2) In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?
Response:
As a key distinction, our study focuses specifically on the main trunk of the contralateral projection of NM axons. This projection features a sequential branching structure known as the delay line, where collateral branches form terminal arbors and connect to the ventral dendritic layer of NL neurons. This structural organization plays a critical role in influencing the dynamic range of ITD detection by regulating conduction delays along the NM axon trunk.
The study by Seidl et al. (2010) is a pioneering work that measured diameter of NM axon using electron microscopy, providing highly reliable data. However, due to the technical limitations of electron microscopy, which does not allow for the continuous tracing of individual axons, it is not entirely clear whether the axons measured in the ventral NL region correspond to terminal arbors of collateral branches or the main trunk of NM axons (see Figure 9E, F in their paper). Instead, they categorized axon diameters based on their distance from NL cell layer, showing that axon diameter increases distally (see Figure 9G in their paper). Notably, the diameters of ventral axons located more than 120 μm away from the NL cell layer is almost identical to those in the midline.
As illustrated in our Figure 4D and Supplementary Video 2, the main trunk of the contralateral NM projection is predominantly located in these distal regions. Therefore, our findings complement those of Seidl et al. (2010) rather than contradicting them. We made this point as clear as possible in text (page 7, line 7).
3) The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?
Response:
In this study, we examined chick embryos from E9 to just before hatching (E21) and post-hatch chicks up to P9. Chickens begin to perceive sound around E12 and possess sound localization abilities at the time of hatching (Grier et al., 1967) (added to page 4, line 12). Therefore, by E21, the sound localization circuit is largely established.
On the other hand, additional refinement of the circuit with aging is certainly possible. A key cue for sound localization, interaural time difference (ITD), depends on the distance between the two ears, which increases as the animal grows. As shown in Figure 2G, internodal length increased by approximately 20% between E18 and P9 while maintaining regional differences. Given that NM axons are nearly fully myelinated by E21 (Figure 4D, 6C), this suggests that myelin extends in proportion to the overall growth of the head and brain volume.
Thus, our study covers not only the early stages of myelination but also the post-functional maturation in the sound localization circuit.
4) The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?
Response:
In this study, we demonstrated that although vesicular release did not affect internodal length, it selectively promoted oligodendrogenesis, thereby supporting the full myelination and hence the pattern of nodal spacing along the NM axons. We believe that this finding falls within the broader scope of 'activity-dependent plasticity' involving oligodendrocytes and nodes.
As summarized in the excellent review by Bonetto et al. (2021), activity-dependent plasticity in oligodendrocytes encompasses a wide range of phenomena, not limited to changes in internodal length but also including oligodendrogenesis. Moreover, the effects of neuronal activity are not uniform but likely depend on the diversity of both neurons and oligodendrocytes. For example, in the mouse visual cortex, activity-dependent myelination occurs in interneurons but not in excitatory neurons (Yang et al., 2020). Additionally, expression of TeNT in axons affected myelination heterogeneously in zebrafish; some axons were impaired in myelination and the others were not affected at all (Koudelka et al., 2016). In the mouse corpus callosum, neuronal activity influences oligodendrogenesis, which in turn facilitates adaptive myelination (Gibson et al., 2014).
Thus, rather than refuting the role of activity-dependent plasticity in nodal spacing, our findings emphasize the diversity of underlying regulatory mechanisms. We described these explicitly in text (page 10, line 18).
Significance
This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.
Response:
This paper does not argue against node plasticity, but rather demonstrates that oligodendrocytes in the NL region exhibit a form of plasticity; they proliferate in response to vesicular release from NM axons, yet do not undergo morphological changes, ensuring adequate oligodendrocyte density for the full myelination of the auditory circuit. Thus, activity-dependent plasticity involving oligodendrocytes would contributes in various ways to each neural circuit, which is presumably attributed to the fact that myelination is driven by complex multicellular interactions between diverse axons and oligodendrocytes. Oligodendrocytes are known to exhibit heterogeneity in morphology, function, responsiveness, and gene profiles (Foerster et al., 2019; Sherafat et al., 2021; Osanai et al., 2022; Valihrach et al., 2022), but functional significance of this heterogeneity remains largely unclear. This paper also provides insight into how oligodendrocyte heterogeneity may contribute to the fine-tuning of neural circuit function, adding further value to our findings. Importantly, our study covers the wide range of development in the sound localization circuit, from the pre-myelination (E9) to the post-functional maturation (P9), revealing how the nodal spacing pattern along the axon in this circuit emerges and matures.
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Reviewer #2
Evidence, reproducibility and clarity
Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.
Major points, detailed below, need to be addressed to overcome some limitations of the study.
Major comments:
1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.
Response:
eTeNT is a widely used genetically encoded silencing tool and constructs similar to the one used in this study have been successfully applied in primates and rodents to suppress target behaviors via genetic dissection of specific pathways (Kinoshita et al., 2012; Sooksawate et al., 2013). However, precisely quantifying the extent of vesicular release inhibition from NM axons in the brainstem auditory circuit is technically problematic.
One major limitation is that while A3V efficiently infects NM neurons, its transduction efficiency does not reach 100%. In electrophysiological evaluations, NL neurons receive inputs from multiple NM axons, meaning that responses may still include input from uninfected axons. Additionally, failure to evoke synaptic responses could either indicate successful silencing or failure to stimulate NM axons, making a clear distinction difficult. Furthermore, unlike in motor circuits, we cannot assess the effect of silencing by observing behavioral outputs.
Thus, we instead opted to quantify the precise expression efficiency of GFP-tagged eTeNT in the cell bodies of NM neurons. The proportion of NM neurons expressing GFP-tagged eTeNT was 89.7 {plus minus} 1.6% (N = 6 chicks), which is consistent with previous reports evaluating A3V transduction efficiency in the brainstem auditory circuit (Matsui et al., 2012). These results strongly suggest that synaptic transmission from NM axons was globally silenced by eTeNT at the NL region. We described these explicitly in text (page 8, line 5).
2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?
Response:
Figure 6D depicts a representative axon selected from a dense population of GFP-positive axons in a 200-μm-thick slice after A3V-eTeNT infection to bilateral NM. As shown in Supplementary Video 1 and 2, densely labeled GFP-positive axons can be traced along the main trunk. To prevent any misinterpretation, we have revised the description of Figure 6 in the main text and Figure legend (page 31, line 9), and stated the A3V-eTeNT infection efficiency was 89.7 {plus minus} 1.6% in NM neurons, as mentioned above. Based on this efficiency, we interpreted that the global occlusion of vesicular release from most of the NM axons altered the pericellular microenvironment of the NL region, which led to the regional effect on the oligodendrocyte density.
On the other hand, your question regarding whether sparse expression of eTeNT still has an effect is highly relevant. As we also discussed in our reply to comment 4 by Reviewer #1, the relationship between neuronal activity and oligodendrocytes is highly diverse. In some types of axons, vesicular release is essential for normal myelination, and this process was disrupted by TeNT (Koudelka et al., 2016), suggesting that direct interaction with oligodendrocytes via vesicle release may actively promote myelination in these types of axons.
To clarify whether the phenotype observed in Figure 6 arises from changes in the pericellular microenvironment at the NL region or from the direct suppression of axon-oligodendrocyte interactions, we plan to add a new Supplementary Figure. Specifically, we will evaluate the node formation on the axon sparsely expressing eTeNT by electroporation into the unilateral NM. Preliminary data indicate that, unlike the results in Figure 6D, sparse eTeNT expression did not contribute to an increase in heminodes and unmyelinated segments. This result would further support our argument that the increase in unmyelinated segments by A3V-eTeNT was due to a disruption of synaptic transmission between NM axons and NL neurons, which in turn altered the pericellular microenvironment at the NL region.
3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:
__Response: __
Thank you for your valuable suggestions to improve the rigor of our statistical analyses. We have reanalyzed all statistical tests using R software. In the revised Methods section and Figure Legends, we have clarified the rationale for selecting each statistical test, specified which test was used for each figure, and explicitly defined both n and N. After reevaluation with the Shapiro-Wilk test, we adjusted some analyses to non-parametric tests where appropriate. However, these adjustments did not alter the statistical significance of our results compared to the original analyses.
3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?
__Response: __
We have revised the explanation of our statistical test choices to provide greater clarity and precision. For example, in Figure 2G, we first assessed the normality of the data in each of the four groups using the Shapiro-Wilk test, which revealed that some datasets did not follow a normal distribution. Given this, we selected the Kruskal-Wallis test, a commonly used non-parametric test for comparisons across three or more groups. Since the Kruskal-Wallis test indicated a significant difference, we conducted a post hoc Steel-Dwass test to determine which specific group comparisons were statistically significant.
3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.
__Response: __
In the case of Figures 3H-K, we compared oligodendrocyte morphology between regions. However, since the number of sparsely labeled oligodendrocytes differs both between regions and across individual samples, there is no strict correspondence between paired measurements. On the other hand, in Figures 5B, C, and E, we compared the density of labeled cells between regions within the same slice, establishing a direct correspondence between paired data points. For these comparisons, we appropriately used a paired t-test.
3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)
__Response: __
We have specified the statistical tests used for each figure in the Methods section and Figure Legends for better clarity. Additionally, we have revised the descriptions for Figure 4G, L, and M and their corresponding Figure Legends to explicitly indicate that Spearman's rank correlation coefficient (rₛ) was used for evaluation.
3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).
__Response: __
We have now clarified what "n" represents in each figure, as well as the number of animals (N) used in each experiment, in the Figure Legends.
In this study, developmental stages of chick embryos were defined by HH stage (Hamburger and Hamilton, 1951), minimizing individual variability. Additionally, since our study focuses on the distribution of morphological characteristics of individual cells, averaging measurements per animal would obscure important cellular-level variability and potentially mislead interpretation of data. Furthermore, we employed a strategy of sparse genetic labeling in many experiments, which naturally results in variability in the number of measurable cells per animal. Given the clear distinctions in our data distributions, we believe that averaging per biological replicate is not essential in this case.
To further ensure the robustness of our statistical analysis, data presented as boxplots were preliminarily assessed using PlotsOfDifferences, a web-based application that calculates and visualizes effect sizes and 95% confidence intervals based on bootstrapping (https://huygens.science.uva.nl/PlotsOfDifferences/; https://doi.org/10.1101/578575). Effect sizes can serve as a valuable alternative to p-values (Ho, 2018; https://www.nature.com/articles/s41592-019-0470-3). The significant differences reported in our study are also supported by clear differences in effect sizes, ensuring that our conclusions remain robust regardless of the statistical approach used.
If requested, we would be happy to provide PlotsOfDifferences outputs as supplementary source data files, similar to those used in eLife publications, for each figure.
3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).
__Response: __
We have now incorporated individual data points into the bar graphs in Figures 5 and 6.
4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?
__Response: __
The morphological differences of oligodendrocytes between white and gray matter are well established (i.e. shorter myelin at gray matter), but their correspondence with the nodal spacing pattern along the long axonal projections of cortical neurons is not well understood. Future research may find similarities with our findings. Additionally, as mentioned in the final section of the Discussion, the mammalian brainstem auditory circuit is functionally analogous to the avian ITD circuit. Regional differences in nodal spacing along axons have also been observed in the mammalian system, raising the important question of whether these differences are supported by regional heterogeneity in oligodendrocytes. Investigating this possibility will facilitate our understanding of the underlying logic and mechanisms for determining node spacing patterns along axons, as well as provide valuable insights into evolutionary convergence in auditory processing mechanisms. We described these explicitly in text (page 11, line 32).
5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.
__Response: __
As you summarized in your comment, our results demonstrated that A3V-eTeNT suppressed oligodendrogenesis in the NL region, leading to a reduction in oligodendrocyte density (Figures 6L, M), which caused the emergence of unmyelinated segments. While this is an indirect manipulation of oligodendrocyte density, it nonetheless provides evidence supporting a causal relationship between oligodendrocyte density and nodal spacing.
The emergence of unmyelinated segments at the NL region further suggests that the myelin extension capacity of oligodendrocytes differs between regions, highlighting regional differences in intrinsic properties of oligodendrocyte as the most prominent determinant of nodal spacing variation. However, as you correctly pointed out, our findings do not establish direct causation.
In the future, developing methods to artificially manipulate myelin length could provide a more definitive demonstration of causality. Given these considerations, we have modified the title to replace "determine" with "underlie", ensuring that our conclusions are presented with appropriate nuance.
6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:
6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)
6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)
6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812). 6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)
__Response: __
Thank you for your insightful suggestions. We have incorporated the relevant references you provided and revised the manuscript accordingly to contextualize our findings within the existing literature.
Minor comments:
7) Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.
__Response: __
Thank you for your suggestion. We have corrected the units in Figure 1G to µm
8) The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?
__Response: __
Oligodendrocytes and OPCs have small cell bodies, often comparable in size to their nuclei. The central void in the ring-like Olig2 staining pattern appears too small to represent the nucleus. Additionally, a similar ring-like appearance is observed in BrdU labeling (Figure 5G), suggesting that this staining pattern may reflect nuclear morphology or other structural features.
Significance
In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.
__Response: __
The main finding of our study is that the primary determinant of the biased nodal spacing pattern in the sound localization circuit is the regional heterogeneity in the morphology of oligodendrocytes due to their intrinsic properties (e.g., their ability to produce and extend myelin sheaths) rather than the density of the cells. This was based on our observations that a reduction of oligodendrocyte density by A3V-eTeNT expression caused unmyelinated segments but did not increase internodal length (Figure 6), further revealing the importance of oligodendrocyte density in ensuring full myelination for the axons with short internodes. Thus, we think that our study could propose the significance of oligodendrocyte heterogeneity in the circuit function as well as in the nodal spacing using experimental manipulation of oligodendrocyte density.
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Reviewer #____3
Evidence, reproducibility and clarity
The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing. I have some major concerns:
1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.
__Response: __
Thank you for your insightful comment regarding the potential role of pre-nodal clusters in determining internodal length. Indeed, studies in zebrafish have suggested that pre-nodal clustering of node components prior to myelination may prefigure internodal length (Vagionitis et al., 2022). We have incorporated a discussion on whether such pre-nodal clusters could contribute to regional differences in nodal spacing in our manuscript (page 9, line 35).
Whether pre-nodal clusters are detectable before myelination appears to depend on neuronal subpopulation (Freeman et al., 2015). To investigate the presence of pre-nodal clusters along NM axons in the brainstem auditory circuit, we previously attempted to visualize AnkG signals at E13 and E14. However, we did not observe clear structures indicative of pre-nodal clusters; instead, we only detected sparse fibrous AnkG signals with weak Nav clustering at their ends, consistent with hemi-node features. This result does not exclude the possibility of pre-nodal clusters on NM axons, as the detection limit of immunostaining cannot be ruled out. In brainstem slices, where axons are densely packed, nodal molecules are expressed at low levels across a wide area, leading to a high background signal in immunostaining, which may mask weak pre-nodal cluster signals prior to myelination. Regarding the comment on Figure 1D, we assume you are referring to Figure 2D based on the context. The lack of clarity in the high-magnification images in Figure 2D results from both the high background signal and the limited penetration of the MAG antibody. Furthermore, we are unable to verify Neurofascin accumulation at pre-nodal clusters, as there is currently no commercially available antibody suitable for use in chickens, despite our over 20 years of efforts to identify one for AIS research. Therefore, current methodologies pose significant challenges in visualizing pre-nodal clusters in our model. Future advancements, such as exogenous expression of fluorescently tagged Neurofascin at appropriate densities or knock-in tagging of endogenous molecules, may help overcome these limitations.
However, a key issue to be discussed in this study is not merely the presence or absence of pre-nodal clusters, but rather whether pre-nodal clusters-if present-would determine regional differences in internodal length. To address this possibility, we have added new data in Figure 6I, measuring the length of unmyelinated segments that emerged following A3V-eTeNT expression. If pre-nodal clusters were fixed before myelination and predetermined internodal length, then the length of unmyelinated segments should be equal to or a multiple of the typical internodal length. However, our data showed that unmyelinated segments in the NL region were less than half the length of the typical NL internodal length, contradicting the hypothesis that fixed pre-nodal clusters determine internodal length along NM axons in this region.
2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.
__Response: __
As mentioned in our reply to comment 2 by Reviewer #1, the diameter of NM axons was already evaluated using electron microscopy (EM) in the pioneering study by Seidl et al., (2010). Additionally, EM-based analysis makes it difficult to clearly distinguish between the main trunk of NM axons and thin collateral branches at the NL region. Accordingly, we did not do the EM analysis in this revision.
In Figure 4, we used palGFP, which is targeted to the cell membrane, allowing us to measure axon diameter by evaluating the distance between two membrane signal peaks. This approach minimizes the influence of the blurring of fluorescence signals on diameter measurements. Thus, we believe that our method is sufficient to evaluate the relative difference in axon diameters between regions and hence to show that axon diameter is not the primary determinant of the 3-fold difference in internodal length between regions.
3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.
__Response: __
The heterogeneity in oligodendrocyte morphology would reflect differences in gene profiles, which, in turn, may arise from differences in their developmental origin and/or pericellular microenvironment of OPCs. We made this point as clear as possible in Discussion (page 9, line 21).
Significance
The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.
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Referee #3
Evidence, reproducibility and clarity
The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.
I have some major concerns:
- The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.
- The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.
- The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.
Significance
The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary.This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.
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Referee #2
Evidence, reproducibility and clarity
Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.
Major points, detailed below, need to be addressed to overcome some limitations of the study.
Major comments:
- It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.
- Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?
- The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:
3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?
3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.
3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)
3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).
3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6). 4. The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit? 5. Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended. 6. The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:
6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)
6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)
6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).
6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)
Minor comments:
- Can the authors amend Fig. 1G with the correct units of measurement, not millimetres.
- The Olig2 staining in Fig 2C does not appear to be nuclear, as would be expected of a transcription factor and as is well established for Olig2, but rather appears to be excluded from the nucleus, as it is in a ring or donut shape. Can the authors comment on this?
Significance
In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Egawa and colleagues investigates differences in nodal spacing in an avian auditory brain stem circuit. The results are clearly presented and data are of very high quality. The authors make two main conclusions:
- Node spacing, i.e. internodal length, is intrinsically specified by the oligodendrocytes in the region they are found in, rather than axonal properties (branching or diameter).
- Activity is necessary (we don't know what kind of signaling) for normal numbers of oligodendrocytes and therefore the extent of myelination.
These are interesting observations, albeit phenomenon. I have only a few criticisms that should be addressed:
- The use of the term 'distribution' when describing the location of nodes is confusing. I think the authors mean rather than the patterns of nodal distribution, the pattern of nodal spacing. They have investigated spacing along the axon. I encourage the authors to substitute node spacing or internodal length for node distribution.
- In Seidl et al. (J Neurosci 2010) it was reported that axon diameter and internodal length (nodal spacing) were different for regions of the circuit. Can the authors help me better understand the difference between the Seidl results and those presented here?
- The authors looked only in very young animals - are the results reported here applicable only to development, or does additional refinement take place with aging?
- The fact that internodal length is specified by the oligodendrocyte suggests that activity may not modify the location of nodes of Ranvier - although again, the authors have only looked during early development. This is quite different than this reviewer's original thoughts - that activity altered internodal length and axon diameter. Thus, the results here argue against node plasticity. The authors may choose to highlight this point or argue for or against it based on results in adult birds?
Significance
This paper may argue against node plasticity as a mechanism for tuning of neural circuits. Myelin plasticity is a very hot topic right now and node plasticity reflects myelin plasticity. this seems to be a circuit where perhaps plasticity is NOT occurring. That would be interesting to test directly. One limitation is that this is limited to development.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Manuscript number: RC-2024-02831
Corresponding author(s): Charisios Tsiairis
1. General Statements [optional]
We are very pleased that all three reviewers found our work to be solid, well-supported by the data, and free of major flaws. It is particularly gratifying that they did not request additional experimental work to support our conclusions. Instead, their comments focused on clarifications, textual improvements, and refinements in data presentation, which we have carefully addressed.
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We have made revisions to improve the clarity of the manuscript, incorporating insightful suggestions from the reviewers. These include refining key explanations, adjusting figure annotations, and modifying the structure of certain sentences. Additionally, we have addressed specific points regarding statistical significance, genome assembly references, and phylogenetic comparisons, ensuring that all aspects of our study are as precise and informative as possible.
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We are confident that these revisions have strengthened the manuscript.
2. Point-by-point description of the revisions
*Reviewer #1 (Evidence, reproducibility and clarity (Required)): *
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*Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes. *
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*General Points: *
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The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood. *
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The observed differences in upregulation are primarily technical. Expression levels are measured relative to unperturbed tissue, and in the control, Zic4 expression in the foot is detected only at noise levels (see figure 2C). As a result, any increase in Zic4 expression upon Gata3 knockdown appears relatively high when normalized to the minimal control levels. In contrast, Gata3 is already present at detectable levels in control samples from the upper body, head, and tentacles (See Fig 2D). Therefore, while its upregulation following Zic4 RNAi appears more modest, we interpret this as a qualitative indication of increased gene expression in the absence of the opposing transcription factor. That said, we acknowledge the possibility of additional regulatory layers contributing to these differences.
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Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced. *
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This is a valid point; however, the interpretation is complicated by the technical limitations of RNAi-based knockdown rather than a complete knockout of Gata3. Over time, the effect of RNAi diminishes, and we have observed that GFP expression returns within four weeks following GFP RNAi, indicating a temporal limit to RNAi-mediated knockdown. Therefore, while an extended time course would be informative, the transient nature of the knockdown makes it challenging to definitively track long-term cell replacement dynamics.
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The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers? *
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We appreciate the reviewer’s comment. In our previous publication (Vogg et al., 2022), we provided evidence supporting this phenomenon. As demonstrated in our data published there, markers of the peduncle, rather than the basal disc—such as manacle (gene ID 100212761) (Bridge et al., 2000) and Bmp5-8 (gene ID 100206618) (Reinhardt et al., 2004)—are also upregulated, suggesting a transition towards a peduncle-like state. However, we opted not to elaborate on this aspect in the current manuscript to maintain focus and avoid redundancy with previously published findings.
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*Specific Points: *
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*Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles. *
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Our intention was to illustrate the general presence of Zic4 in the oral domain, but we acknowledge the reviewer’s point that this could be misleading regarding its precise expression pattern. To address this concern, we have updated the figure panels to more accurately reflect the available in situ hybridization and scRNA-seq data.
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*Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend. *
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We thank the reviewer for catching this error. We have now corrected the references, replacing the numbering with the first authors' last names and publication dates.
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Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?
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We appreciate the reviewer’s observation. The label refers to the budding zone, and we acknowledge our omission in specifying this. We have now updated the figure and its legend to clarify this.
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*Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper? *
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We appreciate the reviewer’s request for clarification. These conditions are indeed similar to those in the previously published Vogg et al. paper. The boxes in the figure represent proximal and distal tentacle regions, each with four replicates. We have now updated the figure and its legend to make this explicit.
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*Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative. *
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*We added the percentage of cells expressing Zic4, Gata3 and both genes on the panel. *
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*Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control. *
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To make the figure clearer, we modified it and grouped each experimental sample with its corresponding control.
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*Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups. *
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We have now expanded the statistical analyses, ensuring that significance tests are presented in all relevant instances. However, we note that while statistical significance is important, it should be interpreted alongside other factors such as the magnitude of the effect, consistent trends across replicates, and biological relevance. Additionally, high standard deviations in certain conditions may influence absolute p-values, and we encourage consideration of the broader context of the data when interpreting these results.
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*Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway). *
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We understand the reviewer’s concern. The p-value for the qPCR data is slightly above 0.05, primarily due to high standard deviation. As the reviewer notes, qPCR on RNAi samples can be noisy, so the data should be interpreted in context. Importantly, the consistent qualitative increase in Gata3 levels after Zic4 knockdown aligns with the RNA-seq results, which, as the reviewer correctly points out, provide a more reliable measurement. Additionally, qPCR samples include a broader portion of head tissue, likely diluting the Gata3 signal from the tentacles and contributing to the observed variability.
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*Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A. *
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We appreciate the reviewer’s suggestion. However, we believe that displaying co-expression UMAPs for Zic4 would be redundant. Additionally, for genes with greater positional overlap, such as FoxI1 and Nfat5, co-expression UMAPs make visualization more challenging. To ensure clarity and optimize the interpretability of the data, we have chosen to present the expression profiles of each gene separately.
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*Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly. *
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Indeed, the study does not focus on the functional role of Gata3 in these neurons. We have revised the sentence, replacing "involvement of Gata3 in specifying neurons" with "expression expression of Gata3 in neurons emerging*" to more accurately reflect the study’s findings. *
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*Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences. *
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We appreciate the reviewer’s concern. However, we have chosen to use the Hydra_RP_1.0 assembly for Figure 1 to maintain consistency with previously published data, which were also mapped to this assembly. Since these publications predate the newer assembly, using the same reference ensures comparability in our analysis. Importantly the assembly used is still downloadable and accessible to every researcher. That said, for the phylogenetic analysis in Figure 2, we have used the latest available genome assemblies and annotations for all species, including Hydra. We have now clarified this in the Methods section.
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*The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper). *
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We appreciate the reviewer’s suggestion and have updated the references accordingly to include Siebert et al., 2019, for data collection and Cazet et al., 2023, for the analysis used in this paper.
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Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.
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We agree with the reviewer that the nematocyte marker HCR provides a more specific identification of nematocytes. This is why we have also used the nematocilin marker in separate samples. However, actin staining adds important information on the morphology of the surrounding epithelial cells, which become indistinguishable from battery cells in Gata3 KDs. Unfortunately, combining actin staining with HCR is technically challenging, as the tissue preparation protocols for these two approaches are not compatible, and we have therefore decided to show both stainings next to each other.
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*Minor Wording Issues: *
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*Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear. *
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We have made the suggested change.
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*Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset. *
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We are grateful for the suggestion, we have changed the text accordingly.
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*p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed. *
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We are referring to the upregulation of gata3 expression in tentacles upon Zic4 knockdown. To make this clearer, we have revised the wording to: “Q-PCR performed on dissected oral and aboral body regions confirmed the upregulation of gata3 upon Zic4 knockdown (Fig. 2C-D).”
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*Reviewer #1 (Significance (Required)): *
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*This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination. *
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*We appreciate the reviewer’s thoughtful summary and recognition of our study’s significance. *
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*Reviewer #2 (Evidence, reproducibility and clarity (Required)): *
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*Summary: *
*The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia. *
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*Major comments: *
*- Are the key conclusions convincing? *
*The key conclusions are convincing. *
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*- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? *
*The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. *
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*- 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. *
*No. *
*- 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? *
*Yes. *
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*Minor comments: *
*- Specific experimental issues that are easily addressable. *
*None. *
*- Are prior studies referenced appropriately? *
*Yes. *
*- Are the text and figures clear and accurate? *
*Yes. The figures are very nice. *
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*- Do you have suggestions that would help the authors improve the presentation of their data and conclusions? *
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*1) Move the phylogenetic comparisons to the end *
*2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results. *
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We thank the reviewer for their positive assessment and constructive suggestions. Below, we comment on each point:
- Placement of cross-species comparison: This suggestion concerns the emphasis and structure of the manuscript. We appreciate the reviewer's interest in the evolutionary aspects of our work. However, we believe that moving this analysis to the end would dilute the main message, which is reinforced by the schematic in Figure 4E-F. We aim to conclude with the experimental results demonstrating the minimization of phenotypic consequences when both factors are knocked down. Therefore, we have chosen to retain the cross-species comparison in its current position to emphasize the conservation of the double-negative interaction before presenting the functional consequences of its perturbation.
- Reordering of Gata3 KD results: We understand the rationale behind this suggestion. However, our sequencing is guided by the fact that foot regeneration deficiency under Gata3 kd has already been documented and presented in previous work (Ferenc et al., 2021). For this reason, we begin with that reference, then build upon it with a deeper examination of the phenotype.
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We are grateful for the reviewer’s feedback and for recognizing the clarity of our figures and analysis.
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***Referee cross-commenting** *
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*I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1. *
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*Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity. *
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We appreciate Reviewer’s #3 remark about the nuance of transcription factor homology. The situation is exactly as described here by Reviewer #2 - The gene names in Figure 1 are based on the results of NCBI automated homology annotation, which we have now clarified in a note in the legend of Figure 1.
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*Reviewer #2 (Significance (Required)): *
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*- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. *
*This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. *
*- Place the work in the context of the existing literature (provide references, where appropriate). *
*The manuscript does a good job of placing the work into the appropriate context. *
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*- State what audience might be interested in and influenced by the reported findings. *
*Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. *
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*- 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. *
*Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution *
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We greatly appreciate the reviewer’s positive feedback and recognition of our study's focus on gene expression in cell fate decisions. We're pleased that our findings on the mutual knockdown and the broader context were well received. Thank you for highlighting the relevance of our work to gene regulation and cell specification.
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*Reviewer #3 (Evidence, reproducibility and clarity (Required)): *
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*Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two. *
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*A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry. *
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The naming of the gene as Sry was indeed based on the NCBI automated homology annotation, and we have clarified this in the revised manuscript. Since we did not pursue further analysis of this gene, we believe that a deeper phylogenetic analysis may not be necessary and could potentially divert attention from the main focus of our study on Gata3's role.
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*Reviewer #3 (Significance (Required)): *
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*This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers. *
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We thank the reviewer for the thoughtful assessment of our work. We appreciate their feedback and the recognition of the quality and significance of our findings.
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Referee #3
Evidence, reproducibility and clarity
Ferenc et al. have studied the role of transcription factors Zic4 and Gata3 in Hydra epithelial cell fate decision. The Tsiairis team has published a paper recently in which they had studied the role of Zic4 in promoting tentacle formation. Here, they discover a negative feedback loop between Zic4 and Gata3 in the context of epithelial cell differentiation. The authors used computational techniques to identify Zic4 binding sited in Hydra promoters of genes that are upregulated in basal disks, known from a previous study, and identified eight candidate genes. Previous studies were also used to narrow down potential Zic4 targets. They argue that Gata3 appears as a strong candidate to be suppressed by Zic4 in the head and being expressed in the foot. Knockdown experiments, followed by qPCR revealed that Gata3 and Zic4 expression is mutually exclusive such that the one represses the other. Next, they report that Gata3 RNAi results in ectopic battery cells at the lower body column, although basal disk cells maintained their identity following Gata3 knockdown. Finally, knocking down both Gata3 and Zic4 resulted in a more normal phenotype, as predicted if a negative feedback loop existed between the two.
A minor comment: one of the predicted Zic4 targets is a gene called Sry. Sry is a mammalian male determinant and a SOX-related protein (SoxA). I was wondering if the authors performed phylogenetic analysis or simply took a BLAST hit as the source for this gene's name. I am unaware of SoxA-like genes in cnidarians . Therefore, I would recommend performing a SOX phylogeny and renaming it according to its closest relatives, which probably won't be Sry.
Significance
This work closes some gaps that remained after publication of previous research by the Tsiairis lab and others. The data are of high quality, solid, and support the authors' conclusions. The manuscript is of general interest for developmental biologists and evodevo workers.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors use the freshwater hydrozoan Hydra as a model to investigate mechanisms of cell fate decisions in the context of terminal epithelial differentiation. The epithelia migrates towards the extremities of the animal and takes on one of two fates: elongated battery cells that house the cnidocytes ( stinging cells ) in the oral ( head ) end of the animal, or more compact secretory basal disc cells at the aboral ( foot ) end. In this manuscript the authors build on previous work that showed the transcription factor Zic4 is necessary for battery cell formation. The authors use in situ hybridization and additional labelling techniques to assess cell fate under a variety of conditions. The authors first screen for Zic4 binding sites in the promoter regions of aboral genes that previously were demonstrated to be up-regulated in response to Zic4 knockdown, and survey publicly available expression databases to identify GATA3 as a candidate transcription factor that shows complementary expression patterns. The authors also screen the promoter regions of Zic4 and GATA3 from a number of other cnidarians and find reciprocal binding sites in all but one case. This is interpreted by the authors as evidence for a Zic4/GATA3 cnidarian regulatory motif. The authors demonstrate that KD of GATA3 results in the opposite phenotype: ectopic differentiation of oral battery cells, and that animals with perturbed GATA3 function fail to regenerate the aboral basal disk cells but rather show oral battery cell phenotype. Further, KD of both genes (Zic4: battery cells and GATA3: pedal disc cells) results in a rescue of the phenotype of either single KD, thereby illustrating that together these two genes function as a negative feedback loop controlling the terminal differentiation of the ectodermal epithelia.
Major comments:
- Are the key conclusions convincing?
The key conclusions are convincing. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
The cross species comparison of binding sites is insightful, but is presented very early in the manuscript. This would be better placed as a final piece, to place the Hydra-specific findings in a larger context. - 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.
No. - 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?
Yes.
Minor comments:
- Specific experimental issues that are easily addressable.
None. - Are prior studies referenced appropriately?
Yes. - Are the text and figures clear and accurate?
Yes. The figures are very nice. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
1) Move the phylogenetic comparisons to the end
2) Similarly, in the section on GATA3 KD, present the normal condition first, and then the regeneration experiment results.
Referee cross-commenting
I have read the other two reviews and find that we are all in agreement that the work presented in this manuscript is sound and is a valuable scientific contribution. I would encourage the authors to consider my own suggests for order of presentation of data, to retain a specific to broad theme (normal then regeneration / hydra then comparisons) and to incorporated the detailed corrections highlighted by reviewer 1.
Regarding reviewer 3's comment regarding SoxA in cnidarians. This is likely true and the nomenclature of the gene likely comes from an automated pipeline to infer gene identities. Unless the authors follow up on this gene, I don't think the onus is on the authors to confirm the identity.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
This paper is a beautiful illustration of the importance of relative gene expression levels in controlling cell fate decisions. Together with their previous works, the role of both transcription factors in specifying one of two possible terminal fates is very clearly illustrated. The final observation, that a mutual knockdown of both factors leads to a rescue of the polarity of the cell type balance is an excellent example of the importance of relative gene expression levels in controlling homeostatic balance between two mutually exclusive cell fates. - Place the work in the context of the existing literature (provide references, where appropriate).
The manuscript does a good job of placing the work into the appropriate context. - State what audience might be interested in and influenced by the reported findings.
Readers with interest in gene regulation, cell specification, and mechanisms of cell type diversification would find these results of interest. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.
Comparative invertebrate embryogenesis; Single cell transcriptomics; Cell and tissue evolution
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Referee #1
Evidence, reproducibility and clarity
Overall, the paper is well-written, the figures are easy to interpret, and the conclusions are well supported by the data. Most of the points discussed below could be addressed with simple text changes.
General Points:
- The upregulation of Gata3 in response to Zic4 RNAi is relatively modest compared to the more pronounced upregulation of Zic4 following Gata3 knockdown, but this point is not really addressed. While these issues could be simply technical, they might also hint at additional layers of regulation that are not yet fully understood.
- Extending the time course would strengthen the conclusion that, in the Gata3 knockdown, the existing basal disk cells remain stable while body column cells migrating into the region differentiate into tentacle cells. If this hypothesis is correct, one would predict that by approximately 20 days, the basal disk cells would be completely replaced.
- The conclusion that tentacle cells transdifferentiate into basal disc cells in the Zic4 knockdown may require more nuance, as only the tips of the tentacles express peroxidase. Do the more proximal regions of the tentacle express peduncle markers?
Specific Points:
Figure 1A, Figure 4E: The pictorial representation of Zic4 expression may need to be revised, as in situ hybridization data from Vogg et al., 2022, suggests that Zic4 is absent from the hypostome and tentacle tips. While in situ hybridization can sometimes lack precision due to variability in staining protocols and subjective decisions on when to stop the reaction, this observation aligns with scRNA-seq data, which also indicates a lack of Zic4 expression in the hypostome and tips of the tentacles.
Figure 1 Legend: For panel D, the legend says "data taken from 28" but the references are not numbered. Same problem for panel E legend.
Figure 1D: There may be a mistake in the Hydra body part labeling. Is "B" supposed to be "P" for peduncle?
Figure 1 Panel E: Please provide clarification regarding what each box means. Are these 8 replicates of the same condition, or are these the proximal and distal regions of the tentacles as was collected in the Vogg paper?
Figure 2A: Consider using the co-expression stats from Fig S2, which are very informative.
Figure 2E, F: It would be more intuitive to group each experimental sample with its corresponding control.
Figure 2C-F: Consider conducting statistical tests of significance between control and treatment groups.
Figure 2 E - Considering the error bars, Gata3 upregulation in response to Zic4 knockdown does not look significant based on qPCR. Showing the significance of the up-regulation in the RNA-seq data may be more convincing. (I believe RNA-seq to be more reliable anyway).
Figure S2: Might be helpful to show co-expression UMAPs here, like what is shown in Figure 2A.
Page 4: "Interestingly, a similar binary choice pattern appears in certain neuronal lineages as well. A recent study demonstrated the involvement of Gata3 in specifying neurons at the aboral end (Primack et al. 2023), suggesting that this cross-regulation between Zic4 and Gata3 may extend beyond the epidermal lineage." Just a note that this paper shows expression, but doesn't show function as the statement implies, so the statement should be changed accordingly.
Page 10: "Transcription Factor Binding site analysis... Hydra promoter sequences were compiled from the NCBI Hydra RP 105 assembly." Authors should provide a repository identifier for the genome they are using. Based on the information provided, it appears the authors are using Genome assembly "Hydra_RP_1.0" RefSeq GCF_000004095.1. However, that genome assembly has been suppressed for the following reason: "superseded by newer assembly for species". Authors should consider updating the reference assembly they are using to map their sequencing data and identify promoter sequences.
The paper makes great use of the Hydra scRNA-seq data set! Minor point, when referring to the Hydra scRNA-seq data set, please cite Siebert et al., 2019 (data collection) and Cazet et al., 2023 (analysis that is being used in this paper).
Something to keep in mind: To an audience without expertise in Hydra cell type morphology, the nematocyte marker HCR will likely be more convincing than the actin staining in Figure 3D to identify and quantify nematocytes.
Minor Wording Issues:
Page 2. "However, the mechanism by which Zic4 prevents the battery cell program from misexpression in normal tentacles remained unclear." Could read more clearly as: However, the mechanism by which Zic4 prevents the misexpression of the battery cell program in normal tentacles remained unclear.
Page 2. "Potential candidates for this function could be found among TFs with highly enriched binding sites in the dataset, which are themselves Zic4 targets." Could read more clearly as: We reasoned that this intermediary factor, likely a target of Zic4, would be a transcription factor with highly enriched binding sites in the dataset.
p3-4. "Q-PCR performed on dissected oral and aboral body regions confirmed this finding (Fig. 2C-D)" It is unclear which "finding" is being confirmed.
Significance
This compelling study from the Tsiairis lab uncovers a double-negative feedback loop between the transcription factors Zic4 and Gata3, functioning as a toggle switch to control oral and aboral fates in Hydra's epidermal lineage. Addressing fundamental questions in developmental biology, this research sheds light on the mechanisms underlying cell fate determination in relationship to their spatial organization. In Hydra, Wnt signaling, a conserved pathway critical for establishing primary body axes, promotes oral fate, emanating from an organizer at the oral end. Hydra body column epidermal cells can differentiate into distinct cell types, including oral battery cells and aboral basal disk cells, but the regulatory mechanisms remained elusive. Recent research from the Tsiairis lab identified Zic4 as a direct Wnt signaling target necessary for repressing basal disk-specific genes. Knocking down Zic4 caused battery cells to transform into basal disk cells, though Zic4 did not directly activate basal disk-specific genes, pointing to an intermediary regulator. This study identifies Gata3 as a key regulator of basal disk gene expression, as it is highly expressed at the aboral end, is inversely correlated with Zic4, and is upregulated in Zic4 knockouts. Functional experiments revealed mutual inhibition between Zic4 and Gata3: knocking down Gata3 led to differentiation of battery cells at the aboral end, while simultaneous knockdowns of Zic4 and Gata3 rescued the phenotypes of individual knockdowns. These findings demonstrate a finely tuned balance between Zic4 and Gata3 in regulating cell fate along the oral-aboral axis in Hydra. This paper therefore offers new insights into the spatial organization of cell type specification in Hydra and into broader principles of cell fate determination.
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Reply to the reviewers
What follows is our revision Plan.
Manuscript number: RC-2024-02794
Corresponding author(s): Jo Morris
[The "revision plan" should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.
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The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.
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If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]
1. General Statements [optional]
We feel the reviewers understood the paper well and made many reasonable points for improvement.
In response to Reviewer three's concern about the reliance on SAE2 over-expression, in the 'Significance' section "One limitation is the strong reliance on the use of an actyl-mimicking mutant". We were minded not to rely on the mutant. Hence, the paper contains considerable data onthe HDCAC6 deacteylase, responsible for SEA2 deacetylation. We show that HDAC6 inhibition phenocopies SAE2-K164Q expression and, moreover, that the approaches which rescue the mitotic defects of SAE2-K164Q expression cells also rescue the defects of HDCA6 inhibited cells. These observations, we believe, overcome the concern.
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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Revisions.
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R1: As the authors state, SUMO1 conjugates decrease during mitosis and this is somewhat at odds with the proposed model regarding NuMA. The authors can detect a SUMOylated NuMA conjugate (fig. 4a). To test whether the proposed model is correct, the authors could check: a. Whether this form is indeed SUMO1-NuMA b. Whether it decreases upon expression of the SAE2K164Q variant.
R2: Figure 4:The authors show a ML792 sensitive high molecular weight smear of NUMA in nocodazole treated cells. It would be very convincing if the authors could demonstrate whether endogenous NUMA is conjugated to SUMO1 or SUMO2 in mitosis by SUMO IPs and whether they can detect a change upon expression of SAE2 variants as in Figure 3a. By replicating this experiment, it would be important to demonstrate the presence of both free and conjugated SUMO paralogs in the input and paralog specific sumoylation in general (smear) and of NUMA in the IP.
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Response:These are important points. We intend to perform the suggested experiments to address which isoform NuMa is modified by, and what the impact of the variant is.
R2:Figures 2 C/Supplementary Figure 3c: The enzyme concentrations used in these reactions are much too high. To discriminate between thioester- and isopeptide-linked SUMO, the same samples should be analyzed in the absence (detection of thioester and isopeptide linkages) and presence of high concentrations of DTT (detection of isopeptide-linked SUMO only). The presented assay is problematic as it shows dimeric SUMO and RanGAP1:SUMO bands in the absence of ATP and no UBC9 but SAE2 thioester/isopetide formation in the absence of RanGAP1 (preferentially UBC9 should form a thioester/isopetide bond in this condition as higher molarities of UBC9 over E1 are used). Dimeric SUMO should not be detected unless disulfide bridges are formed between cysteines - this happens when DTT is not present in the reaction - under such conditions, SAE2 and UBC9 can also form disulfide bridges via their catalytic cysteines, impairing their enzymatic activity. In order to interpret the results correctly, it is important to add low concentrations of DTT (~0.1 mM) even in thioester reactions and to distinguish between thioester and isopeptide linkages.
R2: Figure 2F/ Supplementary Figure 3d: Again, the enzyme concentrations are much too high and need to be reduced to a concentration where mainly RanGAP1 monosumoylation with SUMO1 is detected. As RanGAP1 is the most efficient SUMO substrate known, the enzyme concentrations and reaction time can be greatly reduced to limit the auto-modification of the enzymes and SUMO chain formation. Due to the efficient chain-forming activity of SUMO2, this is more difficult with SUMO2, but can be reduced by limiting the concentration of UBC9 in particular or by using a SUMO2 KallR mutant. In the reaction shown, the authors used only twice the molarity of SUMO compared to the substrate, too low taking into account SUMO2 chain formation, enzyme and substrate modification (The reaction should be limited by enzyme activity not by SUMO2). How can the authors be sure that the band they report as RanGAP1 high MW SUMO2 is indeed RanGAP1 modified and not SAE2 (in comparison to Suppl Figure 3b)?
Response: We intend to repeat these assays, as suggested by the reviewer, reducing the enzyme concentrations and using low-concentration DTT. With the relevant controls and blots to show the identity of the RanGAP-SUMO2 product. Further, we will show control experiments with and without DTT that demonstrate the sensitivity of the SAE2~SUMO band to the reducing agent.
R2: Figure 3 nicely shows that ML792-resistant SAE2 variants conjugate SUMO2 equally well, whereas SAE2 K163R is reduced and SAE2 K163Q appears to be abolished in SUMO1 conjugation. However, only high molecular weight SUMO conjugates are shown. What are the levels of free SUMO after overexpression of SAE2 variants and the indicated treatments?
Response: We will attempt to show free SUMO levels in mitotic cells.
R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?
Response: We will examine the localisation of the SAE2 variants (see section below for the SUMO proteins).
R3: It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.
Response: We intend to perform the first approach suggested, making and examining the double mutant's activity as suggested.
R3: The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.
Response. This is an excellent suggestion. We intend to make the constructs suggested and perform this experiment for our revision.
R3. It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures.
Response. We will attempt to gain a view of SAE2 acetylation over the cell cycle, which requires the precipitation of endogenous SAE following synchronisation.
R3: Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.
Response: We will change this.
R3: The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.
Response: We have considered: "HDAC6-Dependent Deacetylation of SAE2 enhances SUMO1 Conjugation for Mitotic Integrity", we have not changed it on the current manuscript so as not to confuse the reader - we will change it at the journal level.
R3: Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.
Response: The data will be normalised to the WT condition (1 instead of 0), and raw data shown.
R3: Fig 2c: Please also show representative raw data.
Response: Representative images will be shown.
R3: Fig 2d,f: Again, the legend should explain what the plots were normalized to.
Response: Inserted in the legend for Fig. 2d&f: 'The RanGAP1-SUMO1 products are normalised to the WT SAE1:SAE2:SUMO1-only condition (top) and the RanGAP1-SUMO2 products are normalised to the WT SAE1:SAE2:SUMO2-only condition (bottom).'
R3 Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.
Response: Agreed. The figure will be redrawn, and the possibility will be discussed.
R1 Fig. 2c: Why does C173G form a thioester with SUMO2 up to 40% of the WT?
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Response: We believe this arose in measuring background density in the blots in error. We will re-assess the method used.
R3: Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.
Response: We will change one of the colours.
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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R1:2. Please clarify the use of Dox addition in the text and legend earlier (is found currently in Supp. Fig 4).
Response: Inserted before first result using doxycycline: 'Furthermore, we generated U2OS with a doxycycline-inducible (wild-type) WT FLAG-SAE2 or a FLAG-SAE2-K164R mutant.'
R1.3. Fig. 4f: what is the difference between the first (invisible NUMA) bipolar and the second, NuMA visible bipolar spindle?
Response: Fig. 4f now annotated with 'Untransfected' and 'GFP-NuMA transfected'.
R1.4. ML972- should read ML792 on pg 8.
Response: Corrected.
R3: All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed?
Response: Supplemental Figure 4a illustrates that with the exception of the C173G mutant, the remainder WT, and K164-mutants are all expressed at near WT-levels and not over-expressed. The C-G-mutant is highly expressed.
R3: On page 3, the authors could introduce a justification of why they tested IR treatment.
Response: now justified.
R3: The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.
Response: Agreed. Done.
R3: Page 8: "confirmation" should be "conformation".
Response: Done.
R3:Page 8: "While we find a little role for..." - delete "a"
Response: Done.
R2: Supplementary Figure 2: Please indicate the size of the marker bands, the fraction numbers and which fractions were pooled for further analysis. Is there any explanation why SAE1:SAE2K164R eluates in two peaks, suggesting two complexes? How different are they in size?
Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates fractions pooled. Figure updated with relevant recombinant protein preps generated for updated in vitro experiments. The additional SAE1:SAE2-K164R peak which appeared in the previous manuscript Supp. Fig. 2a eluted in the void volume and so we think it comprised aggregated SAE1:SAE2 protein, more recent preparations do not show it.
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R3: The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?
Response: Yes, good point. Now included:
R3: Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?
Response: Comment included.
R3: Fig 3h: typo ("Trasfect")
Response: Done.
R3: Fig 4f: The DAPI signal is hardly visible - better contrast would help.
Response: Improved.
R3: Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.
Response: Ladder markers added to each gel image. Fraction numbers added. Black box indicates the fractions pooled.
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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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R2: According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells?
Response: We have investigated SUMO isoform location. However, in our hands, using a range of SUMO antibodies, we do not see the previously reported localisations in mitotic wild-type cells, and thus, we are not able to assess the impact of the SAE variants. As our phenotypes are restricted to mitosis, we do not consider it worthwhile to look at interphase.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In this manuscript, the authors report on an interesting regulatory mechanism that influences the balance between conjugation of the different SUMO isoforms, SUMO1 versus SUMO2/3. The authors describe that acetylation of a specific residue, K164, in the SUMO activating enzyme (E1) subunit, SAE2, biases the E1's preference towards SUMO2/3. Specifically, they use an acetylation-mimicking K164Q mutation to show that the acetylation state of SAE2 likely affects the affinity of the E1 to SUMO and the rate of thioester formation. With an antibody, they demonstrate the acetylation of SAE2 in cells. Mechanistically, they locate the cause of the isoform bias to a residue in the C-terminus of SUMO in proximity to K164 or SAE2, where SUMO1 carries glutamate, while SUMO2/3 has glutamine. Switching these residues between the SUMO isoforms reverses the isoform preference of the E1. Phenotypically, the SAE2 K164Q mutant induces mitotic problems that the authors attribute to the SUMOylation of the NuMA complex. They assign the deacetylation of SAE1 to HDAC6 and report that deacetylation occurs during mitosis. These results are consistent with a model that SUMO1 modification of the NuMA complex in mitosis is important for mitotic fidelity and that the cell cycle-dependent changes in the acetylation status of SAE2 promote this. Accordingly, fusion of SUMO1 to a NuMA subunit partially overcomes the problems induced by the K164Q mutant or the inhibition of HDAC6.
Major comments:
The experiments are largely performed in a well-controlled manner, and overall, the study is very convincing. I would like to suggest a few experiments that would strengthen the authors' conclusions, and there are a few minor issues with some of the figures.
- It would be helpful if the authors could more clearly separate the two steps catalyzed by the E1. This would be needed to determine whether the accumulation of the SUMO1-AMP intermediate by the K164Q mutant is due to a faster rate of formation or a reduced rate of conversion to the thioester. They could test the AMP formation step in isolation in a straightforward manner by using the double mutant K164Q C173G and measuring a time course of SUMO1-AMP versus SUMO2-AMP build-up. Alternatively, they could try to isolate the second step by adding SUMO1-AMP versus SUMO2-AMP to the E1 de novo - although isolation of the intermediates may be more involved.
- The reason for the isoform selectivity in the context of NuMA SUMOylation remains unresolved. The study would be significantly strengthened if the authors could address the question of whether the mitotic defects come from a lack of NuMA SUMOylation or the wrong type of SUMOylation. In other words, does it matter which isoform of SUMO is attached to NuMA? This could be addressed by also creating a SUMO2 fusion construct and testing whether that suppresses some of the phenotypes observed with the K164Q mutant and upon HDAC6 inhibition.
- The authors should include a more detailed discussion of the importance of the absolute and relative concentrations of free SUMO1 versus SUMO2/3 as a possible mechanism to impose isoform bias. Specifically, they should consider the different KM values of the E1 for the isoforms. The literature says that the E1 has a lower KM (higher affinity) for SUMO1 than SUMO2/3 but also a lower kcat (considering both steps of its reaction together), resulting in an approximately equal Kcat/KM. This would mean that at low overall SUMO concentrations, SUMO1 would have an advantage, whereas with rising SUMO concentrations SUMO2/3 would be favoured (which might be particularly important during stress conditions). What part of the curve does the cellular environment reflect?
- It would be helpful to show a time course of endogenous SAE2 acetylation over the cell cycle, using synchronized cultures. All the experiments showing acetylation are done with transfected FLAG-tagged constructs - are they overexpressed? Is is not possible to work with endogenous SAE2?
Minor comments:
- The title is not immediately understandable. "SUMO protein bias for mitotic stability" sounds a bit awkward. It would be clearer to be more explicit about isoforms.
- On page 3, the authors could introduce a justification of why they tested IR treatment.
- The authors repeatedly use the word "codon" when they describe a site in the protein. Codon refers to mRNA, so the word "residue" would be more appropriate when talking about a protein.
- Page 8: "confirmation" should be "conformation".
- Page 8: "While we find a little role for..." - delete "a"
- Fig 2a: The figure would be easier to understand if the same colour scheme was used for S1 versus S2 to aid the comparison.
- Fig 2b: I don't understand the units of this graph. Why does normalization result in a value of zero, not 1? On this scale, what would a value of 1 signify? How can a value become negative? I would have expected values relative to the WT, with the WT being set to 1 or to 100%. The authors should also show the raw data for this plot.
- Fig 2c: Please also show representative raw data.
- Fig 2d,f: Again, the legend should explain what the plots were normalized to.
- Fig 3g: Could the authors comment on the detrimental effects of both SUMO1 and SUMO2 in the WT background?
- Fig 3h: typo ("Trasfect")
- Fig 4b: The images have very poor contrast. In addition, the merged image would be clearer if two different colours were used.
- Fig 4f: The DAPI signal is hardly visible - better contrast would help.
- Fig S2: It would be appropriate to indicate which fractions were actually collected or combined during the purification.
- Fig S5b: The authors argue with the hydrogen bonding capacities of the different pairings. However, acetylation at K164 should not necessarily prevent a hydrogen bond to SUMO1-E93, considering that the "NH" group is likely still at a comparable distance to the carboxylate of E93 and could in principle undergo H-bonding unless prevented by the steric bulk introduced by the acetyl group. On the other hand, the K164-E93 interaction is the only electrostatic interaction among the 4 possible combinations. While a contribution is not easy to prove experimentally, I think the possibility of charge-charge interactions having an impact should be considered in the discussion.
Significance
The results presented here are interesting and novel. Importantly, the authors provide a molecular model for a new mechanism of how the SUMO system achieves isoform specificity, which is a still very poorly understood phenomenon. The manuscript makes a significant advance by contributing an important new aspect of how the SUMO conjugation machinery chooses between isoforms. The manuscript is strong by providing very good evidence for its conclusions. One limitation is the strong reliance on the use of an actyl-mimicking mutant; this limitation could be overcome by placing a bit more emphasis on detecting endogenous SAE2 acetylation.
Audience: The study should be relevant to a broad audience, given the impact of the SUMO system on cellular regulation; after all, the study addresses a very fundamental problem in the field. In addition, it should be of interest to researchers studying regulation of mitosis.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Walker et al characterized lysine 164 acetylation of the catalytic SUMO activating enzyme subunit SAE2 and observed that this modification causes a bias towards SUMO2/3 over SUMO1 involving their C-terminal tails. While several enzymes appear to mediate SAE2 acetylation, HDAC6 is responsible for deacetylating SAE2 in mitosis, thereby promoting mitotic SUMO1 modification. The nuclear mitotic apparatus, NuMA, was identified as a putative mitotic SUMO1 substate upon SAE2 deacetylation. Replacement of endogenous SAE2 with an acetylation mimetic SAE2-K164Q mutant restricts SUMO1 conjugation of NuMA resulting in multipolar spindle formation that can be rescued either by overexpression of SUMO1 or by SUMO1-NuMA fusion.
Major comments:
- Figures 2 C/Supplementary Figure 3c: The enzyme concentrations used in these reactions are much too high. To discriminate between thioester- and isopeptide-linked SUMO, the same samples should be analyzed in the absence (detection of thioester and isopeptide linkages) and presence of high concentrations of DTT (detection of isopeptide-linked SUMO only). The presented assay is problematic as it shows dimeric SUMO and RanGAP1:SUMO bands in the absence of ATP and no UBC9 but SAE2 thioester/isopetide formation in the absence of RanGAP1 (preferentially UBC9 should form a thioester/isopetide bond in this condition as higher molarities of UBC9 over E1 are used). Dimeric SUMO should not be detected unless disulfide bridges are formed between cysteines - this happens when DTT is not present in the reaction - under such conditions, SAE2 and UBC9 can also form disulfide bridges via their catalytic cysteines, impairing their enzymatic activity. In order to interpret the results correctly, it is important to add low concentrations of DTT (~0.1 mM) even in thioester reactions and to distinguish between thioester and isopeptide linkages.
- Figure 2F/ Supplementary Figure 3d: Again, the enzyme concentrations are much too high and need to be reduced to a concentration where mainly RanGAP1 monosumoylation with SUMO1 is detected. As RanGAP1 is the most efficient SUMO substrate known, the enzyme concentrations and reaction time can be greatly reduced to limit the auto-modification of the enzymes and SUMO chain formation. Due to the efficient chain-forming activity of SUMO2, this is more difficult with SUMO2, but can be reduced by limiting the concentration of UBC9 in particular or by using a SUMO2 KallR mutant. In the reaction shown, the authors used only twice the molarity of SUMO compared to the substrate, too low taking into account SUMO2 chain formation, enzyme and substrate modification (The reaction should be limited by enzyme activity not by SUMO2). How can the authors be sure that the band they report as RanGAP1 high MW SUMO2 is indeed RanGAP1 modified and not SAE2 (in comparison to Suppl Figure 3b)?
- Figure 3 nicely shows that ML792-resistant SAE2 variants conjugate SUMO2 equally well, whereas SAE2 K163R is reduced and SAE2 K163Q appears to be abolished in SUMO1 conjugation. However, only high molecular weight SUMO conjugates are shown. What are the levels of free SUMO after overexpression of SAE2 variants and the indicated treatments? According to the work of Zhang et al from the Matunis lab (cited as reference 39 in the proposed study), SUMO conjugation is greatly reduced in nocodazole-arrested cells, but is restored after release in G1. Furthermore, SUMO1 and SUMO2 localize to different subcellular regions during mitosis. Have the authors tested whether SAE2 variants differ in their intracellular localization or alter the subcellular localization of SUMO1 and SUMO2 in interphase and mitotic cells? Can the authors comment on the proportion of SAE2 that is acetylated?
- Figure 4:The authors show a ML792 sensitive high molecular weight smear of NUMA in nocodazole treated cells. It would be very convincing if the authors could demonstrate whether endogenous NUMA is conjugated to SUMO1 or SUMO2 in mitosis by SUMO IPs and whether they can detect a change upon expression of SAE2 variants as in Figure 3a. By replicating this experiment, it would be important to demonstrate the presence of both free and conjugated SUMO paralogs in the input and paralog specific sumoylation in general (smear) and of NUMA in the IP.
Minor comments:
- Supplementary Figure 2: Please indicate the size of the marker bands, the fraction numbers and which fractions were pooled for further analysis. Is there any explanation why SAE1:SAE2K164R eluates in two peaks, suggesting two complexes? How different are they in size?
Significance
The finding that E1 acetylation regulates SUMO paralog specificity is very exciting, particularly because of its link to key regulatory mitotic functions. Overall, the findings are intriguing and supported in part by various biological and biochemical methods. However, some concerns remain unsatisfactorily addressed, as outlined above.
The findings provide a novel basic concept of how E1 enzyme regulation contributes to the specification of modifier selectivity, demonstrates cross-talk with other PTMs and reveals a biological function. Therefore, the study is of interest to a broad audience.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In their manuscript, Walker et al. investigate the physiological role of deacetylation of the SAE2 subunit of the SUMO E1 enzyme. They find that SAE1:SAE2-acK164 is deacetylated in an HDAC6-dependend manner and use a series of biochemical assays to show that deacetylation of the SAE2 subunit shifts the bias of the SUMO E1 towards SUMO1 conjugation in vitro, proposing a mechanism that is similar to the one that the NEDD8 E1 employs to discriminate between NEDD8 and ubiquitin.
The authors continue to examine the role of different SAE2 variants in different cellular stresses and show that the acetyl-mimicking SAE2K164Q variant displays reduced levels of high molecular weight SUMO1 conjugates in mitotic cells. This variant cannot support proper mitotic spindle formation leading to the appearance of multipolar spindles and centromere-containing micronuclei. Finally, they go on to identify the mechanism underlying these phenotypes and examine NuMA SUMOylation. They test SUMOylation-refractive NuMA variants as well as an already published SUMO1-NuMA fusion that mimics the SUMOylated protein form. They propose a model in which deacetylation of SAE2 changes the bias of the SUMO E1 to increase SUMO1-NuMA conjugation during mitosis, promoting bipolar spindle formation.
Major point:
As the authors state, SUMO1 conjugates decrease during mitosis and this is somewhat at odds with the proposed model regarding NuMA. The authors can detect a SUMOylated NuMA conjugate (fig. 4a). To test whether the proposed model is correct, the authors could check:
a. Whether this form is indeed SUMO1-NuMA
b. Whether it decreases upon expression of the SAE2K164Q variant.
Minor points:
- Fig. 2c: Why does C173G form a thioester with SUMO2 up to 40% of the WT?
- Please clarify the use of Dox addition in the text and legend earlier (is found currently in Supp. Fig 4).
- Fig. 4f: what is the difference between the first (invisible NUMA) bipolar and the second, NuMA visible bipolar spindle?
- ML972- should read ML792 on pg 8.
Significance
General assessment:
This is a thorough study with complex but well controlled experiments and contains a large amount of valuable information. A point could be further clarified in order to provide further support the proposed model.
Advance:
The document brings understanding on the regulation of the SUMO conjugation system a step forward and links it to a physiological context.
Audience:
basic science: the Ubiquitin family field and also the mitosis-cytoskeleton field. Applied science concerning the use of SUMO inhibitors in cancer.
Expertise: SUMO regulation of the cytoskeleton during mitosis (yeast system)
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Manuscript number: RC-2024-02767
Corresponding author(s): Kazuaki Maruyama
1. General Statements
Response to Reviewer #1:
We sincerely appreciate your thoughtful review of our manuscript. Our primary objective is to elucidate the pathogenic mechanisms underlying congenital low-flow vascular malformations, thereby informing the development of novel therapeutic strategies. We recognize that, given the dual nature of our study encompassing both fundamental and clinical science, the presentation may have appeared somewhat convoluted. In response, we have revised the manuscript to clarify these points and have reformatted the text corresponding to your comments—originally presented as a single continuous block—into defined, numbered sections to enhance readability.
Response to Reviewer #2:
We are deeply grateful for the time and effort you have dedicated to reviewing our manuscript despite your busy schedule. Your comments have been particularly insightful, especially regarding the section on the preclinical mouse model. In light of your suggestions, we have conducted additional experiments and revised the manuscript accordingly. We trust that these modifications address your concerns and contribute to the overall improvement of our work.
The revised sections have been highlighted in red in the text.
2. Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required):
The authors investigate the pathogenesis of congenital vascular malformations by overexpressing the Pik3caH1047R mutation under the R26 locus in different cell populations and developmental stages using various Cre and CreERT2 lines, including endothelial-specific and different mesoderm precursor lines. The authors provide a thorough characterization of the vascular malformation phenotypes across models. Specifically, they claim that expressing Pik3caH1047R in the cardiopharyngeal mesoderm (CPM) precursors results in vascular abnormalities localized to the head and neck region of the embryo. The study also includes scRNAseq data analyses, including from previously published data and new data generated by the authors. Trajectory inference analysis of a previous scRNA-seq dataset revealed that Isl1+ mesodermal cells can differentiate into ETV2+ cells, directly giving rise to Prox1+ lymphatic endothelial cell progenitors, bypassing the venous stage. Single-cell RNA sequencing of their CPM model and other in vitro datasets show that Pik3caH1047R upregulates VEGF-A via HIF-1α-mediated hypoxia signaling, findings further corroborated in human samples. Finally, preclinical studies in adult mice confirm that pharmacological inhibition of HIF-1α and VEGF-A reduces the number and size of mutant vessels.
Major comments
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While the study provides a nice characterization of Pik3caH1047R-derived vascular phenotypes induce by expressing this mutation in different cells, the main message of the study is unclear. What is the main question that the authors want to address with this manuscript?
Response:
Our main message is as follows:
- __ Elucidation of pathogenesis based on developmental cellular origins:__ This study focuses on using embryonic models to elucidate the mechanism by which the Pik3caH1047R mutation induces low-flow vascular malformations. Specifically, we demonstrate that expression of Pik3caH1047R in cells derived from the cardiopharyngeal mesoderm (CPM) induces vascular abnormalities that are confined to the head and neck region. Furthermore, vascular malformations originating from another cell type—for example, Pax3+ cells—are confined to the lower body. This suggests that the embryonic origin of endothelial cells may determine the anatomical location of vascular malformations, with important implications for clinical severity and treatment strategies.
Molecular ____s____i____gnaling pathways and targeted therapeutic approaches:
Through single-cell RNA sequencing, we have identified hypoxia signaling—particularly via HIF-1α and VEGF-A—as central to the pathogenesis of these malformations. Moreover, preclinical mouse model experiments demonstrate that pharmacological inhibition of HIF-1α and VEGF-A significantly reduces lesion formation, supporting the potential of targeting these pathways as a novel therapeutic strategy.
In summary, our main message is that by elucidating the developmental and molecular mechanisms underlying Pik3caH1047R-driven low-flow vascular malformations—especially the pivotal role of hypoxia signaling via HIF-1α/VEGF-A—we provide a strong rationale for novel therapeutic strategies aimed at these challenging conditions
To further clarify these points, we have revised the manuscript by incorporating additional experiments and reorganizing the text into clearly defined sections.
The precursor type form where these lesions appear, that venous and lymphatic malformations emerge independently, when and where this phenotype appear?
Response:
In Tie2-Cre; R26R-Pik3caH1047R mutant embryos, no prominent phenotype was observed at E9.5 or E11.5. Vascular (venous) malformations are evident from E12.5, whereas lymphatic malformations become prominent from E13.5. We propose that the emergence of the lymphatic phenotype after E13.5 is due to the fact that lymphatic vessels, particularly in the upper body, begin forming a luminal structure mainly from E13.5 onward(Maruyama et al, 2022) . For further details, please refer to the explanation provided in Question 6.
To address this, we have newly included Supplemental Figure 2 and revised the Results section as follows:
Whereas clear phenotypes were evident at E12.5 and E13.5, no pronounced external abnormalities were observed at E9.5 or E11.5 (Supplemental Figure 2A–B). Similarly, histological examination revealed no significant differences in the short-axis diameter of the PECAM+ CV or in the number of Prox1+ LECs surrounding the CV between control and mutant embryos at E11.5 (Supplemental Figure 2C–F). We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 157–165)
The manuscript needs some work to make the sections more cohesive and to structure better the main findings and the rationale for choosing the models. Authors should explain better when and where the pathogenic phenotypes refer to blood and/or lymphatic malformations. From the quantifications provided in Figure 1, Pik3caH1047R leads to different phenotypes in blood and lymphatic vessels. These are larger diameters with no difference in the number of blood vessels (are you quantifying all pecam1 positive? Vein, arteries, capillaries?), and an increase in the number of lymphatics vessels. Please clarify and discuss.
Response:
We interpreted this as a question regarding which vessels were quantified. The answer to this question is provided in Question 4.
Which vessel types are considered for the quantifications shown in Fig. 1I, M, Q? All Pecam1+ vessels, including lymphatic, vein, capillaries and arteries or which ones? Provide clarifications.
__Response: __
Vessel types were characterized based on anatomical and histological features. For the anatomical details, we referred to The Atlas of Mouse Development by M.H. Kaufman.
This aspect is described in the Methods section, as follows:
Veins and arteries were classified based on anatomical criteria. Vessels demonstrating continuity with a clearly identifiable vein (e.g., the anterior cardinal vein) in serial sections were defined as veins. In contrast, the aorta and pulmonary artery, each exhibiting a distinct wall structure indicative of a direct connection to the heart, were designated as arteries. Lymphatic vessels were identified based on the combined expression of Prox1, VEGFR3, and PECAM, along with the developmental stage, morphology, and anatomical location as described in our previous studies (Maruyama et al, 2019, 2022, 2021) . PECAM+ vessels that lacked a definitive wall structure, did not express lymphatic markers, or did not exhibit clearly identifiable continuity necessary for classification as veins or capillaries were collectively designated as blood vessels or vasculatures. (Page 16, lines 530-539)
Regarding Figure 1I:
In the tongue and mandible, the facial vein—which branches from the anterior cardinal vein—is dilated, and its continuity with the venous system is confirmed. In contrast, Figure 1J shows the number of PECAM+ vasculatures; however, for smaller vessels, continuity is not always demonstrable, so these are designated as vasculatures according to the criteria.
Regarding Figures 1M and N:
In the liver, the dilated vessels are classified as veins because they exhibit continuity with the inferior vena cava. Even in the control group, the central veins tend to have relatively large diameters. Therefore, we compared the average area and quantified the number of abnormal central veins—defined as those contiguous with a vein and exceeding a specified area.
Regarding Figures 1Q and R:
Cerebral vessels are classified as veins due to their continuity with the common cardinal and jugular veins. However, as these vessels extend into the periphery, this continuity becomes less distinct, and they are consequently designated as blood vessels lacking Prox1 expression.
The authors propose that the CPM model results in localized head and neck vascular malformations. However, I am not convinced. The images supporting the neck defects are evident, but it is unclear whether there are phenotypes in the head.
Response:
Perhaps the discrepancy arises from a terminological issue. According to the WHO Classification of Tumours, commonly used in clinical settings, the term "Head and Neck" refers to the facial and cervical regions (including the oral cavity, larynx, pharynx, salivary glands, nasal cavity, etc.) and excludes the central nervous system. The inclusion of the brain in Figure 1O-R may have led to some confusion. We included the brain because cerebral cavernous malformations are classified as venous malformations, and thus serve as an example of common sites for venous malformations in humans. To clarify this point, we have made slight revisions to the first part of the Introduction, as follows:
They frequently manifest in the head and neck region—here defined as the orofacial and cervical areas, excluding the brain. (Page2, lines 52-53)
Why are half of the experiments with the Tie2-Cre model conducted at E12.5 (e.g., validation of recombination, signaling, proliferation) and the others at E13.5? It becomes confusing for the reader why the authors start the results section with E13.5 and then study E12.5.
Response:
This is also related to the previous question (Question 4). We decided to include extensive anatomical information in a single figure. In Supplemental Figure 1, sagittal sections at E12.5 were used so that the pulmonary artery, aorta, and dilated common cardinal vein could be visualized within one sample. This allowed us to demonstrate that the Pik3caH1047R mutation does not affect arteries by contrasting them with the dilated veins. At E13.5, in addition to the dilation observed at E12.5, the common cardinal vein becomes markedly dilated and compresses the surrounding structures. Capturing both veins and arteries simultaneously would require multiple images, which could potentially confuse the reader. Moreover, lymphatic and other organ phenotypes (e.g., in the liver) are more prominent at E13.5. Therefore, we selectively employed both E12.5 and E13.5 stages to suit our specific objectives.
The quantifications provided do not clarify what the "n" represents or how many embryos or litters were analyzed.
Response:
Thank you for your feedback. We have now incorporated the sample size (n) directly into the graphs and figure legends.
Blasio et al. (2018), Hare et al (2015) reported that Pik3caH1047R with Tie2-Cre embryos die before E10.5. How do the authors explain the increase in survival here? Were embryos at E13.5alive? What was the Mendelian ratio observed by the authors? Please provide this information and discuss this point.
Response:
Two types of Tie2-Cre lines are widely used worldwide. The mouse line employed by Blasio et al. (2018) differs from that used in our study (their manuscript did not specify whether the background was B6 or a mixed strain). In contrast, although Hare et al. (2015) used the same mouse line as we did, they maintained a C57BL/6 background. We selected a mixed background of B6 and ICR, as we believe that a heterogeneous genetic background more accurately reflects the diversity of human pathology. We examined five pregnant females, which yielded approximately 30 embryos from five pregnant mice, of which only two survived until E14.0. Based on these observations, we consider E13.5 to be the appropriate survival limit (see Supplemental Figure 2G for additional details). In our breeding strategy, mice in the Tie2-Cre or Tie2-Cre; R26R-eYFP line were maintained as heterozygotes for Tie2-Cre and homozygotes for R26R-eYFP, whereas those carrying the R26R-Pik3caH1047R allele were homozygous. This approach produced control(Cre (-)) and heterozygous offspring in an expected 1:1 ratio at all examined stages: E9.5 (mutant n = 4, control n = 4 from two pregnant females), E11.5 (mutant n = 8, control n = 8 from two pregnant females), E12.5 (mutant n = 4, control n = 4 from two pregnant females), and E13.5 (mutant n = 5, control n = 5 from two pregnant females), with no deviation from the anticipated Mendelian ratio.
Regarding this point, we have described it in the Results section as follows:
Whereas clear phenotypes were evident at E12.5 and E13.5, no pronounced external abnormalities were observed at E9.5 or E11.5 (Supplemental Figure 2A–B). Similarly, histological examination revealed no significant differences in the short-axis diameter of the PECAM+ CV or in the number of Prox1+ LECs surrounding the CV between control and mutant embryos at E11.5 (Supplemental Figure 2C–F). We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 157-165)
Please explain the rationale for using the Cdh5-CreERT2. It is likely due to the lethality observed with Tie2Cre, but this was not mentioned.
Response:
Thank you very much for your comment. As mentioned above, nearly all Tie2‐Cre;Pik3caH1047R embryos fail to survive past E14.0.
The lethality observed with Tie2‐Cre mice is described as follows:
We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 161-165)
The rationale for using CDH5-CreERT2 mice is described as follows:
To investigate whether the resulting human disease subtype (e.g., lesions confined to the head and neck region) is determined by the specific embryonic stage at which Pik3caH1047R is expressed, we crossed tamoxifen-inducible, pan-endothelial CDH5-CreERT2 mice with R26R-Pik3caH1047R mice and analyzed the embryos at E16.5 or E17.5. (Page 5, lines 169-172)
Why were tamoxifen injections done at various time points (E9.5, E12.5, E15.5)? Please clarify the reasoning behind administering tamoxifen at these specific times. Explaining the rationale will help the reader follow the experimental design more easily. Additionally, including an initial diagram summarizing all the strategies to guide the reader from the beginning would be helpful.
Response:
Martinez‐Corral et al. (Nat. Commun., 2020) focused on lymphatic malformations, arguing that the timing of tamoxifen administration during the embryonic period determines the anatomical features of these lesions. They stated, “The majority of lesions appeared as large isolated cysts that were localized mainly to the cervical, and less frequently to the sacral region of the skin (Figure 2)”. Although not stated definitively, their data suggest that early embryonic tamoxifen administration results in the formation of large‐caliber lymphatic vessels with region‐specific distribution in the cervical skin (Figure 2C, Supplemental Figure 2). This description likely reflects an intention to model human vascular malformations, implying that the anatomical characteristics of these malformations are influenced by the developmental stage at which the Pik3caH1047R somatic mutation occurs.
Inspired by these findings, we conducted experiments to determine whether altering the timing of tamoxifen administration would yield region-specific anatomical patterns in vascular malformation development. However, our results indicate that changing the timing of tamoxifen administration does not lead to an anatomical bias similar to that observed in human vascular malformations. Instead, we propose that the embryological cellular origin plays a more significant role in the formation of these human pathologies.
Regarding this section, we have slightly revised the introductory part of the Figure 2 explanation as follows:
To investigate whether the resulting human disease subtype (e.g., lesions confined to the head and neck region) is determined by the specific embryonic stage at which Pik3caH1047R is expressed, we crossed tamoxifen-inducible, pan-endothelial CDH5-CreERT2 mice with R26R-Pik3caH1047R mice and analyzed the embryos at E16.5 or E17.5. (Page 5, lines 169-172)
Additionally, we have added a schematic diagram of the tamoxifen administration schedule at the beginning of Figure 2 and Supplemental Figure 3.
Why do you use the Isl1-Cre constitutive line (instead of the CreERT2)? The former does not allow control of the timing of recombination (targeting specifically your population of interest) and loses the ability to trace the mutant cell behaviors over time. Is the constitutive expression of Pik3caH1047R in Isl1+ cells lethal at any embryonic time, or do the animals survive into adulthood? When you later use the Isl1-CreERT2 line, why do you induce recombination specifically at E8.5? It would be helpful for the reader to have an explanation for this choice, along with a reference to your previous paper.
Response:
Thank you for your comments. We did attempt the same experiments using Isl1-CreERT2 under various conditions. However, administering tamoxifen earlier than E8.5 invariably caused embryonic lethality, likely due to both Pik3ca activity and tamoxifen toxicity, leaving no embryos for analysis. In our previous study, repeated attempts from E6.5 to E16.5 resulted in only two surviving embryos (Maruyama et al., eLife, 2022, Supplemental Figure 3). We also failed to recover any live embryos with tamoxifen administration at E7.5.
Even reducing the tamoxifen dose to one-fifth did not succeed when given before E8.5. Although E8.5 administration was feasible, the observed phenotype remained mild, and no phenotype was detected at E9.5, E11.5, E12.5, or later stages. These findings align with our earlier observations that moving tamoxifen injection from E8.5 to E9.5 markedly diminishes the Isl1+ contribution to the endothelial lineage.
Furthermore, Supplemental Figure 5____ and 6 suggest that a decrease in Isl1 mRNA, which occurs as early as E8.0–E8.25, triggers the shift toward endothelial differentiation. Considering these data and the mild phenotype at E8.5, earlier administration would be ideal for impacting Isl1+ cell fate. However, technical constraints prevented us from doing so, leading us to utilize the constitutive Isl1-Cre line instead.
This section was already included in the Discussion; however, for clarity, we have revised it as follows:
Given that Isl1 expression disappears at a very early stage and contributes to endothelial differentiation, experiments using Isl1-Cre or Isl1-CreERT2 mice cannot clearly distinguish between LMs, VMs, and capillary malformations, In other words, Isl1+ cells likely label a common progenitor population for multiple endothelial subtypes. Consequently, the diverse vascular malformations in the head and neck—including mixed venous-lymphatic and capillary malformations, as well as the macro- and microcystic subtypes of LMs—cannot be fully accounted for by this study alone. (Page 13, lines 419-425)
What is the purpose of using this battery of CreERT2 lines (for example, the Myf5-CreERT2)?
Response:
The head and neck mesoderm arises primarily from the cardiopharyngeal mesoderm and the cranial paraxial mesoderm. Myf5-CreERT2 labels the cranial paraxial mesoderm in the facial region, which gives rise to facial skeletal muscles. Stone et al. (Dev Cell, 2019) reported that a subset of this lineage contributes to head and neck lymphatic vessels, whereas our study (Maruyama et al., eLife, 2022) found no such contribution—an ongoing point of debate. Nevertheless, expressing Pik3caH1047R in this lineage did not induce any vascular malformations.
Pax3-CreERT2 mice label Pax3____⁺ paraxial mesoderm (including cranial paraxial mesoderm), which reportedly contributes to the common cardinal vein and subsequently forms trunk lymphatics (Stone & Stainier, 2019; Lupu et al, 2022) . When Pik3caH1047R was expressed in Pax3⁺ cells, we observed abnormal vasculature in the lower trunk and around the vertebrae, consistent with that report.
Synthesizing these observations with our results from Isl1-Cre, Isl1-CreERT2, and Mef2c-AHF-Cre lines, we propose that Pik3caH1047R mutations within the cardiopharyngeal mesoderm underlie the clinically significant vascular malformations seen in the head and neck region.
We have also incorporated the following explanation into the main text.
Regarding the Pax3-CreERT2:
The head and neck mesoderm arises primarily from the cardiopharyngeal mesoderm and the cranial paraxial mesoderm. In Pax3-CreERT2; R26R-Pik3caH1047R embryos, Pax3+ paraxial mesoderm (including cranial paraxial mesoderm) is labeled; this lineage reportedly contributes to the common cardinal vein and subsequently forms trunk lymphatics(Lupu et al, 2022), (Page 8, lines 247-250)
Regarding the Myf5-CreERT2;
In Myf5-CreERT2; R26R-tdTomato mice—which label the cranial paraxial mesoderm, particularly muscle satellite cells—crossed with R26R-Pik3caH1047R, tamoxifen was administered to pregnant mice at E9.5. (Page 8, lines 255-257)
I find the scRNAseq data in Fig S4 and S5 results very interesting, although I am unsure how they fit with the rest of the story. In principle, a subset of Isl1+ cardiopharyngeal mesoderm (CPM) derivatives into lymphatic endothelial cells was already demonstrated in a previous publication from the group. What is the novelty and purpose here?
Response:
This also addresses Question 11. Our aim in using the Isl1⁺ lineage was to determine the extent of analysis possible with this experimental system. Through reanalysis, we found that the downregulation of Isl1 triggers a switch toward endothelial cell differentiation, with this cell fate decision occurring at a very early embryonic stage. Consequently, our single‐cell analysis supports the conclusion that, regardless of the Isl1-CreERT2 line used or the timing of tamoxifen administration, it is challenging to precisely recapitulate the fine clinical phenotypes observed in humans (e.g., lymphatic or venous malformations) with this experimental system. We believe that this single‐cell analysis provides a theoretical basis for the notion that our Isl1-Cre-based developmental model can only generate a mixed phenotype of vascular and lymphatic malformations.
This section is explained in a similar manner in the revised Discussion for Question 11 as follows:
Given that Isl1 expression disappears at a very early stage and contributes to endothelial differentiation, experiments using Isl1-Cre or Isl1-CreERT2 mice cannot clearly distinguish between LMs, VMs, and capillary malformations, In other words, Isl1+ cells likely label a common progenitor population for multiple endothelial subtypes. Consequently, the diverse vascular malformations in the head and neck—including mixed venous-lymphatic and capillary malformations, as well as the macro- and microcystic subtypes of LMs—cannot be fully accounted for by this study alone. (Page 13, lines 419-425)
Why in Fig. 4 ECs were not subclustered for further analysis (as in Fig. S4,5)? This is a missed opportunity to understand the pathogenic phenotypes.
Response:
Thank you for your question. We performed sub-clustering analysis, particularly focusing on why no phenotype is observed in arteries, as we believed this approach could provide molecular-level insights. Accordingly, we conducted the analysis presented in Figure 1 for Reviewer 1.
Figure legends for Figure ____1 ____for Reviewer 1. The number of endothelial cells was insufficient, making subclustering ineffective.
(Figure for Reviewer 1A, B) Left: UMAP plot showing color-coded clusters (0–3). Subcluster analysis of the Endothelium (Cluster 1) from Fig. 4B. Right: UMAP plot color-coded by condition. (Figure for Reviewer 1C) Heatmap showing the average gene expression of marker genes for each cluster by condition. After cluster annotation, subclusters 0, 1, 2, and 3 were defined as Vein, Capillary, Artery, and Lymphatics, respectively. (Figure for Reviewer 1D) Cell type proportions. (Figure for Reviewer 1E) Number of differentially expressed genes (DEGs) in each sucluster of the PIK3CAH1047R group relative to Control. (Figure for Reviewer 1F) Comparison of enrichment analysis between EC subclusters from scRNA-seq. The bar graph shows the top 20 significantly altered Hallmark gene sets in EC subclusters from scRNA-seq using ssGSEA (escape R package). Red bars represent significantly upregulated Hallmark gene sets in mutants (FDR Initially, we performed sub-clustering on endothelial cells; however, this resulted in a considerably reduced number of cells per sub-cluster, especially in control group (Figure for Reviewer 1A, B). In the control group, there were only approximately 149 endothelial cells in total, and dividing these into four clusters led to very few cells per cluster, thereby introducing statistical instability. Although arterial endothelial cells were relatively well defined by their high expression of Hey1 and Hey2 and lower levels of Nr2f2 and Aplnr, the boundaries between venous, capillary, and lymphatic endothelial cells were less distinct. In particular, defining lymphatic endothelial cells solely by Prox1 expression yielded a very small population; even after incorporating additional lymphatic markers such as Flt4 and Lyve1, it remained challenging to clearly separate the venous, capillary, and lymphatic populations (Figure for Reviewer 1C). Consequently, the proportion of lymphatic endothelial cells was markedly low, and discrepancies with the histological findings further reduced our confidence in this dataset (Figure for Reviewer 1D, E). Moreover, the number of differentially expressed genes (DEGs) increased with the number of cells, and the results of the enrichment analysis as well as the volcano plot were nearly identical to those shown in Figure 4 (Figure for Reviewer 1F, G). In other words, the subclustering process itself had limitations, resulting in the overall outcome being dominated by the most abundant venous cluster.
It is possible that these limitations in sub-clustering are due to the relatively small number of endothelial cells. Nonetheless, a major strength of our single-cell analysis is its ability to compare various cell types derived from Isl1+ lineages, not just endothelial cells. Therefore, the relative scarcity of endothelial cells represents a limitation of this experimental system. For these reasons, we decided to omit this figure from the final version of the manuscript.
This point is described in the Discussion section as follows:
Additionally, we performed endothelial subclustering to explore potential differences in gene expression among arterial, venous, capillary, and lymphatic endothelium. However, in the control embryos, the number of endothelial cells was too low to yield reliable data (data not shown). (Page 13, lines 434-437)
Hypoxia and glycolysis signatures are not specific to mutant ECs. Do the authors have an explanation for this? It is well known that PI3K overactivation increases glycolysis; please acknowledge this.
__Response: __
Thank you for your important comment. We have now incorporated a discussion, along with relevant references, on the section addressing that PI3K overactivation increases glycolysis into the Discussion section as follows:
It is well known that overactivation of PI3K enhances glycolysis(Hu et al, 2016) . In our study, the elevated expression of glycolytic enzymes, including Ldha, suggests a shift toward aerobic glycolysis, consistent with the Warburg effect. (Page 13, lines447-450)
Do you have an explanation for the expression of VEGFA by lymphatic mutant cells?
__Response: __
VEGF-A acts on VEGFR2 expressed on LECs, thereby promoting their proliferation and migration(Hong et al, 2004; Dellinger & Brekken, 2011) .To clarify this point, we have revised the text accordingly and added additional references as follows:
We focused on Vegf-a, a key regulator of ECs proliferation and a downstream target of Hif-1α. Vegf-a likely drives both cell-autonomous and non-cell-autonomous effects on blood ECs , as well as LECs(Hong et al, 2004; Dellinger & Brekken, 2011). (Page 13, lines 445-447)
Likewise, why mesenchymal cells traced from the Islt1-Cre decreased upon expression of Pik3caH1047R?
Response: When comparing the mesenchyme cluster with other mesoderm-derived cells, we observed a marked downregulation of signaling pathways—notably those involved in inhibiting EMT, such as TGF-β, Wnt/βcatenin, and MYC target genes (Supplemental Figure 7B). Many of these pathways are associated with decreased epithelial-to-mesenchymal transition(Xu et al, 2009; Singh et al, 2012; Larue & Bellacosa, 2005; Yu et al, 2015), which could explain the reduction in the number of mesenchymal cells. However, PI3K activation is generally considered to promote EMT, which is at odds with previous studies.
On the other hand, several investigations—including those using ES cells—suggest that PI3K activation could suppress TGF-β signaling via SMAD2/3(Yu et al, 2015) , and in some undifferentiated cell contexts, it may also inhibit the Wnt/β-catenin pathway via Smad2/3(Singh et al, 2012) . These multifaceted roles of PI3K could be particularly important during embryonic development(Larue & Bellacosa, 2005).
Understanding how mesenchymal cell changes under PI3K activation affect endothelial cells is an important issue that requires further study. Accordingly, we have added these points to the Discussion section as follows:
In our data, the mesenchymal cell population was decreased, and within this cluster, pathways typically promoting epithelial mesenchymal tansition (EMT) (e.g., TGF-β, Wnt, and MYC target genes) were downregulated (Supplemental Figure 7B). Although PI3K activation is generally thought to enhance EMT, several studies in undifferentiated cells have reported that PI3K can suppress these signals via SMAD2/3(Singh et al, 2012; Yu et al, 2015) . Elucidating how these changes in the mesenchyme contribute to vascular malformation pathogenesis remains an important avenue for future research. (Page 13, lines 437-444)
Authors need to characterize the preclinical model before conducting any preclinical study. No controls are provided, including wild-type mice and phenotypes, before starting the treatment (day 4).
Response:
Thank you very much for your comment. We have now added new images illustrating skin under three conditions: untreated skin at Day 7, skin from Cre-negative animals that received tamoxifen, and skin from Cre-positive animals examined 4 days after tamoxifen administration. Additionally, we have included the corresponding statistical data for these skin samples (Figure 6C–E).
Why did the authors not use their developmental model of head and neck malformation model for preclinical studies? This would be much more coherent with the first part of the manuscript. Also, how many animals were treated and quantified for the different conditions?
Response:
We have now indicated the number of animals (n) used under each condition directly on the graphs for clarity. As for why we did not use the Isl1-Cre model, we observed that—similar to the Tie2-Cre line—all Isl1-Cre mutant embryos died between E13.5 and E14.0 (indeed, none survived beyond E14.0; see our newly added Figure 3N). Consequently, we could not perform any postnatal treatment experiments. Moreover, as previously noted, the Isl-CreERT2 line has an extremely narrow developmental window for vascular malformation formation, making it less suitable as a general model.
Although we considered potential in utero or maternal interventions (e.g., direct uterine injection or placental transfer), these approaches demand extensive technical optimization and remain an area for future investigation. From a clinical standpoint, postnatal therapy meets a more immediate need: while vascular malformations are congenital, they often enlarge over time(Ryu et al, 2023) , becoming more apparent and more likely to require treatment.
In this study, because embryonic Pik3caH1047R expression was lethal before birth, we generated and treated postnatal cutaneous vascular malformations instead. Although this model does not strictly recapitulate the embryonic disease state, previous studies assessing drug efficacy have similarly employed postnatal tamoxifen-inducible mouse models(Martinez-Corral et al, 2020) , lending validity to this approach. Moreover, because lesions typically become evident later in life rather than in utero, this method more closely aligns with clinical reality and may be more readily translated into practice.
Minor Comments
References in the introduction need to be revised. Specifically, how authors reached the stats on head and neck vascular malformations needs to be clarified. For instance, one of the cited papers refers to all types of vascular malformation, while the other focuses exclusively on lymphatic malformations with PIK3CA mutations. Moreover, in the latter, the groups are divided into orofacial and neck and body categories. How do authors substrate the information from the neck and head here?
Response:
We have clarified our definition of the “head and neck” region early in the Introduction and separated the discussion on anatomical localization from that on PIK3CA genetics. Additionally, we removed the percentage data of localization to avoid potential confusion with the genetic aspects.
In Japan, lymphatic and other vascular malformations of the head and neck typically require complex, multidisciplinary management. Consequently, these conditions are officially designated as “intractable diseases,” and the government provides financial assistance for their treatment. Although most of the information is available only in Japanese, we refer reviewers to the following websites for details on head and neck vascular malformations:
https://www.nanbyou.or.jp/entry/4893 https://www.nanbyou.or.jp/entry/4631 https://www.nanbyou.or.jp/entry/4758.
(Please read with English translator, e.g., Google chrome translator)
We are not aware of a comparable system in other countries. However, it is well recognized that vascular malformations frequently occur in the head and neck region(Nair, 2018; Alsuwailem et al, 2020; Sadick et al, 2017), as evidenced by over 250 PubMed hits when searching for “vascular malformation” and “head and neck.
Incorporating this comment, we have revised the early part of the Introduction as follows:
They frequently manifest in the head and neck region—here defined as the orofacial and cervical areas, excluding the brain (Zenner et al, 2019; Lee & Chung, 2018; Nair, 2018; Alsuwailem et al, 2020). (Page 2, lines 52-53)
Also, in line 79, I need clarification on ref 24 about fibrosis.
__Response: __
Thank you very much for pointing out the error. We have corrected the placement of the reference accordingly.
Include references: Studies in mice have shown that p110α is essential for normal blood and lymphatic vessel development. Please clarify and correct.
__Response: __
Thank you very much. We have now added the references(Graupera et al, 2008; Gupta et al, 2007; Stanczuk et al, 2015).
Please define PIP2 and PIP3
__Response: __
Thank you very much for your comment. We have now added the following definitions to the Introduction:
PIP2: Phosphatidylinositol 4,5-bisphosphate
PIP3: Phosphatidylinositol 3,4,5-trisphosphate
Why is Prox1 showing positivity in erythrocytes in Figure 1?
Response:
We used paraffin-embedded sections to preserve tissue morphology. Although we applied a reagent to suppress autofluorescence, some spillover from excitation around 488 nm was unavoidable. Moreover, in the mutant mice, blood remained within the abnormal vessels rather than being completely flushed out, which further increased the autofluorescence. Despite our efforts to mitigate this, some residual autofluorescence persisted. Consequently, we also employed DAB-based staining to confirm the specificity of Prox1 labeling in other Figures.
Regarding Figure 1, I suggest organizing the quantifications in the same order to facilitate phenotype comparisons. For example, I, J vs. Q, R. What is the difference between M and N?
Response:
To facilitate the comparison between Figures 1I, J and 1Q, R, we have swapped Figures 1Q and R. Regarding Figures 1M and N, these panels represent the average cross-sectional area of an enlarged malformed vessel and the number of vessels exceeding a defined size, respectively. Although some central veins appeared slightly enlarged in the control group, the liver exhibits both a significant dilation of malformed vessels and an increased number of such vessels.
Add the reference of the Bulk RNseq data.
__Response: __
We have added the following references: (Jauhiainen et al, 2023)
Mark in the Fig. 4F that the volcano plots are from cluster one of the scRNASeq (this is explained in text and legend, but when you go to the figure, it isn't very clear).
__Response: __
We have added the label “Cluster 1: Volcano Plot (genes associated with hypoxia/glycolysis)” to
Figure 4F.
Please label Figure 6D/E with the proper labels.
__Response: __
We have provided appropriate labels for Figure 6.
In Fig. 6, it is mentioned that vacuoles are from the tamoxifen injection, how do you know? Do you also see them if you add oil alone (without tamoxifen) or tamoxifen in a WT background?
__Response: __
In Figure 6C, we have included both the image at Day 4 and the condition of Cre(–) animals 7 days after tamoxifen injection.
**Referees cross-commenting**
I complete agree with referee #2 regarding the preclinical studies. Bevacizumab, does not neutralize murine VEGFA. This is a major issue.
__Response: __
As noted in the Reviewer #2 section, there appears to be some effect on mouse vasculature (Lin et al, 2022). However, given the ongoing debate regarding this issue, we performed additional experiments using a neutralizing antibody against mouse VEGF-A (clone 2G11). This antibody has been shown to suppress the proliferation of mouse vascular endothelial cells in vivo, for example(Mashima et al, 2021; Wuest & Carr, 2010). Our results demonstrate that it more sharply suppresses the proliferation of malformed vasculatures (both blood and lymphatic vessels) than bevacizumab. Based on these additional experiments, we revised the figures and updated them as Figure 6.
Reviewer #1 (Significance (Required)):
This study addresses a timely and relevant question: the origins, onset and progression of congenital vascular malformations, a field with limited understanding. The work is novel in its approach, employing complex embryonic models that aim to mimic the disease in its native context. By focusing on the effects of Pik3caH1047R mutations in cardiopharyngeal mesoderm-derived endothelial cells, it sheds light on how these mutations drive phenotypic outcomes through specific pathways, such as HIF-1α and VEGF-A signaling, while also identifying potential therapeutic targets. A strong aspect of the study is the use of embryonic models, which enables the investigation of disease onset in a context that closely resembles the in vivo environment. This is particularly valuable for congenital disorders, where native developmental cues are an integral aspect of disease progression. The study also integrates advanced techniques, including single-cell RNA sequencing, to dissect the cellular and molecular responses induced by the Pik3caH1047R mutation. Moreover, from a translational perspective, it provides novel therapeutic strategies for these diseases. Limitations of the study are (1) unclarity of the main question authors try to address, and main conclusions dereived thereof; (2) the different parts of the manuscripts are not well connected, not clear the rationale; (3) scRNAseq analysis is underdeveloped; (4) characterization of the preclinical model is not provided.
Audience:
The findings presented here interest specialized audiences within developmental biology, vascular biology, and congenital disease research fields, and clinicians by providing new therapies to treat vascular anomalies. Moreover, the study's integration of single-cell and in vivo models could inspire further research in other contexts where understanding clonal behavior and signaling pathways is critical.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
This paper focuses on vascular malformations driven by PI3K mutation, with particular interest on the vascular defects localized at head and neck anatomical sites. The authors exploit the H1047R mutant which has been largely demonstrated to induce both vascular and lymphatic malformation. To limit the effect of H1047R to tissues originated from cardiopharinegal mesoderm, PI3caH1047R mice were crossed with mice expressing Cre under the control of the promoter of Ils1 , a transcription factor that contributes to the development of cardiopharinegal mesoderm-derived tissues. By comparing the embryo phenotype of this model with that observed by inducing at different times of development the expression of PI3caH1047R, the authors conclude that Isl-Cre; PI3caH1047R; R26R-eYFP model recapitulates better the anatomical features of human vascular malformations and in particular those localized at head and neck. In my opinion the new proposed model represents a significant progress to study human vascular malformations. Furthermore, scRNA seq analysis has allowed to propose a mechanism focused on the role of HIF and VEGFA. The authors provides partial evidences that HIF and VEGFA inhibitors halt the development of vascular malformation in VeCAdCre; Pik3caH1047 mice. This experiment is characterized by a conceptual mistake because bevacizumab does not recognize murine VEGFA (see for instance 10.1073/pnas.0611492104; 10.1167/iovs.07-1175. This error dampens my enthusiasm
CRITICISM
- Fig 1A. E13.5 corresponds to the early phase of vascular remodelling. Which is the phenotype at earliest stages (e.g. 9.5 or 10.5)
Response:
Thank you very much for your comment. We have created new Supplemental Figure 2, which demonstrates that no obvious phenotype is observed in mutant embryos at E9.5 and E11.5, and that the survival limit of these mutant embryos is around E13.5 to E14.0.
In response to Reviewer 1’s question, previous study(Hare et al, 2015) have shown that on a B6 background, this mouse model exhibits an earlier onset of phenotype, resulting in early lethality. However we selected a mixed background of B6 and ICR, as we believe that a heterogeneous genetic background more accurately reflects the diversity of human pathology. We examined five pregnant females, which yielded approximately 30 embryos, of which only two survived until E14.0. Based on these observations, we consider E13.5 to E14.0 to be the appropriate survival limit (see Supplemental Figure 2G for additional details).
We have described this in the Results section as follows:
Whereas clear phenotypes were evident at E12.5 and E13.5, no pronounced external abnormalities were observed at E9.5 or E11.5 (Supplemental Figure 2A–B). Similarly, histological examination revealed no significant differences in the short-axis diameter of the PECAM+ CV or in the number of Prox1+ LECs surrounding the CV between control and mutant embryos at E11.5 (Supplemental Figure 2C–F). We also assessed Tie2-Cre; R26R-Pik3caH1047R mutant embryos at E14.0 from five pregnant mice. Only two embryos were alive at this stage, and both showed severe edema and hemorrhaging, indicating they were nearly moribund. These observations suggest that the critical point for survival of these mutant embryos lies between E13.5 and E14.0 (Supplemental Figure 2G). (Page 5, lines 157-165)
Fig 1,2,3. The analysis of VEGFR2 expression is required. This request is important for the paradigmatic and non-overlapping role of this receptor in early and late vascular development. Furthermore ,these data better clarify the mechanism suggested by the experiments reported in fig 5 (VEGFA and HIF expression)
__Response: __
Thank you very much for your comment. For each mouse presented in Figures 1, 2, and 3, we performed VEGFR2 immunostaining on serial sections corresponding to each figure and created a new Supplemental Figure 9. VEGFR2 was broadly expressed in both vascular and lymphatic endothelial cells in control and mutant embryos.
We have described this in the Results section as follows:
Furthermore, to verify whether VEGF‐A can act via VEGFR2, we performed VEGFR2 immunostaining on several mouse models: Tie2‐Cre; R26R‐Pik3caH1047R embryos (E13.5, corresponding to Figure 1), CDH5‐CreERT2; R26R‐Pik3caH1047R embryos (tamoxifen administered at E9.5 and analyzed at E16.5, corresponding to Figure 2), and Isl1‐Cre; R26R‐Pik3caH1047R embryos (E11.5 and E13.5, corresponding to Figure 3). In all cases, both control and mutant embryos exhibited widespread VEGFR2 expression in blood and lymphatic vessels at early and late developmental stages (Supplemental Figure 9A-R’). These findings suggest that Pik3caH1047R may act in an autocrine manner, at least in part via the VEGF‐A/VEGFR2 axis in endothelial cells, potentially explaining the observed phenotype. (Page 11, lines352-361)
As done in Fig 1,2 and 3, data quantification by morphometric analysis is also required for results reported in supplemental figure 3
__Response: __
Thank you for your comment. We have now added additional statistics and graphs for clarity, which are presented as Supplemental Figure 4.
Lines 166-174. I suppose that the reported observations were done at E16.5. What happens later? It's crucial to sustain the statement at lines 187-190
Response:
At E9.5 and E12.5, we reduced the tamoxifen dose to one-fifth of the standard dose. After collecting embryos from approximately 10 pregnant females, we were only able to obtain three embryos at these stages. When tamoxifen was administered at E15.5, three embryos were obtained from two litters. In most cases, miscarriages occurred by E16.5, making further observation difficult. We focused on the time point around E16.5 because it is generally believed that the basic distribution of the lymphatic system throughout the body is established around this stage (Srinivasan et al, 2007; Maruyama et al, 2022).
A similar experiment has been reported using T-CreERT2 to induce mosaic expression of Pik3caH1047R in the mesoderm, which resulted in subcutaneous venous malformations in mice at P1–P5 (Castillo et al, 2016). However, that study did not report whether the mice survived normally after birth. In fact, regarding the survival rate, the authors stated, “Our observations on the lethality and vascular defects in MosMes-Pik3caH1047R (T-CreERT2;R26R-Pik3caH1047R) embryos are similar to the previously reported phenotypes of ubiquitous or EC-specific expression of Pik3caH1047R in the developing embryo (Hare et al, 2015),” suggesting a high mortality rate when Pik3caH1047R is expressed using Tie2-Cre. Moreover, according to Hare et al., analysis of 250 Tie2-Cre; R26R-Pik3caH1047R embryos revealed that all were lethal by E11.5. Thus, considering our results in conjunction with those from previous studies, it appears that expression of Pik3caH1047R in the mesoderm or endothelial cells during embryonic development results in the death of most embryos before birth.
We have supplemented the Results section with the following details:
Since the standard tamoxifen dose (125 mg/kg body weight) leads to miscarriage or embryonic death within 1–2 days, we diluted it to one-fifth of the original concentration. (Pages 5-6, lines 175-177)
scRNAseq was performed at E13.5 (Fig 4). It's mandatory to perform the same analysis at E16.5, which corresponds to the phenotypic analysis shown in fig 3. This experiment is required to understand how hypoxia and glycolysis genes changes along the development of the vascular malformation.
__Response: __
Thank you very much for your comment. First, regarding the experiments using Isl1‐Cre, we would like to clarify that the survival aspect was not adequately addressed. Our Isl1‐Cre embryos die between E13.5 and E14.0, which makes it practically impossible to perform single‐cell analysis beyond this stage (please refer to the newly added Figure 4N). Similarly, for experiments using CDH5‐CreERT2, the limited number of embryos obtained renders further analysis extremely challenging. Additionally, we have supplemented the Results section with the following description:
These Isl1-Cre; R26R-Pik3caH1047R mutant embryos likely died from facial hemorrhaging between E13.5 and E14.0 (Figure 3N). (Page 7, lines 236-237)
Further analysis at later embryonic stages proved challenging. Consequently, we aimed to investigate the effects of Pik3caH1047R on endothelial cells by comparing gene expression at E10.5 with that at E13.5. We performed single‐cell RNA sequencing on E10.5 embryos from both the control (Isl1-Cre; R26R-eYFP) and mutant (Isl1-Cre; R26R-eYFP; R26R-Pik3caH1047R) embryos. Unfortunately, the quality of both datasets was insufficient for reliable analysis. In the control sample, only 40.3% of reads were assigned to cell‐associated barcodes—substantially below the ideal threshold of >70%—with an estimated 790 cells and a median of 598 genes per cell. Similarly, in the mutant sample, only 37.0% of reads were associated with cells, despite an estimated cell count of 7,326 and a median of only 526 genes per cell. These metrics indicate that both datasets were severely compromised by high levels of ambient RNA or by a significant number of cells with low RNA content, precluding robust downstream analysis. This may be due to the fact that immature cells are particularly susceptible to damage incurred during FACS sorting and transportation to the analysis facility. Moreover, the relatively low number of control endothelial cells at E13.5 led us to conclude that performing similar experiments at earlier stages would be difficult. Despite our best efforts, we acknowledge this as a limitation of the present study.
Lines 326-343. In this section the authors provide pharmacological evidences that HIF and VEGFa are involved in vascular malformation caused by H1047R . However , I'm surprised of efficacy of bevacizumab, which neutralizes human but not murine VEGFA. Genetech has developed B20 mAb that specifically neutralizes murine VEGFA. So the data shown require a. clarification by the authors and the experiments must be done with the appropriate reagent. Furthermore, which is the pharmacokynetics of these compounds topically applied?
Response:
Thank you very much for your comment. There are reports that bevacizumab exerts an in vivo inhibitory effect on neovascularization mediated by mouse Vegf-A (Lin et al, 2022). However, given the contentious nature of this issue, we conducted additional experiments. Due to the requirement for an MTA to obtain B20 mAb from Genentech—and considering the time constraints during revision—we opted to use a neutralizing antibody against mouse VEGF-A (clone 2G11) instead. This antibody has been shown to suppress the proliferation of mouse vascular endothelial cells in vivo (Mashima et al, 2021; Wuest & Carr, 2010) .
The dosing regimen for 2G11 was determined based on previous studies (Surve et al, 2024; Churchill et al, 2022). Moreover, an example of effective local administration is provided in (Nagao et al, 2017). Since this product is an antibody drug, it is metabolized and does not function as a prodrug. Although the precise half-life of 2G11 is unknown, rat IgG2a antibodies generally have a circulating half-life of approximately 7–10 days in rats. However, when administered to mice, the half-life is often significantly reduced due to interspecies differences in neonatal Fc receptor (FcRn) binding affinity, with estimates in murine models typically around 2–4 days(Abdiche et al, 2015; Medesan et al, 1998) . However, in our model the injection is subcutaneous—almost equivalent to an intradermal injection (Figure 6B, C). Because this method is expected to provide a more sustained, slow-release effect (similar to the tuberculin reaction), the half-life should be longer than that achieved with intravenous administration. Consequently, we believe that sufficient efficacy is maintained in this model.
Regarding LW-6:
LW-6 is a small molecule that, due to its hydrophobic nature, is believed to freely cross cell membranes. Once inside the cell, it facilitates the degradation of HIF-1α, leading to reduced expression of its downstream targets (Lee et al, 2010). Although its half-life is estimated to be around 30 minutes, the active metabolites may exert sustained secondary effects (Lee et al, 2021). When administered intravenously, peak blood concentrations are reached within 5 minutes, making Cmax a critical parameter due to the rapid onset of action. In our experiments, we based the dosing regimen on previous studies (Lee et al, 2010; Song et al, 2016; Xu et al, 2022, 2024). While those studies administered doses comparable to or twice as high as ours via intravenous, intraperitoneal, or oral routes, our experimental design—in which a single dose was administered on Day 4 and samples were collected on Day 7—necessitated a single-dose protocol.
Regarding Rapamycin:
Several studies have demonstrated that local administration yields anti-inflammatory effects (Takayama et al, 2014; Tyler et al, 2011). Similar outcomes have been observed in vascular malformations (Boscolo et al, 2015; Martinez-Corral et al, 2020). Although the half-life of rapamycin is estimated to be approximately 6 hours following intravenous administration, it may be even shorter (Comas et al, 2012; Popovich et al, 2014).
In light of these comments, we have revised Figure 6. Furthermore, the Results section pertaining to Figure 6 has been updated as follows:
Hif-1α and Vegf-A inhibitors suppress the progression of vascular malformations.
We next examined whether administering Hif-1α and Vegf-A inhibitors could effectively treat vascular malformations. Tamoxifen was administered to 3–4-week-old CDH5-CreERT2;R26R-Pik3caH1047R mice to induce mutations in the dorsal skin. Anti-VEGF-A, a Vegf-A neutralizing antibody; LW6, a Hif-1α inhibitor; and rapamycin, an mTOR inhibitor, were topically applied, and their effects were analyzed (Figure 6A). Both anti-VEGF-A and LW6 reduced the visible swelling in the dorsal skin, whereas the difference between the drug-treated and control groups was less pronounced with rapamycin (Figure 6B). In tamoxifen-treated Cre(–) mice, inflammatory cell infiltration and fibrosis were observed from the dermis to the subcutaneous tissue; however, there were no changes in the number of PECAM⁺ vasculatures or VEGFR3⁺ lymphatic vessels, including their enlarged forms, compared to the untreated control (Figure 6C–E). In contrast, tamoxifen administration to CDH5-CreERT2;R26R-Pik3caH1047R mice resulted in an increase in these vascular structures by day 4 (Figure 6C–E). At day 7, comparing mice with or without treatment using anti-VEGF-A, LW6, or rapamycin, the number of PECAM⁺ vasculatures was reduced in the treated groups; however, in the rapamycin group, the number of enlarged PECAM⁺ vasculatures did not differ from that in the untreated group (Figure 6F–M). Similarly, for VEGFR3⁺ lymphatic vessels, both anti-VEGF-A and LW6 induced a reduction, whereas rapamycin did not produce a statistically significant decrease (Figure 6N–U). (Page 11, lines 363-381)
**Referees cross-commenting**
The issues raised by refereee #1 related to the phenotype analysis are right. In my opinion the Isl model here proposed well mimic human pathology evenf the vascular damage at. head is not so evident
Response:
Perhaps the discrepancy arises from a terminological issue. According to the WHO Classification of Tumours, commonly used in clinical settings, the term "Head and Neck" refers to the facial and cervical regions (including the oral cavity, larynx, pharynx, salivary glands, nasal cavity, etc.) and excludes the central nervous system. The inclusion of the brain in Figure 1O-R may have led to some confusion. We included the brain because cerebral cavernous malformations are classified as venous malformations, and thus serve as an example of common sites for venous malformations in humans.
To clarify this point, we have made slight revisions to the first part of the Introduction, as follows:
They frequently manifest in the head and neck region—here defined as the orofacial and cervical areas, excluding the brain. (Page2, lines 52-53)
Reviewer #2 (Significance (Required)):
General assessment
STRENGTH : a new mouse model seems to well recapitulate human vascular malformation. Possible key molecules have been identified
WEAKNESS. The pharmacological approach to support the role of VEGFA e HIF is not appropriate
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Referee #2
Evidence, reproducibility and clarity
This paper focuses on vascular malformations driven by PI3K mutation, with particular interest on the vascular defects localized at head and neck anatomical sites. The authors exploit the H1047R mutant which has been largely demonstrated to induce both vascular and lymphatic malformation. To limit the effect of H1047R to tissues originated from cardiopharinegal mesoderm, PI3caH1047R mice were crossed with mice expressing Cre under the control of the promoter of Ils1 , a transcription factor that contributes to the development of cardiopharinegal mesoderm-derived tissues. By comparing the embryo phenotype of this model with that observed by inducing at different times of development the expression of PI3caH1047R, the authors conclude that Isl-Cre; PI3caH1047R; R26R-eYFP model recapitulates better the anatomical features of human vascular malformations and in particular those localized at head and neck. In my opinion the new proposed model represents a significant progress to study human vascular malformations. Furthermore, scRNA seq analysis has allowed to propose a mechanism focused on the role of HIF and VEGFA. The authors provides partial evidences that HIF and VEGFA inhibitors halt the development of vascular malformation in VeCAdCre; Pik3caH1047 mice. This experiment is characterized by a conceptual mistake because bevacizumab does not recognize murine VEGFA (see for instance 10.1073/pnas.0611492104; 10.1167/iovs.07-1175. This error dampens my enthusiasm
Criticism
Fig 1A. E13.5 corresponds to the early phase of vascular remodelling. Which is the phenotype at earliest stages (e.g. 9.5 or 10.5)
Fig 1,2,3. The analysis of VEGFR2 expression is required. This request is important for the paradigmatic and non-overlapping role of this receptor in early and late vascular development. Furthermore ,these data better clarify the mechanism suggested by the experiments reported in fig 5 (VEGFA and HIF expression)
As done in Fig 1,2 and 3, data quantification by morphometric analysis is also required for results reported in supplemental figure 3
Lines 166-174. I suppose that the reported observations were done at E16.5. What happens later? It's crucial to sustain the statement at lines 187-190
scRNAseq was performed at E13.5 (Fig 4). It's mandatory to perform the same analysis at E16.5, which corresponds to the phenotypic analysis shown in fig 3. This experiment is required to understand how hypoxia and glycolysis genes changes along the development of the vascular malformation.
Lines 326-343. In this section the authors provide pharmacological evidences that HIF and VEGFa are involved in vascular malformation caused by H1047R . However , I'm surprised of efficacy of bevacizumab, which neutralizes human but not murine VEGFA. Genetech has developed B20 mAb that specifically neutralizes murine VEGFA. So the data shown require a. clarification by the authors and the experiments must be done with the appropriate reagent. Furthermore, which is the pharmacokynetics of these compounds topically applied?
Referees cross-commenting
The issues raised by refereee #1 related to the phenotype analysis are right. In my opinion the Isl model here proposed well mimic human pathology evenf the vascular damage at. head is not so evident
Significance
General assessment
Strength: a new mouse model seems to well recapitulate human vascular malformation. Possible key molecules have been identified
Weakness: The pharmacological approach to support the role of VEGFA e HIF is not appropriate
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #1
Evidence, reproducibility and clarity
The authors investigate the pathogenesis of congenital vascular malformations by overexpressing the Pik3caH1047R mutation under the R26 locus in different cell populations and developmental stages using various Cre and CreERT2 lines, including endothelial-specific and different mesoderm precursor lines. The authors provide a thorough characterization of the vascular malformation phenotypes across models. Specifically, they claim that expressing Pik3caH1047R in the cardiopharyngeal mesoderm (CPM) precursors results in vascular abnormalities localized to the head and neck region of the embryo. The study also includes scRNAseq data analyses, including from previously published data and new data generated by the authors. Trajectory inference analysis of a previous scRNA-seq dataset revealed that Isl1+ mesodermal cells can differentiate into ETV2+ cells, directly giving rise to Prox1+ lymphatic endothelial cell progenitors, bypassing the venous stage. Single-cell RNA sequencing of their CPM model and other in vitro datasets show that Pik3caH1047R upregulates VEGF-A via HIF-1α-mediated hypoxia signaling, findings further corroborated in human samples. Finally, preclinical studies in adult mice confirm that pharmacological inhibition of HIF-1α and VEGF-A reduces the number and size of mutant vessels.
Major comments
While the study provides a nice characterization of Pik3caH1047R-derived vascular phenotypes induce by expressing this mutation in different cells, the main message of the study is unclear. What is the main question that the authors want to address with this manuscript? The precursor type form where these lesions appear, that venous and lymphatic malformations emerge independently, when and where this phenotype appear? The manuscript needs some work to make the sections more cohesive and to structure better the main findings and the rationale for choosing the models. Authors should explain better when and where the pathogenic phenotypes refer to blood and/or lymphatic malformations. From the quantifications provided in Figure 1, Pik3caH1047R leads to different phenotypes in blood and lymphatic vessels. These are larger diameters with no difference in the number of blood vessels (are you quantifying all pecam1 positive? Vein, arteries, capillaries?), and an increase in the number of lymphatics vessels. Please clarify and discuss. Which vessel types are considered for the quantifications shown in Fig. 1I, M, Q? All Pecam1+ vessels, including lymphatic, vein, capillaries and arteries or which ones? Provide clarifications. The authors propose that the CPM model results in localized head and neck vascular malformations. However, I am not convinced. The images supporting the neck defects are evident, but it is unclear whether there are phenotypes in the head. Why are half of the experiments with the Tie2-Cre model conducted at E12.5 (e.g., validation of recombination, signaling, proliferation) and the others at E13.5? It becomes confusing for the reader why the authors start the results section with E13.5 and then study E12.5. The quantifications provided do not clarify what the "n" represents or how many embryos or litters were analyzed. Blasio et al. (2018), Hare et al (2015) reported that Pik3caH1047R with Tie2-Cre embryos die before E10.5. How do the authors explain the increase in survival here? Were embryos at E13.5 alive? What was the Mendelian ratio observed by the authors? Please provide this information and discuss this point. Please explain the rationale for using the Cdh5-CreERT2. It is likely due to the lethality observed with Tie2Cre, but this was not mentioned. Including this information will help readers who may need to become more familiar with the vasculature or the different Cre lines. Why were tamoxifen injections done at various time points (E9.5, E12.5, E15.5)? Please clarify the reasoning behind administering tamoxifen at these specific times. Explaining the rationale will help the reader follow the experimental design more easily. Additionally, including an initial diagram summarizing all the strategies to guide the reader from the beginning would be helpful. Why do you use the Isl1-Cre constitutive line (instead of the CreERT2)? The former does not allow control of the timing of recombination (targeting specifically your population of interest) and loses the ability to trace the mutant cell behaviors over time. Is the constitutive expression of Pik3caH1047R in Isl1+ cells lethal at any embryonic time, or do the animals survive into adulthood? When you later use the Isl1-CreERT2 line, why do you induce recombination specifically at E8.5? It would be helpful for the reader to have an explanation for this choice, along with a reference to your previous paper. What is the purpose of using this battery of CreERT2 lines (for example, the Myf5-CreERT2)? I find the scRNAseq data in Fig S4 and S5 results very interesting, although I am unsure how they fit with the rest of the story. In principle, a subset of Isl1+ cardiopharyngeal mesoderm (CPM) derivatives into lymphatic endothelial cells was already demonstrated in a previous publication from the group. What is the novelty and purpose here? Why in Fig. 4 ECs were not subclustered for further analysis (as in Fig. S4,5)? This is a missed opportunity to understand the pathogenic phenotypes. Hypoxia and glycolysis signatures are not specific to mutant ECs. Do the authors have an explanation for this? It is well known that PI3K overactivation increases glycolysis; please acknowledge this. Do you have an explanation for the expression of VEGFA by lymphatic mutant cells? Likewise, why mesenchymal cells traced from the Islt1-Cre decreased upon expression of Pik3caH1047R? Authors need to characterize the preclinical model before conducting any preclinical study. No controls are provided, including wild-type mice and phenotypes, before starting the treatment (day 4). Why did the authors not use their developmental model of head and neck malformation model for preclinical studies? This would be much more coherent with the first part of the manuscript. Also, how many animals were treated and quantified for the different conditions?
Minor Comments
References in the introduction need to be revised. Specifically, how authors reached the stats on head and neck vascular malformations needs to be clarified. For instance, one of the cited papers refers to all types of vascular malformation, while the other focuses exclusively on lymphatic malformations with PIK3CA mutations. Moreover, in the latter, the groups are divided into orofacial and neck and body categories. How do authors substrate the information from the neck and head here? Also, in line 79, I need clarification on ref 24 about fibrosis. Include references: Studies in mice have shown that p110α is essential for normal blood and lymphatic vessel development. Please clarify and correct. Please define PIP2 and PIP3 Why is Prox1 showing positivity in erythrocytes in Figure 1? Regarding Figure 1, I suggest organizing the quantifications in the same order to facilitate phenotype comparisons. For example, I, J vs. Q, R. What is the difference between M and N? Add the reference of the Bulk RNseq data. Mark in the Fig. 4F that the volcano plots are from cluster one of the scRNASeq (this is explained in text and legend, but when you go to the figure, it isn't very clear). Please label Figure 6D/E with the proper labels. In Fig. 6, it is mentioned that vacuoles are from the tamoxifen injection, how do you know? Do you also see them if you add oil alone (without tamoxifen) or tamoxifen in a WT background?
Referees cross-commenting
I complete agree with referee #2 regarding the preclinical studies. Bevacizumab, does not neutralize murine VEGFA. This is a major issue.
Significance
This study addresses a timely and relevant question: the origins, onset and progression of congenital vascular malformations, a field with limited understanding. The work is novel in its approach, employing complex embryonic models that aim to mimic the disease in its native context. By focusing on the effects of Pik3caH1047R mutations in cardiopharyngeal mesoderm-derived endothelial cells, it sheds light on how these mutations drive phenotypic outcomes through specific pathways, such as HIF-1α and VEGF-A signaling, while also identifying potential therapeutic targets. A strong aspect of the study is the use of embryonic models, which enables the investigation of disease onset in a context that closely resembles the in vivo environment. This is particularly valuable for congenital disorders, where native developmental cues are an integral aspect of disease progression. The study also integrates advanced techniques, including single-cell RNA sequencing, to dissect the cellular and molecular responses induced by the Pik3caH1047R mutation. Moreover, from a translational perspective, it provides novel therapeutic strategies for these diseases.
Limitations of the study are (1) unclarity of the main question authors try to address, and main conclusions dereived thereof; (2) the different parts of the manuscripts are not well connected, not clear the rationale; (3) scRNAseq analysis is underdeveloped; (4) characterization of the preclinical model is not provided.
Audience:
The findings presented here interest specialized audiences within developmental biology, vascular biology, and congenital disease research fields, and clinicians by providing new therapies to treat vascular anomalies. Moreover, the study's integration of single-cell and in vivo models could inspire further research in other contexts where understanding clonal behavior and signaling pathways is critical.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Serra et al have conducted transcriptomic analyses for thalamic Sox2 and Nr2f1 cKO mice, revealing gene regulatory networks underlying development and functions of dLGN which plays pivotal roles in visual sensation. The findings are also potentially important for understanding vision disability in human. Their conclusions are mostly supported by the data, but some reinforcement and additional explanations may further improve the paper.
*We thank the reviewer for their appreciation of our work, and the constructive comments.
*
Major points:
- Although they showed that Sox2 does not regulate Nr2f1 by immunostaining in Fig.1, it would be reinforced by the RNA-seq results. What about evidence for regulation of Sox2 by Nr2f1? I could not find.
*We have now highlighted, in Fig.1D, the requested RNAseq results from Table S1, showing a very limited reduction of expression of Nr2f1 in Sox2 mutant and of Sox2 in Nr2f1 mutants. We further added ISH results confirming this data (Fig. 4A). *
The onset of and specificity among the thalamic nuclei of Sox2 and Nr2f1 expression would better be mentioned in the beginning. As far as I remember, both genes are quite widely expressed in the thalamic nuclei, not necessarily specific to dLGN.
We previously reported in Mercurio et al 2019 (ref. 7) that Sox2 is highly expressed in the dorsal thalamus (precursor to the sensory thalamic nuclei) at least from E15.5 and is later expressed in all the sensory thalamic nuclei, though not in surrounding regions (Mercurio et al 2019 Fig.1). A similar expression pattern was previously reported for Nr2f1 in Chou et al 2013 (ref. 6). A brief mention of this point is now present in Introduction.
Mechanistically, how Sox2 function becomes distinct in neural stem cells and neurons would be of a great interest (e.g., changes in binding partner). But, it might be too much for the present package.
*We agree on the interest of this point. We note that SOX2 binding sites in neurons (but not in stem cells), as detected by CUT&RUN, are enriched for SOX2 and RORA/NRF binding sites. The co-presence of SOX and NRF potential binding motifs (Fig. 2F-G), suggests the possibility of direct physical interaction between SOX2 and NR2F1 mediating joint binding to DNA. This is interesting and will be experimentally addressed in a follow up study. *
Minor points: 1. Explanation for the values in Fig.3A in the text or the figure legend would be helpful for readers unfamiliar with MuSiC.
We clarified the figure legend, better explaining how the plotted were computed and their meaning.
Since Ror-alpha is also expressed layer 4 in the cortex, some explanations for these phenotypes being caused by thalamic defects may be provided. I know that expression of Sox2 and Ror-alpha do not overlap in layer 4, though.
*In fact, we propose that downregulation of RORa in layer 4 maybe caused by reduced thalamic afferents to layer 4, possibly also acting through a reduced delivery of VGF to the cortex; in fact, as the reviewer correctly states Sox2 itself is not expressed in the cortex. *
Why did the authors use two types of Sox2 antibodies in Fig.4A?
We strive to replicate our CUT&RUN data such that we can rely only on the reproducible binding events. We have often noted that – being CUT&RUN a “challenging” application for antibodies – different antibodies yield non-fully overlapping binding profiles. While we do not have a clear explanation for this, we consider more robust converging on those binding events that are obtained by two independent antibodies, when such tools are available. This, in our opinion and experience, drastically decreases the chance of stumbling upon false positive hits.
Quatification for Fig.1A, Fig.2A and 2B may be necessary for the current publication standards.
The requested quantification has been added in Fig. S1A and in Fig. 4C.
In Introduction, NRF1 or NRF is somewhat confusing because there is a different gene named NRF (Nuclear respiratory factor).
*We corrected this. *
Reference 14 is identical to 44.
*We corrected this. *
Reviewer #1 (Significance (Required)):
This work provides a basis of gene regulatory network involved in development and function of dLGN neurons, which may also be important for understanding mechanisms of vision disability in human caused by genetic mutations. Although I am not an expert in this particular field (GRNs in thalamic neurons), a series of the authors' works certainly establish a molecular basis of the roles of Sox2 ranging from neural stem/progenitor cells to neurons. Limitations of the current study in my opinion would be that it only lists up candidate genes for the functions or cause of visual sensations or defects, and thus experimental proof awaits actual biological experiments. Although the results and conclusion provided by the authors are reasonable and convincing, conceptual advance may be limited to some extent. Readers in both basic and clinical researches will be interested in that vision disability caused by mutations in Sox2 and Nr2f1 could be explained by synapse-related genes, axon guidance molecules, or secreting factors like VGF, albeit not with big surprise. My research expertise would be in the field of brain development, particularly in regionalization and morphogenesis of the brain. Yet, I am not particularly familiar with transcriptomic analyses in general.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In the current manuscript, Serra, Mercurio, and colleagues carried out Ror-alpha-Cre specific conditional mutant analysis of Sox2 and Nr2f1 in the thalamus/dLGN. The workflow primarily focused on potential mechanisms underlying transcriptional regulation. With RNA-Seq, the authors identified multiple "common" targets shared by both Sox2 and Nr2f1 factors. In parallel, the authors also carried out CUT-RUN analysis for Sox2 binding patterns in dLGN chromatin.
The current work is built upon the intellectual framework of two papers: the past work led by the senior author in 2019, as well as an earlier work by Chou /O'Leary 2013, in terms of genetic reagents and anatomical and functional analysis. While the newly performed experiments may open some new avenues for future investigation, the current manuscript did NOT vigorously validate bioinformatics predictions using experimental approaches. The current dataset did NOT present any functional and anatomical analysis, esp. in terms of the target gene functions back to the same circuits/connections (thalamus-cortex). The manuscript presented in the current format offers limited biological insights into the neurobiology of dLGN. The limited experimental data also indicated that the manuscript may not be suitable for a very general readership.
We thank the reviewer for pointing out contributions as well as limitations of our work. We are convinced that our work does indeed open up " new avenues for future investigation", reporting for the first time hundreds of targets of SOX2 and NR2F1 as well as hundreds of direct SOX2 binding sites in dLGN neurons that will contribute to future investigations.
Major points: 1. Unless I missed anything - I was not sure why the current Figure 1/ Tables 1&2 took a sharp pause without any in situ/histochemical validations of the "prominent" downstream targets - at minimum, the authors should validate the common targets, including VGF among others;
We now validated the downregulation VGF and Sox5 at the RNA level by ISH confirming SOX5 downregulation by IF. These data are presented in the new Fig. 4, in results page 5 and discussion page 7.
Could the over-expression of any targets (Sox5, etc) reverse the loss of Sox2-phenotypes, esp. in terms of the establishment of thalamic-cortical connections, as assayed by Fig 2A (as well as Mercurio, 2019, Figure4)? Having such an assay would significantly boost the significance of the current study.
The experiment suggested by the reviewer would undoubtly be interesting to address Sox5 contribution to the mutant phenotype; unfortunately, this is too demanding for the present paper.
However, for the sake of data interpretation, we propose that the mutant phenotypes observed rather result from the global deregulation of a set of genes, not just of a single gene. Indeed, we discuss the potential contribution of several different genes, among those co-regulated by SOX2 and NR2F1. From this point of view, we don't necessarily expect the contribution of a specific gene to be prominent. In fact, we believe an interesting result emerging from our work is the identification of a rather numerous set of genes collectively responding to both Sox2 and Nr2f1 mutation, many of which may contribute to the shared phenotypes of the two mutants.
Figure 3 is presented in a very inconvenient manner for any reviewers/future readers to understand and interpret. The plots in B and C are what matter the most, while the raw data in 3A could be included in a table. The presentation and comparison of this figure need some significant work.
We have now modified Fig. 3 as requested and moved the raw data to the Supplementary material (Table S4).
The Cut-n-Run assays offered several dLGN unique (non-neurogenesis) targets. However, the study paused at bioinformatics prediction without experimental validations as well, including the dLGN peaks near Vgf and Sox5.
We are not sure we understand the reviewer's question. The " dLGN unique (non-neurogenesis) targets" that we report are not the results of a bioinformatics prediction, but of the CUT&RUN experiment itself including the dLGN peaks near Vgf and Sox5. In addition, we experimentally validated the downregulation of Vgf and Sox5 by in situ hybridization in the new Figure 4.
Minor points: For general readers, (1) please explicitly document whether Ror-alpha-Cre does NOT(?) impact the retina and cortex;
This is now mentioned in results in agreement with the results in Chou et al. 2013 and Mercurio et al. 2019.
Chou et al mentions explicitly absence of Rora Cre activity in the cortex and this is also in agreement with our own results in Mercurio et al. 2019. As to the retina, we reported not observing any retinal phenotypes in Sox2 mutants in agreement with the absence of any Sox2 deletion within the retina, that would have caused a drastic phenotype as reported in Taranova et al. 2006.
(2) please explain when Ror-alpha-Cre expression timing - is it solely post-mitotic in the dLGN? The authors may have taken these for granted, esp. given Mercurio 2019 and Chou 2013, but such information may help readers outside the field.
The onset of Rora Cre activity is at a stage in which dLGN neurogenesis is completed and most if not all cells are postmitotic as reported in Chou et al. 2013. This point is now more explicitly mentioned in results.
Reviewer #2 (Significance (Required)):
The manuscript offers limited new information to general readers. It might be a good dataset for researchers specialized in transcriptional regulation in terms of finding useful/relevant information to design future experiments. However, the study did NOT offer any histological and functional assays based on bioinformatics tests.
- General assessment: The strengths were a careful analysis of dLGN in early development using both RNA-Seq and Cut-n-Run with a focus on Sox2's post-mitotic role. The limitations were that the study was lack of histological validations and functional tests of the candidate genes.
We now added histological validation of selected targets as requested in the new Fig. 4.
• Advance: The advance of the study is limited, though the experiments were carefully launched.
• Audience: Very limited audience with a specialty in transcription factors in visual system development.
The reviewer is an expert in neurodevelopment using the mouse genetics approach, with primary interests in studying the retina and retino-recipient zone development.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary:
This manuscript investigates the role of Sox2 and Nr2f1 on dLGN development. The authors perform RNA-seq on thalamus-specific conditional knock outs of Sox2 and Nr2f1. The author compile lists of the genes that showed the greatest change in detection between control mice (3 and 3) and mutant mice (3 and 3). The authors find significant overlap in the lists of genes most altered in the mutants and argue that this overlap is consistent with the two transcription factors regulating the same gene network. The authors also perform a CUT&RUN analysis of Sox2 binding sites and find overlap in the list of genes that Sox2 binds to and the genes with altered expression levels in the Sox2-cKO. Regulation of neuron-specific cellular components are highly represented in both the list of binding sites and genes with altered expression levels.
The RNA-seq data and binding site data are valuable resources for researchers trying to understand the development of the dLGN and should be published. However, I am not confident that author's interpretations of their data are supported by what is provided in the manuscript.
Major comments:
Issues with the statistical logic
-Lack of statistical significance is not evidence of equality. The fact that Sox2 and Nr2f1 do not pass the FDR threshold is not evidence that they are unchanged in the two conditional knock-outs.
The meaning of statistical testing and significance in this context is assessing if, and how much, the observed changes in expression in RNA-Seq estimated transcript levels can be due only to experimental variability (not significant) or, vice versa, if there is an additional biological factor (the knock-out of Sox2 or Nr2f1, in this case) behind the changes observed. Clearly, the more “significant” (lower) are the p-value/FDR values associated with changes observed for a gene, the more likely is that the gene transcript levels are affected by the knock outs. Vice versa, if the change is reported to be “not significant”, there isn’t enough evidence – at least from a statistical point of view - that the observed changes in transcript levels are due to the effect of the knock outs. Three replicates per condition are required in order to estimate variance – which is gene specific and estimates what is the “natural” range of variability of each gene due only to experimental variability (and not generated by the knock-outs).
We now report the RNAseq data for Sox2 and Nr2f1 in Fig. 1D and complete them with ISH data in the new Fig. 4. The results are consistent with a limited reduction Nr2f1 in the Sox2 mutants and Sox2 in the Nr2f1 mutants. Though we cannot rule out that they might contribute to some extent to the mutant phenotype, we document a stronger downregulation, in both mutants, of a vast set of other genes (Fig. 1C) onto which our analysis focuses.
-Many arguments are based on the result that Sox2 knock out has a "strong" effect on a gene. FDR and p-values do not provide evidence about effect size beyond "not 0". Average TPN values are provided but, without sorting through thousands of values in the supplementary data, it is not possible to judge the reliability of a claimed effect size. Finally, no biological reference is given for what should be considered a strong effect size besides the relative values within the knockout experiment. I would like to see the replicates for the relevant TPN data presented in the main text and I would like to see the variance between those replicates considered in the author's conclusions. Space in the tables could be saved by reporting fewer digits in the fold changes.
See previous point. The more “significant” are the changes of transcript levels according to statistical testing, the “stronger” the effect of the knock out on them, where by “strong” we mean a more relevant variation of transcript levels. However, since we realized that this term could cause confusion in the reader, we rephrased the relevant parts. Variance is taken into account in the computation of pvalues/FDRs, so the same difference in mean TPM values for two different genes can result to more/less significant according to the estimated variance of the values.
-The authors identify 469 dLGN specific SOX2 binding sites by subtracting the 248 high confidence binding sites identified in non-dLGN cells from the 717 high confidence binding sites identified in dLGN. This subtraction is basically a comparison of p-values with the false assumption that lack of statistical significance means there was no change. The quantitation required to make the claim would be a direct comparison of the two data sets for each binding site.
*We appreciate the concern from the reviewer. CUT&RUN, especially when performed in vivo versus cell lines, has a high intrinsic variability between experiments, and even between technical replicates (DOI: 10.1093/nar/gkae180). While it would be possible to, for example, run DiffBind (built for ChIP-seq), on the dLGN data versus the NS data, these are not, in our opinion, directly comparable as they were not performed in the same batch, on the same type of material (dissected mouse tissue versus cultured cells) or even with the same batches of reagents. Thus, to quantify them in terms of signal at specific loci, without taking into account things like global background, local background, and overall signal to noise ratio, we do not believe is correct. There are many attempts in the field to better quantify CUT&RUN data (spike-in yeast or E. coli DNA at different moments, spike-in drosophila nuclei, etc.) but there remains to be determined a general consensus on what is best or trustworthy. The best way we could do the comparison, with our data as it was generated, was as pointed out above, by comparing the statistically significant events in the dLGN versus those in the NS, that way each dataset is considered independently before the overlap is performed. To help alleviate the reviewers concerns, we have provided here, for the reviewer, signal profiles and heatmaps of the dLGN only regions in both dLGN and NS CUT&RUN. *
Non-quantitative issues:
-It is known that both the Sox2 and Nr2f1 mutants have similar dLGN phenotypes. How, then, can we know if individual changes in gene expression reflect direct regulation by Sox2 and Nr2f1 or the dramatically altered state of the dLGN? The binding data would add to the argument of direct regulation, but it is difficult to judge the specificity of the binding data.
The timepoint of the RNAseq analyses was chosen to precede any phenotypic changes detected in the dLGN based on our previous analyses reported in Mercurio et al. 2019 as stated in Results page 3.
* * -The authors argue that a decrease in layer 4 of the cortex argues that Vgf1 is a likely link between Sox2 and cortical development. However, some decrease in layer 4 thickness is a given if the number of thalamocortical cells in dLGN is reduced.
We agree with the Reviewer. The possible contribution of VGF has been rephrased considering a possible wider contribution of thalamic afferents in general.
-Immuno fluorescence is used to support the idea that the number of cells strongly expressing Sox5 is reduced in the Sox2 cKO. The image shows a reduced patch of Sox5 labeling. However, the dLGN is generally reduced in the Sox2 cKO so it is not clear if there is a difference in the proportion of cells expressing Sox5. The sample size also appears to be 1.
The time of this analysis was chosen to precede dLGN size reduction in mutants, as clearly shown in our previous work Mercurio et al. 2019 and further confirmed by the new ISH for Sox2 and Nr2f1 presented in the new Fig. 4.
The sample size is n=4 as reported in the Figure legend.
Minor
Introduction:
-Writing could be improved.
-Descriptions of effects of Sox2 or Nr2fl using RORalpha-Cre use words like "reduced", "significant", "important". It is unclear what the actual effects or effect sizes are.
We revised the wording for this point.
RESULTS
-What is "Three independent pools of mutant and control dissected visual thalami"? Three mice for each condition (twice for control)?
-Why are there two groups of 3 control mice each and not one group of 6?
As reported in Materials and Methods " RNA sequencing was performed on three independent samples for both mutant and control dLGN. Each sample was composed of dLGNs from three animals of the same genotype pooled together."
*Thalami from 3 mice represent an adequate amount of RNA to perform a single experiment of RNAseq. 3 x 3 represents a biological triplicate for the RNAseq experiment. * Section 2
-For the model in which the probability of genes changing in the same direction is calculated, are all genes assumed to have the same chance of passing the FDR? Gene variance and detection rate will be correlated between conditions. I would suggest a more conservative comparison. What is the correlation of fold change for genes that pass FDR? Of 514 that change in both, 481 go in the same direction and 33 go in a different direction. If everything is random, the number would be 257/257. The claim of four times random overlap does not seem like the conservative estimate.
Genes were selected with the same FDR thresholds in both experiments. The assumption is anyway more simple: the probability of a gene to have a significant change (passing the FDR threshold) in one experiment does not influence its probability to change also in the other, and vice versa. That is, we compute the probability to have a given number of up- or down-regulated genes in common in the two experiments assuming that the two experiments were independent from one another. From another point of view, this is the usual strategy employed in order to assess whether the overlap between two gene sets obtained by two different genome-wide experiments can be considered to be random or not, that is, if the number of genes in the overlap is close to random expected values they can be considered to be independent from one another.
Section 3
-I don't see any basis to judge the p-values in Fig 1D. How do these changes compare to what you would from other dramatic manipulations of neural tissue? Can figure 1D compare to changes in non-neuronal standard? How about metabolism and cell death?
The graph shown represents the most significantly enriched functional annotations (GO annotations, pathways, etc.) among the deregulated genes as computed by Enrichr, one of the many tools developed for this task. And as for all the tools performing this analysis, the p-value means “the probability of having the same number of genes sharing the same functional annotation in a set of genes chosen at random”, computed with the same strategy employed for the overlap between the two deregulated gene sets described before.
Section "Deconvolution..."
-It is great that results for each replicate is presented.
We thank the reviewer.
* * -There are too many significant digits in Fig 3A given the variance.
This has been adjusted as suggested.
-Why do the NR2F1 mutants look more like the Sox2 controls (in terms of excitatory Neurons) than the NR2F1 controls do?
*The graphical presentation of the data in Fig. 3 has been improved, and the numerical data (former panel A) have been moved to the supplementary materials (Table S4) as recommended. *
Controls for Nr2f1 and Sox2 mutants have similar values for excitatory neurons, as expected, see Table S4. Fig. 3 shows the variation between each knock-out and its respective control experiments, and although excitatory neurons are reduced in both mutants the extent of reduction is greater in the Sox2 mutant.
Section "CUT&RUN..."
-How many overlaps (Figure 4B) would you expect by chance?
*This is an extremely difficult number to calculate. It is possible to, for example, generate a random set of genomic fragments of similar length, and check how many of them overlap. This would however be extremely unfair, as CUT&RUN is naturally biased towards open chromatin, and thus would preferentially contain these types of regions in a “randomly” digested set. Additionally, data analysis and mapping biases further increase what overlaps would often occur. To circumvent this, we i) use an IgG control, which should identify and remove regions that are nonspecifically digested and sequenced during the experiment, and ii) performed our analysis after first removing sets of known artifact regions (Nordin et al 2023, ref. 43). *
-Fig 4J needs more description. What does the first full pie represent?
*We have added more description in the figure legend, it now reads: *
- *Schematic depiction of CUT&RUN and RNA-seq overlap, showing Sox2 peak associated genes that are transcribed ( > 5 TPM, 784/1102) and those that are differentially expressed (DEG) in Sox2 mutant dLGN (FDR -Please include the denominator in the binding event argument. It is difficult to judge the specificity of the effect in this section.
We apologize but we don't understand this comment.
Reviewer #3 (Significance (Required)):
The mouse dorsal lateral geniculate nucleus (dLGN) is an important model system for understanding vision and the development of visual circuitry. A considerable literature exists on the role of activity dependent development and molecular gradients in shaping the synaptic connections between the retina and the dLGN. Less is known about the transcriptional networks that regulate dLGN development. Mutations in the transcription factors Sox2 and NR2F1 are associated with severe vision defects and conditional knockout of Sox2 has been shown to cause dramatic defects in dLGN development. The data provided in the current study adds to our understanding of how these transcription factors influence gene expression and circuit formation in the dLGN. Their work points to changes in VGF expression and fewer thalamocortical cells as the most salient effects of Sox2 deletion. These results increase our understanding of the transcriptional networks underlying dLGN development and several visual pathologies.
I think the manuscript should be helpful to researchers interested in the dLGN or researchers interested in the transcription factors important for neural circuit development. My own expertise covers dLGN development but not transcription factors and the interpretation of RNA-seq data. My impression was that the biggest contribution of this manuscript was in obtaining gene expression levels in the Sox2 conditional knockout with multiple RNA-seq replicates. The impact of the paper, as written, is lessened by the fact that the confidence gained by replicating the analysis is not leveraged in the main text of the manuscript.
Performing a RNA-Seq analysis in replicates is common practice, and as we detailed in our replies to the reviewer’s comments the goal of replicates is to have reliable estimations of the parameters needed (mean, variance of each gene) for the subsequent statistical analyses. So, we leveraged the information obtained from the replicates in order to identify with high confidence with genes could be considered to be affected by the knock-outs.
Much of the results, interpretation, and discussion depend on sorting strong effects on genes from weak ones without presenting replicates for effect size or confidence intervals. The replicate data is available in the supplementary data and should be a good resource for future research.
As discussed in the previous responses, the statistical evaluations usually performed on estimated transcript levels and their variance can be translated into a more qualitative evaluation of the effect of the knock-outs performed – the larger is the impact on transcript levels of a gene with respect to its estimated variance (variability) the stronger the effect is assumed to be. Confidence intervals are not usually employed in this context – the “confidence” with which the experimental setting can be assumed to affect gene expression is summarized by the p-values and the subsequent FDR values.
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Referee #3
Evidence, reproducibility and clarity
Summary:
This manuscript investigates the role of Sox2 and Nr2f1 on dLGN development. The authors perform RNA-seq on thalamus-specific conditional knock outs of Sox2 and Nr2f1. The author compile lists of the genes that showed the greatest change in detection between control mice (3 and 3) and mutant mice (3 and 3). The authors find significant overlap in the lists of genes most altered in the mutants and argue that this overlap is consistent with the two transcription factors regulating the same gene network. The authors also perform a CUT&RUN analysis of Sox2 binding sites and find overlap in the list of genes that Sox2 binds to and the genes with altered expression levels in the Sox2-cKO. Regulation of neuron-specific cellular components are highly represented in both the list of binding sites and genes with altered expression levels.
The RNA-seq data and binding site data are valuable resources for researchers trying to understand the development of the dLGN and should be published. However, I am not confident that author's interpretations of their data are supported by what is provided in the manuscript.
Major comments:
Issues with the statistical logic
- Lack of statistical significance is not evidence of equality. The fact that Sox2 and Nr2f1 do not pass the FDR threshold is not evidence that they are unchanged in the two conditional knockouts.
- Many arguments are based on the result that Sox2 knock out has a "strong" effect on a gene. FDR and p-values do not provide evidence about effect size beyond "not 0". Average TPN values are provided but, without sorting through thousands of values in the supplementary data, it is not possible to judge the reliability of a claimed effect size. Finally, no biological reference is given for what should be considered a strong effect size besides the relative values within the knockout experiment. I would like to see the replicates for the relevant TPN data presented in the main text and I would like to see the variance between those replicates considered in the author's conclusions. Space in the tables could be saved by reporting fewer digits in the fold changes.
- The authors identify 469 dLGN specific SOX2 binding sites by subtracting the 248 high confidence binding sites identified in non-dLGN cells from the 717 high confidence binding sites identified in dLGN. This subtraction is basically a comparison of p-values with the false assumption that lack of statistical significance means there was no change. The quantitation required to make the claim would be a direct comparison of the two data sets for each binding site.
Non-quantitative issues:
- It is known that both the Sox2 and Nr2f1 mutants have similar dLGN phenotypes. How, then, can we know if individual changes in gene expression reflect direct regulation by Sox2 and Nr2f1 or the dramatically altered state of the dLGN? The binding data would add to the argument of direct regulation, but it is difficult to judge the specificity of the binding data.
- The authors argue that a decrease in layer 4 of the cortex argues that Vgf1 is a likely link between Sox2 and cortical development. However, some decrease in layer 4 thickness is a given if the number of thalamocortical cells in dLGN is reduced.
- Immuno fluorescence is used to support the idea that the number of cells strongly expressing Sox5 is reduced in the Sox2 cKO. The image shows a reduced patch of Sox5 labeling. However, the dLGN is generally reduced in the Sox2 cKO so it is not clear if there is a difference in the proportion of cells expressing Sox5. The sample size also appears to be 1.
Minor
Introduction:
- Writing could be improved.
- Descriptions of effects of Sox2 or Nr2fl using RORalpha-Cre use words like "reduced", "significant", "important". It is unclear what the actual effects or effect sizes are.
RESULTS
- What is "Three independent pools of mutant and control dissected visual thalami"? Three mice for each condition (twice for control)?
- Why are there two groups of 3 control mice each and not one group of 6?
Section 2
- For the model in which the probability of genes changing in the same direction is calculated, are all genes assumed to have the same chance of passing the FDR? Gene variance and detection rate will be correlated between conditions. I would suggest a more conservative comparison. What is the correlation of fold change for genes that pass FDR? Of 514 that change in both, 481 go in the same direction and 33 go in a different direction. If everything is random, the number would be 257/257. The claim of four times random overlap does not seem like the conservative estimate.
Section 3
- I don't see any basis to judge the p-values in Fig 1D. How do these changes compare to what you would from other dramatic manipulations of neural tissue? Can figure 1D compare to changes in non-neuronal standard? How about metabolism and cell death?
Section "Deconvolution..."
- It is great that results for each replicate is presented.
- There are too many significant digits in Fig 3A given the variance.
- Why do the NR2F1 mutants look more like the Sox2 controls (in terms of excitatory Neurons) than the NR2F1 controls do?
Section "CUT&RUN..."
- How many overlaps (Figure 4B) would you expect by chance?
- Fig 4J needs more description. What does the first full pie represent?
- Please include the denominator in the binding event argument. It is difficult to judge the specificity of the effect in this section.
Significance
The mouse dorsal lateral geniculate nucleus (dLGN) is an important model system for understanding vision and the development of visual circuitry. A considerable literature exists on the role of activity dependent development and molecular gradients in shaping the synaptic connections between the retina and the dLGN. Less is known about the transcriptional networks that regulate dLGN development. Mutations in the transcription factors Sox2 and NR2F1 are associated with severe vision defects and conditional knockout of Sox2 has been shown to cause dramatic defects in dLGN development. The data provided in the current study adds to our understanding of how these transcription factors influence gene expression and circuit formation in the dLGN. Their work points to changes in VGF expression and fewer thalamocortical cells as the most salient effects of Sox2 deletion. These results increase our understanding of the transcriptional networks underlying dLGN development and several visual pathologies.
I think the manuscript should be helpful to researchers interested in the dLGN or researchers interested in the transcription factors important for neural circuit development. My own expertise covers dLGN development but not transcription factors and the interpretation of RNA-seq data. My impression was that the biggest contribution of this manuscript was in obtaining gene expression levels in the Sox2 conditional knockout with multiple RNA-seq replicates. The impact of the paper, as written, is lessened by the fact that the confidence gained by replicating the analysis is not leveraged in the main text of the manuscript. Much of the results, interpretation, and discussion depend on sorting strong effects on genes from weak ones without presenting replicates for effect size or confidence intervals. The replicate data is available in the supplementary data and should be a good resource for future research.
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Referee #2
Evidence, reproducibility and clarity
In the current manuscript, Serra, Mercurio, and colleagues carried out Ror-alpha-Cre specific conditional mutant analysis of Sox2 and Nr2f1 in the thalamus/dLGN. The workflow primarily focused on potential mechanisms underlying transcriptional regulation. With RNA-Seq, the authors identified multiple "common" targets shared by both Sox2 and Nr2f1 factors. In parallel, the authors also carried out CUT-RUN analysis for Sox2 binding patterns in dLGN chromatin.
The current work is built upon the intellectual framework of two papers: the past work led by the senior author in 2019, as well as an earlier work by Chou /O'Leary 2013, in terms of genetic reagents and anatomical and functional analysis. While the newly performed experiments may open some new avenues for future investigation, the current manuscript did NOT vigorously validate bioinformatics predictions using experimental approaches. The current dataset did NOT present any functional and anatomical analysis, esp. in terms of the target gene functions back to the same circuits/connections (thalamus-cortex). The manuscript presented in the current format offers limited biological insights into the neurobiology of dLGN. The limited experimental data also indicated that the manuscript may not be suitable for a very general readership.
Major points:
- Unless I missed anything - I was not sure why the current Figure 1/ Tables 1&2 took a sharp pause without any in situ/histochemical validations of the "prominent" downstream targets - at minimum, the authors should validate the common targets, including VGF among others;
- Could the over-expression of any targets (Sox5, etc) reverse the loss of Sox2-phenotypes, esp. in terms of the establishment of thalamic-cortical connections, as assayed by Fig 2A (as well as Mercurio, 2019, Figure4)? Having such an assay would significantly boost the significance of the current study.
- Figure 3 is presented in a very inconvenient manner for any reviewers/future readers to understand and interpret. The plots in B and C are what matter the most, while the raw data in 3A could be included in a table. The presentation and comparison of this figure need some significant work.
- The Cut-n-Run assays offered several dLGN unique (non-neurogenesis) targets. However, the study paused at bioinformatics prediction without experimental validations as well, including the dLGN peaks near Vgf and Sox5.
Minor points:
For general readers, (1) please explicitly document whether Ror-alpha-Cre does NOT(?) impact the retina and cortex; (2) please explain when Ror-alpha-Cre expression timing - is it solely post-mitotic in the dLGN? The authors may have taken these for granted, esp. given Mercurio 2019 and Chou 2013, but such information may help readers outside the field.
Significance
The manuscript offers limited new information to general readers. It might be a good dataset for researchers specialized in transcriptional regulation in terms of finding useful/relevant information to design future experiments. However, the study did NOT offer any histological and functional assays based on bioinformatics tests.
General assessment:
The strengths were a careful analysis of dLGN in early development using both RNA-Seq and Cut-n-Run with a focus on Sox2's post-mitotic role. The limitations were that the study was lack of histological validations and functional tests of the candidate genes.
Advance:
The advance of the study is limited, though the experiments were carefully launched.
Audience:
Very limited audience with a specialty in transcription factors in visual system development.
The reviewer is an expert in neurodevelopment using the mouse genetics approach, with primary interests in studying the retina and retino-recipient zone development.
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Referee #1
Evidence, reproducibility and clarity
Serra et al have conducted transcriptomic analyses for thalamic Sox2 and Nr2f1 cKO mice, revealing gene regulatory networks underlying development and functions of dLGN which plays pivotal roles in visual sensation. The findings are also potentially important for understanding vision disability in human. Their conclusions are mostly supported by the data, but some reinforcement and additional explanations may further improve the paper.
Major points:
- Although they showed that Sox2 does not regulate Nr2f1 by immunostaining in Fig.1, it would be reinforced by the RNA-seq results. What about evidence for regulation of Sox2 by Nr2f1? I could not find.
- The onset of and specificity among the thalamic nuclei of Sox2 and Nr2f1 expression would better be mentioned in the beginning. As far as I remember, both genes are quite widely expressed in the thalamic nuclei, not necessarily specific to dLGN.
- Mechanistically, how Sox2 function becomes distinct in neural stem cells and neurons would be of a great interest (e.g., changes in binding partner). But, it might be too much for the present package.
Minor points:
- Explanation for the values in Fig.3A in the text or the figure legend would be helpful for readers unfamiliar with MuSiC.
- Since Ror-alpha is also expressed layer 4 in the cortex, some explanations for these phenotypes being caused by thalamic defects may be provided. I know that expression of Sox2 and Ror-alpha do not overlap in layer 4, though.
- Why did the authors use two types of Sox2 antibodies in Fig.4A?
- Quatification for Fig.1A, Fig.2A and 2B may be necessary for the current publication standards.
- In Introduction, NRF1 or NRF is somewhat confusing because there is a different gene named NRF (Nuclear respiratory factor).
- Reference 14 is identical to 44.
Significance
This work provides a basis of gene regulatory network involved in development and function of dLGN neurons, which may also be important for understanding mechanisms of vision disability in human caused by genetic mutations. Although I am not an expert in this particular field (GRNs in thalamic neurons), a series of the authors' works certainly establish a molecular basis of the roles of Sox2 ranging from neural stem/progenitor cells to neurons. Limitations of the current study in my opinion would be that it only lists up candidate genes for the functions or cause of visual sensations or defects, and thus experimental proof awaits actual biological experiments. Although the results and conclusion provided by the authors are reasonable and convincing, conceptual advance may be limited to some extent. Readers in both basic and clinical researches will be interested in that vision disability caused by mutations in Sox2 and Nr2f1 could be explained by synapse-related genes, axon guidance molecules, or secreting factors like VGF, albeit not with big surprise.
My research expertise would be in the field of brain development, particularly in regionalization and morphogenesis of the brain. Yet, I am not particularly familiar with transcriptomic analyses in general.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
1. General Statements [optional]
Our manuscript initially entitled “Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription” investigates the regulation of the initial steps of ribosome biogenesis – the synthesis of large ribosomal RNA precursor by RNA polymerase I.
In our manuscript, we demonstrate for the first time that RNA Polymerase I (Pol I) can prematurely release nascent transcripts at the 5' end of ribosomal DNA transcription units in vivo. This achievement was made possible by comparing wild-type Pol I with a mutant form of Pol I, hereafter called SuperPol previously isolated in our lab (Darrière at al., 2019). By combining in vivo analysis of rRNA synthesis (using pulse-labelling of nascent transcript and cross-linking of nascent transcript - CRAC) with in vitro analysis, we could show that Superpol reduced premature transcript release due to altered elongation dynamics and reduced RNA cleavage activity. Such premature release could reflect regulatory mechanisms controlling rRNA synthesis. Importantly, This increased processivity of SuperPol is correlated with resistance with BMH-21, a novel anti-cancer drugs inhibiting Pol I, showing the relevance of targeting Pol I during transcriptional pauses to kill cancer cells. This work offers critical insights into Pol I dynamics, rRNA transcription regulation, and implications for cancer therapeutics.
We sincerely thank the three reviewers for their insightful comments and recognition of the strengths and weaknesses of our study. Their acknowledgment of our rigorous methodology, the relevance of our findings on rRNA transcription regulation, and the significant enzymatic properties of the SuperPol mutant is highly appreciated. We are particularly grateful for their appreciation of the potential scientific impact of this work. Additionally, we value the reviewer’s suggestion that this article could address a broad scientific community, including in transcription biology and cancer therapy research. These encouraging remarks motivate us to refine and expand upon our findings further.
All three reviewers acknowledged the increased processivity of SuperPol compared to its wild-type counterpart. However, two out of three questions our claims that premature termination of transcription can regulate ribosomal RNA transcription. This conclusion is based on SuperPol mutant increasing rRNA production. Proving that modulation of early transcription termination is used to regulate rRNA production under physiological conditions is beyond the scope of this study. Therefore, we propose to change the title of this manuscript to focus on what we have unambiguously demonstrated:
“Ribosomal RNA synthesis by RNA polymerase I is subjected to premature termination of transcription”.
Reviewer 1 main criticisms centers on the use of the CRAC technique in our study. While we address this point in detail below, we would like to emphasize that, although we agree with the reviewer’s comments regarding its application to Pol II studies, by limiting contamination with mature rRNA, CRAC remains the only suitable method for studying Pol I elongation over the entire transcription units. All other methods are massively contaminated with fragments of mature RNA which prevents any quantitative analysis of read distribution within rDNA. This perspective is widely accepted within the Pol I research community, as CRAC provides a robust approach to capturing transcriptional dynamics specific to Pol I activity.
We hope that these findings will resonate with the readership of your journal and contribute significantly to advancing discussions in transcription biology and related fields.
2. Description of the planned revisions
Despite numerous text modification (see below), we agree that one major point of discussion is the consequence of increased processivity in SuperPol mutant on the “quality” of produced rRNA. Reviewer 3 suggested comparisons with other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al., 2006). This comparison has already been addressed by the Schneider lab (Viktorovskaya OV, Cell Rep., 2013 - PMID: 23994471), which explored Pol II (rpb1-E1103G) and Pol I (rpa190-E1224G). The rpa190-E1224G mutant revealed enhanced pausing in vitro, highlighting key differences between Pol I and Pol II catalytic rate-limiting steps (see David Schneider's review on this topic for further details).
Reviewer 2 and 3 suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Pol I mutant with decreased rRNA cleavage have been characterized previously, and resulted in increased error-rate. We already started to address this point. Preliminary results from *in vitro* experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively. This could provide valuable insights into the mechanistic differences between SuperPol and the wild-type enzyme. SuperPol is the first pol I mutant described with an increased processivity *in vitro* and *in vivo*, and we agree that this might be at the cost of a decreased fidelity.
Regulatory aspect of the process:
To address the reviewer’s remarks, we propose to test our model by performing experiments that would evaluate PTT levels in Pol I mutant’s or under different growth conditions. These experiments would provide crucial data to support our model, which suggests that PTT is a regulatory element of Pol I transcription. By demonstrating how PTT varies with environmental factors, we aim to strengthen the hypothesis that premature termination plays an important role in regulating Pol I activity.
We propose revising the title and conclusions of the manuscript. The updated version will better reflect the study's focus and temper claims regarding the regulatory aspects of termination events, while maintaining the value of our proposed model.
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3. Description of the revisions that have already been incorporated in the transferred manuscript
Some very important modifications have now been incorporated:
Statistical Analyses and CRAC Replicates:
Unlike reviewers 2 and 3, reviewer 1 suggests that we did not analyze the results statistically. In fact, the CRAC analyses were conducted in biological triplicate, ensuring robustness and reproducibility. The statistical analyses are presented in Figure 2C, which highlights significant findings supporting the fact WT Pol I and SuperPol distribution profiles are different. We CRAC replicates exhibit a high correlation and we confirmed significant effect in each region of interest (5’ETS, 18S.2, 25S.1 and 3’ ETS, Figure 1) to confirm consistency across experiments. We finally took care not to overinterpret the results, maintaining a rigorous and cautious approach in our analysis to ensure accurate conclusions.
CRAC vs. Net-seq:
Reviewer 1 ask to comment differences between CRAC and Net-seq. Both methods complement each other but serve different purposes depending on the biological question on the context of transcription analysis. Net-seq has originally been designed for Pol II analysis. It captures nascent RNAs but does not eliminate mature ribosomal RNAs (rRNAs), leading to high levels of contamination. While this is manageable for Pol II analysis (in silico elimination of reads corresponding to rRNAs), it poses a significant problem for Pol I due to the dominance of rRNAs (60% of total RNAs in yeast), which share sequences with nascent Pol I transcripts. As a result, large Net-seq peaks are observed at mature rRNA extremities (Clarke 2018, Jacobs 2022). This limits the interpretation of the results to the short lived pre-rRNA species. In contrast, CRAC has been specifically adapted by the laboratory of David Tollervey to map Pol I distribution while minimizing contamination from mature rRNAs (The CRAC protocol used exclusively recovers RNAs with 3′ hydroxyl groups that represent endogenous 3′ ends of nascent transcripts, thus removing RNAs with 3’-Phosphate, found in mature rRNAs). This makes CRAC more suitable for studying Pol I transcription, including polymerase pausing and distribution along rDNA, providing quantitative dataset for the entire rDNA gene.
CRAC vs. Other Methods:
Reviewer 1 suggests using GRO-seq or TT-seq, but the experiments in Figure 2 aim to assess the distribution profile of Pol I along the rDNA, which requires a method optimized for this specific purpose. While GRO-seq and TT-seq are excellent for measuring RNA synthesis and co-transcriptional processing, they rely on Sarkosyl treatment to permeabilize cellular and nuclear membranes. Sarkosyl is known to artificially induces polymerase pausing and inhibits RNase activities which are involved in the process. To avoid these artifacts, CRAC analysis is a direct and fully in vivo approach. In CRAC experiment, cells are grown exponentially in rich media and arrested via rapid cross-linking, providing precise and artifact-free data on Pol I activity and pausing.
Pol I ChIP Signal Comparison:
The ChIP experiments previously published in Darrière et al. lack the statistical depth and resolution offered by our CRAC analyses. The detailed results obtained through CRAC would have been impossible to detect using classical ChIP. The current study provides a more refined and precise understanding of Pol I distribution and dynamics, highlighting the advantages of CRAC over traditional methods in addressing these complex transcriptional processes.
BMH-21 Effects:
As highlighted by Reviewer 1, the effects of BMH-21 observed in our study differ slightly from those reported in earlier work (Ref Schneider 2022), likely due to variations in experimental conditions, such as methodologies (CRAC vs. Net-seq), as discussed earlier. We also identified variations in the response to BMH-21 treatment associated with differences in cell growth phases and/or cell density. These factors likely contribute to the observed discrepancies, offering a potential explanation for the variations between our findings and those reported in previous studies. In our approach, we prioritized reproducibility by carefully controlling BMH-21 experimental conditions to mitigate these factors. These variables can significantly influence results, potentially leading to subtle discrepancies. Nevertheless, the overall conclusions regarding BMH-21's effects on WT Pol I are largely consistent across studies, with differences primarily observed at the nucleotide resolution. This is a strength of our CRAC-based analysis, which provides precise insights into Pol I activity.
We will address these nuances in the revised manuscript to clarify how such differences may impact results and provide context for interpreting our findings in light of previous studies.
Minor points:
Reviewer #1:
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In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example: On page 14: "SuperPol accumulation is decreased (compared to Pol I)". • *On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph." *
We clarified and increased the global writing style according to reviewer comment.
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*There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references. *
This was corrected in this version of the manuscript.
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*In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA? *
Data are effectively normalized relative to the 5S rRNA, but the value for the 18S rRNA is arbitrarily set to 100%.
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Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling.
The presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. In the absence of these subunits (which can vary depending on the purification batch), Pol I pausing, cleavage and elongation are known to be affected. To strengthen our conclusion, we really wanted to show the subunit composition of the purified enzyme. This important control should be shown, but can indeed be shown in a supplementary figure if desired.
Y-axis is figure 7D is now correctly labelled
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*In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result? *
The 140 nt product is a degradation fragment resulting from trimming, which explains its lower accumulation in the absence of Rrp6. BMH21 significantly affects WT Pol I transcription but has also a mild effect on SuperPol transcription. As a result, the 140 nt product accumulates under these conditions.
Reviewer #2:
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*pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions of the rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates" *
According to reviewer, statement has been simplified in this version of the manuscript.
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*Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors. *
We agree that this is a very important discussion of our results. Transcription is a very dynamic process in which paused polymerase is easily detected using the CRAC assay. Elongated polymerases are distributed over a much larger gene body, and even a small amount of polymerase detected in the gene body can represent a very large rRNA synthesis. This point is of paramount importance and, as suggested by the reviewer, is now discussed in detail.
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*A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility? *
Reviewer suggested that a decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. We already started to address this point. Preliminary results from in vitro experiments suggest that SuperPol mutants exhibit an elevated error rate during transcription. However, these findings remain preliminary and require further experimental validation to confirm their reproducibility and robustness. We propose to consolidate these data and incorporate into the manuscript to address this question comprehensively.
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*pp. 15 and 22: Premature transcription termination as a regulator of gene expression is well-documented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research. *
According to reviewer suggestion, we referenced these studies.
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*p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement. *
This represents some unpublished data from our lab. KR and SuperPol are the only two known mutants resistant to BMH-21. We observed that resistance between both alleles is synergistic, with a much higher resistance to BMH-21 in the double mutant than in each single mutant (data not shown). Comparing their resistance mechanisms is a very important point that we could provide upon request. This was added to the statement.
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*p. 23: "The released of the premature transcript" - this phrase contains a typo *
This is now corrected.
Reviewer #3:
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*Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes. *
The precise objective of this experiment is to avoid the use of the Rrp6 mutant. Under these conditions, we prevent the accumulation of transcripts that would result from a maturation defect. While it is possible to conduct the experiment with the Rrp6 mutant, it would be impossible to draw reliable conclusions due to this artificial accumulation of transcripts.
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*Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly. *
__We corrected Figure 1A. __
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*Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture. *
We addressed this point to make the figure more visually informative. In Northern Blot analysis, we use a TSS (Transcription Start Site) probe, which detects only transcripts containing the 5' extremity. Due to co-transcriptional processing, most of the rRNA undergoing transcription lacks its 5' extremity and is not detectable using this technique. We have the data, but it does not show any difference between Pol I and SuperPol. This information could be included in the supplementary data if asked.
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*"Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C. *
Thanks for pointing out this mistake. it has been corrected.
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*Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region. *
We agree that such an analysis would have been interesting. However, with the exception of the pre-rRNA starting at the transcription start site (TSS) studied here, any cleaved rRNA at its 5' end could result from premature termination and/or abnormal processing events. Exploring the production of other abnormal rRNAs produced by premature termination is a project in itself, beyond this initial work aimed at demonstrating the existence of premature termination events in ribosomal RNA production.
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*Figure 4: should probably be provided as supplementary material. *
As lmentioned earlier (see comments), ____the presence of all Pol I specific subunits (Rpa12, Rpa34 and Rpa49) is crucial for the enzymatic activity we performed. This important control should be shown, but can indeed be shown in a supplementary figure if desired.
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*"While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected. *
We agree with this comment and we modified the text accordingly.
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*Figure 7D: the legend of the y-axis is missing as well as the title of the plot. *
Legend of the y-axis and title of the plot are now present.
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The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.
This was discussed previously. See comment above.
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*Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b. *
Bibliography is now fixed
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4. Description of analyses that authors prefer not to carry out
Does SuperPol mutant produces more functional rRNAs ?
As Reviewer 1 requested, we agree that this point requires clarification. In cells expressing SuperPol, a higher steady state of (pre)-rRNAs is only observed in absence of degradation machinery suggesting that overproduced rRNAs are rapidly eliminated. We know that (pre)-rRNas are unable to accumulate in absence of ribosomal proteins and/or Assembly Factors (AF). In consequence, overproducing rRNAs would not be sufficient to increase ribosome content. This specific point is further address in our lab but is beyond the scope of this article.
__Is premature termination coupled with rRNA processing __
We appreciate the reviewer’s insightful comments. The suggested experiments regarding the UTP-A complex's regulatory potential are valuable and ongoing in our lab, but they extend beyond the scope of this study and are not suitable for inclusion in the current manuscript.
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Referee #3
Evidence, reproducibility and clarity
In the manuscript "Ribosomal RNA synthesis by RNA polymerase I is regulated by premature termination of transcription", Azouzi and co-authors investigate the regulatory mechanisms of ribosomal RNA (rRNA) transcription by RNA Polymerase I (RNAPI) in the budding yeast S. cerevisiae. They follow up on exploring the molecular basis of a mutant allele of the second largest subunit of RNAPI, RPA135-F301S, also dubbed SuperPol, that they had previously reported (Darrière et al, 2019), and which was shown to rescue Rpa49-linked growth defects, possibly by increasing rRNA production.
Through a combination of genomic and in vitro approaches, the authors test the hypothesis that RNAPI activity could be subjected to a Premature Transcription Termination (PPT) mechanism, akin to what is observed for RNA Polymerase II (RNAPII), and which is suggested to be an important step for the quality control of rRNA transcripts. SuperPol is proposed to lack such a regulatory mechanism, due to an increased processivity. In agreement, SuperPol is shown to be resistant to BMH-21, a drug previously shown to impair RNAPI elongation.
Overall, the experiments are performed with rigor and include the appropriate controls and statistical analysis. Both the figures and the text present the data clearly. The Material and Methods section is detailed enough. The reported results are interesting; however, I am not fully convinced of the existence of PPT of RNAPI, and even less of its utmost importance. The existence of PPT of RNAPI would entail an intended regulatory mechanism. The authors propose that PPT could serve as quality control step for the UTP-A complex loading on the rRNA 5'-end. While this hypothesis is enticing and cautiously phrased by the authors, the lack of evidence showing a specific regulatory function (such as UTP-A loading checkpoint or else) limits these termination events to possibly abortive actions of unclear significance. The auhors may want to consider comparisons to other processive alleles, such as the rpb1-E1103G mutant of the RNAPII subunit (Malagon et al, 2006) or the G1136S allele of E. coli RNAP (Bar-Nahum et al., 2005). While clearly mechanistically distinct, these mutations result in similarly processive enzymes that achieve more robust transcription, possibly at the cost of decreased fidelity. Indeed, an alternative possibility explaining these transcripts could be that they originate from unsuccessful resumption of transcription after misincorporation (see below).
I suggest reconsidering the study's main conclusions by limiting claims about the regulatory function of these termination events (the title of the manuscript should be changed accordingly). Alternatively, the authors should provide additional investigation on their regulatory potential, for example by assessing if indeed this quality control is linked to the correct assembly of the UTP-A complex. The expectation would be that SuperPol should rescue at least to some extent the defects observed in the absence of UTP-A components. Moreover, the results using the clv3 substrate suggest the possibility that SuperPol might simply be more able to tolerate mismatches, thus be more processive in transcribing, because not subjected to proof-reading mechanisms, similarly to what observed in Schwank et al., 2022. This could explain many of the observations, and I think it is worth exploring by assessing the fidelity of the enzyme, especially in the frame of suggesting a regulatory function for these termination events.
Minor comments
- Figure 1B: it would be opportune to separate the technique's schematic representation from the actual data. Concerning the data, would the authors consider adding an experiment with rrp6D cells? Some RNAs could be degraded even in such short period of time, as even stated by the authors, so maybe an exosome depleted background could provide a more complete picture. Could also the authors explain why the increase is only observed at the level of 18S and 25S? To further prove the robustness of the Pol I TMA method could be good to add already characterized mutations or other drugs to show that the technique can readily detect also well-known and expected changes.
- Figure 1C: the NTS1 probe signal is missing (it is referenced in Figure 1A but not listed in the Methods section or the oligo table). If this probe was unused, please correct Figure 1A accordingly.
- Figure 2A: the RNAPI occupancy map by CRAC is hard to interpret. The red color (SuperPol) is stacked on top of the blue line, and we are not able to observe the signal of the WT for most of the position along the rDNA unit. It would be preferable to use some kind of opacity that allows to visualize both curves. Moreover, the analysis of the behavior of the polymerase is always restricted to the 5'ETS region in the rest of the manuscript. We are thus not able to observe whether termination events also occur in other regions of the rDNA unit. A Northern blot analysis displaying higher sizes would provide a more complete picture.
- "Importantly, despite some local variations, we could reproducibly observe an increased occupancy of WT Pol I in 5'-ETS compared to SuperPol (Figure 1C)." should be Figure 2C.
- Figure 3D: most of the difference in the cumulative proportion of CRAC reads is observed in the region ~750 to 3000. In line with my previous point, I think it would be worth exploring also termination events beyond the 5'-ETS region.
- Figure 4: should probably be provided as supplementary material.
- "While the growth of cells expressing SuperPol appeared unaffected, the fitness of WT cells was severely reduced under the same conditions." I think the growth of cells expressing SuperPol is slightly affected.
- Figure 6B: can the authors explain why most of bands detected in their Pol I TMA assay in Figure 6B are unchanged? It is unclear to me why only the 18S and 25S bands are decreased following BMH-21 treatment. Moreover, this experiment lacks the corresponding quantification and statistical tests.
- Figure 7D: the legend of the y-axis is missing as well as the title of the plot.
- The statements concerning BMH-21, SuperPol and Rpa190-KR in the Discussion section should be removed, or data should be provided.
- Some references are missing from the Bibliography, for example Merkl et al., 2020; Pilsl et al., 2016a, 2016b.
Significance
Azouzi and co-authors' work builds on their previous study (Darrière et al, 2019) of RPA135-F301S (SuperPol), a mutant allele of the second largest RNAPI subunit, which was shown to compensate for Rpa49 loss, potentially by increasing rRNA production. The work advances the mechanistic understanding of the the SuperPol allele, demonstrating the increased processivity of this enzyme compared to its wild-type counterpart. Such increased processivity "desensitizes" RNAPI from abortive transcription cycles, the existence of which is clearly shown, though the biological significance of this phenomenon remains unclear. The lack of evidence for a regulatory mechanism behind these early termination events is, in my opinion, a limitation of this study, as it does not allow for differentiation between an intended regulatory process and a byproduct of an imperfect system.
This work is of interest for researchers studying transcription regulation, particularly those interested in understanding RNAPI's role and fidelity. Demonstrating PPT as a regulatory quality control for RNAPI could point to common strategies in between RNAPI and RNAPII regulation, where premature termination has been extensively documented. However, without evidence of a specific regulatory function, these findings may currently be limited to descriptive insights.
My expertise lies is RNAPII transcription, transcription termination, and genomic approaches to studying transcription.
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Referee #2
Evidence, reproducibility and clarity
This article presents a study on a mutant form of RNA polymerase I (RNAPI) in yeast, referred to as SuperPol, which demonstrates increased rRNA production compared to the wild-type enzyme. While rRNA production levels are elevated in the mutant, RNAPI occupancy as detected by CRAC is reduced at the 5' end of rDNA transcription units. The authors interpret these findings by proposing that the wild-type RNAPI pauses in the external transcribed spacer (ETS), leading to premature transcription termination (PTT) and degradation of truncated rRNAs by the RNA exosome (Rrp6). They further show that SuperPol's enhanced activity is linked to a lower frequency of PTT events, likely due to altered elongation dynamics and reduced RNA cleavage activity, as supported by both in vivo and in vitro data.
The study also examines the impact of BMH-21, a drug known to inhibit Pol I elongation, and shows that SuperPol is less sensitive to this drug, as demonstrated through genetic, biochemical, and in vivo approaches. The authors show that BMH-21 treatment induces premature termination in wild-type Pol I, but only to a lesser extent in SuperPol. They suggest that BMH-21 promotes termination by targeting paused Pol I complexes and propose that PTT is an important regulatory mechanism for rRNA production in yeast. The data presented are of high quality and support the notion that 1) premature transcription termination occurs at the 5' end of rDNA transcription units; 2) SuperPol has an increased elongation rate with reduced premature termination; and 3) BMH-21 promotes both pausing and termination. The authors employ several complementary methods, including in vitro transcription assays. These results are significant and of interest for a broad audience. Beyond the minor points listed below, my main criticism concerns the interpretation of data in relation to termination. While it is possible that the SuperPol mutation affects the wild-type Pol I's natural propensity for termination, it is also possible that premature termination is simply a consequence of natural or BMH-21-induced Pol I pausing. SuperPol may elongate more efficiently than the wild-type enzyme, pause less frequently, and thus terminate less often. In this light, the notion that termination "regulates" rRNA production might be an overstatement, with pausing as the primary event. Claiming a direct effect on termination by both the mutation and BMH-21 would require showing that with equivalent levels of pausing, termination occurs more or less efficiently, which would be challenging and should not be expected in this study. The authors address this point in the last two paragraphs of the discussion. My suggestion is to temper the claims regarding termination as a regulatory mechanism.
Minor points
- pp. 14-15: The authors note local differences in peak detection in the 5'-ETS among replicates, preventing a nucleotide-resolution analysis of pausing sites. Still, they report consistent global differences between wild-type and SuperPol CRAC signals in the 5'ETS (and other regions o fthe rDNA). These global differences are clear in the quantification shown in Figures 2B-C. A simpler statement might be less confusing, avoiding references to a "first and second set of replicates"
- Figures 2A and 2C: Based on these data and quantification, it appears that SuperPol signals in the body and 3' end of the rDNA unit are higher than those in the wild type. This finding supports the conclusion that reduced pausing (and termination) in the 5'ETS leads to an increased Pol I signal downstream. Since the average increase in the SuperPol signal is distributed over a larger region, this might also explain why even a relatively modest decrease in 5'ETS pausing results in higher rRNA production. This point merits discussion by the authors.
- A decreased efficiency of cleavage upon backtracking might imply an increased error rate in SuperPol compared to the wild-type enzyme. Have the authors observed any evidence supporting this possibility?
- pp. 15 and 22: Premature transcription termination as a regulator of gene expression is well-documented in yeast, with significant contributions from the Corden, Brow, Libri, and Tollervey labs. These studies should be referenced along with relevant bacterial and mammalian research.
- p. 23: "SuperPol and Rpa190-KR have a synergistic effect on BMH-21 resistance." A citation should be added for this statement.
- p. 23: "The released of the premature transcript" - this phrase contains a typo
Significance
These results are significant and of interest for a basic research audience.
This referee has expertise in RNA biology, Pol II transcription and termination.
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Referee #1
Evidence, reproducibility and clarity
The study characterises an RNA polymerase (Pol) I mutant (RPA135-F301S) named SuperPol. This mutant was previously shown to increase yeast ribosomal RNA (rRNA) production by Transcription Run-On (TRO). In this work, the authors confirm this mutation increases rRNA transcription using a slight variation of the TRO method, Transcriptional Monitoring Assay (TMA), which also allows the analysis of partially degraded RNA molecules. The authors show a reduction of abortive rRNA transcription in cells expressing the SuperPol mutant and a modest occupancy decrease at the 5' region of the rRNA genes compared to WT Pol I. These results suggest that the SuperPol mutant displays a lower frequency of premature termination. Using in vitro assays, the authors found that the mutation induces an enhanced elongation speed and a lower cleavage activity on mismatched nucleotides at the 3' end of the RNA. Finally, SuperPol mutant was found to be less sensitive to BMH-21, a DNA intercalating agent that blocks Pol I transcription and triggers the degradation of the Pol I subunit, Rpa190. Compared to WT Pol I, short BMH-21 treatment has little effect on SuperPol transcription activity, and consequently, SuperPol mutation decreases cell sensitivity to BMH-21.
I'd suggest the following points to be taken into consideration:
Major points:
- The differences in the transcriptionally engaged WT Pol I and SuperPol profiles (Figure 2) are very modest, without any statistical analyses. What is the correlation between CRAC replicates? Are they separated in PCA analyses? Please, include more quality control information. In my opinion, these results are not very convincing. Similarly, the effect of BMH-21 on WT Pol I activity (Figure 7) is also very subtle and doesn't match the effect observed in a previous study [1]. Could the author comment on the reasons for these differences? These discrepancies raise concerns about the methodology. In addition, according to the laboratory's previous work [2], Pol I ChIP signal at rDNA is not significantly different in cells expressing WT Pol I and SuperPol. How can these two observations be reconciled? I would suggest using an independent methodology to analyse Pol I transcription, for example, GRO-seq or TT-seq.
- While the experiments clearly show SuperPol mutant increases nascent transcription and decreases the production of abortive promoter-proximal transcripts compared to WT Pol I. RPA135-F301S mutation has a minor impact on total rRNA levels, at least those shown in Figure 3B. Are steady-state rRNA levels higher in cells expressing SuperPol mutant? It would be interesting to know if SuperPol mutant produces more functional rRNAs.
Minor points
- In general, the writing style is not clear, and there are some word mistakes or poor descriptions of the results, for example:<br /> On page 14: "SuperPol accumulation is decreased (compared to Pol I)". On page 16: "Compared to WT Pol I, the cumulative distribution of SuperPol is indeed shifted on the right of the graph."
- There are also issues with the literature, for example: Turowski et al, 2020a and Turowski et al, 2020b are the same article (preprint and peer-reviewed). Is there any reason to include both references? Please, double-check the references.
- In the manuscript, 5S rRNA is mentioned as an internal control for TMA normalisation. Why are Figure 1C data normalised to 18S rRNA instead of 5S rRNA?
- Figure 4 should be a supplementary figure, and Figure 7D doesn't have a y-axis labelling.
- In Figure 7C, BMH-21 treatment causes the accumulation of ~140bp rRNA transcripts only in SuperPol-expressing cells that are Rrp6-sensitive (line 6 vs line 8), suggesting that BHM-21 treatment does affect SuperPol. Could the author comment on the interpretation of this result?
References
- Jacobs RQ, Huffines AK, Laiho M & Schneider DA (2022) The small-molecule BMH-21 directly inhibits transcription elongation and DNA occupancy of RNA polymerase I in vivo and in vitro. J. Biol. Chem. 298: 101450
- Darrière T, Pilsl M, Sarthou M-K, Chauvier A, Genty T, Audibert S, Dez C, Léger-Silvestre I, Normand C, Henras AK, Kwapisz M, Calvo O, Fernández-Tornero C, Tschochner H & Gadal O (2019) Genetic analyses led to the discovery of a super-active mutant of the RNA polymerase I. PLoS Genet. 15: e1008157
Significance
The work further characterises a single amino acid mutation of one of the largest yeast Pol I subunits (RPA135-F301S). While this mutation was previously shown to increase rRNA synthesis, the current work expands the SuperPol mutant characterisation, providing details of how RPA135-F301S modifies the enzymatic properties of yeast Pol I. In addition, their findings suggest that yeast Pol I transcription can be subjected to premature termination in vivo. The molecular basis and potential regulatory functions of this phenomenon could be explored in additional studies.
Our understanding of rRNA transcription is limited, and the findings of this work may be interesting to the transcription community. Moreover, targeting Pol I activity is an open strategy for cancer treatment. Thus, the resistance of SuperPol mutant to BMH-21 might also be of interest to a broader community, although these findings are yet to be confirmed in human Pol I and with more specific Pol I inhibitors in future.
My expertise is human Pol II and Pol III transcription regulation.
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Reply to the reviewers
• Reviewer #1 (Evidence, reproducibility and clarity (Required)): Summary:
In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; diKerent types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes.
The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.
Major comment:
[Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in Delta- Notch signaling, could the authors analyze the eKect of coupling delay on robustness of the segmentation clock against morphogenetic processes?
Response: We thank the reviewer for the suggestion. Owing to the computational demands of including such a delay in the model, we cannot feasibly repeat every simulation analysed here in the presence of delay, and would like to note that the increased computational demand that delays put on the simulations is also the reason why Uriu et al 2021 did not include it, as stated in their published exchange with reviewers. However, analogous to our analysis in figure 7, we can analyse how varying the position of progenitor cell ingression aKects synchrony in the presence of the coupling delay measured in zebrafish by Herrgen et al. (2010). We show this analysis in a new figure 8 (8B, specifically), on page 21, and discuss its implications in the text on pages 20- 22. Our analysis reveals that the model cannot recover synchrony using the default parameters used by Uriu et al. (2021) and reveal a much stronger dependence on the rate of cell mixing (vs) than shown in the instantaneous coupling case (cf. figure 7). However, by systematically varying the value of the delay we find that a relatively minor increase in the delay is suKicient to recover synchrony using the parameter set of Uriu et al. (see figure 8C). Repeating this across the three scenarios of cell ingression we see that the combination of coupling strength and delay determine the robustness of synchrony to varying position of cell ingression. This suggests that the combination of these two parameters constrain the evolution of morphogenesis.
Minor comments:
- PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.
Response: We thank the reviewer for spotting this. This has now been changed throughout to rT, as shorthand for ‘radius of tissue’.
- page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.
Response: We thank the reviewer for spotting this. This has now been corrected.
- Figure 3C: Description of black crosses in the panels is required in the figure legend.
Response: Thank you for spotting this. The legend has now been corrected.
- Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?
Response: We thank the reviewer for the suggestion. We have now plotted the synchrony along the AP axis for varying motility profiles, and this can be seen in figure 3 supplement 1, and is briefly discussed in the text on page 11. We show that while the synchrony varies with x-position (as already expected, see figure 2), there is no trend associated with the shape of the motility profile.
- In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.
Response: These results can be exactly equal despite the simulations being stochastic because they were both initialised using the same ‘seed’ in the source code. However, we now see that this might be confusing to the reader, and we have re-generated this figure but this time initialising the simulations for each ingression scenario using a diKerent seed value. This is now reflected in the text on page 12 and in figure 4.
- The authors analyze the eKect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbours. I think it would be helpful to plot the average number of neighbouring cells in simulations as a function of density to quantitatively support the claim.
Response: We thank the reviewer for their suggestion. Distributions of neighbour numbers for exemplar simulations with varying density can now be found in figure 4 supplementary figure 1 and are referred to in the text on page 11.
- The authors analyze the eKect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.
Response: We thank the reviewer for their suggestion and agree that visualising the data in this way is an excellent idea. We have generated the suggested kymographs and added them to figure 4 as supplements 2 and 4, and discussed these results in the text on page 12.
- I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.
Response: We agree this is an important clarification. We explicitly state this, and briefly justify our choice, in the text on page 16.
- Figure 5-figure supplement 2: panel labels A, B, C are missing.
Response: Thank you for bringing this to our attention. These have now been added.
- Figure 5-figure supplement 3: panel labels A, B, C are missing.
Response: Thank you for bringing this to our attention. These have now been added.
• Reviewer #1 (Significance (Required)):
Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.
Response: We thank the reviewer for their interest in our manuscript and for acknowledging us as one of the first to address the modularity and evolvability of somitogenesis. We hope that this work will encourage others to think about these concepts in this system too. In the original submission, we identified a high enough coupling strength as the main mechanism underlying the identified modularity in somitogenesis. Since, we have included an analysis of the coupling delay and find that it is the interplay between coupling strength and coupling delay that mediate the identified modularity, allowing PSM morphogenesis and the segmentation clock to evolve independently in regions of parameter space that are constrained and determined by the interplay between these two parameters. We have now added an extra figure (figure 8) where we explore this interplay and have discussed it at length in the last section of the results and in the discussion. We again thank the reviewer for encouraging us to include delays in our analysis.
• Reviewer #2 (Evidence, reproducibility and clarity (Required)):
SUMMARY
The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.
Methodology | The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes aKect the segmentation clock.
Model System | The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.
Key Findings and Conclusions | (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.
MAJOR COMMENTS
- The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained and seem reasonable assumptions (from the embryology perspective).
Response: We thank the reviewer for their positive comments about our work
- This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cell-cell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.
Response: We would like to clarify that the model does include cell-cell interactions as cells interact with their neighbours’ clock phase to synchronise and to avoid occupying the same physical space.
- The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.
Response: We support open source coding practices.
- In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'oK' phase of the clock will ingressing cells be in-phase with their neighbours."
However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853])
Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbours when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).
Response: We thank the reviewer for their comments. While it is known that in zebrafish the clock begins oscillating during epiboly and before the onset of segmentation (Riedel-Kruse et al., 2007), to our knowledge no-one has examined whether posteriorly or laterally ingressing progenitor cells express clock genes prior to their ingression into the PSM, which occurs later in development than the first oscillations which give rise to the first somites. We have not found any published evidence of her/hes gene expression in the dorsal donor tissues or lateral tissues surrounding the PSM, however we acknowledge that this has not been actively studied before and our assumption relies on an absence of evidence, rather than evidence of absence.
However, we agree with the reviewer that one should include such an analysis for completeness, and we have now generated additional simulations where progenitor cells ingress with a random clock phase. This data is presented in figure 2 supplement 1 and mentioned in the main text on page 9.
MINOR COMMENTS:
- The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).
Response: We have cited the vast literature on somitogenesis to the best of our ability and do recognise that the work of the Oates lab appears prominently, but this is probably because their experimental data were originally used to parametrise the model in Uriu et al. 2021.
The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.
Response: We are very glad to see that the reviewer found that the manuscript was clearly presented.
- The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.
Response: Thank you
Minor suggestions:
- Page 26: In the Cell addition sub-section of the Methods section, correct all
instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).
Response: We thank the reviewer for raising this, this is a good point. We have now corrected to ‘subdomain’ where appropriate.
- Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).
Response: Thank you for the suggstion, we have now done this (see table 1 page 36).
**Referee Cross-commenting**
I carefully read the reports provided by my fellow reviewers. My cross-comments aim to enhance the collective evaluation of the manuscript by Hammond et al.
• On reviewer #1's Comments:
I agree with Reviewer #1's overall evaluation of the manuscript's value and relevance, and with their general comments. I particularly support the suggestion to optionally include coupling delays known to influence the clock's period, as this would improve the model's completeness and benefit the research community. I also view this as an optional but desirable addition, not mandatory.
Response: As per reviewer #1’s suggestion, we have now included this analysis (figure 8).
In Fig. 4, I agree that showing kymographs, similar to Fig. 2D, for each PSM length would greatly improve the visualization of the results, given the relevance of this result to the manuscript's main message.
Response: As per reviewer #1’s suggestion, we have now included such an analysis (figure 4 supplements 2 and 4) and agree with both reviewers that they improve the communication of our results.
The remaining minor comments are useful and relevant to improving the manuscript.
• On reviewer #3's Comments:
Although I agree with Reviewer #3 that the paper is somewhat lengthy, I find the detailed description of the model in its biological context necessary and welcomed by the embryology research community. Without this detail, the paper might be too 'dry' and lose part of its audience. Conversely, focusing mostly on embryology without detailing the model parameters and simulation findings would deprive it of novelty and critical insights.
Response: We thank Reviewer #2 for this assessment, which we agree with. Nonetheless we have sought to streamline our writing throughout to increase clarity without reducing the content.
Overall, I find Reviewer #3's suggestions scientifically interesting, particularly comments 3, 4, and 5, which express legitimate questions for future study. However, I find them tangential to the main question addressed in this manuscript, which pertains to the modularity of the segmentation clock and morphogenesis. Therefore, I do not see them as significant improvements for the authors to implement in the current study.
Response: We thank Reviewer #2 for their comments here and refer them to our responses to Reviewer #3.
I would like to know how the authors respond to comments 1 and 2, which I do not have the expertise to evaluate.
Response: We have now addressed these concerns in our response to Reviewer #3. Please see below.
I agree with comment 6 that a brief mention of the known pathways/gene networks to which the assumptions apply (in zebrafish) would be a good addition. However, I do not think a detailed discussion is needed, as specific genes/networks can be diKerent for diKerent organisms.
Response: We now justify this assumption in the methods on page 32.
I disagree with comment 7, as Fig. 3 shows that the clock is robust to changes in cell ingression regime across all cell motility profiles tested. This is an important result for the manuscript's take home message, and should remain in the main text, not as a supplementary figure.
Response: We agree with Reviewer #2 and have included this in our response to Reviewer #3.
Finally, regarding Reviewer #3's concern about the incompleteness of the results, I find the results robust given the formalism chosen and within the scenarios where the assumptions hold. I believe this concern applies to the formalism (which is a choice) and not to the quality or relevance of the work presented in the manuscript. Additionally, some of the model's limitations have been adequately addressed by the authors.
Response: We thank Reviewer #2 for their comments.
• Reviewer #2 (Significance (Required)): GENERAL ASSESSMENT
- This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.
ADVANCE
- This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern.
However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.
- The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.
AUDIENCE
- Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.
Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular
mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from diKerent vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.
MY EXPERTISE
My areas of research (relevant for this study): Vertebrate embryo clock oscillations in Gallus gallus; Computational biology.
I can evaluate the relevance and validity of the model, critically evaluate its outputs and parameters, and the significance of the model assumptions for drawing relevant biological insights; however, I am not an expert on this mathematical formalism.
• Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to diKerent biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy. Below are the comments I would like the authors to address:
- The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the eKects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the eKect of diKerent parameters in greater detail.
Response: One of the main objectives of the work we present in this manuscript is to assess how the evolution of PSM morphogenesis affects, or does not affect, segment patterning. The PSM is a three-dimensional tissue with diKering cell rearrangement dynamics along its anterior-posterior axis. In addition, PSM dimension, density, the rearrangement rate, and patterns of cell ingression all vary across vertebrate species, and they are functional, especially cell mixing as it promotes synchronisation and drives elongation. In order to answer questions on the modularity of somitogenesis we therefore consider it absolutely necessary to include a three-dimensional representation of the PSM thatcaptures single cells and their movements. In addition, this will allow us, as Reviewer #2 also pointed out, to reparametrize our model using species-specific data as it becomes available.
While the reviewer is right in that lower dimensional representations would be computationally more efficient, and are generally more tractable, it would not be possible to represent cell mixing in one dimension, as this happens in three dimensions. One could perhaps encode the synchrony-promoting eKect of cell mixing via some coupling function κ(x) that increases towards the posterior, however it is unclear what existing biological data one could use to parameterise this function or determine its form. Cell mixing can be modelled in a two-dimensional framework, however this cannot quantitatively recapture the rate of cell mixing observed in vivo, which is an advantage of this model.
Furthermore, it is unclear how one would simulate processes such as compaction- extension using a one-dimensional model. The two diKerent scenarios of cell ingression which we consider can also not be replicated in a one-dimensional model, as having a population of cells re-acquiring synchrony on the dorsal surface of the tissue while new material is added to the ventral side, creating asynchrony, is qualitatively diKerent than a one-dimensional scenario where cells are introduced continuously along the spatial axis.
I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?
Response: Indeed, such a metric (e.g. as that used by Uriu et al. to quantify synchrony along the x- axis) would be more accurate for determining synchrony within the PSM. However, as per the clock and wavefront model of somitogenesis, only synchrony at the very anterior of the PSM (or at the wavefront, equivalently) is functional for somitogenesis and thus evolution. Therefore, we restrict our analysis to the anterior-most region of the PSM. We now further justify this in the main text on page 9.
While studying the eKect of cellular ingression, the authors study three discrete modes- random, DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes aKected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.
Response: We thank the reviewer for this suggestion; this is a very interesting question. We are currently working on a related computational and experimental project to address the question of how PSM morphogenesis can change over evolutionary time to evolve the diKerent modes that we see across species. As part of this work, we are running precisely the simulations suggested by the reviewer to find regions of parameter space in which all the relevant morphogenetic processes can freely evolve. While interesting, this work is however outside the scope of the current manuscript.
While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two-dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.
Response: We thank the reviewer for their suggestion and agree that this would constitute an interesting addition to the manuscript. We have now generated these data, which are shown in figure 4 supplement 5 and mentioned on page 13. We see no clear relationship between these two variables when co-varying in the presence of random ingression.
Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?
Response: We thank the reviewer for this question. It was not clear to us if this was something the reviewer wants us to address in the study’s background and introduction, or an analysis we should include in the results. Therefore, we have responded to both comprehensively below:
The prevailing conceptual framework for understanding this is the clock and wavefront model (Cooke and Zeeman, 1976), which posits that the somite length is inversely proportional to the frequency of the clock relative to the speed of the wavefront, and that the total number of segments is the relative frequency multiplied by the total duration of somitogenesis.
Experimentally we know that the frequency is determined in part by the coupling strength (Liao, Jorg, and Oates, 2016), and from comparative embryological studies (Gomez et al., 2008; Steventon et al., 2016) we know that changes in the elongation dynamics of the PSM correlate with changes in somite number, presumably by altering the total duration of somitogenesis (Gomez et al., 2009). These changes in elongation are thought to be driven by the changes in cell and tissue mechanics we test in our manuscript.
Within our model, we cannot in general predict how the number of segments responds to changes in either clock parameters or cell mechanical parameters, as we lack understanding of what causes somitogenesis to cease; this is thus not encoded in our model and segmentation can in principle proceed indefinitely. Therefore, we have not performed this analysis.
Similarly, we have not included an analysis of somite length. This is for two reasons: 1) as per the clock and wavefront model, the frequency at the PSM anterior (which we analyse) is equivalent to this measurement, as we assume (in general) the wavefront ($x = x_{a}$) is inertial. 2) the length of the nascent somite is not thought to be of much relevance to the adult phenotype, and by extension evolution. Somites undergo cell division and growth soon after their patterning by the segmentation clock, therefore their final size does not majorly depend on the dynamics of the segmentation clock. Rather, the main function of the clock is to control their number (and polarity).
The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail, for which chemical networks this is a good assumption.
Response: We thank the reviewer for their suggestion and now justify this assumption in the methods on page 31.
Such an assumption is appropriate for the segmentation clock, as the clock in the posterior of the PSM is thought to oscillate with a constant frequency, at least for the majority of somitogenesis although the frequency of somite formation slows towards the end of this process in zebrafish (Giudicelli et al., 2007, PLoS Biol.). In addition, PSM cells isolated and cultured in the presence of FGF (thus replicating the signalling environment of the posterior PSM) will continue to exhibit her1 oscillations with an apparently constant frequency (Webb et al., 2016).
We note that such formulations are widely used within the segmentation clock literature (e.g. Riedel-Kruse et al., 2007, Morelli et al., 2009).
Figure 3 and the associated text shows no eKect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.
Response: Thank you for the suggestion. However, we would argue that the lack of eKect is a crucial result when discussing modularity. Reviewer #2 agrees with this assessment.
• Reviewer #3 (Significance (Required)):
The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.
My research interests are building physics and engineering based models of cell and tissue scale biological phenomena.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, Verd and colleagues explored how various biologically relevant factors influence the robustness of clock dynamics synchronization among neighboring cells within the context of somatogenesis, adapting a mathematical model presented by Urio et. al in 2021 in a similar context. Specifically they show that clock dynamics is robust to different biological mechanisms such as cell infusion, cellular motility, compaction-extension and cell-division. On the other hand , the length of Presomitic Mesoderm (PSM) and density of cells in it has a significant role in the robustness of clock dynamics. While the manuscript is well-written and provides clear descriptions of methods and technical details, it tends to be somewhat lengthy. Below are the comments I would like the authors to address:
- The authors mention that "...the model is three dimensional and so can quantitatively recapture the rates of cell mixing that we observe in the PSM". I am not convinced with this justification of using a 3D model. None of the effects the authors explore in this manuscript requires a three dimensional model or full physical description of the cellular mechanics such as excluded volume interaction etc. A one-dimensional model characterized by cell position along the arclength of PSM and somatic region and segmentation clock phase θ can incorporate all the physics authors described in this manuscript as well as significantly computationally cheap allowing the authors to explore the effect of different parameters in greater detail.
- I am not sure about the justification for limiting the quantification of phase synchrony in a very limited (one cell diameter wide) region at one end of the somatic part (Page 33 below Fig. 9). From my understanding of the manuscript, the segments appear in significant length anterior to this region. Wouldn't an ensemble average of multiple such one cell diameter wide regions in the somatic region be a more accurate metric for quantifying synchrony?
- While studying the effect of cellular ingression, the authors study three discrete modes-random,DP and DP+LV and show that in the DP+LV mode the clock synchrony becomes affected. I would like the authors to explore this in a continuous fashion from a pure DP ingression to Pure LV ingression and intermediates.
- While studying the effect of length and density of cells in PSM on cellular synchrony, the authors restrict to 3 values of density and 6 values of PSM length keeping the other parameter constant. I would be interested to see a phase diagram similar to Fig. 7 in the two dimensional parameter space of L and ρ0. I am curious if a scaling relation exists for the parameter values that partition the parameter space with and without synchrony.
- Both in the abstract and introduction, the authors discuss at a great length about the variability in the number of segments. I am curious how the number and width of the segments observed depend on different parameters related to cellular mechanics and the segmentation clock ?
- The authors assume that the phase dynamics of the chemical network may be described by an oscillator with constant frequency. For the completeness of the manuscript, the author should discuss in detail,for which chemical networks this is a good assumption.
- Figure 3 and the associated text shows no effect of the cellular motility profile in the synchrony of the segmentation clock. This may be moved to the supplementary considering the length of this manuscript.
Significance
The manuscript answers some important questions in the synchrony of segmentation clock in the vertebrates utilizing a model published earlier. However, the presented result is incomplete in some aspects (points 2 to 5 of section A) and that could be overcome by a more detailed analysis using a simpler one dimensional (point 1 of section A). I believe this manuscript could be of interest to an intersecting audience of developmental biologists, systems biologists, and physicists/engineers interested in dynamical systems.
My research interests are building physics and engineering based models of cell and tissue scale biological phenomena
-
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Referee #2
Evidence, reproducibility and clarity
Summary
The manuscript from Hammond et al., investigates the modularity of the segmentation clock and morphogenesis in early vertebrate development, focusing on how these processes might independently evolve to influence the diversity of segment numbers across vertebrates.
Methodology | The study uses a previously published computational model, parameterized for zebrafish, to simulate and analyse the interactions between the segmentation clock and the morphogenesis of the pre-somitic mesoderm (PSM). Their model integrates cell advection, motility, compaction, cell division, and the synchronization of the embryo clock. Three alternative scenarios of PSM morphogenesis were modeled to examine how these changes affect the segmentation clock.
Model System | The computational model system combines a representation of cell movements and the phase oscillator dynamics of the segmentation clock within a three-dimensional horseshoe-shaped domain mimicking the geometry of the vertebrate embryo PSM. The parameters used for the mathematical model are mostly estimated from previously published experimental findings.
Key Findings and Conclusions | (1) The segmentation clock was found to be broadly robust against variations in morphogenetic processes such as cell ingression and motility; (2) Changes in the length of the PSM and the strength of phase coupling within the clock significantly influenced the system's robustness; (3) The authors conclude that the segmentation clock and PSM morphogenesis exhibited developmental modularity (i.e. relative independence), allowing these two phenomena to evolve independently, and therefore possibly contributing to the diverse segment numbers observed in vertebrates.
Major comments
- The key conclusion drawn by the authors (that there is robustness, and therefore modularity, between the morphogenetic cellular processes modeled and the embryo clock synchronization) stems directly from the modeling results appropriately presented and discussed in the manuscript. The model comprises some strong assumptions, however all have been clearly explained and the parameterization choices are supported by experimental findings, providing biological meaning to the model. Estimated parameters are well explained, and seem reasonable assumptions (from the embryology perspective).
- This study, as is, achieves its proposed goal of evaluating the potential robustness of the embryo clock to changes in (some) morphogenetic processes. The authors do not claim that the model used is complete, and they properly identify some limitations, including the lack of cell-cell interactions. Given the recognized importance of cellular physical interactions for successful embryo development, including them in the model would be a significant addition in future studies.
- The authors have deposited all the code used for analysis in a public GitHub repository that is updated and available for the research community.
- In page 6, the authors justify their choice of clock parameters for cells ingressing the PSM: "As ingressing cells do not appear to express segmentation clock genes (Mara et al. (2007)), the position at which cells ingress into the PSM can create challenges for clock patterning, as only in the 'off' phase of the clock will ingressing cells be in-phase with their neighbors."
However, there are several lines of evidence (in chick and mouse), that some oscillatory clock genes are already being expressed as early as in the gastrulation phase (so prior to PSM ingression) (Feitas et al, 2001 [10.1242/dev.128.24.5139]; Jouve et al, 2002 [10.1242/dev.129.5.1107]; Maia-Fernandes at al, 2024 [10.1371/journal.pone.0297853]).
Question: Is this also true in zebrafish? (I.e. is there any recent experimental evidence that the clock genes are not expressed at ingression, since the paper cited to support this assumption is from 2007). If they are expressed in zebrafish (as they are in mouse and chick), then the cell addition should have random clock gene periods when they enter the PSM and not start all with a constant initial phase of zero. Probably this will not impact the results since the cells will also be out of phase with their neighbors when they "ingress", however, it will model more closely the biological scenario (and avoid such criticism).
Minor comments
- The citations are appropriate and cover the major labs that have published work related to this study (although with some overrepresentation of the lab that published the model used).
- The text is clear, carefully written, and both the methods and the reasoning behind them are clearly explained and supported by proper citations.
- The figures are comprehensive, properly annotated, with explanatory self-contained legends. I have no comments regarding the presentation of the results.
- Minor suggestions:
- Page 26: In the Cell addition sub-section of the Methods section, correct all instances where the word domain is used, but subdomain should be used (for clarity and coherence with the description of the model, stated as having a single domain comprising 3 subdomains).
- Page 32: Table 1. Parameter values used in our work, unless otherwise stated -> Suggestion: Add a column with the individual citations used for each parameter (to facilitate the confirmation of each corresponding reference).
Referee Cross-commenting
I carefully read the reports provided by my fellow reviewers. My cross-comments aim to enhance the collective evaluation of the manuscript by Hammond et al.
Reviewer #1's Comments:
I agree with Reviewer #1's overall evaluation of the manuscript's value and relevance, and with their general comments. I particularly support the suggestion to optionally include coupling delays known to influence the clock's period, as this would improve the model's completeness and benefit the research community. I also view this as an optional but desirable addition, not mandatory.
In Fig. 4, I agree that showing kymographs, similar to Fig. 2D, for each PSM length would greatly improve the visualization of the results, given the relevance of this result to the manuscript's main message.
The remaining minor comments are useful and relevant to improving the manuscript.
Reviewer #3's Comments:
Although I agree with Reviewer #3 that the paper is somewhat lengthy, I find the detailed description of the model in its biological context necessary and welcomed by the embryology research community. Without this detail, the paper might be too 'dry' and lose part of its audience. Conversely, focusing mostly on embryology without detailing the model parameters and simulation findings would deprive it of novelty and critical insights.
Overall, I find Reviewer #3's suggestions scientifically interesting, particularly comments 3, 4, and 5, which express legitimate questions for future study. However, I find them tangential to the main question addressed in this manuscript, which pertains to the modularity of the segmentation clock and morphogenesis. Therefore, I do not see them as significant improvements for the authors to implement in the current study.
I would like to know how the authors respond to comments 1 and 2, which I do not have the expertise to evaluate.
I agree with comment 6 that a brief mention of the known pathways/gene networks to which the assumptions apply (in zebrafish) would be a good addition. However, I do not think a detailed discussion is needed, as specific genes/networks can be different for different organisms.
I disagree with comment 7, as Fig. 3 shows that the clock is robust to changes in cell ingression regime across all cell motility profiles tested. This is an important result for the manuscript's take home message, and should remain in the main text, not as a supplementary figure.
Finally, regarding Reviewer #3's concern about the incompleteness of the results, I find the results robust given the formalism chosen and within the scenarios where the assumptions hold. I believe this concern applies to the formalism (which is a choice) and not to the quality or relevance of the work presented in the manuscript. Additionally, some of the model's limitations have been adequately addressed by the authors.
Significance
GENERAL ASSESSMENT
- This study uses a previously published model to simulate alternative scenarios of morphogenetic parameters to infer the potential independence (termed here modularity) between the segmentation clock and a set of morphogenetic processes, arguing that such modularity could allow the evolution of more flexible body plans, therefore partially explaining the variability in the number of segments observed in the vertebrates. This question is fundamental and relevant, yet still poorly researched. This work provides a comprehensive simulation with a model that tries to simplify the many morphogenetic processes described in the literature, reducing it to a few core fundamental processes that allow drawing the conclusions seeked. It provides theoretical insight to support a conceptual advance in the field of evolutionary vertebrate embryology.
ADVANCE
- This study builds on a model recently published by Uriu et al. (eLife, 2021) that incorporates quantitative experimental data within a modeling framework including cell and tissue-level parameters, allowing the study of multiscale phenomena active during zebrafish embryo segmentation. Uriu's publication reports many relevant and often non-intuitive insights uncovered by the model, most notably the description of phase vortices formed by the synchronizing genetic oscillators interfering with the traveling-wave front pattern. However, this model can be further explored to ask additional questions beyond those described in the original paper. A good example is the present study, which uses this mathematical framework to investigate the potential independence between two of the modeled processes, thereby extracting extra knowledge from it. Accordingly, the present study represents a step forward in the direction of using relevant theoretical frameworks to quantitatively explore the landscape of complex molecular hypotheses in silico, and with it shed some light on fundamental open questions or inform the design of future experiments in the lab.
- The study incorporates a wide range of existing literature on the developmental biology of vertebrates. It comprehensively cites prior work, such as the foundational studies by Cooke and Zeeman on the segmentation clock and the role of FGF signaling in PSM development as discussed by Gomez et al. The literature properly covers the breadth of knowledge in this field.
AUDIENCE
- Target audience | This study is relevant for fundamental research in developmental biology, specifically targeting researchers who focus on early embryo development and morphogenesis from both experimental and theoretical perspectives. It is also relevant for evolutionary biologists investigating the genetic factors that influence vertebrate evolution, as well as to computational biologists and bioinformatics researchers studying developmental processes and embryology.
Developmental researchers studying the segmentation clock in other vertebrate model organisms (namely mouse and chick), will find this publication especially valuable since it provides insights that can help them formulate new hypotheses to elucidate the molecular mechanisms of the clock (for example finding a set of evolutionarily divergent genes that might interfere with PSM length). Additionally, this study provides a set of cellular parameters that have yet to be measured in mouse and chick, therefore guiding the design of future experiments to measure them, allowing the simulation of the same model with sets of parameters from different vertebrate model organisms, therefore testing the robustness of the findings reported for zebrafish.
MY EXPERTISE
My areas of research (relevant for this study): Vertebrate embryo clock oscillations in Gallus gallus; Computational biology.
I can evaluate the relevance and validity of the model, critically evaluate its outputs and parameters, and the significance of the model assumptions for drawing relevant biological insights; however, I am not an expert on this mathematical formalism.
-
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this manuscript, Hammond et al. study robustness of the vertebrate segmentation clock against morphogenetic processes such as cell ingression, cell movement and cell division to ask whether the segmentation clock and morphogenesis are modular or not. The modularity of these two would be important for evolvability of the segmenting system. The authors adopt a previously proposed 3D model of the presomitic mesoderm (Uriu et al. 2021 eLife) and include new elements; different types of cell ingression, tissue compaction and cell cycles. Based on the results of numerical simulations that synchrony of the segmentation clock is robust, the authors conclude that there is a modularity in the segmentation clock and morphogenetic processes.
The presented results support the conclusion. The manuscript is clearly written. I have several comments that could help the authors further strengthen their arguments.
Major comment:
[Optional] In both the current model and Uriu et al. 2021, coupling delay in phase oscillator model is not considered. Given that several previous studies (e.g. Lewis 2003, Herrgen et al. 2010, Yoshioka-Kobayashi et al. 2020) suggested the presence of coupling delays in Delta-Notch signaling, could the authors analyze the effect of coupling delay on robustness of the segmentation clock against morphogenetic processes?
Minor comments:
- PSM radius and oscillation synchrony are both denoted by the same alphabet r. The authors should use different alphabets for these two to avoid confusion.
- page 5 Figure 1 caption: (x-x_a/L) should be (x-x_a)/L.
- Figure 3C: Description of black crosses in the panels is required in the figure legend.
- Figure 3C another comment: In this panel, synchrony r at the anterior PSM is shown. It is true that synchrony at anterior PSM is most relevant for normal segment formation. However, in this case, the mobility profile is changed, so it may be appropriate to show how synchrony at mid and posterior PSM would depend on changes in mobility profile. Is synchrony improved by cell mobility at the region where cell ingression happens?
- In page 12, the authors state that "the results for the DP and DP+LV cases are exactly equal for L = 185 um, as .... and the two ingression methods are numerically equivalent in the model". I understood that in this case two ingression methods are equivalent, but I do not understand why the results are "exactly" equal, given the presence of stochasticity in the model.
- The authors analyze the effect of cell density on oscillation synchrony in Fig. 4 and they mention that higher density increases robustness of the clock by increasing the average number of interacting neighbors. I think it would be helpful to plot the average number of neighboring cells in simulations as a function of density to quantitatively support the claim.
- The authors analyze the effect of PSM length on synchrony in Fig. 4. I think kymographs of synchrony r as shown in Fig. 2D would also be helpful to show that indeed cells get synchronized while advecting through a longer PSM.
- I understand that cells in M phase can interact with neighboring cells with the same coupling strength kappa in the model, although their clocks are arrested. If so, this aspect should be also mentioned in the main text in page 16, as this coupling can be another noise source for synchrony.
- Figure 5-figure supplement 2: panel labels A, B, C are missing.
- Figure 5-figure supplement 3: panel labels A, B, C are missing.
Significance
Synchronization of the segmentation clock has been studied by mathematical modeling, but most previous studies considered cells in a static tissue without morphogenesis. In the previous study by Uriu et al. 2021, morphogenetic processes such as cell advection due to tissue elongation, tissue shortening, and cell mobility were considered in synchronization. The current manuscript provides methodological advances in this aspect by newly including cell ingression, tissue compaction and cell cycle. In addition, the authors bring a concept of modularity and evolvability to the field of the vertebrate segmentation clock, which is new. On the other hand, the manuscript confirms that the synchronization of the segmentation clock is robust by careful simulations, but it does not propose or reveal new mechanisms for making it robust or modular. The main targets of the manuscript will be researchers working on somitogenesis and evolutionary biologists who are interested in evolution of developmental systems. The manuscript will also be interested by broader audiences, like developmental biologists, biophysicists, and physicists and computer scientists who are working on dynamical systems.
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General Statements<br /> The reviewer comments helped us improve the paper by including new computations, figures, and analyses related to vasopressin, drug dosages, and treatment cessation. We have also removed confusing terminology from the text. We believe that the paper is now more comprehensive, clear, and rigorous.
Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
The authors address the question of lowering long-term elevated cortisol levels by affecting the parameters in a previously published mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis. The parameters are related to various pathways. The elevation in cortisol levels is related to diseases e.g. mood disorders and Cushing's syndrome.<br /> The authors conducted a systematic in silico analysis of various points of intervention in the HPA axis. They found that only two interventions targeting corticotropin-releasing hormone (CRH) can lower long-term cortisol. Other drug targets either fail to lower cortisol due to gland-mass compensation or lower cortisol but harm other aspects of the HPA axis. Thus, they identify potential drug targets, including CRH-neutralizing antibodies and CRH synthesis inhibitors, for lowering long-term cortisol in mood disorders and in those suffering from chronic stress.<br /> The method used is in silico investigations of the mathematical model.<br /> The draft is well written with a single typo in line 270. I have no further comments!
Response: The typo is fixed.
Reviewer #1 (Significance):
In silico predictions without direct use of data is a weakness but the conducted analysis is convincing. An improved understanding of why some drugs work and others do not is important and is postulated to agree with clinical evidence.
Response: We thank the reviewer for this endorsement.
Reviewer #2 (Evidence, reproducibility and clarity):
Summary<br /> The authors utilise a mathematical model of the hypothalamic-pituitary-adrenal axis to address the utility of interventions altering its various outputs (CRH, ACTH and cortisol) to ameliorate axis disruption in response to chronic stress. They show that a lowering of circulating CRH by either blocking its synthesis or increasing its clearance is effective at returning the HPA axis to basal activity at all levels. In contrast, interventions altering ACTH or cortisol production, their circulating levels or actions are ineffective in the model. This is consistent with data on the long-term efficacy of drugs reducing excess corticosteroids in patients and animal models. The use of mathematical models to describe complex interactions in endocrine systems is a valuable advance in our understanding of potential mechanisms and therapies and this is an excellent example.
Response: We thank the reviewer for this endorsement.
Major comments<br /> 1. The model of the HPA axis that the authors have described previously is a little simplistic when considering the known physiology. Specifically, this model ignores the contribution of vasopressin to the axis, which has been described as being the primary hypothalamic factor driving HPA axis activity in chronic stress (see doi.org/10.1016/S0079-6123(08)00403-2). Including this may be beyond the scope of the current model, however it should be considered and at least commented on. It is notable that the model fits the clinical and animal model data, which may suggest that the contribution of vasopressin in the long term may be overestimated, possibly as a result of differential effects of the two hypothalamic factors, with CRH driving ACTH release and POMC gene expression, whilst vasopressin only increases ACTH release without augmenting POMC expression. This is worthy of discussion.
Response: We thank the reviewer for this comment which helped us discuss vasopressin. We agree that adding it as a variable in the model is beyond the scope of the current study. We describe its effects in the introduction and discussion sections. Interestingly, when one considers the best characterized effect of vasopressin, namely enhancing CRH-dependent ACTH release, one can use this model to investigate the effects of inhibiting vasopressin. We predict that vasopressin inhibition is unlikely to be an effective strategy for lowering long-term cortisol and alleviating stress-related mental disorders, as evidenced by the failure of clinical trials.
In the introduction we add:<br /> 1. “CRH stimulates the secretion of adrenocorticotropic hormone (ACTH) by corticotroph cells in the anterior pituitary, an effect enhanced by vasopressin (Aguilera et al, 2008; Antoni, 2017).” (lines 35-37)<br /> 2. Clinical trials for two vasopressin 1b receptor antagonist candidates, SSR149415 and TS-121, in the table of HPA-related clinical trials (Table 1)
In the discussion we add (lines 398-409): ”One important factor not explicitly considered in the model is the contribution of vasopressin to the axis. Vasopressin potentiates the CRH-dependent release of ACTH from pituitary corticotrophs by acting on the V1b receptor (V1bR) (Aguilera et al, 2008; Antoni, 2017). Including this hormone explicitly is beyond the current scope. However, a simple analysis indicates that the effect of elevated vasopressin can be modeled by increasing the ACTH secretion parameter b2. This suggests that vasopressin V1b receptor antagonists should have effects similar to inhibitors of ACTH production. As such, vasopressin receptor antagonists should be compensated by the HPA axis without long-term effects on cortisol. Accordingly, V1bR antagonists did not show statistically significant efficacy in clinical trials for major depressive disorder and generalized anxiety disorder (Griebel et al, 2012; Chaki, 2021; Kamiya et al, 2020). However, vasopressin may have additional relevant effects on the HPA axis and the central nervous system which warrant a more detailed modeling analysis.”
- The model that this study relies on is dependent on slow changes in the various levels of the endocrine axis and the authors have focused on alterations in cell number as the process leading to a prolongation of their dysfunction. For the stress axis, the evidence for changes in corticotroph cell number is weak and the recent paper of Lopez et al (DOI: 10.1126/sciadv.abe44) suggests that chronic stress, at least over a period of 3 weeks does not lead to an alteration in the number of corticotrophs, despite cell population changes in the adrenal gland. There are other processes which could lead to prolonged alteration of corticotroph output and it would be better to focus (as the authors have in places) on functional mass, rather than cell number which may suggest it is not the trophic effect of CRH that is important for increased functional mass.
Response: We thank the reviewer for this. We now refer only to functional mass changes. We corrected all places in which hyperplasia of corticotrophs is mentioned. We also state in lines 125-126 that the model is agnostic as to whether growth in functional mass is due to hyperplasia or hypertrophy.<br /> We also added a citation for Lopez et al. 2021 (line 86) to support the growth of cortisol-secreting cells in the zona fasciculata of the adrenal gland under stress conditions.
- The parameters in the model for interventions are described as simply being less than or greater than one- to what extent are the effects of these interventions dependent on their specific value? For example, presumably if the I1 value is close to zero, then the CRH-synthesis inhibitor would be ineffective. Likewise, if it were close to 1 then there would be negligible release of CRH in response to stress, and the preservation of a response to acute stress would be lost. Can the authors show the range of values for I1, C1 and A1 where the interventions are effective at normalising HPA axis function whilst (for I1 and A1) still preserving the acute stress response?
Response: We thank the reviewer for this comment that helped us to add a new section in the results on dose response, and three new figures (Figure 4, Figure S2 and Figure S3):
“CRH interventions have a dose-dependent response in the model<br /> We computed the effects of drug doses by varying the relevant model parameter, where zero dose means no change in the parameter and high doses mean large changes in the parameter. We find that both candidate interventions for lowering cortisol - CRH-synthesis inhibitors and CRH-blocking antibodies - cause a dose-dependent reduction of steady-state cortisol (Figure 4A). This indicates that putative treatment may require finding the appropriate dose to return the patients to their normal cortisol baseline range. Other drug candidates have no effect on long-term cortisol steady state (Figure S2).
At all doses, the steady states of CRH and ACTH remain normal (Figure 4B-C). The acute stress response, defined as peak cortisol upon acute stress input relative to steady-state cortisol, is dose dependent (Figure 4D and Figure S3). At a dose that returns cortisol to the normal range, the acute response is also normalized.
We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F and Figure S4).
Figure 4. Predicted effective interventions have a dose-dependent effect on cortisol, and cortisol abruptly rises when treatment is ceased. (A) Cortisol steady state in the model upon changes in doses of CRH-synthesis inhibitors and CRH-blocking antibodies. (B-C) The same changes in drug doses have no effect on ACTH (B) and CRH (C) steady state levels. (D) Cortisol peak response to an acute stress relative to steady state for different drug doses. (E-F) HPA dynamics upon cessation of CRH-synthesis inhibitors (E) and anti-CRH antibodies (F) after 50 days.”
In the supplemental information:
“Cortisol dose response to HPA-targeting drugs
Figure S2. Cortisol steady state dose response to HPA-targeting drugs, related to Figure 4.
Figure S3. Cortisol peak response to acute stressor under varying concentrations of HPA-targeting drugs, related to Figure 4.”
- In the models that the authors describe with CRH interventions, what is the impact of stopping the intervention on axis output in the short and long-term? Presumably ceasing the use of CRH antagonists would lead to much more severe axis dysregulation than CRH neutralising antibodies or CRH synthesis inhibitors.
Response: We have now added new analysis on drug cessation (new figure 4E-F, Figure S4). After a 50 day treatment, sudden cessation caused a rapid return to hypercortisolemia:<br /> We added in lines 277-278: “We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F).”
Reviewer #2 (Significance):
Whilst the study builds on the use of a previously described mathematical model, its utility in identifying potential targets for therapy within the important area of chronic stress makes it an important example of the value of the modelling approach to decisions on appropriate targets for therapy. The model does not include important known factors which have been described as being important in the HPA axis response to chronic stress and would be considerably improved if these could be incorporated.<br /> The study builds on conceptual insights into the role a delayed or slow functional mass change might play in dysregulation of endocrine axes and this could be applied to other physiological systems and will be of interest to modellers and physiologists alike. The authors are leaders in this field and there are few other modellers considering systems level interactions over this timescale.
Response: We thank the reviewer for this endorsement.
As a pituitary physiologist, my review has focused on the interactions between the various players in HPA axis function, I do not have the expertise to comment on mathematical modelling aspects.
Reviewer #3 (Evidence, reproducibility and clarity):
This extremely interesting paper asks why various attempts to treat depression and bipolar disorder with glucocorticoid antagonists or cortisol synthesis inhibitors have failed. The starting point for their analysis is a simple computational model that, importantly, includes the facts that CRH stimulates not only ACTH release but also corticotroph growth and ACTH stimulates not only cortisol production but also the growth of cells in the adrenal cortex. They call this the "gland mass model". According to the model, if the hypothalamus receives a continuous stress input, all of the HPA hormones will be elevated-CRH transiently and the others in a sustained fashion. Adding a sufficient dose of a CRH inhibitor (decreasing the rate constant b1 in the model) or a CRH antibody (increasing the rate constant a1) normalizes the hormone levels, whereas blocking cortisol function or production does not. This is demonstrated by numerical simulations and backed up by deriving analytical expressions for the hormone concentrations at steady state. The paper provides a plausible explanation for why past therapeutic efforts have failed and points to a couple of approaches that might succeed. These conclusions are hypotheses-they haven't been tested experimentally and we really don't know how accurately the system is described by this nice, simple model-but they are really intriguing hypotheses that could lead to therapeutic breakthroughs. I strongly recommend publication.
Response: We thank the reviewer for this endorsement.
My only criticisms are minor:
- The authors should specify what exact change in the model's parameters they are making to implement their therapeutic interventions. E.g. in Fig 1B top left and 2A, what is the change in the value of b1 that corresponds to the addition of a CRH-synthesis inhibitor? (I'd guess it's being dropped to zero, but if this is stated, I missed it)
Response: We thank the reviewer for that comment which helped us to clarify what is the required parameter change to normalize cortisol. We have now added in lines 173-175: “According to equation (1), as a general guideline, treating cortisol levels that are x-fold higher than baseline requires a drug dose that alters the relevant parameter (e.g., CRH production or removal rate) by a similar x-fold.”
- I think it would also be useful to show a dose-response relationship for the various interventions.
Response: We thank the reviewer for this comment that helped us to add a new section in the results on dose response, and three new figures (Figure 4, Figure S2 and Figure S3):
“CRH interventions have a dose-dependent response in the model<br /> We computed the effects of drug doses by varying the relevant model parameter, where zero dose means no change in the parameter and high doses mean large changes in the parameter. We find that both candidate interventions for lowering cortisol - CRH-synthesis inhibitors and CRH-blocking antibodies - cause a dose-dependent reduction of steady-state cortisol (Figure 4A). This indicates that putative treatment may require finding the appropriate dose to return the patients to their normal cortisol baseline range. Other drug candidates have no effect on long-term cortisol steady state (Figure S2).
At all doses, the steady states of CRH and ACTH remain normal (Figure 4B-C). The acute stress response, defined as peak cortisol upon acute stress input relative to steady-state cortisol, is dose dependent (Figure 4D and Figure S3). At a dose that returns cortisol to the normal range, the acute response is also normalized.
We also tested the effects of abrupt treatment cessation. For both CRH interventions, stopping treatment led to a rapid return to hypercortisolemia (Figure 4E-F and Figure S4).
Figure 4. Predicted effective interventions have a dose-dependent effect on cortisol, and cortisol abruptly rises when treatment is ceased. (A) Cortisol steady state in the model upon changes in doses of CRH-synthesis inhibitors and CRH-blocking antibodies. (B-C) The same changes in drug doses have no effect on ACTH (B) and CRH (C) steady state levels. (D) Cortisol peak response to an acute stress relative to steady state for different drug doses. (E-F) HPA dynamics upon cessation of CRH-synthesis inhibitors (E) and anti-CRH antibodies (F) after 50 days.”
In the supplemental information:
“Cortisol dose response to HPA-targeting drugs
Figure S2. Cortisol steady state dose response to HPA-targeting drugs, related to Figure 4.
Figure S3. Cortisol peak response to acute stressor under varying concentrations of HPA-targeting drugs, related to Figure 4.”
*Referees cross-commenting*
It looks like we are all enthusiastic about this work.
Response: Thank you.
Reviewer #3 (Significance):
Strengths: It's a beautiful new insight on a really important topic, extracted from a simple, understandable mathematical model of the HPA axis.
Weaknesses: It is based on a model and the model could be wrong. This does not however diminish my enthusiasm for this provocative work.
Advance: It is highly original.
Audience: I hope attracts a wide audience--modelers, endocrinologists, psychiatrists, drug developers.
My expertise: I am a systems biologist, have taught psychopharmacology to medical students, and have an interest in endocrine signaling.
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Referee #3
Evidence, reproducibility and clarity
This extremely interesting paper asks why various attempts to treat depression and bipolar disorder with glucocorticoid antagonists or cortisol synthesis inhibitors have failed. The starting point for their analysis is a simple computational model that, importantly, includes the facts that CRH stimulates not only ACTH release but also corticotroph growth and ACTH stimulates not only cortisol production but also the growth of cells in the adrenal cortex. They call this the "gland mass model". According to the model, if the hypothalamus receives a continuous stress input, all of the HPA hormones will be elevated-CRH transiently and the others in a sustained fashion. Adding a sufficient dose of a CRH inhibitor (decreasing the rate constant b1 in the model) or a CRH antibody (increasing the rate constant a1) normalizes the hormone levels, whereas blocking cortisol function or production does not. This is demonstrated by numerical simulations and backed up by deriving analytical expressions for the hormone concentrations at steady state. The paper provides a plausible explanation for why past therapeutic efforts have failed and points to a couple of approaches that might succeed. These conclusions are hypotheses-they haven't been tested experimentally and we really don't know how accurately the system is described by this nice, simple model-but they are really intriguing hypotheses that could lead to therapeutic breakthroughs. I strongly recommend publication.
My only criticisms are minor:
- The authors should specify what exact change in the model's parameters they are making to implement their therapeutic interventions. E.g. in Fig 1B top left and 2A, what is the change in the value of b1 that corresponds to the addition of a CRH-synthesis inhibitor? (I'd guess it's being dropped to zero, but if this is stated, I missed it)
- I think it would also be useful to show a dose-response relationship for the various interventions.
Referees cross-commenting
It looks like we are all enthusiastic about this work.
Significance
Strengths: It's a beautiful new insight on a really important topic, extracted from a simple, understandable mathematical model of the HPA axis.
Weaknesses: It is based on a model and the model could be wrong. This does not however diminish my enthusiasm for this provocative work.
Advance: It is highly original.
Audience: I hope attracts a wide audience--modelers, endocrinologists, psychiatrists, drug developers.
My expertise: I am a systems biologist, have taught psychopharmacology to medical students, and have an interest in endocrine signaling.
-
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Referee #2
Evidence, reproducibility and clarity
Summary
The authors utilise a mathematical model of the hypothalamic-pituitary-adrenal axis to address the utility of interventions altering its various outputs (CRH, ACTH and cortisol) to ameliorate axis disruption in response to chronic stress. They show that a lowering of circulating CRH by either blocking its synthesis or increasing its clearance is effective at returning the HPA axis to basal activity at all levels. In contrast, interventions altering ACTH or cortisol production, their circulating levels or actions are ineffective in the model. This is consistent with data on the long-term efficacy of drugs reducing excess corticosteroids in patients and animal models. The use of mathematical models to describe complex interactions in endocrine systems is a valuable advance in our understanding of potential mechanisms and therapies and this is an excellent example.
Major comments
- The model of the HPA axis that the authors have described previously is a little simplistic when considering the known physiology. Specifically, this model ignores the contribution of vasopressin to the axis, which has been described as being the primary hypothalamic factor driving HPA axis activity in chronic stress (see doi.org/10.1016/S0079-6123(08)00403-2). Including this may be beyond the scope of the current model, however it should be considered and at least commented on. It is notable that the model fits the clinical and animal model data, which may suggest that the contribution of vasopressin in the long term may be overestimated, possibly as a result of differential effects of the two hypothalamic factors, with CRH driving ACTH release and POMC gene expression, whilst vasopressin only increases ACTH release without augmenting POMC expression. This is worthy of discussion.
- The model that this study relies on is dependent on slow changes in the various levels of the endocrine axis and the authors have focused on alterations in cell number as the process leading to a prolongation of their dysfunction. For the stress axis, the evidence for changes in corticotroph cell number is weak and the recent paper of Lopez et al (DOI: 10.1126/sciadv.abe44) suggests that chronic stress, at least over a period of 3 weeks does not lead to an alteration in the number of corticotrophs, despite cell population changes in the adrenal gland. There are other processes which could lead to prolonged alteration of corticotroph output and it would be better to focus (as the authors have in places) on functional mass, rather than cell number which may suggest it is not the trophic effect of CRH that is important for increased functional mass.
- The parameters in the model for interventions are described as simply being less than or greater than one- to what extent are the effects of these interventions dependent on their specific value? For example, presumably if the I1 value is close to zero, then the CRH-synthesis inhibitor would be ineffective. Likewise, if it were close to 1 then there would be negligible release of CRH in response to stress, and the preservation of a response to acute stress would be lost. Can the authors show the range of values for I1, C1 and A1 where the interventions are effective at normalising HPA axis function whilst (for I1 and A1) still preserving the acute stress response?
- In the models that the authors describe with CRH interventions, what is the impact of stopping the intervention on axis output in the short and long-term? Presumably ceasing the use of CRH antagonists would lead to much more severe axis dysregulation than CRH neutralising antibodies or CRH synthesis inhibitors.
Significance
Whilst the study builds on the use of a previously described mathematical model, its utility in identifying potential targets for therapy within the important area of chronic stress makes it an important example of the value of the modelling approach to decisions on appropriate targets for therapy. The model does not include important known factors which have been described as being important in the HPA axis response to chronic stress and would be considerably improved if these could be incorporated.<br /> The study builds on conceptual insights into the role a delayed or slow functional mass change might play in dysregulation of endocrine axes and this could be applied to other physiological systems and will be of interest to modellers and physiologists alike. The authors are leaders in this field and there are few other modellers considering systems level interactions over this timescale.
As a pituitary physiologist, my review has focused on the interactions between the various players in HPA axis function, I do not have the expertise to comment on mathematical modelling aspects.
-
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Referee #1
Evidence, reproducibility and clarity
The authors address the question of lowering long-term elevated cortisol levels by affecting the parameters in a previously published mathematical model of the hypothalamic-pituitary-adrenal (HPA) axis. The parameters are related to various pathways. The elevation in cortisol levels is related to diseases e.g. mood disorders and Cushing's syndrome.<br /> The authors conducted a systematic in silico analysis of various points of intervention in the HPA axis. They found that only two interventions targeting corticotropin-releasing hormone (CRH) can lower long-term cortisol. Other drug targets either fail to lower cortisol due to gland-mass compensation or lower cortisol but harm other aspects of the HPA axis. Thus, they identify potential drug targets, including CRH-neutralizing antibodies and CRH synthesis inhibitors, for lowering long-term cortisol in mood disorders and in those suffering from chronic stress.<br /> The method used is in silico investigations of the mathematical model.<br /> The draft is well written with a single typo in line 270. I have no further comments!
Significance
In silico predictions without direct use of data is a weakness but the conducted analysis is convincing. An improved understanding of why some drugs work and others do not is important and is postulated to agree with clinical evidence.
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Reply to the reviewers
Detailed response to Reviewer comments
We thank the reviewers for their positive and constructive evaluation of the paper. We have addressed in full the concerns raised as detailed below. We apologize for the long time it took us to respond, which was a consequence of local circumstances in the last year.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary:
The authors analyzed circulating cell-free DNA for COVID-19 using deep sequencing of the methylation and histone modification. The major output was cell-specific quantification. The study involved 120 unvaccinated, hospitalized patients, 19 asymptomatic/mild cases, and 40 controls. Between COVID-19 and controls, they found significant differences in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts. The latter two cell types had significant differences even in the asymptomatic patients. It is unclear if the damage seen is related to COVID-19 specifically, or related to general inflammation or infection.
Strengths of the study include relatively high WGBS/targeted sequencing, along with fragment-level analysis with methods described in their previous work (Loyfer et al. Nature). In addition, they add and ChIP-seq data using their published methods. The work comes from a group with leading expertise in methylation cell-free DNA analysis.
Overall, the work is most comprehensive analysis to date for COVID-19, and the data would be a valuable resource to the research community. We have major and minor comments that do not necessarily require additional experimental work.
We thank the reviewer for these supportive comments.
Major comments:
- There is a lack of data and the methods are presented in such a way that the results and conclusion can be reproduced and evaluated. Neither the code nor the data to generate the results are available. Both need to be made available during the peer review process.
Missing data: Fragment-level FASTQ, BAM, or PAT files are needed to reproduce the results. Missing Scripts, for example in Github, is standard and reasonable for reproducing the figures shown. Missing targeted assay method details: - The authors should show the data, methods, and details for: "The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed."
Thank you. WGBS data files are currently being uploaded to GEO and are waiting for an accession number.
For the validation of targeted markers, we added a new supplemental table (S11) with data on the methylation status of the loci used in this study in different cells and tissues (i.e. marker specificity), and provided a detailed text and references to the methods used.
The authors did not list the major limitations of the study in the discussion or elsewhere. These should include (or be addressed with experimental or conclusion changes):
1) The small sample size of the asymptomatic/mild group (if the emphasis of the paper, as suggested by the title, is on the asymptomatic/mild group - see the next major point.
Thank you, indeed this is a limitation, we have now addressed this issue in the text. Despite this limitation, findings regarding to this population were statistically significant.
2) The targeted assay is used on the vast majority of samples, including all of the asymptomatic/mild group. However, it is limited to a particular subset of cell types (total defined by all possible cell types in the body). Those cell types were determined based on WGBS data on only 6 COVID-19 cases.
Thank you, indeed this is a limitation. WGBS was done on 6 critically ill patients, to uncover the potential cell types that will be of most interest in the targeted assay. In comparison to the WGBS, the targeted assay has a deeper coverage and therefore greater sensitivity. We have now addressed this issue in the limitations section.
3) The methylation references for the WGBS data were limited to a fraction of all human cell types. For example, this paper was not able to evaluate Schwann cells or peripheral nerves, which was a significant finding for COVID-19 related multisystem inflammatory syndrome (PMID 37279751).
The WGBS atlas (PMID: 36599988) consists of ~40 cell types that we were able to isolate at a high purity. While this is the most complete methylome atlas of human cell types generated to date, it is indeed incomplete. Unfortunately the scarcity of Schwann cells prevented us from determining the methylome of this cell type, and the matter is to be investigated in future studies. Note that the study referred to by the reviewer described the cell-free transcriptome rather than the cfDNA methylome of patients. cfDNA methylation analysis of Schwann cells remains a challenge to be addressed in future studies. This limitation is explained in the revised text.
4) The case and control groups (severe, asymptomatic mild, and control) were collected at different times and circumstances, allowing for potential pre-analytical confounders.
We now addressed this limitation in the text.
5) cfDNA levels can be influenced by several unmeasured factors, including death, replication leading to more turnover, clearance/stability, and movement from tissue into circulation. The methods used cannot distinguish between these possibilities .
Indeed, the mechanism by which cfDNA concentration is increased is not fully understood, but is certainly correlated with pathology. We clarify this in the revised text.
6) (if true) the controls used for the targeted assay were not age/sex matched. The median age for the controls skew younger per Table S1, S2, S3.
We used control samples that were collected before the pandemic, to make sure that they were not infected with COVID-19. Consequently, there are minor demographic differences (e.g. controls tend to be younger than the hospitalized patients, though similar age to the asymptomatic donors).
Note that in previous studies, cfDNA levels and origins did not show differences in sex.
In the WGBS samples, we did age and sex matched the samples.
We explain this issue in the revised text.
7) (optional) It is unclear whether the differences found are attributable to COVID-19, coronavirus infection, viral infections, infections in general, or inflammation in general. The appropriate alternative controls were not addressed in this study. The paper shows some degree of correlation with acute inflammatory markers (CRP, ferritin, neutrophil contribution).
Indeed, elevated cfDNA from specific tissues reflects tissue turnover or death, with no indication of the cause of pathology. We now addressed this limitation in the text.
The title is a bit misleading in that it revolves around the asymptotic patients. However, this is also the group with the lowest representation at n=19. The vast majority of the data is related to the hospitalized patients. While other studies may have looked at hospitalized patients, I agree with the authors that there is merit in deep sequencing and the correlated clinical data.
Thank you. We chose to highlight in the title the most novel and provocative finding of the study.
More details on the patient inclusion criteria are needed. Were the asymptomatic/mild positive by PCR test or a point of care immunoassay? We know the viral load is quite dynamic for these patients. What was the timing of the blood draw?
Likewise, how did you find the hospitalized patients? Was it comprehensive over a period of time? These details help reveal any potential biases in the selection process.
We do not have information on the viral load in patients. All were positive for a PCR test. For the asymptomatic cases we know the time of the test, and this information is now added in Supplemental Table S2.
Hospitalized patients were recruited and consented at the Shaare Zedek Medical Center in a rather comprehensive manner – we recruited all patients that we could during May-June 2020. This is explained in the revised methods section.
Minor comments:
- The abstract states: "Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage." However, in Fig 5, there seems to be a bimodal distribution for the lung epithelial and cardiomyocytes. Unclear if that is an artifact of the graph.
It is quite interesting that the asymptomatic/mild group seems to have a bimodal distribution in lung epithelial and cardiomyocyte cfDNA. Perhaps this data is not available, and the sample size is small, but could there have been a clinical difference between the two groups (e.g. asymptomatic versus mild, or had symptoms later?). It is unclear how precise the measurements are for the lung epithelial cells.
Thank you for this comment. Since cfDNA levels of the hospitalized patients are increased by orders of magnitude, we have arranged the graphs in logarithmic scale. Consequently, the bimodality that the reviewer mentions reflects only a slight absolute difference of cfDNA levels from lung and cardiomyocytes: +-1 GE/ml, and we assume that this difference does not reflect clinical significance (and is not statistically different from the controls). This is referred to in the revised text.
The authors listed 2 prior studies that looked at cell type or tissue damage during COVID-19. There are 2 other studies that I am aware of: PMID: 33651717 (n=84 with n=18 nonhospitalized) but probably shallow WGBS, and 37279751 (n=205 pediatric patients). Importantly, the latter paper found Schwann cells were significantly elevated, which is missing from the current study's assessment. In addition, citation 14 from the same group already found significantly increased vascular endothelial cfDNA in COVID-19 patients with severe disease versus mild. While some findings are consistent, there are also discrepancies.
As explained above, our DNA methylation atlas does not contain a Schwann cell entry, so we cannot refer to cfDNA from this cell type; the mentioned study used cfRNA to assess this population. This is mentioned in the limitations of the study.
We now cite more comprehensively existing literature of liquid biopsies in Covid-19, and discuss the potential sources of discrepancy. We believe these result from differences in the methylome atlas, from the higher depth of the targeted assay compared with WGBS, and from our assessment of a unique population of asymptomatic patients.
Is Fig 2 necessary? Fig. 5 seems to display the same data but with the asymptomatic group.
Indeed there is some redundancy. Figure 2 shows data on hospitalized patients, and Figure 5 focuses on asymptomatic patients but uses as reference the same controls and severe patients as in Figure 2. We believe that this arrangement helps clarity.
"Elevated lung cfDNA reflects excessive lung cell death" - recommend this statement is tempered as direct evidence is not available in this study. An alternative explanation could be that endothelial cells are damaged, and it is easier for lung cfDNA to enter blood circulation rather than the respiratory system.
Thank you for this comment. We have addressed this possibility in the revised Discussion.
Fig 6: Add unit of measure to heatmap.
Added.
Supplemental Fig 1.: Add label to unit of measure in caption or figure. Average or median beta value over a series of CpGs?
Added. Each row represents a single CpG beta value.
The authors state the targeted assay "allows for a more accurate and sensitive detection of cfDNA from a given source", which should be tempered unless clear evidence is presented for these statements. In addition, it targets only a small subset of all cell types. The highest cell type contribution from MK cells is only represented by 2 markers
We now discuss this in more detail and with caution. Indeed targeted assays may not be more accurate given the use of few markers, but we do believe they are at least theoretically more sensitive given the use of PCR and deep sequencing.
Targeted assay has a few caveats that the authors should mention or fix:
The method is not described in detail.
More details are now provided, including multiplex PCR method and a reference to the script used for interpreting sequence data.
Methods besides WGBS can have biases in methylation representation and a beta correlation between the 12 samples that underwent WGBS and the targeted assay would be reassuring.
We have added a graph (new __Supplementary Figure S3) showing a good correlation of Covid-19 WGBS data and targeted analysis of the same samples.__
The level of precision at the lower end of cellular contribution would be helpful too. The lung epithelial and cardiomyocyte cells were present at the lower end of the spectrum. This can be shown in a titration of the purified cells into plasma, or at least an in silico titration analyzed with only the targeted markers.
Thank you. The targeted methylation assay is capable of detecting ~0.1% contribution of DNA from a given source, or 1-5 genome equivalents from this source. This is true also for our lung and cardiomyocyte markers, as previously shown (PMID 35450968, 29691397).
The authors state "(i) Evidence of frequent cardiomyocyte death in hospitalized patients... it has not been appreciated that cardiac cell death is a feature shared by most hospitalized patients." However, COVID-19 patients have elevated troponin.
Thank you. Evidence for troponin elevation was indeed reported in some, but not most of the hospitalized patients (see PMID: 32652195, 33121710, 32219356, 32211816). Note that troponin is not a definitive evidence of cardiac cell death (e.g. the significance of elevated troponin after a marathon or in patients with kidney disease is not clear). This provides a justification for the use of cfDNA for this purpose, as we have shown previously (PMID: 37290439). This is clarified in the revised Discussion.
The authors state "This signal presumably reflects elevated turnover of red blood cells and increased rate of erythropoiesis". However, could it be also higher nucleated RBCs released into circulation as the authors cited?
Thank you. Both of these possibilities are valid, and are not mutually exclusive. Elevated NRBC was reported in severe COVID-19, and is strongly associated with higher erythropoiesis. This is clarified in the revised Discussion.
Fig 2, 4, 5: The graphs seem to suggest that the authors picked 0.001 GE/mL as not detected. Should they label that point appropriately as "not detected" or "ND"? It is not clear why 0.001 GE/mL was picked, and the analytical sensitivity of the targeted test is not reported.
Right, this was due to the non-zero limit of log graphs. We explain this in the text.
How many mLs of plasma were used?
We have now added to supplemental tables the amount of plasma that was used for each patient.
__Reviewer #1 (Significance (Required))____: __ - General assessment: Strengths - 1) Interesting topic: Non-invasive tabulation using deep methylation sequencing of cell type shedding into circulation of an important disease (COVID-19). 2) Deep sequencing using methylation and histone output is a significant improvement on past studies. Although targeting limits the scope of the cell types, the targeting was based on relatively deep WGBS sequencing on 6 cases and 6 controls.
Limitations - The unique aspects (targeted assay and deep sequencing) are missing data and detailed methodology for reanalysis and reproducibility. See major comment 2.
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Advance: The authors used deep sequencing through brute force (WGBS) and a unique targeted assay to study COVID-19 from a large group (n=120 patients). They found that endothelial and erythroblast lineages are overrepresented based on the presence and severity of the COVID-19 infection. Their findings are significant and go beyond what has been published. The methodologies and data (i.e. the controls) would be a great resource to the community that can be used beyond the scope of COVID-19.
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Audience: This article would be appealing to a broad, translational/clinical audience. The authors have published on methylation deconvolution several times before, but to my knowledge, the broader targeted assay is unique and there is a large dataset with correlated clinical information that may be of broad utility.
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Reviewer expertise: technical expertise with circulating cell-free DNA. translational/clinical expertise.
__Reviewer #2 (Evidence, reproducibility and clarity (Required))____: __ they performed deep WGBS on severe COVID-19 and HC plasma samples, applied the novel UXM algorithm that includes 40 human cell types to identify the tissue origins of cfDNA, and showed increased cfDNA from diverse cell and tissue types in COVID-19 patients than healthy controls. Besides WGBS, they also performed targeted methylation assay to measure cellular turnovers/death and tissue injury from major cell and tissue types involved in COVID-19 pathogenesis and used as a predictor of poor outcome. Finally, they showed that cfChIP-seq can identify heightened immune responses associated with COVID-19 and asymptomatic patients. Previous studies have shown that cfDNA has a great potential to map tissue injuries in COVID-19 and predict patient outcomes (Cheng et al., 2021 & Andargie et al., 2021). The expanded reference methylation atlas and the addition of targeted methylation assay and cfCHIP-seq in this study are very informative and fascinating. Please allow me to congratulate Ben-Ami and colleagues for this wonderful work.
Thank you for this encouraging feedback.
Below are some points that need to be addressed to improve the manuscript: Major 1. Given the heterogeneous nature of COVID-19 clinical manifestation, the limited number of patients (n=6) raises concern about the significance of WGBS analysis. The authors need to provide further details as to why they performed WGBS only from 6 samples out of 120 subjects and what was the selection criteria
Study design was impacted by resource limitations. We were able to perform deep WGBS only on a small number of samples, so have used this as a guide to the general nature of tissue turnover in COVID-19 patients, and later used a narrower, highly sensitive, more affordable and more broadly available targeted assay. This is clarified in the revised text (Discussion, section on limitations of study).
The gene expression analysis with cfCHIp-seq is interesting. Likewise, Differentially Methylated Regions (DMR) can infer gene expression. Is the methylation analysis also showing increased interferon response in COVID-19 patients? This study also showed increased cfDNA from monocytes that is not reflected in blood cell counts. Does cfCHIP-seq identify inflammatory response-related genes in monocytes/macrophages? Hadjadj et al. 2020 (PMID: 32661059: Science) reported impaired interferon response in severe COVID-19 patients. Whereas this study showed heightened interferon response in severe and asymptomatic/mild COVID-19 patients compared to healthy controls, there was no difference between Mild and Severe COVID-19 patients. The author should consider validating their finding with plasma cytokine measurement. cfChip-seq also identifies cfDNA tissues-of-origin (PMID: 33432199). How is the correlation between these three assays (WGBS, targeted methylation assay and cfCHIP-seq) to detect cell death/turnover?
- Thank you for these comments. While cfChip does indeed reflect gene expression patterns in the cells that released cfDNA, cfDNA methylation patterns are indicative of cell identity (i.e. tissue of origin) but not dynamic gene expression (PMID: 30100054). __Unfortunately, current cfChip technology while revealing gene expression patterns in the cells that released cfChromatin, does not inform which cell types have expressed these genes (e.g. monocytes or T cells). Thus we can state that the cells releasing cfDNA expressed interferon stimulated genes, but we cannot say which cells were expressing these genes. __
We were unable to perform additional measurements e.g. cytokines since our blood samples are almost entirely depleted.
With regards to the tissue origins of cfDNA: as shown in the paper, there is a general good agreement between WGBS and the targeted assay. In the revised version we show a good correlation between findings in specific samples that were subject to both WGBS and the targeted assay (Supplemental Figure S3). In our hands the sensitivity and specificity of cfChip-seq for detecting tissue origins of cfDNA are lower than cfDNA methylation, hence we elected to use the cfChip information only for inference of gene expression.
It is unclear whether hospitalized COVID-19 subjects experienced particular organ involvement. It would be interesting to link the tissue-specific cfDNA to different COVID-19 endotypes. For instance, cardiac involvement and cardiomyocyte cfDNA.
Indeed, linking tissue-specific cfDNA to clinical phenotype has been challenging. Elevated lung cfDNA is correlated with disease severity (which is well established to be associated with pulmonary damage). We were unable to link elevated cardiac cfDNA to a clinical cardiac phenotype, also because of the limited cardiac assays that were performed on the hospitalized patients e.g troponin and cardiac eco.
Previous studies reported cfDNA concentration in healthy controls ranges between 3 and 15 ng/mL. This study's median cfDNA level for asymptomatic COVID-19 patients falls within that range. It would be interesting if the authors comment on the methodology differences, including plasma volume, correction for extraction efficiency, and cfDNA assay type.
Indeed, asymptomatic patients had a mild, though highly statistically significant elevation in total cfDNA concentration relative to controls, as shown in Figure 5. Samples of asymptomatic patients and controls were obtained and processed identically using the Qiasymphony liquid handling robot. This is described in the revised methods. Plasma volume collected for each sample is now shown in Supp Tables S1-4.
Were the asymptomatic/Mild case samples collected in the same time frame as Hospitalized patients? It would be interesting if the authors comment on the effect of SARSCOV-2 variants and viral loads on plasma cfDNA level.
Yes, all collected at the same period (May-October 2020). This is stated in the revised methods. Unfortunately we do not have information on specific variants on viral loads.
The author showed cfDNA from total T cells and CD8 cells in particular. The authors should comment on why CD4+ T was not shown instead of T cells (which includes both CD4 and CD8 cells).
Unfortunately our current methylome atlas does not allow for identification of specific methylation markers for CD4+ cells (PMID: 34842142).
Considering the expensive nature of deep sequencing, it would be interesting if the authors comment on applying the UXM algorithm for low and medium- and low-coverage sequenced samples.
The algorithm applies to WGBS samples regardless of depth, obviously with reduced performance in low coverage sequencing. Formal analysis of performance on multiple WGBS samples is ongoing.
Minor 1. The timing of blood sample collection from hospital admission or testing positive for COVID-19 is important to use cfDNA as a predictor of outcome. The authors should explain when the sample was collected for asymptomatic/mild patients. If it's not in the "acute phase" it should also be clarified for comparison with hospitalized COVID-19.
We have now added the time of sampling – typically a week or two after diagnosis (Supplemental Table S2).
Is there a reason the authors included repeated measures of cfDNA within the same subject (N=120, n-142; Figure 1A)? The author should consider statistical correction for repeated measures. This is important to reduce bias.
Thank you, we have now reanalyzed the data including only one sample for each patient. The results are largely the same as the original analysis (for reviewer eyes only).
I believe the authors forgot to include "Code and data availability" declaration. I encourage the authors to make publicly available the WGBS data and deconvolution algorithm for reproducibility purposes.
WGBS data files are currently being uploaded to GEO and are waiting for an accession number.
Figure 1D should show individual data points to see the pattern of tissue-specific cfDNA better, especially as COVID-19 shows heterogeneous clinical presentation. Please consider overlaying the data point on the histogram.
Thank you, we have changed the graph to show each datapoint.
Methods - Page 27, the first sentences from the last paragraph, please include the unit
Thank you, we have changed the paragraph.
after the number "75".... In fact, this paragraph is identical to the previous paper (PMID: 33432199); please consider paraphrasing the section.
Done.
Please clearly define "deteriorated." What WHO score or range is considered as deteriorated?
Deteriorated patients were defined as [maximal WHO score post sample] – [WHO score at sampling day] > 0. This is now clarified in the revised results section.
The authors mix between 40 and 37 reference cell types. Please be consistent.
Thank you. Done.
Page 6, line 3, please replace erythrocyte with erythroblast.
Done.
Page 28, line 10, please replace COVID with COVID-19.
Done.
Figure 5D needs a key for recovered versus deteriorated.
Done (figure 4D).
Figure 5, legend title, please fix the number of healthy controls.... (n=30-45).
Done.
__Reviewer #2 (Significance (Required))____: __ This manuscript used a deep WGBS approach with an expanded human cell-type methylation atlas and novel deconvolution algorithm, targeted methylation assay (which makes the cfDNA test easy to use in a clinical lab setting) and cfChIP-seq on plasma cfDNA based on epigenetic markers to identify specific cellular/organ involved in COVID-19 pathogenesis and identify potential mechanistic insights associated with heightened inflammatory response. Compared to the previous study, the limited sample size raises concerns about the significance of whole-genome bisulfite sequencing data in COVID-19 patients. Additionally, whether the tissue-specific cfDNA tracks specific COVID-19-associated endotypes has yet to be discussed. Taken together, this cfDNA may help to understand COVID-19 pathogenesis and define tissue or organ injuries.
My expertise is in Genomics and Immunology.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript entitled "Epigenetic liquid biopsies reveal elevated vascular endothelial cell turnover and erythropoiesis in asymptomatic COVID-19 patients," Ben-Ami and colleagues perform WGBS, targeted methylation assay and cfChIP-seq to measure cellular turnover/death or tissue injuries and infer gene expression profile in COVID-19 patients and healthy controls. First, they performed deep WGBS on severe COVID-19 and HC plasma samples, applied the novel UXM algorithm that includes 40 human cell types to identify the tissue origins of cfDNA, and showed increased cfDNA from diverse cell and tissue types in COVID-19 patients than healthy controls. Besides WGBS, they also performed targeted methylation assay to measure cellular turnovers/death and tissue injury from major cell and tissue types involved in COVID-19 pathogenesis and used as a predictor of poor outcome. Finally, they showed that cfChIP-seq can identify heightened immune responses associated with COVID-19 and asymptomatic patients. Previous studies have shown that cfDNA has a great potential to map tissue injuries in COVID-19 and predict patient outcomes (Cheng et al., 2021 & Andargie et al., 2021). The expanded reference methylation atlas and the addition of targeted methylation assay and cfCHIP-seq in this study are very informative and fascinating. Please allow me to congratulate Ben-Ami and colleagues for this wonderful work.
Below are some points that need to be addressed to improve the manuscript:
Major
- Given the heterogeneous nature of COVID-19 clinical manifestation, the limited number of patients (n=6) raises concern about the significance of WGBS analysis. The authors need to provide further details as to why they performed WGBS only from 6 samples out of 120 subjects and what was the selection criteria.
- The gene expression analysis with cfCHIp-seq is interesting. Likewise, Differentially Methylated Regions (DMR) can infer gene expression. Is the methylation analysis also showing increased interferon response in COVID-19 patients? This study also showed increased cfDNA from monocytes that is not reflected in blood cell counts. Does cfCHIP-seq identify inflammatory response-related genes in monocytes/macrophages? Hadjadj et al. 2020 (PMID: 32661059: Science) reported impaired interferon response in severe COVID-19 patients. Whereas this study showed heightened interferon response in severe and asymptomatic/mild COVID-19 patients compared to healthy controls, there was no difference between Mild and Severe COVID-19 patients. The author should consider validating their finding with plasma cytokine measurement. cfChip-seq also identifies cfDNA tissues-of-origin (PMID: 33432199). How is the correlation between these three assays (WGBS, targeted methylation assay and cfCHIP-seq) to detect cell death/turnover?
- It is unclear whether hospitalized COVID-19 subjects experienced particular organ involvement. It would be interesting to link the tissue-specific cfDNA to different COVID-19 endotypes. For instance, cardiac involvement and cardiomyocyte cfDNA.
- Previous studies reported cfDNA concentration in healthy controls ranges between 3 and 15 ng/mL. This study's median cfDNA level for asymptomatic COVID-19 patients falls within that range. It would be interesting if the authors comment on the methodology differences, including plasma volume, correction for extraction efficiency, and cfDNA assay type.
- Were the asymptomatic/Mild case samples collected in the same time frame as Hospitalized patients? It would be interesting if the authors comment on the effect of SARSCOV-2 variants and viral loads on plasma cfDNA level.
- The author showed cfDNA from total T cells and CD8 cells in particular. The authors should comment on why CD4+ T was not shown instead of T cells (which includes both CD4 and CD8 cells).
- Considering the expensive nature of deep sequencing, it would be interesting if the authors comment on applying the UXM algorithm for low and medium- and low-coverage sequenced samples.
Minor
- The timing of blood sample collection from hospital admission or testing positive for COVID-19 is important to use cfDNA as a predictor of outcome. The authors should explain when the sample was collected for asymptomatic/mild patients. If it's not in the "acute phase, " it should also be clarified for comparison with hospitalized COVID-19.
- Is there a reason the authors included repeated measures of cfDNA within the same subject (N=120, n-142; Figure 1A)? The author should consider statistical correction for repeated measures. This is important to reduce bias.
- I believe the authors forgot to include "Code and data availability" declaration. I encourage the authors to make publicly available the WGBS data and deconvolution algorithm for reproducibility purposes.
- Figure 1D should show individual data points to see the pattern of tissue-specific cfDNA better, especially as COVID-19 shows heterogeneous clinical presentation. Please consider overlaying the data point on the histogram.
- Methods - Page 27, the first sentences from the last paragraph, please include the unit.
- after the number "75".... In fact, this paragraph is identical to the previous paper (PMID: 33432199); please consider paraphrasing the section.
- Please clearly define "deteriorated." What WHO score or range is considered as deteriorated?
- The authors mix between 40 and 37 reference cell types. Please be consistent.
- Page 6, line 3, please replace erythrocyte with erythroblast.
- Page 28, line 10, please replace COVID with COVID-19.
- Figure 5D needs a key for recovered versus deteriorated.
- Figure 5, legend title, please fix the number of healthy controls.... (n=30-45).
Significance
This manuscript used a deep WGBS approach with an expanded human cell-type methylation atlas and novel deconvolution algorithm, targeted methylation assay (which makes the cfDNA test easy to use in a clinical lab setting) and cfChIP-seq on plasma cfDNA based on epigenetic markers to identify specific cellular/organ involved in COVID-19 pathogenesis and identify potential mechanistic insights associated with heightened inflammatory response. Compared to the previous study, the limited sample size raises concerns about the significance of whole-genome bisulfite sequencing data in COVID-19 patients. Additionally, whether the tissue-specific cfDNA tracks specific COVID-19-associated endotypes has yet to be discussed. Taken together, this cfDNA may help to understand COVID-19 pathogenesis and define tissue or organ injuries.
My expertise is in Genomics and Immunology.
-
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Referee #1
Evidence, reproducibility and clarity
Summary:
The authors analyzed circulating cell-free DNA for COVID-19 using deep sequencing of the methylation and histone modification. The major output was cell-specific quantification. The study involved 120 unvaccinated, hospitalized patients, 19 asymptomatic/mild cases, and 40 controls. Between COVID-19 and controls, they found significant differences in lung epithelial cells, cardiomyocytes, vascular endothelial cells and erythroblasts. The latter two cell types had significant differences even in the asymptomatic patients. It is unclear if the damage seen is related to COVID-19 specifically, or related to general inflammation or infection.
Strengths of the study include relatively high WGBS/targeted sequencing, along with fragment-level analysis with methods described in their previous work (Loyfer et al. Nature). In addition, they add and ChIP-seq data using their published methods. The work comes from a group with leading expertise in methylation cell-free DNA analysis.
Overall, the work is most comprehensive analysis to date for COVID-19, and the data would be a valuable resource to the research community. We have major and minor comments that do not necessarily require additional experimental work.
Major comments:
- There is a lack of data and the methods are presented in such a way that the results and conclusion can be reproduced and evaluated. Neither the code nor the data to generate the results are available. Both need to be made available during the peer review process.
Missing data: Fragment-level FASTQ, BAM, or PAT files are needed to reproduce the results. Missing Scripts, for example in Github, is standard and reasonable for reproducing the figures shown. Missing targeted assay method details: - The authors should show the data, methods, and details for: "The validation of markers was done using DNA extracted from different cells and tissues, and the methylation status of the CpG block was assessed."
- The authors did not list the major limitations of the study in the discussion or elsewhere. These should include (or be addressed with experimental or conclusion changes):
- The small sample size of the asymptomatic/mild group (if the emphasis of the paper, as suggested by the title, is on the asymptomatic/mild group - see the next major point).
- The targeted assay is used on the vast majority of samples, including all of the asymptomatic/mild group. However, it is limited to a particular subset of cell types (total defined by all possible cell types in the body). Those cell types were determined based on WGBS data on only 6 COVID-19 cases.
- The methylation references for the WGBS data were limited to a fraction of all human cell types. For example, this paper was not able to evaluate Schwann cells or peripheral nerves, which was a significant finding for COVID-19 related multisystem inflammatory syndrome (PMID 37279751).
- The case and control groups (severe, asymptomatic mild, and control) were collected at different times and circumstances, allowing for potential pre-analytical confounders.
- cfDNA levels can be influenced by several unmeasured factors, including death, replication leading to more turnover, clearance/stability, and movement from tissue into circulation. The methods used cannot distinguish between these possibilities.
- (if true) the controls used for the targeted assay were not age/sex matched. The median age for the controls skew younger per Table S1, S2, S3.
- (optional) It is unclear whether the differences found are attributable to COVID-19, coronavirus infection, viral infections, infections in general, or inflammation in general. The appropriate alternative controls were not addressed in this study. The paper shows some degree of correlation with acute inflammatory markers (CRP, ferritin, neutrophil contribution).
- The title is a bit misleading in that it revolves around the asymptotic patients. However, this is also the group with the lowest representation at n=19. The vast majority of the data is related to the hospitalized patients. While other studies may have looked at hospitalized patients, I agree with the authors that there is merit in deep sequencing and the correlated clinical data.
- More details on the patient inclusion criteria are needed. Were the asymptomatic/mild positive by PCR test or a point of care immunoassay? We know the viral load is quite dynamic for these patients. What was the timing of the blood draw?
Likewise, how did you find the hospitalized patients? Was it comprehensive over a period of time? These details help reveal any potential biases in the selection process.
Minor comments:
- The abstract states: "Asymptomatic patients had elevated levels of immune-derived cfDNA but did not show evidence of pulmonary or cardiac damage." However, in Fig 5, there seems to be a bimodal distribution for the lung epithelial and cardiomyocytes. Unclear if that is an artifact of the graph.
It is quite interesting that the asymptomatic/mild group seems to have a bimodal distribution in lung epithelial and cardiomyocyte cfDNA. Perhaps this data is not available, and the sample size is small, but could there have been a clinical difference between the two groups (e.g. asymptomatic versus mild, or had symptoms later?). It is unclear how precise the measurements are for the lung epithelial cells. 2. The authors listed 2 prior studies that looked at cell type or tissue damage during COVID-19. There are 2 other studies that I am aware of: PMID: 33651717 (n=84 with n=18 nonhospitalized) but probably shallow WGBS, and 37279751 (n=205 pediatric patients). Importantly, the latter paper found Schwann cells were significantly elevated, which is missing from the current study's assessment. In addition, citation 14 from the same group already found significantly increased vascular endothelial cfDNA in COVID-19 patients with severe disease versus mild. While some findings are consistent, there are also discrepancies. 3. Is Fig 2 necessary? Fig. 5 seems to display the same data but with the asymptomatic group. 4. "Elevated lung cfDNA reflects excessive lung cell death" - recommend this statement is tempered as direct evidence is not available in this study. An alternative explanation could be that endothelial cells are damaged, and it is easier for lung cfDNA to enter blood circulation rather than the respiratory system. 5. Fig 6: Add unit of measure to heatmap. Supplemental Fig 1.: Add label to unit of measure in caption or figure. Average or median beta value over a series of CpGs? 6. The authors state the targeted assay "allows for a more accurate and sensitive detection of cfDNA from a given source", which should be tempered unless clear evidence is presented for these statements. In addition, it targets only a small subset of all cell types. The highest cell type contribution from MK cells is only represented by 2 markers. 7. Targeted assay has a few caveats that the authors should mention or fix: The method is not described in detail. Methods besides WGBS can have biases in methylation representation and a beta correlation between the 12 samples that underwent WGBS and the targeted assay would be reassuring. The level of precision at the lower end of cellular contribution would be helpful too. The lung epithelial and cardiomyocyte cells were present at the lower end of the spectrum. This can be shown in a titration of the purified cells into plasma, or at least an in silico titration analyzed with only the targeted markers. 8. The authors state "(i) Evidence of frequent cardiomyocyte death in hospitalized patients... it has not been appreciated that cardiac cell death is a feature shared by most hospitalized patients." However, COVID-19 patients have elevated troponin.<br /> 9. The authors state "This signal presumably reflects elevated turnover of red blood cells and increased rate of erythropoiesis". However, could it be also higher nucleated RBCs released into circulation as the authors cited? 10. Fig 2, 4, 5: The graphs seem to suggest that the authors picked 0.001 GE/mL as not detected. Should they label that point appropriately as "not detected" or "ND"? It is not clear why 0.001 GE/mL was picked, and the analytical sensitivity of the targeted test is not reported. 11. How many mLs of plasma were used?
Significance
General assessment:
Strengths 1. Interesting topic: Non-invasive tabulation using deep methylation sequencing of cell type shedding into circulation of an important disease (COVID-19). 2. Deep sequencing using methylation and histone output is a significant improvement on past studies. Although targeting limits the scope of the cell types, the targeting was based on relatively deep WGBS sequencing on 6 cases and 6 controls.
Limitations The unique aspects (targeted assay and deep sequencing) are missing data and detailed methodology for reanalysis and reproducibility. See major comment 2.
Advance: The authors used deep sequencing through brute force (WGBS) and a unique targeted assay to study COVID-19 from a large group (n=120 patients). They found that endothelial and erythroblast lineages are overrepresented based on the presence and severity of the COVID-19 infection. Their findings are significant and go beyond what has been published. The methodologies and data (i.e. the controls) would be a great resource to the community that can be used beyond the scope of COVID-19.
Audience: This article would be appealing to a broad, translational/clinical audience. The authors have published on methylation deconvolution several times before, but to my knowledge, the broader targeted assay is unique and there is a large dataset with correlated clinical information that may be of broad utility.
Reviewer expertise: technical expertise with circulating cell-free DNA. translational/clinical expertise.
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Reply to the reviewers
Response to Reviewers
We thank the three reviewers for their insightful and constructive comments, which have helped improve the manuscript. Our replies to each comment are provided below.
Reviewer #1
Evidence, reproducibility and clarity
The abscission checkpoint, also known as NoCut, is a genome protection mechanism that remains poorly understood. This pathway is conserved from yeast to humans and protects the genome against chromosome bridges, a dangerous missegregation event that can have catastrophic consequences on genome stability. Dam et al now report the role of Srs2, a DNA helicase, as a key factor in the abscission checkpoint. The authors establish Srs2 as bona fide factor in this pathway by showing its involvement in abscission delays when chromatin bridges are induced. Importantly, yeast defective for Srs2 show increased levels of DNA damage when the frequency of chromatin bridges is increased. The authors also provide genetic evidence supporting a model whereby the interaction of SrS2 with PCNA s required for abscission regulation. In the second part of the manuscript, the authors study the human homologue of SRS2, PARI, in abscission regulation. The manuscript provides convincing evidence that PARI is also required for abscission delays in the presence of chromatin bridges. Critically, this role is specific for chromosome missegregation as abscission delays in response to nucleoporin depletion remain intact in PARI-depleted cells. Thus there is a conserved requirement for these DNA helicases in the abscission checkpoint.
* Overall, these are important advances in our understanding of the abscission checkpoint. The data is high quality and convincing in general. However, the impact of PARI depletion on genome stability needs to be further demonstrated to support key claims in the manuscript. Specifically:*
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Disruptions of the abscission checkpoint in human cells result in bi-nucleation or increased levels of DNA damage. In this context, the authors need to show that PARI-depleted cells with increased frequency of chromatin bridges exhibit increased levels of bi-nucleation, DNA damage or both.
We thank the reviewer for its positive assessment of our work. While our data establish that Srs2 inhibits abscission to prevent DNA damage in yeast, we agree with the reviewer that we have not tested the consequences of PARI loss on DNA damage or cytokinesis failure in HeLa cells. We will address this in the revised version of our study.
Significance
The abscission checkpoint, remains poorly understood. There is evidence in the literature that disruptions in this pathway increase susceptibility to cancer. The identification of the Srs2/PARI helicases as key components in this pathway is a considerable step forward in this field.
Reviewer #2
__Evidence, reproducibility and clarity __
The Aurora B-mediated abscission checkpoint ("NoCut" in yeast) prevents tetraploidization or chromatin breakage in the presence of chromatin bridges in cytokinesis and the mechanisms of its activation are a matter of active investigation. In the present study, Dam et al propose that the conserved Srs2/PARI DNA helicase is required for the activation of the abscission checkpoint in response to chromatin bridges generated by DNA replication stress or topoisomerase inhibition. This is a timely and very interesting topic and the potential identification of a novel regulatory protein that activates the abscission checkpoint would be important. However, in my opinion, some Figures are of relatively low quality and need improving, there are apparent discrepancies between data and important control experiments are missing, which preclude the reader from fully evaluating the conclusions of this study. Some direct evidence of the role of Srs2/PARI on DNA bridges is also required. Also, it would be nice to investigate mechanistic details of the potential Srs2/PARI functions in the abscission checkpoint, and how it fits with other recently published signaling pathways that activate the abscission checkpoint in cytokinesis.
Specific comments: 1. The DNA channel (Ht2B-mCherry) in Figure 1A is of very low quality to be able to verify the authors interpretations of when the individual chromatin bridges are resolved (probably broken). For example, in the WT movie, they claim that the bridge is intact in frames 10 min and 14 min (yellow arrow) and that the bridge is resolved at 16 min (asterisk); however, I'm not convinced this is the case, because I can only see a very small portion of the bridge already at the 10 min and 14 min time-points. In my opinion, this bridge could have been broken much earlier, probably at 10 min. Also, WT +HU, is this bridge really intact at 10 min and at 14 min? In Srs2Δ + HU, the bridge appears broken to me much earlier, perhaps at 30 min. There is a distinct possibility that the authors could not calculate the resolution times accurately from these movies (please also see my next comment, #2). The authors could perhaps use a more sensitive bridge marker such as GFP-BAF.
To clarify our approach, chromosome segregation was considered complete only when bridges were no longer detectable, while discontinuous or faint bridges were still classified as unresolved, as stretched DNA may result in weak nucleosome signals. This definition aligns with the bridge resolution times reported in Figure 1B-E. To improve clarity, we have revised the Results section to specify our classification criteria, and added all frames from the time-lapse movies in Figure 1A as a new figure (Supplementary Figure S1).
In Figure 1B, they conclude that Srs2Δ cells treated with HU exhibit increased time from anaphase onset to bridge resolution compared with WT or Srs2Δ cells. This result appears at odds with data from Fig. 2C showing that Srs2Δ+HU finish abscission at similar times to WT or Srs2Δ cells as judged by plasma membrane morphology. (final cut). Given that the final cut of the plasma membrane should cause chromatin bridges to break, if Srs2 is required for an abscission delay in response to HU-induced chromatin bridges, I would expect Srs2Δ + HU cells to exhibit accelerated plasma membrane cut and also faster chromatin bridge resolution compared with controls. This discrepancy could at least in part be caused by the relatively low quality of movies used for the calculations in Fig. 1.
This is a perceptive point. To clarify, we analyzed the timing of chromosome segregation, membrane ingression at the abscission site, and abscission relative to anaphase onset, as shown in the new Supplementary Figure S2. In HU-treated cells (both WT and srs2∆), bridge resolution and membrane ingression occur around the same time (~10 minutes after anaphase onset), with srs2∆ cells exhibiting slightly earlier membrane contraction. This suggests that bridges resolve during cytokinesis (see also our reply to the next comment) but does not distinguish whether they break prematurely or resolve normally. Our key finding is that membrane abscission is delayed in HU-treated cells in an Srs2-dependent manner, raising the question of whether this delay is important to prevent bridge breakage. This hypothesis is tested and supported by Figure 2D, where delaying cytokinesis (via cyk3∆) reveals the protective role of Srs2.
Fig. 2 shows faster abscission times (membrane cut) in Srs2Δ+HU cells compared with WT+HU. The authors interpret this data as evidence for a role of Srs2 in abscission delay in response to HU-induced chromatin bridges (page 7 and elsewhere). However, there is no direct evidence that the cells analyzed in Fig.2 exhibited DNA bridges in cytokinesis. One could argue that HU-induced DNA replication stress caused DNA lesions at the nuclear chromatin, which affected completion of cytokinesis in the absence or presence of Srs2. What proportion of HU-treated cells in cytokinesis exhibit DNA bridges? Judging from Fig. 1D this could be as low as 0-20%. The authors should analyze HU-treated cells that clearly exhibit DNA bridges, either by live-cell imaging or in fixed cells experiments. As it stands and together with my previous comments #1 and 2, I'm not convinced this data fully supports a role for Srs2 in the abscission delay in response to HU-induced DNA bridges.
We appreciate the reviewer's concern. The presence of chromatin bridges in HU-treated cells during cytokinesis (membrane ingression) is documented in the new Supplementary Figure S2, as noted in our response to the previous comment. Additionally, our previous study (Amaral 2016, PMID: 27111841, Figure 1D) demonstrated that under the same HU treatment conditions used here, >90% of wild-type cells exhibit chromatin bridges during cytokinesis. This strongly supports the conclusion that the effects observed in Figure 2 are linked to the presence of DNA bridges.
In Fig. 2D, there is no evidence to support that Mre11 foci are caused by bridge breakage, and not by replication-stress induced DNA lesions at the main nucleus (no DNA bridge is evident, also see comment #3).
The use of the cyk3 mutant in Figure 2D specifically addresses this concern. If Mre11 foci resulted from replication stress-induced lesions in the main nucleus, delaying cytokinesis should have no impact on damage levels. However, we observe that delaying cytokinesis via the cyk3 mutation significantly reduces Mre11 foci, strongly suggesting that these foci arise from chromatin bridge breakage rather than replication stress, and that delaying cytokinesis provides extra time to solve the chromosome segregation problem. This conclusion is further supported by previous studies showing that cyk3∆ delays cytokinesis (Amaral 2016, PMID: 27111841, Figure 2C; Onishi et al. 2013, PMID: 23878277). We have clarified this point in the revised text.
Figure 3: the authors use a top2-4 mutant strain to generate DNA bridges from catenated DNA and investigate the potential role of Srs2 in the abscission delay. However, no DNA bridges are obvious in the cells shown in Fig. 3. What proportion of top2-4 mutant cells in cytokinesis exhibit DNA bridges? Does this explain the striking difference in the percentage of cells that haven't completed abscission after 30-60 min in WT+HU vs Top2-4 cells? Please also see my previous comments above.
The top2-4 mutant is well-characterized, and under the conditions used here, 100% of cells exhibit DNA bridges during cytokinesis (see for example Amaral et al., 2016, Figure 3A). We have clarified this point in the revised text. Notably, previous work has shown that top2-4-induced bridges are thicker and more persistent than those caused by HU-induced replication stress. This difference might contribute to the more severe abscission defect observed in top2-4 cells, though we have not directly tested this.
The authors propose that association of Srs2 with PCNA is required for complete inhibition of abscission in top2-4 mutant cells with chromatin bridges. Assuming a role for Srs2 in abscission timing in cytokinesis with chromatin bridges is fully proven, it is essential that the authors also investigate the localization of Srs2 and PCNA on chromatin bridges, using GFP-tagged proteins or appropriate antibodies in fixed and/or living cells. This would suggest a direct role of these proteins on chromatin bridges and considerably strengthen the authors hypothesis. Alternatively, Srs2 and PCNA may indirectly affect abscission timing through their well-established roles at nuclear chromatin.
The perturbations used in Figure 4 have been previously shown to disrupt Srs2-PCNA and PCNA-chromatin interactions (Armstrong et al., 2012; Ayyagari et al., 1995; Johnson et al., 2016; Kubota et al., 2013), as referenced in our manuscript. Given this well-established evidence, we believe additional imaging experiments would be redundant. Moreover, we do not claim that Srs2 or PCNA must specifically localize to chromatin bridges for NoCut function. Instead, our data demonstrate their genetic requirement for abscission inhibition in the presence of bridges. Whether these proteins localize exclusively on bridges or more broadly on chromatin remains unresolved, a point we explicitly discuss in the manuscript.
In Fig. 4D, the authors show an abscission delay in elg1Δ mutant cells in the presence of dicentric bridges compared with cytokinesis without bridges and interpret this as evidence that artificially retaining PCNA on dicentric chromatin bridges is sufficient to inhibit abscission. It is important that the authors demonstrate that PCNA localizes to dicentric bridges in elg1Δ mutant, but not in ELG1 control, cells, e.g., by immunofluorescence, to support their claim and their proposed model.
As noted in our previous response, the association of PCNA with chromatin throughout the cell cycle and its regulation by Elg1 have been extensively characterized in prior studies. Given this established evidence, additional imaging experiments would be redundant.
We also clarify that we do not claim that PCNA is specifically retained on chromatin bridges in elg1Δ mutants. Rather, our model is based on the overall retention of PCNA on chromatin in elg1Δ cells, as demonstrated in published studies.
Notably, elg1Δ mutants without dicentric bridges retain PCNA on chromatin but do not exhibit delayed abscission. However, only elg1Δ mutants with chromatin bridges inhibit abscission, indicating that PCNA retention alone is not sufficient—it is the presence of a bridge with retained PCNA that is critical. This distinction has been clarified in the revised manuscript.
In Fig. 5, the authors claim that HeLa cells treated with the Top2 inhibitor ICRF193 exhibit delayed midbody resolution compared with controls and that depletion of PARI by siRNA accelerates abscission in ICRF-treated cells. They interpret this as evidence for a role of PARI in the abscission delay in response to ICRF-induced chromatin bridges. However, no bridges are visible at any time-frame in cells in Fig. 5B raising the possibility that the observed time-differences are due to some effect of ICRF in cytokinesis without bridges. I'm also not convinced that in Fig. 5B the midbodies in NT/ICRF/230 min, siPARI/DMSO/110 min and siPARI/ICRF/150 min were resolved as indicated by the authors, as I can definitely see both midbody arms very clearly in these photos. The p-values are also just below the p
We acknowledge that the chromatin bridges in Figure 5B are challenging to visualize and may appear discontinuous. This is not due to poor image quality but likely reflects the low chromatin density of these structures. To clarify this, we now include magnified and contrast-enhanced images to better highlight the bridges, and quantification in Fig. 5C. Additionally, in the revised manuscript, we will provide new images using GFP-BAF, which directly binds DNA, to more clearly demonstrate the presence of chromatin bridges in ICRF-treated cells. These data will confirm that most cytokinetic cells in ICRF-treated conditions exhibit bridges.
Regarding the midbodies shown in Figure 5B, the presence of one or both arms intact does not indicate unresolved abscission but rather that the midbody has been severed, a distinction we explicitly describe in the manuscript.
Concerning the statistical analysis, we note that the p-value threshold of 0.05 is a widely accepted convention for statistical significance, and we have applied it appropriately in our analysis.
Finally, regarding the EM images in Figure 5C, these are single-section images, which do not allow us to determine definitively whether the bridges are physically broken when they appear discontinuous. It is possible that portions of the bridge extend outside the sectioned image. Regardless, we do not claim that these bridges are intact or broken. Rather, our key conclusion is that their presence at the abscission site in ICRF-treated cells is not affected by PARI knockdown, supporting our model.
In Fig. 6, the authors examine actin patches in PARI-depleted and control cells as a marker of abscission. Although a role for PARI in actin patch formation would be very interesting, I'm not sure how it fits with the present story. The actin inside the intercellular canal described by Bai et al (removal of which correlates with abscission) appears very different to the accumulations of actin at the base of the intercellular canal described by Sreigemann et al and by Dandoulaki et al. I can definitely see actin patches (similar to the ones in Steigemann et al) in Fig. 6 NT/ICRF, but I can't see any at the other treatments (I disagree with the arrows). Incidentally, I can see a DNA bridge only in NT/ICRF, but not in the other treatments.
We have revised our description of this figure for greater clarity. In control cells, actin accumulates at the cleavage furrow during anaphase and gradually disperses (clears) as cytokinesis progresses. We do not see patches in untreated cells, and we have updated the y-axis label in Figure 5B from “% of cells with actin patches” to “% of cells with actin clearance” to better reflect our observations.
Actin patches were observed only in ICRF-193-treated cells and were often associated with chromatin bridges. Cells that successfully disassembled these actin patches were classified as having completed actin clearance. Our data indicate that PARI depletion increases the fraction of cells that clear chromatin from the division plane, facilitating actin patch disassembly.
The actin patches observed in our study closely resemble those reported by Steigemann et al., and notably, we used the same cell line as in that study. Regarding Bai et al., they used both phalloidin and actin-GFP. For example, Figure 5C in Bai et al., shows examples of both actin patches near chromatin bridges, which resemble those in our study, and filamentous actin structures within the intercellular canal, which appear distinct.
Finally, a bridge fragment lacking actin patches is visible in PARI knockdown cells treated with ICRF, and we have now highlighted this in the revised figure.
- Midbody resolutions are clearer in Fig. 7, perhaps with the exception of siPARI/DMSO. However, no DNA bridges are visible, raising again the possibility that the authors investigate effects in cytokinesis without DNA bridges.
See our response to point 8: while bridges are difficult to visualize, our analysis confirms that ICRF treatment induces bridges that persist during cytokinesis.
Can the authors investigate whether the helicase activity of PARI is required for the abscission checkpoint, by depletion-reconstitution experiments with a helicase-mutant protein?
PARI lacks detectable Walker motifs and associated ATPase activity, suggesting PARI lacks helicase activity (Moldovan et al., 2012). Therefore, we have not pursued depletion-reconstitution experiments with a helicase-mutant protein.
The authors should investigate localization of PARI to the midbody/ DNA bridge in cytokinesis with chromatin bridges. Recent reports have proposed that a Top2-MRN-ATM-Chk2 pathway activates the Aurora B-dependent abscission checkpoint in human cells (PMIDs: 37638884, 33355621). The authors should examine localization of Aurora B and some of the above proteins in control and PARI-deficient cells to establish if/how PARI fits in the above pathway.
As noted in our manuscript, we attempted to visualize PARI at midbodies and DNA bridges but were unable to detect any signal. This could be due to either its absence in these regions or its low concentration, making detection challenging.
We agree that investigating the Top2-MRN-ATM-Chk2 pathway in this context is important. We will examine the localization of key pathway components, including Aurora B, in control and PARI-deficient cells, and include the results in the revised manuscript.
- The authors use ICRF to generate chromatin bridges. If ICRF is continuously present in their assays, one would expect it to inhibit Top2 and impair the abscission checkpoint (PMIDs: 37638884, 33355621). How do the authors reconcile this with their proposed model?
This is an important point. Studies from the Zachos lab have shown that Topoisomerase IIα-DNA covalent complexes (Top2ccs) accumulate near the midbody in cells with chromatin bridges and play a key role in initiating abscission checkpoint signaling by recruiting MRN, ATM, and Aurora B. Supporting this model, ICRF-193 treatment does not alter midbody disassembly timing in HeLa cells, as shown in Petsalaki et al., 2023 (Figure S4D).
However, our results indicate that ICRF-193-treated HeLa cells exhibit delayed midbody severing, suggesting that at least some aspects of abscission checkpoint signaling remain active under these conditions. One possible explanation for this discrepancy is the difference in ICRF-193 concentration: our study uses a low dose (250 nM) versus 10 µM in the Zachos group study. We favor the hypothesis that this lower dose preserves sufficient Top2 activity to support some level of checkpoint signaling while still effectively generating chromatin bridges.
Additional comments:
Page 8: "Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with PCNA". The authors should test this experimentally or provide appropriate references supporting this claim.
We have clarified our statement and provided the reference: Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with non-SUMOylated PCNA (Armstrong et al. 2012).
- Page 6: "Deletion of SRS2 further increased the fraction of anaphase cells with RPA foci, rising to approximately 30% in the absence of HU..."; however, this rise was not statistically significant as indicated in Fig. 1C.
Thank you for noting this - we have removed this statement.
Fig. 1C, D: SDs are missing. Fig. 1E: please show the p-values.
These data in Figures 1C-D represent percentages from cells pooled from two independent experiments with similar results. P-values were calculated using Dunn’s multiple comparison test. Standard deviations are not applicable in this case. We have included the p-values for Figure 1E.
Fig. 2D: please show SDs and individual values.
These data represent percentages from cells pooled from independent experiments with similar results. P-values were calculated using Fisher’s exact test. Standard deviations and individual values are not applicable in this case.
- Why do the authors show the spindle pole body in their movies?
We do this to infer the time of anaphase onset; see our response to points 1-3 and Fig. S2.
Fig. 4A: WT and top2-4 cells have the same symbol in the graph.
We have changed the symbols.
Significance
Strengths: potentially novel regulator of the abscission checkpoint. Timely and interesting topic of broad scientific interest.
Limitations: problems with quality of some data and withy the interpretation. Also, more mechanistic evidence is required to significantly advance our knowledge in the field.
Reviewer #3
Evidence, reproducibility and clarity:
Summary: Building on the specific connection between DNA bridges that bear marks of replication stress and the NoCut checkpoint (Amaral 2016, 2017), which prevents completion of cytokinesis, Dam et al. test the helicase Srs2/PARI for a role in this checkpoint pathway. The authors have produced a thorough study investigating the role of this helicase in both yeast and mammalian cells in the presence of DNA bridges. The manuscript includes clear evidence that Srs2 is important to resolve chromatin bridges, remove replication protein A (RPA) from chromatin, and delay cytokinesis under replication stress. Further, the authors show that loss of Srs2 under replication stress increases DNA damage, marked by elevated MRE11 foci in a manner dependent on cytokinesis (i.e., dependent on Cyk3). Srs2 deletion also partially abrogates the abscission delay seen upon topo-II inactivation. They further report that Srs2 must interact with PCNA to delay abscission in S. cerevisiae. While chromatin bridges formed when a dicentric chromosome is present escape detection by the NoCut checkpoint, inactivation of Elg1, which unloads PCNA and associated factors following DNA replication, results in delayed abscission. In HeLa cells, the Srs2 ortholog PARI is shown to similarly help promote abscission delay in the presence of DNA bridges following topoisomerase inhibition, as loss of PARI through siRNA knockdown prevents this abscission delay. Mechanistically, when PARI levels are reduced in HeLa cells, actin patches that function to stabilize the midbody and protect DNA bridges do not form/persist robustly as in cells with intact PARI. Consistent with a specific role in sensing the presence of a DNA bridge, depletion of PARI did not impact abscission checkpoint activity in response to depletion of the NPC component, Nup153. Finally, the authors show that PARI depletion reduced time to abscission to the same extent as treatment with an Aurora B inhibitor, and PARI depletion in conjunction with Aurora B inhibition did not reduce abscission timing further than singular treatments, suggesting that PARI works within the Aurora B-mediated NoCut signaling cascade.
Major comments: The manuscript is well written and, in general, the conclusions are thoroughly supported, but there are a few recommendations for addition or revision.
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The first of these is for a more thorough introduction of helicases potentially involved in cytokinesis and more clear rationale for why the focus is on Srs2.
We appreciate the reviewer’s suggestion and have expanded the introduction to better contextualize helicases in cytokinesis and clarify our focus on Srs2.
Figure 1 E lacks statistical analysis. In addition, the text referring to 1E leads to confusion because the distinction between "RPA foci during anaphase" and "RPA coated chromatin bridges" is not made clear. The authors should clarify that the data presented in 1E shows quantification of cells with RPA foci during anaphase, not RPA coated chromatin bridges, and use consistent wording between the text and figure/figure legend. Further, how cells with RPA foci were identified, and what is classified as an RPA focus from images should be described in the methods.
We appreciate the reviewer’s feedback. In the revised manuscript, we have included statistical analysis for Figure 1E and clarified the distinction between "RPA foci during anaphase" and "RPA-coated chromatin bridges" to ensure consistency. Additionally, we have updated the Methods section to specify how cells with RPA foci were identified and what criteria were used to classify RPA foci based on the imaging data.
In some cases, it is unclear whether DNA bridge formation is prevented vs aberrantly broken. For example, under Top2 inactivation, does the absence of Srs2 prevent bridge formation or promote their breakage along with premature midbody abscission? Confirming the frequency of chromatin bridge formation would address this and, further, monitoring RPA persistence would validate whether RPA clearance from bridges is consistently correlated with Srs2 activity (an interesting observation from Figure 1 that is not followed up on). Similarly, other conditions that appear to interfere with abscission delay (e.g., disrupting Srs2-PCNA interaction) should be monitored for whether the formation of DNA bridges has been altered.
We agree this is important and will address it in a full revision. We will quantify chromatin bridge formation under Top2 inactivation to determine whether Srs2 mutations affect bridge frequency or stability. Additionally, we will monitor RPA persistence in top2 cells to assess whether RPA clearance correlates with Srs2 activity. While we find it unlikely that bridge formation is prevented by srs2 mutations, as Top2 is essential for decatenation, our experiments will directly test this possibility.
In Figure 4A, the data show that the PIP-box is required for timely abscission. Imaging data from yeast strains with the PIP-box deletion alone should be included, rather than only showing the deletion in combination with the SIM deletion.
We agree with the reviewer’s suggestion, and will include imaging data from yeast strains with the PIP-box deletion alone in the revised manuscript.
While the authors state that PARI and PCNA were not detectable at bridges in mammalian cells, it would be worth examining whether RPA is persistent on DNA bridges in mammalian cells depleted of PARI to understand how closely this pathway resembles the features found in yeast.
Here too, we agree with the reviewer’s suggestion, and will include imaging data from HeLa cells visualizing RPA in the revised manuscript.
In Figure 6, the authors should describe in the methods how cells with actin patches were identified and quantified and explain what criteria must be met to be identified as an actin patch. Actin patches were described as "disassembling more quickly" in PARI-depleted cells, but the images look as if actin patches are not forming properly in these cells. The images are crisp and clear, but a change in wording may be necessary to accurately describe the data.
Thank you for pointing this out. We agree that the wording was confusing (see our reply to reviewer 2, comment 9) and have revised our description of this figure for greater clarity. In control cells, actin accumulates at the cleavage furrow during anaphase and gradually disperses (clears) as cytokinesis progresses. We do not see patches in untreated cells, and we have updated the y-axis label in Figure 5B from “% of cells with actin patches” to “% of cells with actin clearance” to better reflect our observations. Actin patches were observed only in ICRF-193-treated cells and were often associated with chromatin bridges. Cells that successfully disassembled these actin patches were classified as having completed actin clearance. Our data indicate that PARI depletion increases the fraction of cells that clear chromatin from the division plane, facilitating actin patch disassembly.
Minor suggestions to improve the manuscript are:
Include a diagram that shows hallmarks of cell division and what is being tracked in particular assays (e.g., DNA bridge duration vs time to abscission).
Thank you for this suggestion, which we have implemented in Figure S2A.
In the elegant CLEM experiments presented in Figure 5, organelle labels could be added to orient the readers.
We added organelle labels to CLEM images.
The data in supplemental Figure 2 should be moved to Figure 5. The fact that there are similar levels of chromatin bridges is vital information and stresses that the defect lies in detection and response to the bridge as opposed to formation of bridges when PARI is depleted.
We agree, and have moved Figure S2 to Figure 5 (now Figure 5C).
Significance
The link between DNA bridges and NoCut/abscission checkpoint signaling is a fundamental aspect of cell cycle regulation. This manuscript makes a significant contribution to our understanding of this pathway by introducing a novel role for the helicase Srs2/PARI in execution of an abscission delay in the presence of DNA bridges. This is an important contribution as there is sparse information about cellular factors that mediate detection and response to DNA bridges, which is vital to protecting genome integrity. Although, as the authors themselves state, "the molecular mechanisms by which Srs2 and PARI function in NoCut remain unclear," this study, with some revisions, merits publication as it reveals a conserved role for a factor in this important response pathway and provides new insights into why certain DNA bridges (i.e., bridges formed by dicentric chromosomes) are not recognized by the NoCut pathway.
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Referee #3
Evidence, reproducibility and clarity
Summary: Building on the specific connection between DNA bridges that bear marks of replication stress and the NoCut checkpoint (Amaral 2016, 2017), which prevents completion of cytokinesis, Dam et al. test the helicase Srs2/PARI for a role in this checkpoint pathway. The authors have produced a thorough study investigating the role of this helicase in both yeast and mammalian cells in the presence of DNA bridges. The manuscript includes clear evidence that Srs2 is important to resolve chromatin bridges, remove replication protein A (RPA) from chromatin, and delay cytokinesis under replication stress. Further, the authors show that loss of Srs2 under replication stress increases DNA damage, marked by elevated MRE11 foci in a manner dependent on cytokinesis (i.e., dependent on Cyk3). Srs2 deletion also partially abrogates the abscission delay seen upon topo-II inactivation. They further report that Srs2 must interact with PCNA to delay abscission in S. cerevisiae. While chromatin bridges formed when a dicentric chromosome is present escape detection by the NoCut checkpoint, inactivation of Elg1, which unloads PCNA and associated factors following DNA replication, results in delayed abscission. In HeLa cells, the Srs2 ortholog PARI is shown to similarly help promote abscission delay in the presence of DNA bridges following topoisomerase inhibition, as loss of PARI through siRNA knockdown prevents this abscission delay. Mechanistically, when PARI levels are reduced in HeLa cells, actin patches that function to stabilize the midbody and protect DNA bridges do not form/persist robustly as in cells with intact PARI. Consistent with a specific role in sensing the presence of a DNA bridge, depletion of PARI did not impact abscission checkpoint activity in response to depletion of the NPC component, Nup153. Finally, the authors show that PARI depletion reduced time to abscission to the same extent as treatment with an Aurora B inhibitor, and PARI depletion in conjunction with Aurora B inhibition did not reduce abscission timing further than singular treatments, suggesting that PARI works within the Aurora B-mediated NoCut signaling cascade.
Major comments: The manuscript is well written and, in general, the conclusions are thoroughly supported, but there are a few recommendations for addition or revision. The first of these is for a more thorough introduction of helicases potentially involved in cytokinesis and more clear rationale for why the focus is on Srs2.
Figure 1 E lacks statistical analysis. In addition, the text referring to 1E leads to confusion because the distinction between "RPA foci during anaphase" and "RPA coated chromatin bridges" is not made clear. The authors should clarify that the data presented in 1E shows quantification of cells with RPA foci during anaphase, not RPA coated chromatin bridges, and use consistent wording between the text and figure/figure legend. Further, how cells with RPA foci were identified, and what is classified as an RPA focus from images should be described in the methods.
In some cases, it is unclear whether DNA bridge formation is prevented vs aberrantly broken. For example, under Top2 inactivation, does the absence of Srs2 prevent bridge formation or promote their breakage along with premature midbody abscission? Confirming the frequency of chromatin bridge formation would address this and, further, monitoring RPA persistence would validate whether RPA clearance from bridges is consistently correlated with Srs2 activity (an interesting observation from Figure 1 that is not followed up on). Similarly, other conditions that appear to interfere with abscission delay (e.g., disrupting Srs2-PCNA interaction) should be monitored for whether the formation of DNA bridges has been altered.
In Figure 4A, the data show that the PIP-box is required for timely abscission. Imaging data from yeast strains with the PIP-box deletion alone should be included, rather than only showing the deletion in combination with the SIM deletion.
While the authors state that PARI and PCNA were not detectable at bridges in mammalian cells, it would be worth examining whether RPA is persistent on DNA bridges in mammalian cells depleted of PARI to understand how closely this pathway resembles the features found in yeast.
In Figure 6, the authors should describe in the methods how cells with actin patches were identified and quantified and explain what criteria must be met to be identified as an actin patch. Actin patches were described as "disassembling more quickly" in PARI-depleted cells, but the images look as if actin patches are not forming properly in these cells. The images are crisp and clear, but a change in wording may be necessary to accurately describe the data.
Minor suggestions to improve the manuscript are:
Include a diagram that shows hallmarks of cell division and what is being tracked in particular assays (e.g., DNA bridge duration vs time to abscission).
In the elegant CLEM experiments presented in Figure 5, organelle labels could be added to orient the readers.
The data in supplemental Figure 2 should be moved to Figure 5. The fact that there are similar levels of chromatin bridges is vital information and stresses that the defect lies in detection and response to the bridge as opposed to formation of bridges when PARI is depleted.
Significance
The link between DNA bridges and NoCut/abscission checkpoint signaling is a fundamental aspect of cell cycle regulation. This manuscript makes a significant contribution to our understanding of this pathway by introducing a novel role for the helicase Srs2/PARI in execution of an abscission delay in the presence of DNA bridges. This is an important contribution as there is sparse information about cellular factors that mediate detection and response to DNA bridges, which is vital to protecting genome integrity. Although, as the authors themselves state, "the molecular mechanisms by which Srs2 and PARI function in NoCut remain unclear," this study, with some revisions, merits publication as it reveals a conserved role for a factor in this important response pathway and provides new insights into why certain DNA bridges (i.e., bridges formed by dicentric chromosomes) are not recognized by the NoCut pathway.
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #2
Evidence, reproducibility and clarity
The Aurora B-mediated abscission checkpoint ("NoCut" in yeast) prevents tetraploidization or chromatin breakage in the presence of chromatin bridges in cytokinesis and the mechanisms of its activation are a matter of active investigation. In the present study, Dam et al propose that the conserved Srs2/PARI DNA helicase is required for the activation of the abscission checkpoint in response to chromatin bridges generated by DNA replication stress or topoisomerase inhibition. This is a timely and very interesting topic and the potential identification of a novel regulatory protein that activates the abscission checkpoint would be important. However, in my opinion, some Figures are of relatively low quality and need improving, there are apparent discrepancies between data and important control experiments are missing, which preclude the reader from fully evaluating the conclusions of this study. Some direct evidence of the role of Srs2/PARI on DNA bridges is also required. Also, it would be nice to investigate mechanistic details of the potential Srs2/PARI functions in the abscission checkpoint, and how it fits with other recently published signaling pathways that activate the abscission checkpoint in cytokinesis.
Specific comments:
- The DNA channel (Ht2B-mCherry) in Figure 1A is of very low quality to be able to verify the authors interpretations of when the individual chromatin bridges are resolved (probably broken). For example, in the WT movie, they claim that the bridge is intact in frames 10 min and 14 min (yellow arrow) and that the bridge is resolved at 16 min (asterisk); however, I'm not convinced this is the case, because I can only see a very small portion of the bridge already at the 10 min and 14 min time-points. In my opinion, this bridge could have been broken much earlier, probably at 10 min. Also, WT +HU, is this bridge really intact at 10 min and at 14 min? In Srs2Δ + HU, the bridge appears broken to me much earlier, perhaps at 30 min. There is a distinct possibility that the authors could not calculate the resolution times accurately from these movies (please also see my next comment, #2). The authors could perhaps use a more sensitive bridge marker such as GFP-BAF.
- In Figure 1B, they conclude that Srs2Δ cells treated with HU exhibit increased time from anaphase onset to bridge resolution compared with WT or Srs2Δ cells. This result appears at odds with data from Fig. 2C showing that Srs2Δ+HU finish abscission at similar times to WT or Srs2Δ cells as judged by plasma membrane morphology. (final cut). Given that the final cut of the plasma membrane should cause chromatin bridges to break, if Srs2 is required for an abscission delay in response to HU-induced chromatin bridges, I would expect Srs2Δ + HU cells to exhibit accelerated plasma membrane cut and also faster chromatin bridge resolution compared with controls. This discrepancy could at least in part be caused by the relatively low quality of movies used for the calculations in Fig. 1.
- Fig. 2 shows faster abscission times (membrane cut) in Srs2Δ+HU cells compared with WT+HU. The authors interpret this data as evidence for a role of Srs2 in abscission delay in response to HU-induced chromatin bridges (page 7 and elsewhere). However, there is no direct evidence that the cells analyzed in Fig.2 exhibited DNA bridges in cytokinesis. One could argue that HU-induced DNA replication stress caused DNA lesions at the nuclear chromatin, which affected completion of cytokinesis in the absence or presence of Srs2. What proportion of HU-treated cells in cytokinesis exhibit DNA bridges? Judging from Fig. 1D this could be as low as 0-20%. The authors should analyze HU-treated cells that clearly exhibit DNA bridges, either by live-cell imaging or in fixed cells experiments. As it stands and together with my previous comments #1 and 2, I'm not convinced this data fully supports a role for Srs2 in the abscission delay in response to HU-induced DNA bridges.
- In Fig. 2D, there is no evidence to support that Mre11 foci are caused by bridge breakage, and not by replication-stress induced DNA lesions at the main nucleus (no DNA bridge is evident, also see comment #3).
- Figure 3: the authors use a top2-4 mutant strain to generate DNA bridges from catenated DNA and investigate the potential role of Srs2 in the abscission delay. However, no DNA bridges are obvious in the cells shown in Fig. 3. What proportion of top2-4 mutant cells in cytokinesis exhibit DNA bridges? Does this explain the striking difference in the percentage of cells that haven't completed abscission after 30-60 min in WT+HU vs Top2-4 cells? Please also see my previous comments above.
- The authors propose that association of Srs2 with PCNA is required for complete inhibition of abscission in top2-4 mutant cells with chromatin bridges. Assuming a role for Srs2 in abscission timing in cytokinesis with chromatin bridges is fully proven, it is essential that the authors also investigate the localization of Srs2 and PCNA on chromatin bridges, using GFP-tagged proteins or appropriate antibodies in fixed and/or living cells. This would suggest a direct role of these proteins on chromatin bridges and considerably strengthen the authors hypothesis. Alternatively, Srs2 and PCNA may indirectly affect abscission timing through their well-established roles at nuclear chromatin.
- In Fig. 4D, the authors show an abscission delay in elg1Δ mutant cells in the presence of dicentric bridges compared with cytokinesis without bridges and interpret this as evidence that artificially retaining PCNA on dicentric chromatin bridges is sufficient to inhibit abscission. It is important that the authors demonstrate that PCNA localizes to dicentric bridges in elg1Δ mutant, but not in ELG1 control, cells, e.g., by immunofluorescence, to support their claim and their proposed model.
- In Fig. 5, the authors claim that HeLa cells treated with the Top2 inhibitor ICRF193 exhibit delayed midbody resolution compared with controls and that depletion of PARI by siRNA accelerates abscission in ICRF-treated cells. They interpret this as evidence for a role of PARI in the abscission delay in response to ICRF-induced chromatin bridges. However, no bridges are visible at any time-frame in cells in Fig. 5B raising the possibility that the observed time-differences are due to some effect of ICRF in cytokinesis without bridges. I'm also not convinced that in Fig. 5B the midbodies in NT/ICRF/230 min, siPARI/DMSO/110 min and siPARI/ICRF/150 min were resolved as indicated by the authors, as I can definitely see both midbody arms very clearly in these photos. The p-values are also just below the p<0.05 threshold, which could in part be due to the quality of the movies quantified. Also, in Fig. 5C, the authors show evidence of DNA at the midbody in ICRF-treated cells by CLEM; however, this DNA appears broken before abscission in both cases and could not have been derived from premature abscission.
- In Fig. 6, the authors examine actin patches in PARI-depleted and control cells as a marker of abscission. Although a role for PARI in actin patch formation would be very interesting, I'm not sure how it fits with the present story. The actin inside the intercellular canal described by Bai et al (removal of which correlates with abscission) appears very different to the accumulations of actin at the base of the intercellular canal described by Sreigemann et al and by Dandoulaki et al. I can definitely see actin patches (similar to the ones in Steigemann et al) in Fig. 6 NT/ICRF, but I can't see any at the other treatments (I disagree with the arrows). Incidentally, I can see a DNA bridge only in NT/ICRF, but not in the other treatments.
- Midbody resolutions are clearer in Fig. 7, perhaps with the exception of siPARI/DMSO. However, no DNA bridges are visible, raising again the possibility that the authors investigate effects in cytokinesis without DNA bridges.
- Can the authors investigate whether the helicase activity of PARI is required for the abscission checkpoint, by depletion-reconstitution experiments with a helicase-mutant protein?
- The authors should investigate localization of PARI to the midbody/ DNA bridge in cytokinesis with chromatin bridges. Recent reports have proposed that a Top2-MRN-ATM-Chk2 pathway activates the Aurora B-dependent abscission checkpoint in human cells (PMIDs: 37638884, 33355621). The authors should examine localization of Aurora B and some of the above proteins in control and PARI-deficient cells to establish if/how PARI fits in the above pathway.
- The authors use ICRF to generate chromatin bridges. If ICRF is continuously present in their assays, one would expect it to inhibit Top2 and impair the abscission checkpoint (PMIDs: 37638884, 33355621). How do the authors reconcile this with their proposed model?
Additional comments: 14. Page 8: "Although SIM-defective Srs2 has a lower affinity to SUMOylated PCNA, it can still interact with PCNA". The authors should test this experimentally or provide appropriate references supporting this claim. 15. Page 6: "Deletion of SRS2 further increased the fraction of anaphase cells with RPA foci, rising to approximately 30% in the absence of HU..."; however, this rise was not statistically significant as indicated in Fig. 1C. 16. Fig. 1C, D: SDs are missing. Fig. 1E: please show the p-values. 17. Fig. 2D: please show SDs and individual values. 18. Why do the authors show the spindle pole body in their movies? 19. Fig. 4A: WT and top2-4 cells have the same symbol in the graph.
Significance
Strengths: potentially novel regulator of the abscission checkpoint. Timely and interesting topic of broad scientific interest.
Limitations: problems with quality of some data and withy the interpretation. Also, more mechanistic evidence is required to significantly advance our knowledge in the field.
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Referee #1
Evidence, reproducibility and clarity
The abscission checkpoint, also known as NoCut, is a genome protection mechanism that remains poorly understood. This pathway is conserved from yeast to humans and protects the genome against chromosome bridges, a dangerous missegregation event that can have catastrophic consequences on genome stability. Dam et al now report the role of Srs2, a DNA helicase, as a key factor in the abscission checkpoint. The authors establish Srs2 as bona fide factor in this pathway by showing its involvement in abscission delays when chromatin bridges are induced. Importantly, yeast defective for Srs2 show increased levels of DNA damage when the frequency of chromatin bridges is increased. The authors also provide genetic evidence supporting a model whereby the interaction of SrS2 with PCNA s required for abscission regulation. In the second part of the manuscript, the authors study the human homologue of SRS2, PARI, in abscission regulation. The manuscript provides convincing evidence that PARI is also required for abscission delays in the presence of chromatin bridges. Critically, this role is specific for chromosome missegregation as abscission delays in response to nucleoporin depletion remain intact in PARI-depleted cells. Thus there is a conserved requirement for these DNA helicases in the abscission checkpoint. Overall, these are important advances in our understanding of the abscission checkpoint. The data is high quality and convincing in general. However, the impact of PARI depletion on genome stability needs to be further demonstrated to support key claims in the manuscript. Specifically: Disruptions of the abscission checkpoint in human cells result in bi-nucleation or increased levels of DNA damage. In this context, the authors need to show that PARI-depleted cells with increased frequency of chromatin bridges exhibit increased levels of bi-nucleation, DNA damage or both.
Significance
The abscission checkpoint, remains poorly understood. There is evidence in the literature that disruptions in this pathway increase susceptibility to cancer. The identification of the Srs2/PARI helicases as key components in this pathway is a considerable step forward in this field.
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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 study the authors sought to identify novel mechanisms underlying the progression of kidney fibrosis, by activating myofibroblast formation of a human kidney fibroblast cell line with TGF-beta, and collecting a time-series data set of transcriptome, proteome, phosphoproteome and secretome. They then performed a number of computational analyses to identify the key pathways and regulators that were driving the TGF-beta mediated responses in the early and late time points. They further validated several candidates experimentally with siRNA knockdowns, confirming FLI1 and E2F1 as two primary suppressors for myofibroblast activation.
Major comments: while all the experiments and data collections appeared to be carried out carefully, all data essentially came from one human PDGFRβ+ cell line derived from a previous study. Can this cell line fully represent the fibroblast populations in human kidneys? I could not find much information such as donor age, sex, or clinical conditions of the donor. It is unclear how much the cell line has been passaged, what is the level of clonality or the level of replication-induced senescence. How can we ensure that the mechanisms identified from one single cell line are robust and generalizable, truly representative of common kidney fibroblast cells or fibroblasts in general? The amount of multi-omics data collection was quite impressive, and I don't think it is realistic to repeat all those data generation experiments across multiple cell lines. Nonetheless, I feel that it is important to selectively validate some of the key findings on additional cell lines. On a related note, myofibroblast activation can be different between male and female in vivo and in vitro (https://www.biorxiv.org/content/10.1101/2024.10.02.615251v1.abstract). Is any of the findings in this study sex specific?
Minor comments:
Results section 2.1. Authors state "Specifically, we observed the activation of myofibroblast-specific gene expression as the fibrotic process progresses linking long-term patient data with in vitro data obtained over the course of hours". However, the transcriptomic data (Figure 1F) shows very low # of hits for these myofibroblast specific genes. Does this indicate that these cells are already in the myofibroblast state and that this is a model for TGFB stimulation of myofibroblasts? More clarification on this and what is being modeled (including starting and ending state of these cells) is needed. The authors tend to overstate how this in vitro model reflects complex disease phenotypes. The main issue is what is being modeled, which appears to be mostly TGF-B induced ECM production and possibly enhanced myofibroblast state signatures? On page 23: "To summarize, the integration of multi-omic data into time-resolved network models of early and late fibrotic responses revealed dynamic shifts in signaling pathways, transcription factor activities, and protein interactions, highlighting the temporal complexity of kidney fibrosis progression and identifying both well-known and novel regulatory factors for further investigation." Here it is not clear that the timeline used in this paper is recapitulating "late fibrotic processes" seen in vivo nor how it truly relates to kidney fibrosis progression. Also section 2.4: "To further validate the role of these transcription factors in the development of fibrotic diseases...". This is not something that this in vitro model can achieve. In section 2.4, the paragraph discussing E2F1 is poorly written, over uses the word "activity", and is not clear. Figure 3E: it is a bit of surprise to see HDAC1 being a node there connecting RELA to KLF4/FLI1. HDAC1 deacetylates histones and many transcription factors, hence the effects are likely to be very broad. Can the authors explain why it has such a high specificity in this context?
Significance
Overall, this is a nice study with several strengths. The time-series multi-omics data along the course of myofibroblast activation generated in this study is very impressive. While transcriptomic data collection is quite routine, the proteomics, phosphoproteomics, and secretomics data really lifted the significance of this study to another level. As demonstrated in their study, these data allowed the authors to carry out much more sophisticated computational analyses (which is another major strengths of this study), examining the responses in terms of gene regulation, protein production, modification, secretion at the early and late stages of fibrotic activation, formulating a mechanistic model. This study managed to get much closer to determining causal and direct regulation, compared with many other previous studies staying at the level of correlation and enrichments. Finally, some of the key regulators identified in their analyses were validated experimentally by siRNA knockdowns.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors presented a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFRβ+ mesenchymal cells aimed at unraveling disease mechanisms. This research advanced our understanding of the pathogenesis of kidney fibrosis. However, this reviewer has several concerns.
Major comments:
1.Why does the 0.08h group not exist in Fig S1? What's more, the detection of ECM appears to be insufficient as it only reveals COL1 expression. 2.Fig S2A shows that p-smad2 has 11 bands, whereas Smad2 has 12 bands. Moreover, the repeatability of the two repeated trials is not very excellent. Additionally, why not look at the phosphoproteomics data to see how p-smad2 changes? 3.The early-activated transcription factors screened by the author, including FLI1 and E2F1, act as negative regulators of collagen deposition, needs further verification.
Minor comments:
1.The graphical abstract and the abstract don't agree on how many time points there are-is it seven or eight? 2.For every group in the multi-omics, what is the n value?
Significance
The insights gained from this study not only advance our understanding of kidney fibrosis but also pave the way for the development of novel therapeutic strategies targeting this challenging condition. There is still much to be done, though. For instance, the author's screening of early-activated transcription factors, such as FLI1 and E2F1, which function as negative regulators of collagen deposition, requires additional confirmation.
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Referee #1
Evidence, reproducibility and clarity
Summary
This study showed measurements and integration of time-series multiple omics data of the human kidney PDGFR beta+ cells responding to TGF-beta stimuli. The authors also presented key pathways that were inferred based on estimating activities of TFs and kinases, and confirmed by knockdown experiments whose phenotypes can be observed by means of imaging.
Major concerns
- The content of Discussion is too thin. Particularly, it is uncommon to see a discussion section with no citations like this manuscript. Cite related studies and compare with the own results so that the authors can argue originality and novelty of this work. I also see some citations in Results. Usually it is opposite: little citations in Results section and many citations in Discussions.
- Put more emphasis on presenting biological relevances in order for readers to easily recognize them. I guess that Figs. 4C and 4F are examples of such biological findings.
- Draw the whole picture(s) of the integrated networks, not only subnetworks. If too much complicated, the complexity itself will be important information for readers.
- On SMAD2:
4a) The responses of p-SMAD2 in Fig. S2 are remarkably different in the two batches. The authors should discuss the reason of these outcomes. Which of the two batches exhibited similar responses to the phosphoproteome data?
4b) What possible reasons do authors think about that SMAD2/3 are not included in the transcriptional regulatory networks presented in Figs. 3 and 4 in spite of their importance in the TGFbeta signaling? Should be argued.
4c) What molecular mechanism can cause the increase in SERPINE1 expression dependent on TGFbeta? The mechanism may involve SMAD2/3 but neither presented nor argued. Should be clarified.
4d) It seems inconsistent that knockdown of the early-activated TFs cause extensive ECM accumulation in the knockdown experiment presented in Fig. 4B. Did the authors see suppression of ECM accumulation by knockdown of SMAD2/3? Should be presented.
Minor concerns
- Fig. 1D: Numbers in the Venn diagram of 'proteomics technologies' do not match with the numbers in another Venn diagram on the right hand side. Should be corrected or explained.
- Fig. 2B: 'INFalpha' should be IFNalpha, so is 'INFgamma'.
- Fig. 2B, Fig. S4C: What does the sign of 'Pathway enrichment score' mean? How is it calculated? Should be explained.
- Do not fit curves to data that should be drawn in line graphs (e.g. Figs. 3F, 4E, 4G etc.).
- How did the authors plot the regression curves presented in Fig. 4D? Should be clarified.
- What is 'PKN'? Maybe 'Prior Knowledge Network', but clearly spelled out when it first appears.
- Did the PNK-nodes in the networks exhibit quantitative changes in any of the omics data?
- What do the axes of the heatmaps mean in Fig. S3A? Why are there more categories than total sample numbers? Should be clarified.
Significance
The omics data were well measured under appropriate quality controls. Hence, this study will attract interests from specialists of kidney fibrosis and systems biologists. But there still remains concerns regarding arguments and data presentation of the manuscript.
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Reply to the reviewers
Manuscript number: RC-2024-02810
Corresponding author(s): Eric CHEVET
1. General Statements [optional]
This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.
We would like to thank the reviewers who pointed towards specific points in our manuscript which once addressed will make the work stronger.
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 (General comments) raised the possibility that some interactions are post-lysis artifacts as ER lumen proteins are biotinylated. This is indeed true and this was our first reaction when analyzing the data. We and others previously demonstrated that a subset of ER luminal proteins can reflux (PMID: 38865586) out of the ER to the cytosol in both mammalian cells (PMID: 33710763, PMID: 37925033) and yeast (PMID: 32246734, PMID: 31101715) upon ER stress notably in mammalian cells some PDIs (PMID: 33710763) or some chaperones such as BiP (PMID: 37487081). To address whether PDIA4 could possibly be biotinylated by BirA*, we tested if PDIA4 could be found in the cytosolic fraction (using methodologies previously reported) (Fig. 1) (see also section 3).
These experiments show that PDIA4 can be found in the cytosol under ER stress conditions and thereby become a substrate for our fusion IRE1-BirA* protein. Moreover, our interactome study we found other ER-resident proteins, actually also found in other IRE1 proximitome approaches using TurboID (PMID: 38727283) such as HSP90AB1. This information will be added in the revised manuscript as well. To further address this reviewer’s comment, we propose, using the subcellular-fractionation protocol previously used, to assess the presence of other ER luminal protein from our BioID experiment (such as HSP90AB1 or GRP78/BiP) in the cytosol upon basal and ER stress conditions and test the interaction IRE1/PDIA4 using in situ cross-linking followed by a co-immunoprecipitation approach with or without ER stress.
- Reviewer 1 & 2 (Specific points):
- Figure2D: Reviewer 1 cannot appreciate the ER stress-induced expression of XBP1s.
- Reviewer 2 questions the uses of different stressors along the paper.
We agree that these points could be significantly improved. We will address these specific points by transfecting HEK293T cells with BirA* alone or IRE1-BirA and stressing the cells with 3 different ER stressors used in this study (DTT, Tg, Tm) and then evaluate XBP1 mRNA splicing using RT-qPCR and XBP1s expression using Western blotting. IRE1-BirA overexpression will be quantified compared to endogenous IRE1. Regarding Fig 2D the WB in MA2-KO cells with increasing amount of transfected IRE1-BirA will be repeated to show a better image of the XBP1s blot.
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Reviewer 1 (Specific point) suggests that BirA might not be expressed since the protein is not visible on the western blot Fig2E. The cytosolic BirA* (cBirA*) has been expressed and was detected by mass spectrometry. All the mass spectrometry data presented in the manuscript corresponded to those found using IRE1-BirA* of which those found with cBirA* alone were removed. This information was indeed missing and will be added in the revised version as well as the datasets corresponding to cBirA* alone. In addition, we will show the western blot on cBirA transfected cells.
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Reviewer 1 & 3 (Specific points):
- Figure7: Reviewer 1 asks for a IRE1/hnRNPL co-immunoprecipitation.
- Figure7: Reviewer 3 asks to develop the results obtained on hnRNPL. Does the depletion of HNRNPL influence the expression of SEL1L? Does it influence some other aspect of IRE1 stability maybe through a protein-protein interactions?
We will perform IRE1 immunoprecipitation by transfecting HEK293T cells with IRE1-flag and then blot hnRNRPL, SEL1L and SYNV1. We will also test the expression of SEL1L upon hnRNRPL knockdown and test other ERAD proteins clients by western blotting to address whether our result is specific to IRE1. Moreover, to further document the role of hnRNRPL on the biology of IRE1 we will evaluate how the absence of hnRNPL impacts on IRE1 signaling through comparison of RNAseq data from IRE1 deficient cells (or IRE1 RNase inhibitor treated cells) with those obtained from hnRNPL silenced cells. This should allow us to identify gene networks specific of IRE1 (or IRE1 RNase) and common to those impacted by hnRNPL silencing. At last, we will evaluate how the relationship hnRNPL/IRE1 impacts on cells’ ability to cope with chemically induced ER stress. To do so we first propose to compare ER stress-induced cell death in cells invalidated for IRE1 (genetically or pharmacologically) and others silenced for hnRNPL. These results will be confronted to those obtained in vivo in the fly (collaboration Pedro Domingos ongoing).
- Reviewer 2 & 3 (Specific points):
- Reviewer 2 raised the possibility that the large basal interactor might be due to the very long time periods in the BioID process. The reviewer asks if we did perform a time course of biotin treatment.
- Reviewer 3 asks for a timecourse of ER stress (with treatment shorter than 16h) to better catch the dynamic nature of IRE1 PPIs that regulate IRE1 activity.
We agree with these comments. We used a BirA* enzyme to characterize the IRE1 interactome, this enzyme (BirA*) which requires at least 16h to label efficiently proteins at proximity with biotin. To validate (or not) our interactome data, we propose to perform experiments with shorter labelling time, and use an IRE1-TurboID and perform different time course (ranging from 30min to 8h) with or without stress in the presence of biotin. Biotinylated proteins will be purified and we will test the presence of different proteins that have been captured in our first IRE1-BioID analysis using Western blotting with specific antibodies.
- Reviewer 3 (Specific points):
- Reviewer 3 says that other RIDD targets should be tested, notably BLOS1 (Fig5D). Moreover, the reviewer suggests to include a condition with a RNAse inhibitor as positive control.
We will perform transfection in HEK293T cells with the different siRNA candidates as we did in Fig5D. Then we will assess the effect of the different knockdown on RIDD targets by testing BLOS1 and DGAT2, two robust RIDD targets, by RT-qPCR. This experiment will be performed with or without stress, in the presence or not of MKC8866 and in the presence of Actinomycin D in order to block transcription which could lead to confounding effects in terms of gene expression.
- Reviewer 3 (Specific points):
- Reviewer 3 asks to validate the direct interaction between PTPN1 and IRE1 and to further developed the role of IRE1/PTPN1 interaction in the splicing activity of IRE1.
To test the direct interaction between IRE1 and PTPN1, we are planning to use GST-PTPN1 (commercially available) and HIS-IRE1 recombinant proteins produced in the laboratory (either WT or N638D) as previously reported by us (PMID: 20237204). We will then perform successive GST-pulldown in presence of GST-PTPN1 and HIS-IRE1. In addition, we are also planning to measure XBP1 mRNA splicing by RT-qPCR upon PTPN1 knockdown in HEK293Tcells expressing IRE1 WT or IRE1 N638D mutant and treated, or not, with ER stress inducers. In these conditions, the activity of IRE1 and its mutant in terms of RNase activity (XBP1 mRNA splicing and RIDD) will be evaluated.
- The reviewers asked for some precisions that could be answered directly in the manuscript. Here are the modifications of the text.
- Reviewer 1 (specific point) found that Figure 1 is misleading.
The meta-analysis depicted in Figure 1 of the manuscript includes data from many studies aiming at identifying IRE1 interactors using high-throughput methods. However, one must consider that those interactors were studied in different backgrounds: different cell types, technics and treatments. In addition, considering the low abundance of IRE1 and the high number of interactors shown in Figure 1, it should be highly improbable that all those IRE1 interactions occur at the same time. The comment of Figure 1 will be modified to better appreciate the way this network was built alongside its associated bias. We agree that we could use this figure in supplemental material to justify our strategy for in situ proximity labelling.
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Reviewer 1 (specific point) asks how the MS analysis was carried out to avoid false positive. Mass spectrometry data were indeed analyzed by subtracting the hits found in control conditions (cBirA*) from the hits detected with IRE1-BirA*, as hypothesized by the reviewer. The manuscript text will be modified accordingly to better appreciate the curation that was performed and the cBirA* dataset added on the ProteomeXchange database.
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Reviewer 1 (minor points) argues that apoptosis is not a major cluster from the stressed interactome. Here, we highlight that the term “Regulation of apoptotic process” is exclusively enriched in the stressed interactome, therefore referring to terminal UPR that occurs during prolonged stress. Also, this term includes 16 IRE1 interactors (which corresponds to 30% of the stressed interactome and 7% of the global interactome). Altogether, this explains why we considered this term to comment to comment the Gene Ontology. The manuscript will be modified to better illustrate the choice of this term.
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Reviewer 1 (minor points) asks to discuss the possibility of interactions due to IRE1 overexpression and the bias associated with the technic (plus how authors fixed these issues). Bias due to IRE1 overexpression are discussed in the Section “Approach limitations” as follows: “Since we used transient overexpression of IRE1 for our BioID study, there might be an increased basal level of ER stress compared to stable transfection, modifying the basal UPR signaling properties.” This will be modified to discuss a potential increase in the number of IRE1 interactors due to IRE1 overexpression. Regarding the technical approach, our BioID approach does not allow to detect transient interactions, a limitation that will be commented to this section.
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Reviewer 2 (specific points) argues that addition of the bars from Figure S2C should reach 100%. The analysis carried out for Fig S2C uses the COMPARTMENTS plugin on Cytoscape (Binder et al. 2014) and does not aim to add up the percentage to 100%. In detail, this plugin individually calculates a score (from 0 to 5) for a protein in each subcellular compartments listed in the panel, based on manually curated literature, high-throughput screens, automatic text mining, and sequence-based prediction methods. Then for each compartment, we counted the number of proteins with a score higher than 4,75 (= 95% of 5) and calculated the abundance percentage relatively to the total number of proteins of the datasets (for BioID or Ref independently), providing the values displayed in the panel S2C. The fact that each analysis is independent from one another and that one protein may be counted in several compartments makes the addition to 100% irrelevant.
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Reviewer 2 (minor points) specifies that the Adamson dataset used in our analysis is a Perturb-Seq. We thank the reviewer for noticing this imprecision. The manuscript will be revised to be more specific about the nature of the Adamson dataset (e.g. replacing CRISPR screen by CRISPRi screen coupled with Perturb-Seq).
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Reviewer 2 (minor points) asks to rework some figures to enlarge the size of the font and to better separate the panels of some figures. Additionally, he suggests that the manuscript could benefit of a careful English editing. We thank the reviewer for this comment. Figures will be reworked for improved readability (e.g. font size and panel boundaries). Regarding the manuscript, it will be reworked to improve the writing quality and correct the mistakes.
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Reviewer 2 (minor points) pointed on page 10 the sentence “Thus, IRE1 BioID identified new IRE1 interactors and revealed that IRE1 interactions are responsive to stress” while the majority of the interactions occur in basal. We thank the reviewer for this comment and agree that the sentence could be clarified. The fact that 25% of the interactions appear specifically during ER stress treatment despite the stress already induced by IRE1 overexpression suggests that the exogenous stress is still able to modify IRE1 interactions. It therefore indicates that overexpressed IRE1 interacts with a different landscape of proteins upon induced ER stress.
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Reviewer 3 (specific points) asks for some precision about the duration of the stress treatments used for the BioID. We thank the reviewer for noticing some of these inconsistencies in the manuscript. To be precise, the stress treatment (Tg or TM) of the BioID carried out for mass spectrometry is concomitant to the addition of exogenous biotin, which is indeed 16h treatment. While we agree such stress treatment is longer than usual, we highlight that both biotin and ER stress treatment had to be added for the same duration, to allow the detection of ER stress interactors during the slow kinetic of BirA* dependent biotinylation. The results section, figure legends and Materials and Methods will be edited to harmonize the concomitant ER stress/biotin treatment for BioID coupled with mass spectrometry.
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 (General comments) raised the possibility that some interactions are post-lysis artifacts as ER lumen proteins are biotinylated. PDIA4 is an ER luminal protein identified by our cytosolic BioID. To test whether this protein could be found in the cytosol, we performed subcellular fractionation and were able to observe PDIA4 in the cytosolic fraction (Fig 1 Revision). This was confirmed by quantifying the relative signal between PDIA4 and Calnexin used as the ER marker. The experiment will be expanded to other ER luminal proteins found in our interactome.
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Reviewer 1 (Specific point) suggests that BirA might not be expressed since the protein is not visible on the western blot Fig2. In addition, Reviewer 1 asks how the MS analysis was carried out to avoid false positive. As mentioned above, BirA has not been detected by western blot so far. However, it was by mass spectrometry, as shown by the table displaying BirA Signal Intensity (Fig 2 Revision). BirA is less expressed in control condition than fused with IRE1, which may explain a low signal exerted by the streptavidin-HRP blot.
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Reviewer 1 (Specific point) asks for an improved visualization of panel 5A, showing a NATIVE-PAGE with higher exposure associated quantification of %oligomerization. Also, reviewer 1 suggests adding a corresponding SDS-PAGE for IRE1. Regarding IRE1 oligomerization, Panel 5A has been reworked according to the reviewer’s comment (Fig 3 Revision). A higher exposed picture of the NATIVE-PAGE is provided and SDS-PAGE in the same conditions is shown. Quantification of % IRE1 oligomerization is also provided to better appreciate this result. Figure 5 of the manuscript will be reworked to implement such modifications.
Figure ____3____ Revision: Rework of panel 5A with IRE1 SDS-PAGE and quantification of IRE1 oligomerization.
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Reviewer 3 (specific point) asks for a quantification of IRE1-BirA overexpression compared to WT. To address this reviewer’s comment, a preliminary result has been obtained using Western blot, regarding the comparison of the expression between overexpressed IRE1-BirA* and WT IRE1. This shows that IRE1-BirA* is expressed between 5 to 8 times more than WT, independently of ER stress induction by DTT (Fig 4 Revision). This will be repeated at least twice independently to consolidate the data.
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Reviewer 3 (Specific point) asks for a comparison of the IRE1 BioID with the Turbo-ID recently published by Ahmed et al. Ahmed et al identified 155 interactors for IRE1α and 137 for IRE1β in the HMC1.2 leukemia cell line. Yet, the entire list of these interactors is neither available in the manuscript nor on the ProteomeXchange database. When comparing our interactors with the hits released in their work (Ahmed et al. 2024), we find 20 (including IRE1) that are shared with our dataset (__Fig 5 Revision, __IRE1 is not indicated on the Venn diagram).
Figure ____5____ Revision: Venn diagram of IRE1 shared interactors between Le Goupil et al BioID and the available data in Ahmed et al TurboID 2024 (data on ProteomeXchange PXD047343 not yet available).
Considering that the approach Ahmed et al. used relies on another proximity labeling method, that the experiment was carried out in another cell line and that the total number of hits is of the same order of magnitude as that obtained in our analysis, one can be relatively confident about our results. We agree that a full comparison will be more informative (we will provide a full comparison in the revised version by using the proteomeXchange dataset if available, if not, we will contact them directly).
- Reviewer 3 (Specific point) asks whether the IRE1 N683D mutant could exert a different basal activity than the WT IRE1. The IRE1α mutant N683D has been controlled upon reception. Preliminary results measuring the splicing of XBP1 by RT-qPCR in basal conditions showed that the mutant’s basal activity is at a steady-state level through time, comparable to the WT (Fig 6 Revision). Provided that this mutant is expressed at a lower level than IRE1 WT, one might consider that the ability of N683D to exert a higher XBP1 mRNA splicing activity on its own than the WT is neglectable.
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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 2 (Specific point) suggests to develop the results regarding the comparison between IRE1α, IRE1β and PERK interactors. Regarding the IRE1α/PERK comparison, both interactome was performed in HEK293T cells using the BirA* system (PMID: 37366380), minoring the issues regarding methodological bias. Functionally, both sensors aim to alleviate ER stress, and one might hypothesize that these interactors commonly regulate IRE1 and PERK pathways, either to promote or limit the ER stress response. In accordance the GO further suggests that these interactors are closely associated with ER stress regulation. When focusing on structural aspects, IRE1 and PERK both display a kinase domain. Alignment of the sequence of IRE1α and PERK kinase domain only shows a limited conservation (24% identity calculated with Clustal Omega), however, when looking at 3D structures of the respective kinase domain (PDB: 4G31 for PERK and PDB: 4YZ9 for IRE1), we observe common features (e.g. N-lobe, 7 α-helixes in the C-lobe), which might underline similar ways of interactor-dependent regulation.
We agree with this reviewer that the comparison of the different interactomes is of great interest and that this will be part of our investigations in the future. At present time, we provide
below a Venn diagram that integrates data from different datasets (our data on IRE1α and b bioID interactomes in HEK293T cells (https://doi.org/10.1101/2024.10.27.620453), the PERK bioID interactome in HEK293T cells (PMID: 37366380), the IRE1α turboID interactome in HMC1.2 cells (PMID: 38727283) and the IRE1b IP/MS interactome in goblet cell lines (PMID: 38177501)).
Figure 6: Venn diagram of the shared interactors between IRE1a, IRE1b, and PERK from several studies.
This shows that the IRE1α and PERK interactomes, generated using BirA* fusions in HEK293T cells share 43 proteins which may be of course highly interesting to evaluate whether these interactions could occur through IRE1α and/or PERK kinase domains (e.g., PERK and IRE1α interaction with PTPN1). Regarding the IRE1α/IRE1β comparison, the IRE1β interactome was evaluated either using bioID (our data) or using IP-MS in LS174T goblet-like cells (PMID: 38177501) - provided that data from Ahmed et al. not available yet. Hence, we agree here that these differences impose biases that are not optimal to compare the interactomes (for instance AGR2 is not endogenously expressed in HEK293T cells). Overall, we do not plan to extend the experiments on these topics, as this is not directly aligned with the main scope of our study, but are definitely interested in pursuing the relevance of the shared interactomes in future studies. As the manuscript does not provide much explanation of these panels in the results section, we are considering either improving the discussion of existing panels, or deleting them from the manuscript.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors utilize a proximity ligation approach to probe protein-protein interactions involved in regulating the activity and stability of the ER stress sensing protein IRE1. Specifically, they express an IRE1-BirA fusion protein that they use to identify specific protein-protein interactions that influence the relative IRE1 RNAse activities of XBP1 splicing and RIDD. They go on to focus on two hits, PTPN1 and HNRPL, showing that these proteins influence IRE1 RNAse activity and stability, respectively.
Overall, the primary value of this manuscript is the list of potential interactors that is generated through this approach. Limitations are largely discussed in the manuscript. These include the fact that only interactors in the cytosol are accurately profiled owing the construct design and the potential for overexpression artifacts. Apart from those, there are some other issues with the manuscript that should be addressed, which are highlighted in more detail below. Ultimately, this manuscript doesn't provide a lot to move the field forward apart from providing another list of potential IRE1 interactors. The two 'hits' pursued are not sufficiently developed to reveal new insights into IRE1 regulation, as the mechanisms are not well developed and it isn't clear something 'new' has been discovered that directly relates to IRE1. I strongly recommend that the authors advance on of these hits to more deeply understand the mechanistic insights related to their (potential) involvement of IRE1 regulation.
Specific Comments.
- The authors bring up the potential for overexpression artifacts, but they should define how much overexpression is observed by comparing the relative expression of overexpressed protein to endogenous IRE1 by western blotting.
- There is some confusion regarding the timing of the BioID experiments, especially as it relates to the addition of ER stress. In the text, it seems that the authors treat with ER stress for 16 h, while the legend suggests 6 h treatments. A 16 h treatment is far too long to interpret potential regulators of IRE1 activity, so this is an important point. Related, the authors should do a timecourse of ER stress to better catch the dynamic nature of IRE1 PPIs that regulate IRE1 activity (but this should be a short timecourse).
- Along the same lines as above, Ahmed et al recently published another proximity ligase profile for IRE1, as highlighted by the authors. Yet, the authors do not show any comparisons between their list and the list generated by Ahmed et al. This is critical, as it could help generate a more reliable list of IRE1 interactors identified by this approach. In many ways, as alluded to by the authors, the more rapid labeling afforded by TurboID used by Ahmed et al would show a better snapshot of IRE1 interactors, limiting the potential impact of this study, so it is essential to benchmark their approach to the previous manuscript.
- The authors use CD59 as a putative RIDD target for the studies described in Fig. 5D. Other targets should also be used to convince that these effects can be attributed to RIDD. Notably, the canonical RIDD target BLOS1 should be used. Further, the authors should show that the Tg-dependent reduction in CD59 is sensitive to co-treatment with IRE1 RNAse inhibitors. Without further experiments on this point, these experiments are difficult to interpret as RIDD targets (apart from BLOS1) are well established to not be canonical across cell types.
- The authors have previously demonstrated that PTPN1 is involved in regulating XBP1 splicing, although the work presented here is suggested to reveal a new importance for direct interactions with IRE1. However, this needs to be further developed. The authors use a bioinformatic approach termed iPIN to suggest interactions, although this appears to be a proprietary software that has not been published. The identify a potential interface for this interaction and then show that some mutations near this potential site of interaction seem to reduce IRE1 stability, while increasing interactions with PTPN1 (overexpressed) and XBP1 splicing. However, there are a number of concerns here. Does the mutation, N638D basally increase the specific activity of splicing, which can be measured using recombinant proteins. Further, the co-IPs are not well controlled, as there is no evidence that PTPN1-mCherry doesn't come down with beads or any other protein. In other words, the potential role for PTPN1 in regulating XBP1 splicing needs to be better developed to convince that this represents an important activity mediated through direct IRE1 interactions.
- Similarly, the results with HNRNPL need to be further developed. It is well established that IRE1 ERAD is regulated by the activity of SYNV1 (HRD1) and SEL1L. So does genetic depletion of HNRPNL influence expression of these factors (HRD1 is shown but not SEL1L). Does it affect their interaction? Or does it influence some other aspect of IRE1 stability maybe through a protein-protein interactions? Again, more information is needed to determine the potential importance of HNRNPL in IRE1 stabilization.
Significance
Overall, the primary value of this manuscript is the list of potential interactors that is generated through this approach. Limitations are largely discussed in the manuscript. These include the fact that only interactors in the cytosol are accurately profiled owing the construct design and the potential for overexpression artifacts.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Le Goupil et al. presents the results of a protein proximity screen for the UPR sensor IRE1 using the method BioID. The data include a list of interactors, their comparison with computational analysis of curated databases as well as previously published experimental data such as genome wide siRNA or CRISPRi screens and focused Perturb-Seq data. By focusing on the intersection of these data sets, the authors putatively connect IRE1 to previously unknown cellular activities. The authors also make an effort to validate these data by couple of examples where they identify HNRNPL as an interacting partner and stabilizer of IRE1. Overall, this manuscript makes important contributions towards establishing a framework to understand IRE1 biology more fully; however, significant validation and functional characterization would be required to fully evaluate the robustness/utility of the IRE1 interactome that is presented.
Specific points:
- What is the reason to use different ER stressors in different experiments, i.e. DTT, TG, or TM?
- Figure S2C: percentages should add up to 100% for enabling meaningful comparison of the two.
- Are the number of common interactors between IRE1 and PERK too high for structurally different proteins? Is it because they are embedded in the same membrane and thus there may be some ´non-specific´ interactors? It may also be due to long incubation periods (see below). For proper examination of this, of course, requires BioID experiment in the same cell type under the same conditions. This should be underlined in the text. The same goes for the comparison of IRE1 and IRE1
- It may be surprising that the great majority of the interactors are at the basal level, without stress. Since IRE1 activity is stress-induced, how are these basal interactors change IRE1 activity upon stress? Could this large basal interactor set be due to the very long time periods in the BioID process (18-24 h)? Or are the majority of the interactors mediating non-canonical IRE1 functions, as suggested in the literature (even some of these are stress activated)? Regarding this, did the authors do a time course to identify the optimal time of biotin treatment, the time point at which a plateau is reached in terms of approximate number of proteins associated?
Minor points:
- The manuscript will significantly benefit from careful English language editing. There are spelling errors, omission of punctuations, half sentences, and repetitive language.
- The data from Adamson et al. paper referenced on page 6 is a CRISPRi screen coupled to Perturb-Seq, not a simple CRISPR screen.
- 50 nM Thapsigargin is referred to as a mild stressor, but it is actually a strong stressor that can even kill some cell types.
- Figure texts are often too small and hard to follow, e.g. in the Venn-diagrams.
- Boundries of Figures S2D-E-F are too difficult to discern.
- Statement on top of page 10: ¨Thus, IRE1 BioID identified new IRE1 interactors and revealed that IRE1 interactions are responsive to stress¨. However, the majority of the interactors ara basal, not responsive to stress.
Significance
Strengths:
Robust experimental approach with a well-established technique that provides in situ interactome data for a central protein in proteostasis.
Weakness:
Lack of further experimental validation of the data. This is, however, a big task, and will take significant additional effort and time.
Advance:
The study makes conceptual and incremental increase in defining the IRE1 interactome and opens the way for further studies.
Audience:
The findings of this study is of interest to basic molecular and cell biologists with an interest in intracellular signaling, as well as those that may be interested in UPR-disease connection, e.g. cancer and neurodegenerative disease.
Reviewer Expertise:
UPR biology in normal and pathological conditions.
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Referee #1
Evidence, reproducibility and clarity
Goupil et al. developed a proximity labeling approach using BioID and identified many interacting proteins for the conserved ER stress sensor. The authors validated their results by comparing previously known IRE1a interacting proteins with their list. Indeed, many interacting proteins are in their list, including HSPA5, HSP90B1, PTPN1, and UPF1. Surprisingly, some of these proteins are localized in the ER lumen, which should not be biotinylated by BirA*, thus raising the possibility that some interactions are post-lysis artifacts. The authors also identify HNRNPL as a novel interacting protein of IRE1a. They further demonstrate that the depletion of HNRNPL leads to faster degradation under basal conditions but not during ER stress. Overall, the authors have employed the BioID approach to map the interactome of IRE1. However, the authors should be cautioned to give the impression to readers that all these interactions are true, and many of them could be false positives due to overexpression of IRE1a and highly sensitive mass spectrometry.
Major Comments:
The logic of analyzing existing data in Figure 1 is unclear to me. As I mentioned in my summary, it misleads the readers that all these components of biological pathways directly interact with IRE1. Biochemical and functional studies have never been done to support many high-throughput interaction studies. Also, IRE1 is an extremely low abundant protein (~416 molecules/HeLa cell) (PMID: 24487582). How do such low-interacting proteins interact with hundreds of proteins unless using an overexpression system? While Figure 2C shows a nice ER stress-dependent induction of XBP1s, it is not easy to appreciate the ER stress-induced expression of XBP1s in Figure 2D. The authors need to show better XBP1s blot. Surprisingly, biotinylated proteins were not detected when cytosolic BirA was expressed, suggesting that the construct was not expressed, missing a crucial control. Figure 3: Simply enriching biotinylated proteins from IRE1a-BirA expressing cells could yield false positives. This is because of the half-life of the biotin adenylate ester on the minute scale. The best way to avoid false positives is to subtract the signal from hits obtained from the cytosolic BirA* cells. It is unclear whether the authors used such an approach to prevent false positives. Figure 5A: IRE1a oligomerization on Native PAGE immunoblotting cannot be readily appreciated. They should show a longer exposure and quantify the % of oligomers relative to the total signal. They should also include IRE1a and Tubulin immunoblots performed using a standard SDS PAGE. The role of HNRNPL in protecting IRE1 from degradation is convincing in Figure 7. The data could be further supported by showing the interaction between IRE1a and HNRNPL by co-immunoprecipitation.
Minor Comments:
On page 6, the author mentions "protein processing in the ER and apoptosis as major clusters." While protein processing is a major cluster, apoptosis is not compared to other pathways. Authors often mention direct interactions between IRE1a and other proteins. I would be cautious in saying this unless these interactions were truly demonstrated using purified IRE1 and the partner protein. Otherwise, the interaction could be mediated by other factors in cells. The authors need to discuss the possibility of non-specific interactions due to IRE1a overexpression and intrinsic flaws of BioID and what steps the authors took to mitigate these effects.
Significance
The study is significant as it identifies new interacting proteins for IRE1a, a conserved ER stress sensor protein.
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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: It has been known for many years that some peroxisomal proteins are imported by the major peroxisomal protein import receptor Pex5, which recognises the C terminal targeting signal PTS1, despite either lacking a PTS1 or if the PTS1 is blocked. Some proteins are also able to 'piggyback' into peroxisomes by binding to a partner which possesses a PTS. Eci1, the subject of this study is such a protein. This manuscript identified a PTS1-independent, non-canonical interaction interface between S. cerevisiae PEX5 and imported protein Eci1. Confocal imaging was used to observe the PTS1-independent import of Eci1 into peroxisomes and to establish dependence of Pex5 even in the absence of its piggyback partner Dci1. The authors purified the Pex5-Eci1 complex and used Cryo-EM to provide a structure of the purified PEX5-Eci1 complex. In general, this manuscript is well written and easy to read.
Major points
Most of the experiments presented are well-designed and accompanied with appropriate controls. However, please mention how many times the experiments have been repeated and how many biological samples were used in the analysis.The authors should also consider the following suggestions substantiate their conclusions:
Figure 1A: Include full-length Eci1 with an N-terminal fluorophore, Eci1 PTS1-deletion with N-terminal fluorophore, and the PTS1 deletion with a C-terminal fluorophore, to control for any disturbance of targeting by the C terminal NG tag.
Figure 1C: Confirm the Eci1 and Dci1 levels (if an antibody is available for the latter) by western blot. It is difficult to compare expression levels when comparing just a small number of cells in the microscope. Western blot would give a more robust evaluation of protein levels and help corroborate the claim that Eci1 expression is decreased in the absence of Dci1 if the authors wish to stand by this conclusion.
Figure 2: confirm the deletion and overexpression of PEX9, PEX5, and PEX7 by western blot of the relevant strains. The production of these strains is not described in the manuscript. If they have been previously described this should be referenced if not it should be included.
Figure 2: Validate these strains by checking import of a canonical PTS1 and canonical PTS2 and pex9 dependent protein to ensure they function as they should, unless these strains have been published elsewhere in which case their characterisation can be referenced.
Figure 3: The gel should include a standard of a known amount of the lysate used in the pull down to enable a semi-quantitative estimation of the amount of Eci1 protein captured by PEX5 with and without its PTS1. Also include Eci1 with a C-terminal fluorophore to be comparable with the in vivo data in Figs 1 and 2. A control with no pex5 for background would be useful. A full Coomassie-blue stained gel (not western blot) is required to demonstrate the direct interaction as with the western blot it cannot be excluded that other proteins bridge the interaction since this is a pull down from lysate not purified proteins. OPTIONAL:Interestingly the surface on Eci1 which binds pex5 is where CoA binds in the active enzyme. Would CoA compete for binding to Pex5? (could add it into the pull down expt?)
Figure S2: The complex between pex5 and eci1 is solved by cryo EM. Eci1 is hexameric usually 1 but sometimes 2 or 3 pex5s are bound to the complex. The size-exclusion chromatography figure with calculated molecular weight is required to support the stoichiometry. A native gel to show the complex, as well as a denaturing gel (using the complex) to show the individual proteins will be beneficial.
Figure S9: Would Eci1 compete with Dci1 to bind to Pex5 since they share highly conserved interfaces? If so, why did the deletion of Dci1 impair Eci1 location? Or is this just reduced expression in the dci1 deletion background? (See point 2) This seems counterintuitive/contradictory so please comment.
OPTIONAL: As the authors acknowledge this work is in vitro. It would have been interesting to examine the role of this interface in vivo by mutating one or more of the residues in Eci 1 identified as being important for the interaction. Granted that mutation can affect the folding of the protein, but the binding region is on the surface so it may not, and this can be readily checked e.g by enzyme activity or limited proteolysis.
OPTIONAL: Similarly, it would have been interesting to see if mutating the residues of PEX5 involved in the interface affect the import of other cargoes than eci1 or if reciprocal mutations in pex5 and Eci1 e.g switching charges could restore an import defect.
OPTIONAL If 8 & 9 isn't possible could a co-evolutionary analysis of the interface residues provide further independent evidence for their functional importance? They have looked at conservation of residues in Eci1 but this could be extended to a co-evolution analysis.
Minor points
Figure 1C and throughout the manuscript state clearly whether the same confocal settings are used when comparing fluorescence intensity of different images/samples.
Figure S2B: Please use different colours for PEX5 and Eci1 for clarity.
Figure 4A: please indicate the PTS1 for the other 5 molecules of Eci1. Are they buried? Or not seen? Please add explanation.
Figure 4B, C, and D: please colour the circled helix in PEX5 so that it can be more easily seen.
Please indicate the EBI-mediated interaction in Figure 4C. The relationship between 4C and 4D could be explained better as they are not viewed from the same direction
Figure S3: As the authors indicated, Pex5 binds with multiple conformations and forms a variable interface with an Eci1 subunit. Does this mean different types of non-canonical interface are possible? Please discuss this.
Figure 5A and B: they should be labelled as PEX5 TPR domain
Figure S8 is very helpful in understanding the interface and could be included in Figure 5.
Significance
While cargo recognition by Pex5-PTS1 is well understood in molecular detail there are proteins which either lack a PTS1 or have a nonessential PTS1 that still require Pex5 for import into peroxisomes. This study provides a structural view of interaction between Pex5 and its cargo Eci1, a protein that does have a PTS1 but which is not essential for import. It's not the first example of a PEX5-cargo structure to show a non-canonical binding interface and the results are compared to the human pex5-AGT structure. It is an important addition to understanding how so-called PTS context dependent or non1 non2 proteins can be imported. Is this the first structure showing Pex5 bound to an oligomer cargo? Previous work is appropriately cited in the manuscript.
The study will be of interest to audiences interested in protein-protein interaction and in protein targeting to organelles. This manuscript presents additional knowledge on how an oligomeric PTS1-independent protein can be imported into peroxisomes. The potential of other proteins using the similar importing mechanism can be tested to understand how one receptor can use apparently multiple binding modes to import a wide range of different proteins.
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Referee #2
Evidence, reproducibility and clarity
Peroxisomes are single membrane organelles conserved in all eukaryotes and play important roles in various metabolic reactions, such as beta oxidation of fatty acids. In general, proteins localized in the peroxisomal matrix encode either a C-terminal PTS1 signal or an N-terminal PTS2 signal, and Pex5 acts as a cargo receptor in the PTS1 pathway and Pex7 in the PTS2 pathway, respectively. Previous studies have suggested that some matrix proteins (e.g., Eci1) are transported into the peroxisomal matrix in the PTS1-independent manner, but the mechanism is still unclear. In the present study, Peer et al. determined the Cryo-EM structure of the Pex5-Eci1 complex, which revealed a new interaction site that is distinct from the recognition site of the canonical PTS1 signal, providing important insight into the PTS1-independent, but the Pex5-dependent matrix protein transport. This study by Peer et al. will be of interest to a broad readership in basic cell biology other than peroxisomes.
The reviewer feels that the manuscript needs to be revised in the following points.
Major comments
- The authors showed that Pex5 binds to Eci1 in a PTS1 signal-independent manner from pull-down experiments in Figure 2, but this result is qualitative. If the authors add quantitative data on the interaction between Pex5 and Eci1 from isothermal titration calorimetry (ITC) or surface plasmon resonance (SPR), this would make this paper more convincing. This could be done in 2 months.
- It is not clear to what extent the new interaction sites between Pex5 and Eci1 is important for transport to peroxisomes, as revealed in this study. I suggest, for example, expressing Eci1 with a mutation at a site involved in interaction with Pex5 in yeast and analyzing its effect on peroxisomal localization as additional experiments anew. I believes that this could be done in about 2 months.
Minor comments
- The results of yeast cell imaging in Figures 1, 2 and S1 are all qualitative and not quantitative. Furthermore, there are no descriptions of the experimental reproducibility of the data. I suggest that these points need to be improved.
- I feel that information of sample preparation for cryo-EM analysis of the Pex5-Eci1 complex is not enough since it is only described in the methods. I suggest the authors to add the results of gel-filtration chromatography and CBB-stained SDS-PAGE in the manuscript.
- The authors discuss the interaction interface between Pex5 and Eci1 in Figures 4 and 5, but the manuscript presented at this stage is difficult at least for me to understand the interaction between them. I recommend the authors to add new figure(s) to show more detailed interaction. Also, I suggest that cryo-EM density map around the interaction region between Pex5 and Eci1 should be presented more detail.
Significance
My expertise is in yeast cell biology and structural biology. From this perspective, I think that the strengths of this study are, first, that Pex5-dependent peroxisomal transport of Eci1 in yeast cells occurs independently of PTS1 signal and its paralog Dci1, and that the cryo-EM structure of the Pex5-Eci1 complex reveals a new interaction site other than PTS1 between Pex5 and Eci1. This work is of broad interest not only to peroxisomes, but also to many cell biologists specializing in organelles, and ultimately to structural biologists. On the other hand, the authors' cryo-EM data suggest that 2-3 molecules of Pex5 bind to the Eci1 hexamer. However, it is unclear how the binding of multiple Pex5 molecules to the Eci1 hexamer affects their transport to peroxisomes, and further analysis is needed to elucidate the transport mechanism in more detail.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Proteins are imported into peroxisomes by mobile receptors such as PEX5. PEX5 recognizes cargo proteins in the cytosol by their peroxisome targeting signal (PTS) and then shuttles them across the peroxisomal membrane into the matrix. While most peroxisomal proteins contain well-characterized signals that bind to PEX5 either directly (PTS1) or through PEX7 (PTS2), some proteins interact with PEX5 independently of these canonical signals. The molecular basis of these unconventional interactions has been poorly understood.
The manuscript by Peer et al. deals with one such protein called Eci1 in yeast. Eci1 has a PTS1 signal at its C terminus and a putative PTS2 signal at its N terminus, yet the authors show that neither of these signals is required for import of Eci1 into peroxisomes. They also show that import of Eci1 cannot be entirely explained by piggy-backing on its paralog Dci1. Regardless, import of Eci1 depends entirely on PEX5, indicating that Eci1 can bind to PEX5 unconventionally. To identify this additional interface, the authors solve the cryo-EM structure of PEX5 bound to Eci1 (which is a hexamer). Surprisingly, the structure reveals that PEX5 binds to only one of the six Eci1 subunits, and that two distinct interfaces are apparent. One reflects the canonical interaction between the PTS1 signal of Eci1 and the receptor's cognate PTS1-binding TPR domain. The other interface is novel and of potential interest. It involves a region of Eci1 that engages a segment of PEX5 upstream of the TPR domain. This segment has not been previously implicated in binding protein cargo.
Major issues:
- The major issue with the paper is that the novel interface between Eci1 and PEX5 has not been demonstrated to be important for import into peroxisomes. Specifically, mutagenesis of both sides of the interface is required to demonstrate that this interaction mediates import of Eci1 lacking the canonical PTS1 signal (and also in the absence of the paralog Dci1). Such data are indisputably a precondition for publication of this paper. Pull-down experiments should also be performed to demonstrate that the interface is sufficient for interacting with PEX5 in the absence of the PTS1 signal on Eci1.
- The paper hinges on the demonstration of a residual interaction between PEX5 and Eci1 lacking its PTS1 signal. However, the pull-down experiment in Figure 3 that allegedly shows this result lacks a critical control for non-specific binding of Eci1 to the nickel beads alone. Also, this experiment does not show a direct interaction between PEX5 and Eci1, since the two proteins are co-expressed in bacteria and then pulled down using an engineered His-tag in PEX5. This experiment should be repeated using PEX5 and Eci1 purified separately and then mixed in vitro. Please show a coomassie-stained SDS-PAGE gel to assess protein purity in addition to the immunoblot, and please show the pull-down in a more conventional way comparing the input and the bound fraction (it is unclear what is meant by soluble and elution fractions).
- The presentation of the structure in Figure 4 should be improved. An overview of the complex should be shown first, and then each interface should be pointed out in a different view (and accordingly labeled). It is distracting and not necessary to show all six subunits of Eci1 in different colors. The non-conventional interface should be shown more clearly, with key amino acids numbered and labeled, and the configurations of their side chains highlighted. Please also highlight the salt bridges and hydrogen bonds at this interface that are mentioned in the text but never illustrated.
- The data in Figs. S2 and S3 raise doubts about the reported resolution of PEX5 in the cryo-EM structure. Please provide examples of the density map and the fit to the model.
- Please provide data for the purification of the complex between PEX5 and Eci1, including a gel-filtration chromatogram and an SDS-PAGE gel of the purified sample used for cryo-EM.
- OPTIONAL: The observation that the non-conventional interface between PEX5 with Eci1 corresponds to the site of CoA binding is interesting. This interaction might keep the enzyme inactive while in the cytosol and bound to PEX5, until it would be correctly delivered into peroxisomes and released from the receptor. Alternatively, it could also reflect regulation of Eci1 import by CoA. This idea could easily be tested by pull-down experiments performed with or without CoA, or perhaps by an in vitro Eci1 activity assay in the presence or absence of PEX5. The significance of the paper would be considerably improved if this interaction reflected a mechanism to regulate Eci1 activity or import.
Minor issues:
- The manuscript has many grammatical mistakes which should be addressed. The absence of line numbers precludes us from indicating specific issues.
- In general, when referring to a single subunit from the Eci1 hexamer, please use the terms subunit or protomer, and avoid the use of the term monomer which is misleading.
- In Fig. 1C, it is unclear whether the experiment was performed in the absence or presence of PEX11. Since the paper hinges on the demonstration of an unconventional interaction between Eci1 and PEX5, perhaps this experiment should be performed in pex11 knockout cells (to enlarge peroxisomes as in Fig. 1B) to show that the residual peroxisomal localization indeed corresponds to the matrix.
- In Fig. 6, it would help to show each structure individually and then the overlay.
- Fig. S4 should include a scale bar and box size.
- Why are phosphorylation sites indicated in Fig. S6?
- In Fig. S8, please show the structures of Eci1 bound to PEX5 and to CoA individually, and then the overlay. The figure is very diffucult to understand otherwise.
- In Fig. S9, please label the homologous interface residues on Eci1 and Dci1 in individual views, and then show the overlay.
Significance
The main finding of the paper is a noncanonical interaction between Eci1 and the peroxisomal import receptor PEX5. This interaction could solve a longstanding mystery about how Eci1 can be targeted to peroxisomes in the absence of its canonical peroxisome targeting signal. Because the authors have not demonstrated that this interaction is sufficient for import of Eci1 in vivo, this key conclusion of the paper remains unconfirmed. If this omission were corrected, the paper would add another example to the growing list of proteins that are imported into peroxisomes by binding unconventionally to PEX5.
The authors employ an interesting strategy to confirm that Eci1 is correctly imported into the peroxisomal matrix in vivo (and not just recruited to the cytosolic surface of the peroxisomal membrane). This strategy involves enlarging peroxisomes (which normally are diffraction limited) by knocking out a factor required for peroxisome division, allowing the matrix to be resolved from the limiting membrane by light microscopy. Failure to adequately demonstrate import into the matrix had plagued many earlier studies on protein targeting to peroxisomes. The strategy employed in this paper could therefore be useful to other researchers.
In its current form, the manuscript would be of some interest to the peroxisomal community and perhaps also to researchers studying protein targeting to membrane-bounded organelles. However, if the authors could show that the novel interface between PEX5 and Eci1 functions in part to regulate Eci1 enzymatic activity (or conversely, Eci1 import by CoA), then the paper would be of much broader interest to the fields of metabolism and metabolic regulation.
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Reply to the reviewers
We are grateful to the reviewers for their detailed evaluation and insightful comments, which have improved the clarity and readability of this manuscript. We have addressed all reviewer comments and incorporated their suggested changes into the text and figures. The line numbers in our response correspond to those in the revised manuscript. Following reviewer 3’s comment, we have repeated the structural refinement of G234A and G234V apo crystal structures without water molecules, which improved the reliability of the data.
Reviewer #1
- Abstract: The current abstract is challenging to follow. For instance, the phrase "The detached head preferentially binds to the forward tubulin-binding site after ATP binding, but the mechanism preventing premature binding to the microtubule while awaiting ATP remains unknown" could imply that the tethered head binds ATP, which is misleading. A clearer statement would be: "The detached head preferentially binds to the forward tubulin-binding site after ATP binding to the leading, microtubule-bound head, but the mechanism preventing premature binding to the microtubule while its partner awaits ATP remains unknown." Response: We thank the reviewer for the suggestion to improve clarity. We have revised the indicated sentence and updated the abstract to enhance clarity.
Terminology: In the introduction, consider rephrasing to "...its two motor domains ("heads")."
Response: We have corrected the phrase accordingly (line 44).
Lines 71-72: The sentences "This mechanism explains how the tethered head preferentially binds to the forward-binding site 'after ATP binding.' However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site 'before ATP binding'" could be rephrased for clarity. A suggested revision is: "This mechanism explains how the tethered head preferentially binds to the forward-binding site after ATP binding to the microtubule-bound, leading head. However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site before ATP binds to the leading head."
Response: We appreciate the suggestion for clarification. We have corrected the phrase accordingly (lines 72-75).
Line 98: Consider revising "could release both ADP" to "could release both ADPs" or "could release both ADP molecules."
Response: We have corrected the phrase accordingly (line 100).
Lines 103-104: The statement "Therefore, these results suggest the tension posed to the neck linker plays a critical role in suppressing microtubule-binding of the tethered head" should be clarified. Since tension only develops in the two-heads-bound state, using "steric hindrance" instead of "tension" may improve precision.
Response: We have corrected this sentence as follows: “These findings suggest that constraints on the neck linker (whether from steric hindrance or interactions with the head or microtubule) are crucial in preventing the tethered head from binding to microtubule” (lines 105-107).
Lines 374-375: Replace "...before ATP-binding triggers the forward stepping..." with "...before ATP binding to the leading head triggers the forward stepping..."
Response: We have corrected the phrase accordingly (line 374-375).
Tense Consistency: Ensure consistent use of present or past tense throughout the manuscript for clarity.
Response: We have reviewed the manuscript and corrected the verb tenses.
Reviewer #2
- Lines 72-73 can be deleted as they are repetitive with lines 95-96. Response: While I acknowledge the reviewer’s point about redundancy, we would like to retain this sentence as it provides an important connection to the opening sentence of the next paragraph, where we explain why the rear-head gating model is required.
Line 87: The authors should cite Mickolaczyk et al. PNAS 2015 and Sudhakar et al. Science 2021 as these studies also observed that the trailing head takes a sub-step and is located on the right side of the leading head before it moves forward and completes the step.
Response: We did not cite these two papers as they contradict the statement of this sentence and rather suggest that kinesin waits for ATP-binding in the “two-head-bound” state. We interpreted this discrepancy as follows: 1) Mickolaczyk’s observations likely represent multiple motor-driven movement. Ensuring mono-valency of bead labeling is essential. In optical trapping assays, it is established that >98% of the bead motility is driven by a single motor when less than 50% of beads moved along the microtubule when brought into contact with microtubule using optical trap. The corresponding author has extensive experience preparing monovalent probes for optical trapping bead assays and high-speed single-molecule assays using gold probe (Tomishige et al., J. Cell Biol. 142, 989 (1998)), having established reliable protocols for monovalent labeling of kinesin with gold probes (refer to methods in Isojima et al., Nat. Chem. Biol. 2016 and Niitani et al. biorxiv 2024). The colloidal gold was coated with three SAMs (self-assembled monolayers) in a ratio of 1:10:10 (biotin-SAM:carboxy-SAM:hydroxy-SAM) to reduce surface biotin molecules and non-specific kinesin binding. The gold particles and kinesin-streptavidin complex were mixed at a 1:1 ratio, though this mixing ratio does not guarantee that 100% of the gold particle movements along microtubule are driven by single motors. We established that standard deviations (s.d.) of on- and off-axis displacements (especially that of off-axis) are key indicators for distinguishing between single- and multiple-motor driven motility of the gold probe. Under the above single-molecule conditions, majority of off-axis s.d. traces exhibited clear two-state transitions between microtubule-bound (low s.d.) and -unbound (high s.d.) states of the gold-labeled head, while under multivalent conditions (with higher kinesin:gold ratio and/or higher biotin-SAM ratio on the gold surface), most traces showed sub-steps but lacked these two-state transitions, instead displaying uncorrelated on- and off-axis s.d. traces. In contrast, Mickolajczyk et al. used commercial streptavidin-coated gold nanoparticles mixed with kinesin at a 6:1 motor-to-gold ratio. While their 2016 and 2017 papers did not show s.d. traces, their Biophys. J. 2019 paper (Fig.4) displayed s.d. traces that are characteristic of multivalent bead motility according to the criteria described above. 2) Sudhakar et al.’s interpretation that rapid sub-steps between 8-nms steps represent tethered head movement (illustrated in Fig 4 of their paper) is likely incorrect. The optical trap force acts on the neck linker of the microtubule-bound head, not to the neck linker of the tethered head. Consequently, trailing head detachment should not cause significant displacement of the trapped bead (as illustrated in Fig. 4 of Carter and Cross, Nature 2005). Instead, conformational changes in the neck linker of the microtubule-bound head (i.e., cover-neck bundle formation after ATP binding (Hwang et al. Structure 2008)) would cause bead displacement, supporting that kinesin waits for ATP in the “one-head-bound state”.
Lines 103: The authors should cite Benoit et al. kinesin14 and Kif1A structures as these studies directly show the conformations of the neck-linkers when both heads are bound to the microtubule.
Response: We cited the paper (line 105).
Line 113: There is an extra "e" on "nucleotide".
Response: We have corrected the typo (line 117).
Line 118: I would delete "universal" as it is not clear whether all kinesins use a tension-based mechanism.
Response: We agree with the reviewer’s comment. Further, reviewer 3 noted that recent studies showed that kinesin-3 may not be explained by this mechanism, so we have removed the word “universal” from this sentence as well as from the Abstract and Discussion.
Line 132: Why did the authors decide to use a cys-lite mutant for X-ray and cryo-EM studies?
Response: We used the Cys-light mutant to maintain consistency across various experimental techniques in this paper and to enable direct comparison with the nucleotide-free kinesin-1 structures reported by Cao et al. (2014, 2017), who used the same Cys-light construct. To express this, we revised the sentence as follows: “For consistency across experimental techniques and comparison with the previously solved nucleotide-free kinesin-1 structures, we used a cysteine-light mutant kinesin, where surface-exposed cysteines were replaced with either Ala or Ser” (lines 135-138).
Line 192: The authors refer to Figures 3A and B when they discuss ATP-like and ADP-like conformations. However, these figures refer to open, semi-open, and closed conformations. Things become clear later in the text, but this is confusing, as is. I recommend the authors either show ATP-like and ADP-like classification as a supplemental figure and refer to that figure or not refer to the figure in this sentence.
Response: To explain the result in this paragraph, we should reference these figures, while we acknowledge the reviewer’s comment about the confusing nomenclature in Fig.3. To address this, Fig. 3A now lists both the old terminology (nucleotide-free, ADP-like, and ATP-like) alongside the new terminology (open, semi-open, and closed).
Lines 259-260: I would delete "as evidenced by..." and just cite those papers.
Response: We have corrected this sentence accordingly (line 265-266).
Lines 262-276: The authors should cite the relevant literature in this paragraph as most of their conclusions here were already shown by previous structural studies.
Response: Reviewer 3 also noted that this paragraph outlines our current understanding, which seems out of place in the Results and more relevant for the Discussion. Therefore, we have moved this paragraph to the Discussion section and added relevant citations from the literature (lines 390-406).
Recent biophysical studies claim that neck-linker docking is a two-step process that occurs in ATP binding and ATP hydrolysis. Do the authors agree with this model? Can they comment on why the neck-linker only partially docks during ATP binding, and require ATP hydrolysis to complete the docking? If they disagree with this model, this should be explained in the Discussion.
Response: This paper focuses on the neck linker’s extensibility in coordinated motility rather than its docking onto the head. The correlation between ATP binding/hydrolysis and neck linker-docking has been examined in a concurrent paper by Niitani et al. (biorxiv 10.1101/2024.09.19.613828) and is discussed in their Discussion section. In this paper, using loose backward constraint on the neck linker, we demonstrated that docking of the initial neck linker segment is sufficient to half-open the gate. Furthermore, extending the neck linker length increased the ATP off-rate of the rear E236A head, indicating that forward neck linker strain plays a crucial role in stabilizing the closed state. These findings support the hypothesis that neck linker docking remains partially unstable in the one-head-bound state and achieves full stabilization only after transitioning to the two-head-bound state.
Lines 285: The authors should cite Benoit et al. as they showed this clearly in their structure. Benoit et al. showed that, even though both heads are bound to AMP-PNP, the neck linkers are pointed in opposite directions and the rigid body conformations of the trailing and leading heads are different. Do the authors take this into account when they model the Topen-Lopen state? Can they also comment on why the heads can have different rigid body conformations even though they are bound to the same nucleotide? Is this because tension on the neck-linker is too high if both heads are in the open conformation?
Response: We have added a citation to Benoit et al. 2021. The Topen-Lopen state is an off-pathway conformational state that differs from the on-pathway two-head-bound states (Tclosed-Lopen) studied using cryoEM. Using smFRET, we showed this state appeared only in the neck linker extended mutants, for which no cryoEM observation exist. Therefore, we modeled the Topen-Lopen state by assuming both heads adopt identical conformations in the open state, and showed that this off-pathway transition is suppressed because it would cause an intolerable increase in neck linker tension. Benoit et al.’s finding that the front open head can bind AMPPNP aligns with Niitani et al.’s observation (bioRxiv 2024) that while the front head can bind ATP, it maintains a low ATP affinity state—unlike the rear head, which exhibits high ATP affinity. This suggests that ATP binding (nucleotide state) is not tightly coupled to the open-to-closed conformational transition of the head.
Line 308: How do the authors estimate the tension on the neck linker? This needs to be explained briefly in the main text as it is central to the conclusions of this work.
Response: While we briefly described the method to estimate the tension in the text, we did not specify which part of the disordered neck linker was used for this calculation. We have now added this explanation as follows: “To estimate the amount of this tension, we isolated the disordered neck linker segments from both the leading and trailing heads that are stretched between the motor domains without steric hindrance or docking onto the head (Fig. S4 D). Then, we applied a harmonic potential to the Cα atoms at both ends of the stretched region and calculated the tension from the average displacement of the Cα atom from the potential minimum using MD simulations (Fig. 7, A and B)” (lines 300-306)
Line 308: Calculated tension is a lot higher than the force needed to pull a tubulin out from its tail from the microtubule (Kuo et al. Nat Comms 2022). Even the lowest tension they reported is a lot higher than the estimates made by Clancy et al. and Hyeon and Onuchic. The authors should comment on why this might be the case.
Response: The neck linker tension between two heads differs from the force applied by the optical trap to the bead attached to the coiled-coil stalk. Because these forces act in different direction and the coiled-coil stalk contains flexible hinges, torques, rather than forces, should be compared, though this is difficult to estimate (as described in Figure S16 in Hwang and Karplus, Structure 16, 62-71 (2008)). Hyeon & Onuchi (2007) and Hariharan & Hancock (2009) calculated the neck linker tension using a worm-like chain model, yielding different results of 12-15 pN and 28 pN, respectively (Clancy et al. cited these results). This discrepancy stems from different end-to-end distances used in their calculations (3.1 nm versus 4 nm). The 4 nm distance used by Hariharan and Hancock likely represents the tension in the two-head-bound state, as it equals half the distance between two heads on adjacent tubulin-binding sites. Using MD simulation, Hariharan and Hancock further estimated the neck linker tension of 15 pN in constraint force mode and 35 pN in force-clamp mode. Our estimated tension (39 pN) in Tclosed-Lopen state is comparable to the upper limit of these calculations. This estimated tension using isolated neck linkers is likely an overestimate, since the stretched neck linker in the presence of the motor domain includes an additional energetic contribution from its direct interaction with the leading head, which will be described in detail in our response to the reviewer 2’s comment #16. To address this, we have included the following sentence: “The tension in the Tclosed-Lopen state is likely an overestimate since this measurement excludes the enthalpic component discussed above, though it is comparable to previous MD measurements and theoretical calculations using a worm-like chain model (Hariharan and Hancock, 2009).” (lines 307-311)
Line 321: I would also cite Shastry and Hancock here.
Response: We have cited this paper (line 322).
Lines 387: "...the transition from one-head-bound to two-head-bound Topen-Lopen state".
Response: We have corrected the phrase accordingly (lines 387-388).
Lines 418-428: The authors assume that the neck-linker extension is purely entropic. However, neck linkers are almost fully stretched especially in unfavorable two-head-bound conformations, and they can potentially make contact with the motor domains. Therefore, this process may not be purely entropic and may also involve energetic terms when considering the free energy of neck linker docking.
Response: We appreciate the reviewer’s comment, as we had overlooked this important point. After examining the simulation movies of neck linker dynamics in Topen-Lopen and Tclosed-Lopen states (Fig. S4B, C and Videos 3, 4), we found that the stretched neck linker region in the Topen-Lopen state was displaced from the head and showed no interaction with the head during the simulation period. However, in the Tclosed-Lopen state, we observed a stable interaction between the K326 residue in the neck linker and the D37 and F48 residues of the leading open head (which can be seen in Video 4). This interaction was not included in our tension estimation (Fig. S4D), which assumed the tension had a purely entropic origin. Therefore, the estimated tension in the Tclosed-Lopen state is likely an overestimate, while the tension in the Topen-Lopen state remains purely entropic. We have added two sentences to describe these observations as follows: “Throughout the simulation, the stretched neck linker remained displaced from the head without any interaction, suggesting that the neck linker behaves as an entropic spring.” (lines 288-290), and “During this simulation, we observed a stable contact between the K326 side chain of the disordered neck linker and the D37 and F48 residues of the leading head (see Video 4), suggesting that the neck linker tension in Tclose-Lopen state includes an energetic component.” (lines 293-296)
Lines 452-454: I think this sentence summarizes the most significant contribution of this work and should be clearly mentioned in the abstract.
Response: We thank the reviewer for this suggestion and have incorporated the sentence into the abstract.
Lines 476-479: This sentence claims that neck linker docking is not necessary. Instead, rotation of the R-sub domain of the motor domain is sufficient to trigger the forward step. I would omit this sentence, as the rationale is not well explained, and it conflicts with a large body of literature on neck-linker docking. This could be an interesting idea to discuss in a perspective article or a topic of future research, but it may unnecessarily confuse the reader at the conclusion of this work.
Response: We included this sentence because it provides a testable prediction for neck linker-docking independent stepping, and we are preparing a manuscript to experimentally test this hypothesis. However, we agree with the reviewer’s comment that this statement conflicts with the common view in this field, and without additional verification or statement, it would confuse readers. Therefore, we have removed this sentence from the manuscript.
Reviewer #3
Major Comments:
- The Abstract is not clearly written to distinguish which kinesin head is being discussed.
Response: We revised the second sentence in the abstract to distinguish between the tethered and microtubule-bound heads and updated the abstract to enhance clarity.
The authors describe the bulge formed by the terminus if helix 4 as an obstruction that is "creating an intolerable increase in neck linker tension", but could it not simply be that forward head binding is conformationally disfavoured? Perhaps these ideas are not mutually exclusive.
Response: We agree with the reviewer that in the ATP-waiting state, the tethered head might also be prevented from binding to the tubulin-binding site due to the neck linker requiring a highly stretched configuration—this could occur before the tension increase that accompanies the transition from semi-open to open conformation. While we addressed this possibility in the Discussion section (lines 398-405 of the original version), our explanation was not sufficiently clear. We have therefore revised the sentence to clarify this point as follows: “Therefore, we can only speculate that the tension would lie somewhere between that of the Tclose-Lopen and Topen-Lopen states, and that microtubule binding of the tethered semi-open head may be restricted because the disordered neck linkers would need to adopt highly stretched configurations.” (lines 421-424)
The term "universal" in describing this tension-based regulation mechanism seems unjustified without examination of other kinesins. They might consider Kif1A as a subject given its shorter and seemingly more entropically-constrained neck linker. Recent structures of Kif1A bound to MTs in two-heads bound states have recently been described by Benoit et al. (Nat Comm. 2024).
Response: We agree with the reviewer and acknowledge that this tension-based regulation mechanism may not apply to some other kinesin subfamilies, which have different neck linker properties, such as varying neck linker lengths or specific interactions with the motor domain. We removed the word “universal” from the Abstract, Introduction and Discussion and added a final sentence to the Discussion as follows: “Additionally, studies are needed to examine whether this mechanism extends to other kinesin subfamilies with different neck linker properties, such as varying neck linker lengths (kinesin-2: Hariharan and Hancock, 2009; kinesin-3: Benoit et al., 2024) or specific interactions with the motor domain (kinesin-6: Guan et al., 2016; Ranaivoson et al., 2023).” (lines 501-505).
The authors should consider discussing how having two chains in the asymmetric unit of the APO motor impacts the NL structure.
Response: The G234A apo and G234V apo crystals share the same asymmetric unit since the G234A crystal was grown from a G234V crystal seed. We inspected the structures near the proximal end of the neck linker (or the C-terminus of the a6 helix connected to neck linker) that could cause steric hindrance or direct interaction with the initial segment of the neck linker. The closest element of the adjacent chain was L5, which was separated by 1.1 nm from the proximal end of the neck linker (K324 residue) and did not interact with it. The proximal ends of the neck linkers of chains A and B face each other, with a cylindrical cavity between them. This cavity in G234V apo allows an antiparallel β-sheet formation between the two stretched neck linkers of chain A and B (Figure S2A). However, we did not observe density corresponding to the antiparallel β-sheet in the cavity of G234A apo, likely due to its slightly smaller cavity size. Notably, this antiparallel β-sheet formation would be geometrically impossible for the two neck linkers in a dimer since their C-termini are joined in parallel by the neck coiled-coil. These explanations have been added to the text (lines 154-156) and the legend of Figure S2.
At barely 3 angstroms, how are waters modelled and how is it their B-factors are so low? Rfact and Rfree are also quite divergent for the GA mutant (APO) structure.
Response: To improve the R-factor, we placed water molecules to account for unmodeled and discontinuous electron density peaks that were too small to be interpreted as polypeptides. However, this treatment was likely incorrect and is the primary reason for both the low B-factor and Rfree values, which led to the large discrepancy between Rwork and Rfree. To address this issue, we repeated the structural refinement of G234A and G234V apo structures by removing water molecules placed on unmodeled density peaks. We retained only one water molecule in the nucleotide pocket of chain A in the G234A apo structure due to its well-defined density (Figure S1). This improved refinement significantly reduced the discrepancy between Rwork and Rfree of G234A apo from 20.0/28.1% to 20.7/26.5%. For G234V apo, while the discrepancy remained unchanged, the overall values were improved from 24.4/29.2% to 20.0/25.8%. We updated Table 1 and deposited these refined structures to the Protein Data Bank (PDB# 9L78 and 9L6K) with details provided in the “Data availability” section.
Lines 262-276: This section describes our current understanding of the mechanism of neck linker docking in accord with NP closure, which seems out of place in the Results and more relevant for the Discussion. Likewise, the two paragraphs before and after the description of the gold nanocluster study describe a re-evaluation and graphical/animated description of others' findings (Figure 4 and videos 1 and 2), rather than analysis of structural data obtained experimentally in this study.
Response: We acknowledge that this paragraph describes previous findings rather than current results. Therefore, we have relocated it to the Discussion section with appropriate citations from the literatures (lines 390-406). In addition, the paragraph, which precedes the gold nanocluster study, draws from previous research using different subdomain boundaries, so we added the relevant citations accordingly (line 238).
It is mentioned in the Discussion that the neck linker-docking is not necessary to trigger the forward step after ATP binding, but rather the rotation of the R-domain is sufficient to diminish the steric hindrance that limits tethered head binding. Are they suggesting that the neck linker could be undocked or disordered when making the forward step of a two-headed motor? According to other structural studies, a fully docked neck-linker is required to adopt the closed conformation. Moreover, binding of the leading head to the MT is necessary for complete closure of the nucleotide-binding pocket of the trailing head.
Response: This sentence was included because it offers a testable prediction for neck linker-docking independent stepping, and we are currently preparing a manuscript to test this hypothesis experimentally. The prediction is supported by Niitani et al.’s finding (biorxiv 10.1101/2024.09.19.613828) that loose neck linker crosslinking, which allows docking of the initial segment of the neck linker onto the head but prevents complete neck docking, reduced ATP-induced microtubule detachment rate by half. However, since this statement challenges the conventional understanding in this field and requires further verification, as noted by reviewer 2, we have removed it to avoid confusion.
Minor Comments:
Line 113 - "nucletodiee-free" spelling.
Response: We have corrected the typo (line 117).
Lines 118-122 - Final sentence of Introduction needs improvement: "Moderate neck-linker extension"? Terms are not defined/vague.
Response: To clarify this point, we revised this sentence as follows: “among possible conformational transitions, the one that requires less entropy reduction from stretching the disordered neck linker is favored” (lines 123-125).
Line 131 - Possible Error: "N-terminal motor domain (1-332 residues)" - should this be 1-322?
Response: This is our mistakes and we corrected the number of residues (line 134).
It could be difficult for some readers to follow the naming convention used Tapo-Lapo which is equivalent to Topen-Lopen in the final mechanistic model figure.
Response: In response to the reviewer’s comment, we have removed the reference to the Tapo-Lapo state from the Introduction and revised the notation in the Result section from Tapo-Lapo to Topen-Lopen.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The manuscript by Makino et al. investigates the mechanistic basis by which the two motor domains (heads) of kinesin dimers coordinate binding and release from their microtubule (MT) trackway in a productive manner for motility (i.e., in a way that limits backsteps or abandoned steps and encourages directional movement).
Earlier studies have provided structural, biochemical, and indirect visual evidence that kinesin dimers first associate with MTs with one head. This MT interaction opens the head's nucleotide pocket so its bound ADP can be released. ATP can then enter, and the nucleotide pocket will close around it when the neck linker at the C-terminus of the motor domain is able to dock against the side of the motor domain. The docking of the neck linker directs the tethered motor domain forward to the next available binding site on the MT, but before this happens, it is possible that the tethered head can still engage the MT, either in front of, or behind, the MT-bound head. Similarly, after taking a step, a head that has disengaged from the MT could rebind its previous site, or swing ahead of its partner motor domain to engage the next binding site on the MT.
This paper used structural methods, computational modelling, molecular dynamics, and biophysical measurements of labeled mutant kinesin dimers to understand how these tethered head-MT interactions are restricted from happening until the other MT-bound head is in the correct catalytic state for the tethered head-MT interaction to be productive. Their goal was to understand the mechanism that prevents premature binding of the tethered head to the microtubule during ATP-waiting state.
Their X-ray crystallographic and cryo-EM structures of monomeric kinesin-1 heads that were mutated to facilitate capture of the APO or "Open" nucleotide pocket state showed that the kinesin neck linker doesn't interact specifically or stably with either the motor domain or the microtubule surface in the nucleotide-free state. It appears that the neck linker is inhibited from docking and extending toward the MT plus end by a bulge made by the end of helix 4. This bulge would increase the distance the neck linker would have to stretch if it were connected to the neck linker of its MT-bound partner head. Thus, they proposed that this bulge deters kinesin dimers from being able to form complexes with MTs in which both the forward and rearward head are both bound to the MT and contain empty nucleotide pockets (i.e., both heads are in the 'open' state). Tension on the normal-length neck must therefore restrict unproductive MT binding events.
Overall, this study makes interesting links between the asymmetries in neck linker tension, entropy levels, and nucleotide pocket status of each dimeric kinesin head.
Major Comments:
- The Abstract is not clearly written to distinguish which kinesin head is being discussed.
- The authors describe the bulge formed by the terminus if helix 4 as an obstruction that is "creating an intolerable increase in neck linker tension", but could it not simply be that forward head binding is conformationally disfavoured? Perhaps these ideas are not mutually exclusive.
- The term "universal" in describing this tension-based regulation mechanism seems unjustified without examination of other kinesins. They might consider Kif1A as a subject given its shorter and seemingly more entropically-constrained neck linker. Recent structures of Kif1A bound to MTs in two-heads bound states have recently been described by Benoit et al. (Nat Comm. 2024).
- The authors should consider discussing how having two chains in the asymmetric unit of the APO motor impacts the NL structure.
- At barely 3 angstroms, how are waters modelled and how is it their B-factors are so low? Rfact and Rfree are also quite divergent for the GA mutant (APO) structure.
- Lines 262-276: This section describes our current understanding of the mechanism of neck linker docking in accord with NP closure, which seems out of place in the Results and more relevant for the Discussion. Likewise, the two paragraphs before and after the description of the gold nanocluster study describe a re-evaluation and graphical/animated description of others' findings (Figure 4 and videos 1 and 2), rather than analysis of structural data obtained experimentally in this study.
- It is mentioned in the Discussion that the neck linker-docking is not necessary to trigger the forward step after ATP binding, but rather the rotation of the R-domain is sufficient to diminish the steric hindrance that limits tethered head binding. Are they suggesting that the neck linker could be undocked or disordered when making the forward step of a two-headed motor? According to other structural studies, a fully docked neck-linker is required to adopt the closed conformation. Moreover, binding of the leading head to the MT is necessary for complete closure of the nucleotide-binding pocket of the trailing head.
Minor Comments:
Line 113 - "nucletodiee-free" spelling.
Lines 118-122 - Final sentence of Introduction needs improvement: "Moderate neck-linker extension"? Terms are not defined/vague.
Line 131 - Possible Error: "N-terminal motor domain (1-332 residues)" - should this be 1-322?
It could be difficult for some readers to follow the naming convention used Tapo-Lapo which is equivalent to Topen-Lopen in the final mechanistic model figure.
Significance
The manuscript by Makino et al. explores the coordination of kinesin dimer motor domains during microtubule (MT) motility, focusing on the mechanism that prevents premature tethered head binding in the ATP-waiting state. The combination of structural biology (X-ray crystallography, cryo-EM), computational modeling, molecular dynamics, and biophysical studies on mutant kinesins is a strength of the study and has allowed the authors to provide insights into how neck linker tension, nucleotide pocket status, and structural features like a helix 4 bulge influence kinesin dynamics.
Strengths
- The identification of the helix 4 bulge as a determinant of neck linker tension adds to our understanding of kinesin head coordination and motility.
- The study draws interesting links between entropy, structural asymmetries, and functional outcomes in kinesin dimer motility.
- The findings hint at conserved mechanisms regulating kinesin family motor dynamics, although this remains to be experimentally confirmed.
Limitations
- Claims of universal applicability for the tension-based regulation mechanism are premature without examining other kinesins, such as Kif1A.
- The role of neck linker docking in forward stepping and the potential for undocked states during motility need clearer resolution against prior studies.
In conclusion, the study contributes valuable mechanistic insights into kinesin motility and raises intriguing questions about its broader applicability across kinesin families, warranting further investigation. This study should be of general interest to the cytoskeletal motors community.
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Referee #2
Evidence, reproducibility and clarity
This manuscript investigates the role of the neck linker in coordinating the stepping cycles of the two heads of a kinesin-1 motor.
Previous studies in the field showed that kinesin walks by alternating stepping of its heads, referred to as hand-over-hand. In solution, both heads are in the ADP-bound state and have low affinity for MTs. One of the heads collides with the microtubule and releases its ADP while the other head remains in the ADP-bound state and does not interact with the microtubule. ATP binding to the bound head results in partial docking of its neck linker, which pulls the unbound head by 8.2 nm towards the plus end. ATP hydrolysis in the bound head completes its neck-linker docking and pulls the unbound head further towards the tubulin adjacent to the plus end side of the bound head. In this state, both heads are bound to the microtubule, the trailing head is bound to ADP.Pi and the leading head is nucleotide-free. ATP binding to the leading head is gated until the trailing head releases Pi and dissociates from the microtubule. After microtubule release, the head remains in the trailing position near its tubulin binding site as kinesin-1 waits for ATP binding to the leading head to start the next ATPase cycle.
The authors of this study ask an important question: After the trailing head releases from the microtubule, what prevents it from binding the tubulin on the minus-end or plus-end side of the leading head as the motor waits for ATP binding to the leading head? They first obtained the crystal structure of the kinesin head plus its neck linker in the nucleotide-free and ADP-bound conditions. Next, they solved the microtubule-bound structure of a kinesin head in a nucleotide-free condition using cryo-EM. Using their structures and previous structural studies of kinesin motors, they discovered that rigid body motions within the kinesin motor domain upon ADP release result in a steric clash between the C-terminus of helix4 and the distal end of helix 6 where the neck-linker is connected. They claim that this steric clash imposes an asymmetric constraint on the mobility of the neck linker: it can stretch freely backward but not forward in this state. They supported their model by labeling the middle of the neck linker with a gold nanoparticle and finding its position relative to the motor domain bound to the microtubule using cryo-EM. They observed that the gold density is positioned backward and located on the right-hand side of the motor domain, providing an explanation for why the trailing head takes steps from the right side of the leading head as kinesin walks. Consistent with previous work, they showed that ATP binding to the head releases this constraint, the first two residues of the neck-linker extend helix 6, while the rest docks onto a hydrophobic pocket on the motor domain and forms a beta-sheet with the neck cover strand, completing the neck-linker docking. Towards the conclusion of this work, the authors built a model for the two-head-bound state of kinesin on the microtubule and calculated the tension on the neck linkers based on the rigid body conformations of the motor domains. Using MD simulations, they estimated that the heads experience 50-100 pN tension through the extension of their neck linkers to support both heads to bind to the microtubule. The tension is lowest when the trailing head is ATP-bound and the leading head is nucleotide-free (which is the estimated state of kinesin right after neck-linker docking and the forward stepping of the trailing head), whereas tension is prohibitively too high when both heads are in the nucleotide-free state or the trailing head is in the nucleotide-free state and the leading head is in the ATP bound state. These results are consistent with a large body of work in literature and suggest that tension on the linkers prevents rebinding of the trailing head to the microtubule, keeps the two heads out of phase, and coordinates the stepping cycle of the kinesin heads to proceed in the forward direction, rather than backward. Finally, they perform smFRET measurements on kinesin mutants with extended neck linkers and show that extension of the neck linkers allows both heads of a kinesin dimer to simultaneously bind to the microtubule, demonstrating that it is the tension that prohibits the trailing head from binding to the microtubule in the ATP waiting state and keeps kinesin in a one-head-bound state for the majority of its mechanochemical cycle.
I only have several suggestions to improve the clarity and more balanced citation of the previous literature.
- Lines 72-73 can be deleted as they are repetitive with lines 95-96.
- Line 87: The authors should cite Mickolaczyk et al. PNAS 2015 and Sudhakar et al. Science 2021 as these studies also observed that the trailing head takes a sub-step and is located on the right side of the leading head before it moves forward and completes the step.
- Lines 103: The authors should cite Benoit et al. kinesin14 and Kif1A structures as these studies directly show the conformations of the neck-linkers when both heads are bound to the microtubule.
- Line 113: There is an extra "e" on "nucleotide".
- Line 118: I would delete "universal" as it is not clear whether all kinesins use a tension-based mechanism.
- Line 132: Why did the authors decide to use a cys-lite mutant for X-ray and cryo-EM studies?
- Line 192: The authors refer to Figures 3A and B when they discuss ATP-like and ADP-like conformations. However, these figures refer to open, semi-open, and closed conformations. Things become clear later in the text, but this is confusing, as is. I recommend the authors either show ATP-like and ADP-like classification as a supplemental figure and refer to that figure or not refer to the figure in this sentence.
- Lines 259-260: I would delete "as evidenced by..." and just cite those papers.
- Lines 262-276: The authors should cite the relevant literature in this paragraph as most of their conclusions here were already shown by previous structural studies.
- Recent biophysical studies claim that neck-linker docking is a two-step process that occurs in ATP binding and ATP hydrolysis. Do the authors agree with this model? Can they comment on why the neck-linker only partially docks during ATP binding, and require ATP hydrolysis to complete the docking? If they disagree with this model, this should be explained in the Discussion.
- Lines 285: The authors should cite Benoit et al. as they showed this clearly in their structure. Benoit et al. showed that, even though both heads are bound to AMP-PNP, the neck linkers are pointed in opposite directions and the rigid body conformations of the trailing and leading heads are different. Do the authors take this into account when they model the Topen-Lopen state? Can they also comment on why the heads can have different rigid body conformations even though they are bound to the same nucleotide? Is this because tension on the neck-linker is too high if both heads are in the open conformation?
- Line 308: How do the authors estimate the tension on the neck linker? This needs to be explained briefly in the main text as it is central to the conclusions of this work.
- Line 308: Calculated tension is a lot higher than the force needed to pull a tubulin out from its tail from the microtubule (Kuo et al. Nat Comms 2022). Even the lowest tension they reported is a lot higher than the estimates made by Clancy et al. and Hyeon and Onuchic. The authors should comment on why this might be the case.
- Line 321: I would also cite Shastry and Hancock here.
- Lines 387: "...the transition from one-head-bound to two-head-bound Topen-Lopen state".
- Lines 418-428: The authors assume that the neck-linker extension is purely entropic. However, neck linkers are almost fully stretched especially in unfavorable two-head-bound conformations, and they can potentially make contact with the motor domains. Therefore, this process may not be purely entropic and may also involve energetic terms when considering the free energy of neck linker docking.
- Lines 452-454: I think this sentence summarizes the most significant contribution of this work and should be clearly mentioned in the abstract.
- Lines 476-479: This sentence claims that neck linker docking is not necessary. Instead, rotation of the R-sub domain of the motor domain is sufficient to trigger the forward step. I would omit this sentence, as the rationale is not well explained, and it conflicts with a large body of literature on neck-linker docking. This could be an interesting idea to discuss in a perspective article or a topic of future research, but it may unnecessarily confuse the reader at the conclusion of this work.
Significance
Overall, this work is highly interesting and valuable to the kinesin field. It addresses an important question about the role of neck-linkers in the kinesin mechanism and provides meaningful explanations for some fo the previous observations made in the field.
Expertise: I am a single-molecule biophysicist interested in the mechanism and regulation of microtubule motors.
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Referee #1
Evidence, reproducibility and clarity
In this study, Makino et al. investigate how tension within the neck linker regulates the coordinated stepping of kinesin-1, a dimeric motor protein with two motor domains (or "heads"). Using high-resolution structural analyses, the authors identify a bulge near the neck linker's base in the nucleotide-free head that restricts forward extension, increasing steric hindrance when extended forward. This hindrance, they propose, prevents the tethered head from prematurely binding to the microtubule while the leading, microtubule-bound head awaits ATP. Molecular dynamic simulations and single-molecule fluorescence assays support this steric hindrance-based model, suggesting a mechanism that thermodynamically suppresses off-pathway transitions, thereby guiding kinesin-1's processive movement along microtubules. I recommend acceptance of the manuscript, subject to the following revisions:
- Abstract: The current abstract is challenging to follow. For instance, the phrase "The detached head preferentially binds to the forward tubulin-binding site after ATP binding, but the mechanism preventing premature binding to the microtubule while awaiting ATP remains unknown" could imply that the tethered head binds ATP, which is misleading. A clearer statement would be: "The detached head preferentially binds to the forward tubulin-binding site after ATP binding to the leading, microtubule-bound head, but the mechanism preventing premature binding to the microtubule while its partner awaits ATP remains unknown."
- Terminology: In the introduction, consider rephrasing to "...its two motor domains ("heads")."
- Lines 71-72: The sentences "This mechanism explains how the tethered head preferentially binds to the forward-binding site 'after ATP binding.' However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site 'before ATP binding'" could be rephrased for clarity. A suggested revision is: "This mechanism explains how the tethered head preferentially binds to the forward-binding site after ATP binding to the microtubule-bound, leading head. However, it does not clarify how the tethered head is prevented from rebinding to the rear-tubulin binding site before ATP binds to the leading head."
- Line 98: Consider revising "could release both ADP" to "could release both ADPs" or "could release both ADP molecules."
- Lines 103-104: The statement "Therefore, these results suggest the tension posed to the neck linker plays a critical role in suppressing microtubule-binding of the tethered head" should be clarified. Since tension only develops in the two-heads-bound state, using "steric hindrance" instead of "tension" may improve precision.
- Lines 374-375: Replace "...before ATP-binding triggers the forward stepping..." with "...before ATP binding to the leading head triggers the forward stepping..."
- Tense Consistency: Ensure consistent use of present or past tense throughout the manuscript for clarity.
Significance
The conclusions are supported by the data provided, offering valuable insights into the coordination of kinesin's motor domains during movement. These findings help address a knowledge gap in kinesin stepping mechanics, making this work relevant to researchers studying cytoskeletal motor proteins.
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Reply to the reviewers
We thank the reviewers for the detailed evaluations and thoughtful comments, which have improved the clarity and readability of this manuscript. We have responded to all reviewer comments and incorporated their suggested changes into the text and figures. The line numbers in our response correspond to those in the revised manuscript. We have also included new experimental results suggested by reviewer 2, which further strengthen our main conclusion.
Reviewer #1
- Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.
- Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads." Response: We thank the reviewer for pointing out our misunderstandings. We have corrected these sentences accordingly (lines 45-47 and lines 111-112).
Reviewer #2
MAJOR CONCERNS Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.
__Response: __We have now reiterated some of the key caveats in the Discussion, and newly described in the Results section those points not mentioned in the original manuscript that do not affect the conclusion. We also added a summary of the limitations and caveats into the first paragraph of the Discussion section (lines 425-431).
1) We added a sentence in the Results section to describe that the ATP-binding kinetics of the Cys-light mutant remained consistent with previous studies as follows: “First, we demonstrated that k+1 and k-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004)” (lines 163-166). The reduced kcat values of cysteine pair-added mutants before crosslinking were primarily due to reduced microtubule association rate (data not included in this manuscript). We have added a sentence in the Results section describing the kcat results as follows: “The reduced ATPase activity primarily results from a decreased microtubule association rate (data to be presented elsewhere) with little change in ATP binding or microtubule dissociation rates (Table 1).” (lines 144-146).
2) Fluorescently-labeled ATP was used to determine the ATP off-rates of the E236A mutant monomer and E236A rear head of the E236A/WT heterodimer. Two caveats in these measurements could lead to underestimating the ATP off-rate: 1) The off rate of Alexa-ATP from the head may be reduced compared to unmodified ATP, as Alexa-ATP driven motility showed a 10-fold reduce velocity. 2) The ATP off-rate of the E236A mutant may differ from that of the rear head in the wild-type dimer, since the E236A mutant likely stabilizes the neck linker-docked state more strongly than in the rear head of the wild-type dimer. These points are crucial for evaluating the results of ATP off-rate and the affinity for ATP, so we have added sentences in the Discussion section as follows: “We note, however, that this Kd of ATP may somewhat underestimate the true value in wild-type kinesin for two reasons: first, the E236A mutation likely stabilizes the neck linker-docked, closed state more than in the rear head of the wild-type dimer (Rice et al., 1999), and second, the Alexa-ATP used to measure the ATP off-rate of E236A head showed ~10-fold smaller velocity compared to unmodified ATP, partly due to a slower ATP off-rate (Figure 2____-____figure supplement 3).” (lines 449-454).
3) Under reducing condition, the rear head crosslink contained 30% crosslinked species, while under oxidized condition, the front head crosslink contained 11% un-crosslinked species (Figure 1____-____figure supplement 1). These heterogeneities likely affect the rate constants of k-1 for rear head crosslink and k2 for front head crosslink, as crosslinked and un-crosslinked species showed significantly different rate constants. However, we did not use the rear head crosslink result to determine k-1, since ATP hydrolysis likely occurred before reversible ATP dissociation. Instead, we used E236A monomer to estimate the k-1 of the rear head. In addition, the result for k2 of the front head crosslink was further validated using the E236A/WT heterodimer, which will be described in the next section.
4) This is an important point, and therefore, we conducted experiments using the E236A/WT heterodimer (including new experimental results of ATP binding kinetics of the front head) and obtained consistent results. To address this point, we have revised the following sentences in the Discussion: “In the front head, backward orientation of the neck linker has little effect on ATP binding and dissociation rates, both when measured for a monomer crosslink (Figure 2A, B) and for the front head of a E236A-WT heterodimer (Figure 4B, C, F).” (lines 432-433); “However, we found that the ATP-induced detachment rates from microtubule (k2) were similarly reduced for both the front head crosslink (7.0 s-1; Figure 3A) and the front WT head of the E236A/WT heterodimer (6.3 s-1; Figures 6D), suggesting that a step subsequent to ATP binding is gated in the front head.” (lines 437-441).
Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.
Response: The low microtubule affinity of the rear-head-like crosslink stems from an extremely slow ADP release rate upon microtubule binding, not from a fast microtubule-detachment rate. Using stopped-flow measurements of microtubule-binding kinetics (microtubule-stimulated mant-ADP release and microtubule association rates), we found that the rear-head-crosslink resulted in a 2,000-fold decrease in the microtubule-stimulated ADP-release rate. This finding also explains the reduced ATPase of the rear-head-crosslink (Figure 1E). Since this low microtubule-affinity state occurs in the ADP-bound state rather than the ATP-bound state, we hypothesized that the neck-linker docked ADP-bound state cannot effectively bind to microtubules, requiring neck-linker undocking for microtubule binding (Mattson-Hoss et al., Proc. Natl. Acad. Sci., 111, 7000-7005 (2014)). While we acknowledge that understanding slow microtubule binding in the neck linker docked state is important for elucidating the mechanism and regulation of microtubule-binding of the head, this paper focuses specifically on the mechanism and regulation of “microtubule-detachment”. We plan to present these microtubule-binding kinetics data in a separate manuscript currently in preparation.
To explain the low microtubule affinity of the rear-head-crosslink, we added this explanation to the text; “because this constraint on the neck linker dramatically reduces the microtubule-activated ADP release rate (data to be presented elsewhere), creating a weak microtubule binding state” (lines 226-228).
Although the rear head crosslinking construct under oxidative condition showed fewer fluorescent spots per kymographs (images) due to its low microtubule binding rate, we collected more than one hundred spots by recording additional microscope movies (N=140; Figure 3-figure supplement 2B), ensuring sufficient data for statistical analysis.
Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?
Response: The results of ATP on/off rates are comparable to the previous stopped flow measurements of ATP binding to monomeric kinesin-1 on microtubule, which are 2-5 µM-1s-1 and ~150 s-1, respectively (summarized in the review by Cross (2004)). We added a sentence as follows: “First, we demonstrated that k+1 and k-1 of the wild-type head without Cys-modification were unchanged after oxidization (Table 1) and were comparable to those previously reported (Cross, 2004).” (lines 163-166).
As the reviewer pointed out, the rapid mixing and smFRET data cannot be directly compared due to the differences in temporal resolution and fluorescent probe used. In Figure 2E (2F in the revised version), we measured ATP dissociation rate for both WT and E236A using smFRET. Due to the lower temporal resolution, we could not accurately determine ATP binding rate using smFRET. Therefore, to compare the ATP binding rate between WT and E236A heads, we now have added stopped-flow measurements of mant-ATP binding to the E236A monomer, as shown in Fig. 2C and Figure 2-supplement 2, and described in the text (lines 182-185).
Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?
Response: We agree with the reviewer that our finding about ATP binding to the front head is potentially significant in the kinesin field, as it has been widely believed that ATP-binding is suppressed in the front head. In our original manuscript, this conclusion was supported only by the measurement of ATP on-rate of the front-head-crosslink, which may differ from the front head of a dimer in which the backward orientation of the neck linker is maintained by the backward strain. Although the reviewer suggested performing smFRET experiments using E236A/WT heterodimer, smFRET have relatively low temporal resolution (50-100 fps) and cannot accurately measure the frequency of ATP binding, so we used this technique only to determine ATP off rates. In this revised manuscript, we now have added stopped-flow experiments to separately measure the ATP binding to the front and rear heads of the E236A/WT heterodimer. By labeling the rear E236A head with a fluorophore to quench the mant-ATP signal bound to the rear head, we successfully measured mant-ATP binding rate to the front head. We found that the ATP-binding rate to the front head was comparable to that of an unconstrained monomer head, providing direct evidence for our conclusion. The revised version includes Fig. 4 A-C (with Figure 4-supplement 2; Figs. 4 and 5 are swapped in order) showing the kinetics of ATP binding to the front and rear heads of the E236A/WT heterodimer, with corresponding text in the result section (lines 315-324).
MINOR CONCERNS Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates. Line 41: Replace "cellar" with "cellular". Line 83: The authors should cite Andreasson et al. here.
Response: We have corrected these sentences accordingly (lines 31, 40, 85).
Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).
Response: This statement lacks experimental evidence and may confuse readers, so we have removed it for clarity.
Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.
Response: A conclusion sentence of this paragraph has been added: “These results demonstrate that neck linker constraints in both forward and rearward orientations inhibit specific steps in the mechanochemical cycle of the head (lines 151-153)”.
Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"
Response: We have corrected these sentences accordingly (line 179-181).
Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.
Response: This sentence explains how the dark-field microscopy data was analyzed to determine whether the labeled head was in the leading or trailing position before detaching from the microtubule, but the explanation needs clarification. We removed the phrase “which was rarely observed for E236A-WT heterodimer” and simplified this sentence as follows: “Moreover, these observations allow us to distinguish whether the gold-labeled WT head was in the leading or trailing position just before microtubule detachment; the backward displacement of the detached head indicates that the labeled WT head occupied the leading position prior to detachment (Figure 5____-____figure supplement ____1).” (lines 347-351).
Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?
Response: We demonstrated that the k2 (microtubule-detachment rate) of the front head matched the ATP turnover rate of the E236A/WT heterodimer (Figure 6 B and E), suggesting that the inhibited step occurs after ATP binding in the front head. In contrast, the rear E236A head showed virtually no ATP hydrolysis activity, since in high-speed dark field microscopy, we observed forward step caused by rear E236A head detachment from microtubule only rarely, approximately once every few seconds (Figure 5-figure supplement 1). We added a sentence in the text as follows: “As described later, the reduced ATPase rate results from suppressed microtubule detachment of the front WT head, while the rear E236A head is virtually unable to detach from microtubules” (lines 311-313).
Line 323: Is the unbound dwell time unchanged?
Response: The unbound dwell time exhibited a weak ATP-dependence, which we described only in Figure 5-supplement 2 (Figure 4-supplement 2 in the old version). We observed three distinct phases in the unbound dwell time based on mobility differences, with ATP dependence appearing only in the third phase. This finding suggests that ATP binding to the microtubule-bound E236A head is sometimes necessary for the detached WT head to rebind to the forward-tubulin binding site, indicating that the microtubule-bound E236A head occasionally releases ATP during the one-head-bound state (without the forward neck linker strain). To describe the ATP-dependence of the unbound dwell time, we added a sentence in the main text as follows: “In contrast, the dwell time of the unbound state of the gold-labeled WT head showed weak ATP dependence (Figure 5____-____figure supplement 2), indicating that the rear E236A head occasionally releases ATP when the front head detaches from the microtubule and the neck linker of E236A head becomes unconstrainted. This finding further supports the idea that forward neck linker strain plays a crucial role in reducing the reversible ATP release rate.” (lines 372-377).
Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.
Response: We have revised the phrase accordingly (line 371).
Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.
Response: We agree to the reviewer’s comment and have corrected this sentence accordingly (line 338).
Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.
Response: To improve the readability, we have reorganized Figures 4-6, such that all the figure panels related to the neck linker extended mutants are shown in Figure 6; Figure 5D has been moved to Figure 6F.
Line 421: delete "not" before "does not".
Response: We have corrected this typo.
Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.
Response: The slip motion demonstrated by Sudhakar et al. (2021) differs from the backstep motion reported by Carter and Cross (and many other laboratories). Slip motion occurs after kinesin detaches from the microtubule and continues until the bead returns to the trap center. In contrast, backstep motion occurs during processive movement when the trap force either exceeds or approaches the stall force. The kinetics of these motions also differ significantly: slip steps occur with a dwell time of 71 µs and are independent of ATP concentration, while backsteps take ~0.3 s (at 1 mM ATP) and depend on ATP concentration. These differences indicate that slip motion is phenomenologically distinct from backsteps occurring under supra-stall or near-stall force.
Line 474: Replace "suppresses" with "suppressed".
Response: We have corrected this typo.
Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.
Response: We formatted Figure 4E to match Figure 4b from Isojima et al. (Nature Chem. Biol. 2015), to emphasize the difference in ATP dependence of the front and rear head.
Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).
Response: The method was described in the Materials and Methods section, but we have now described how to distinguish between bound and unbound states in the main text as follows: “Unlike the unbound trailing head of wild-type dimer that showed continuous mobility (Isojima et al., 2016), the unbound WT head of E236A-WT heterodimer exhibited a low-fluctuation state in the middle (Figure 5B, s.d. trace). This low-fluctuation unbound state was distinguishable from the typical microtubule-bound state, having a shorter dwell time of ~5 ms compared to the bound state and positioning backward, closer to the E236A head, relative to the bound state (Figure 5____-____figure supplement 2).” (lines 351-356).
__Reviewer #3____
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Minor Issues:
- Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.
Response: We changed the word “manner” to “process” (line 23).
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Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.
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Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."
Response: We have corrected these sentences accordingly (line 30, 32).
- Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.
Response: We have revised the last sentence of the abstract accordingly by specifying the neck linker’s conformation as follows: ”In combination, these conformation-specific effects of the neck linker favor ATP hydrolysis and dissociation of the rear head prior to microtubule detachment of the front head, thereby providing a kinetic explanation for the coordinated walking mechanism of dimeric kinesin.” (lines 34-37).
- Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.
Response: We measured the ATP-induced detachment rate k2 of the front head of the E236A-WT heterodimer to validate our findings from the front-head-crosslinked monomer experiments, which demonstrated reduced k2 after oxidation. To clarify this point, we have now included ATP binding kinetics measurements for both front and rear heads of the E236A-WT heterodimer, as suggested by reviewer 2. These additional data demonstrate consistency between the results from the crosslinked monomer and E236A-WT heterodimer experiments.
- Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.
Response: We have addressed this point by revising this sentence as follows: “These results are consistent with an inability of the front head to fully close its nucleotide pocket to promote ATP hydrolysis and Pi release (Benoit et al., 2023), as will be discussed later.” (lines 441-443)
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Referee #3
Evidence, reproducibility and clarity
Summary:
Kinesin-1 is a dimeric motor protein that transports cargo along microtubules via an ATP-powered, hand-over-hand stepping mechanism. Its processive movement is driven by coordinated ATPase cycles in the two motor domains (heads), which ensures sequential, alternating microtubule binding and detachment for unidirectional motility. The goal of this study by Niitani et al. was to investigate how the neck linker, a region extending from the motor domain's C-terminus that undergoes large conformational changes, differentially regulates microtubule affinity and nucleotide turnover in each of the two heads. The authors employed a combination of pre-steady-state and single-molecule analyses to separately measure the ATP-binding and microtubule-detachment kinetics of the front and rear heads.
To isolate the kinetics of each head, the authors used disulfide crosslinking to trap the front and rear head states of a monomeric kinesin as well as a heterodimeric kinesin in which one chain was locked in the rear head conformation. These experiments revealed that the backward-facing neck linker of the front head reduces microtubule detachment, while the forward-facing neck linker of the rear head enhances ATP affinity. This finding is consistent with cryo-EM structures of two-head-bound kinesins, which show that the front head has an open nucleotide pocket with the neck linker pulled backward, while the rear head has a closed nucleotide pocket with the neck linker docked and extended toward the front head, regardless of the nucleotide bound.
The authors used pre-steady-state kinetics and single-molecule assays to explore how the neck linker conformation influences kinesin's motility cycle. ATP binding rates to the kinesin head on microtubules were measured through stopped-flow experiments with mant-ATP and nucleotide-free kinesin-microtubule complexes. These results showed that crosslinking the rear head reduced the ATP dissociation rate, while crosslinking the front head had no significant effect on ATP binding kinetics. The dissociation of ATP from the rear head was further investigated by trapping it in a pre-ATP hydrolysis state using a kinesin mutant (E236A) that significantly slows ATP hydrolysis and stabilizes the neck-linker docked state.
The authors also investigated the impact of neck-linker orientation (forward vs. backward) on kinesin detachment from microtubules by measuring turbidity changes after rapidly mixing nucleotide-free, crosslinked kinesin-microtubule complexes with ATP using a stopped-flow apparatus. Their results demonstrated that the forward neck linker conformation in the rear head promotes microtubule detachment, while the backward-oriented neck linker in the front head reduces the detachment rate. This suggests that the neck linker conformation mediates gating of microtubule affinity and nucleotide binding. Additionally, they show that partial docking of the neck linker onto the head is sufficient to partially open the gating mechanism.
To further investigate the role of neck linker tension in front head gating, the authors created an E236A-WT heterodimer. In this dimer, the E236A mutant functions as a long-lived rear head, trapping it in a neck-linker docked state, while the WT head is positioned at the front in the presence of ATP. The analysis of microtubule detachment kinetics and ATPase activity in this heterodimer revealed that although the front WT head can hydrolyze ATP, its catalytic activity is suppressed by the E236A mutant in the rear head.
Overall, this is a biochemically rigorous study that supports recent structural findings, particularly by highlighting the existence and role of the asymmetry of the neck linkers in two-head-bound kinesin dimers and the distinct conformations of their motor domains.
Minor Issues:
- Line 22, Abstract - The phrase "move in a hand-over-hand manner" could be clearer if phrased as "move in a hand-over-hand fashion" to improve readability.
- Abstract - Neck linker conformation in the leading head: The sentence "We demonstrate that the neck linker conformation in the leading kinesin head increases microtubule affinity without altering ATP affinity" would benefit from defining this conformation as "backward" for clarity.
- Abstract - Neck linker conformation in the trailing head: The sentence "The neck linker conformation in the trailing kinesin head increases ATP affinity by several thousand-fold compared to the leading head, with minimal impact on microtubule affinity" should also clarify that this conformation is "forward."
- Abstract - Conformation-specific effects: The authors mention conformation-specific effects in the neck linker structure but do not define the neck linker's conformation or the motor domain's (MD) conformation. Clarifying these conformational changes would improve the explanation of how they promote ATP hydrolysis and dissociation of the trailing head before the leading head detaches from the microtubule, thereby providing a kinetic basis for kinesin's coordinated walking mechanism.
- Line 306 - Use of ATP in the E236A-WT heterodimer: In discussing the "ATP-induced detachment rate of the WT head in the E236A-WT heterodimer," the authors should consider justifying their choice of ATP over ADP for inducing microtubule (MT) dissociation. Since ATP typically promotes tighter MT binding and ATP turnover is reduced in forward-positioned WT heads, it may be unclear to some readers why ATP was chosen.
- Discussion - Backward-oriented neck linker in the front head: The discussion mentions that the backward-oriented neck linker in the front head reduces its ATP-induced detachment rate, suggesting that a step after ATP binding (e.g., isomerization, ATP hydrolysis, or phosphate release) is gated in the front head. However, the authors do not clarify that the backward neck linker orientation would imply the nucleotide pocket should be open or at least not fully closed, thus inhibiting ATP turnover. This is important because, as demonstrated in other studies, full closure of the nucleotide pocket is linked to neck linker docking. This point should be addressed earlier in the discussion.
Significance
As indicated in the notes to the authors, I'm supportive of the work reported and view the a biochemically rigorous approach has been used to understand how the neck linker element has such a significant influence on the chemomechanical cycle of each motor domain.
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Referee #2
Evidence, reproducibility and clarity
This manuscript investigates the role of the neck linker in coordinating the stepping cycles of the two heads of a kinesin-1 motor. Previous studies in the field showed that kinesin walks by alternating stepping of its heads, referred to as hand-over-hand. In this stepping mechanism, the front head of a kinesin dimer must remain bound until the rear head dissociates from the microtubule, moves forward, and rebinds to the tubulin on the plus-end side of the front head. There is a large body of work done to address this question. These studies all point to the central role of the 14 amino acid extension, a neck-linker, which connects the two heads to a common stalk, in coordination of kinesin motility. In a two-head-bound state, the motor domains (heads) are oriented parallel to the microtubule, but the neck linkers are orienting toward each other, thereby, breaking the symmetry in a homodimeric motor. In addition, the neck linkers are quite short, almost stretching to their near contour length to accommodate the microtubule binding of both heads. Previous studies pointed out that either the opposing orientation or the intramolecular tension of the neck linkers coordinate the stepping cycle.
However, we still do not know which step(s) in the chemo-mechanical cycle is controlled by the neck-linker to keep the two heads out of phase. The front head gating model postulates that ATP binding to the front head is gated until the rear head detaches from the microtubule. The rear head gating model proposes that the neck linker accelerates the detachment of the rear head from the microtubule. In this study, the authors use pre-steady state kinetics and smFRET to address this question. They measured ATP binding and microtubule detachment kinetics of kinesin's catalytic domain with neck linker constraints 1) imposed by disulfide crosslinking of the neck linker in monomeric kinesin in backward (rear head-like) and forward (front head-like) orientations, and 2) using the E236A-WT heterodimer to create a two-head microtubule-bound state with the mutant and WT heads occupying the rear and front positions respectively. They found that neck-linker conformation of the rear head reduces the ATP dissociation rate but has little effect on microtubule affinity. In comparison, the neck-linker conformation of the front head does not change ATP binding to the front head, but it reduces ATP-induced detachment of the front head, suggesting that a step after ATP binding (i.e. ATP hydrolysis or Pi release) is gated in the front head.
Major Concerns
Limitations of this study: The authors need to discuss the limitations of their work. 1) They used a cys-lite kinesins mutant and introduced new surface-exposed cysteines. These mutants have lower kcat values than WT. 2) They used fluorescently labeled ATP molecules, which are hydrolyzed 10 times slower than unlabeled nucleotides. 3) They still observe crosslinking under reducing conditions and partial (but almost complete) crosslinking under oxidized conditions. 4)They assumed that cysteine crosslinked orientation mimics the orientation of the neck-linker in the front and rear conditions. The authors clearly pointed to these issues in the Results section. While these assumptions are also supported by several control experiments, the authors need to acknowledge some of these limitations in the Discussion as well.
Line 238, the authors wrote that "forward constraint on the neck linker in the rear head does not significantly accelerate the detachment from the microtubule." Can the authors comment on why the read-head-like construct has a low affinity for microtubules even in the absence of ATP (Line 220)? I believe that the low affinity of the head in this conformation is more striking (and potentially more important) than the changes they observe in detachment rates. The authors should also consider that they might not be able to reliably measure the changes in the dissociation rate in single molecule assays of this construct (especially if the release rate of the rear head in the oxidized condition increases a lot higher than that of WT). The kymographs show infrequent and brief events, which raises doubts about how reliably they can measure the release rates under those imaging conditions. Higher motor concentrations and faster imaging rates may address this concern.
Figure 2: How do the rates shown in Figure 2A-B compare to the previous kinetics studies in the field? The authors compare the dissociation rate of WT measured in rapid mixing experiments to that of E236A in smFRET assays. It is not clear whether these comparisons can be made reliably using different assays. Can the authors perform rapid mixing of E236A or try to determine the rate for the WT from smFRET trajectories?
Line 396: One of the most significant conclusions of this work is that the backward orientation of the neck linker has little effect on ATP binding to the front head. This is only supported by the results shown in Fig. 2A-B. Can the authors perform/analyze smFRET assays on the E236A/WT heterodimer to directly show whether the ATP binding rate to the WT head is affected or not affected by the orientation of the neck linker of the WT head?
Minor Concerns
Lines 31 and 32: I recommend replacing "ATP affinity" with "ATP binding rate" or "the dissociation of ATP" to be more specific. This is because they do not directly measure the affinity (Kd), but instead measure the on or off rates.
Line 41: Replace "cellar" with "cellular".
Line 83: The authors should cite Andreasson et al. here.
Lines 83-86: It seems this sentence belongs to the next paragraph. It also needs a citation(s).
Line 151: It would be helpful to add a conclusion sentence at the end of this paragraph to explain what these results mean to the reader.
Lines 175-180: I recommend combining and shortening these sentences, as follows, to avoid confusing the reader: "To detect the ATP dissociation event of the rear head, we employed a mutant kinesin with a point mutation of E236A in the switch II loop, which almost abolishes ATPase hydrolysis and traps in the microtubule-bound, neck-linker docked state,"
Line 314: "which was rarely observed ...". This is out of place and confusing as is. I recommend moving this sentence after the sentence that ends in Line 295.
Line 300: Can the authors comment on why E236A/WT has a substantially lower ATPase rate than WT homodimer? Is it possible to determine which step in the catalytic cycle is inhibited?
Line 323: Is the unbound dwell time unchanged?
Line 331: I recommend replacing "ATP-induced detachment" with "nucleotide-induced detachment" for clarity.
Line 344: I recommend replacing "affinity" with "forward strain prevents the release of the nucleotide" or similar to avoid confusion. Forward strain reduces the off-rate of the bound nucleotide, rather than allowing ATP to bind more efficiently to the rear head.
Lines 376-385: G7-12 constructs are introduced in Figure 6, but the results in this paragraph are shown in Figure 5. They should be moved to Figure 6 to avoid confusion.
Line 421: delete "not" before "does not".
Lines 433-441: Unless I am mistaken, more recent work in the kinesin field showed that backward trajectories of kinesin 1 reported by Carter and Cross are due to slips from the microtubule rather than backward processive runs of the motor.
Line 474: Replace "suppresses" with "suppressed".
Figure 4E: I would plot these results with increasing ATP concentration on the x-axis.
Figure 4B: The authors should explain how they distinguish between bound and unbound states in the main text or figure legends. For example, it is not clear how the authors score when the motor rebinds to the microtubule in the first unbinding event shown in Figure 4B (displacement plot).
Significance
I believe that this work will make an important contribution to the large body of literature focused on the mechanism of kinesin, which serves as an excellent model system to understand the kinetics and mechanics of a molecular motor. The mechanism proposed by the authors modifies the front-head gating model and is in agreement with recent structural work done on a kinesin dimer bound to a microtubule. Overall, the work is well performed, and the conclusions are well supported by the experimental data. I have several major and minor concerns to improve the clarity of this work and strengthen its conclusions.
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Referee #1
Evidence, reproducibility and clarity
In this study, the authors investigate the molecular mechanism behind kinesin-1's coordinated movement along microtubules, with a focus on how ATP binding, hydrolysis, and microtubule attachment/detachment are regulated in the leading and trailing heads. Using pre-steady state kinetics and single-molecule assays, they show that the neck linker's conformation modulates nucleotide affinity and detachment rates in each head differently, establishing an asynchronous chemo-mechanical cycle that prevents simultaneous detachment. Supported by cryo-EM structural data, their findings suggest that strain-induced conformational changes in the nucleotide-binding pockets are crucial for kinesin's hand-over-hand movement, presenting a detailed kinetic model of its stepping mechanism. The manuscript is well-crafted, technically rigorous, and should be of significant interest to cell biology and cytoskeletal motor researchers. I recommend acceptance with the following minor changes:
- Introduction, page 3: The statement "Single dimeric kinesin moves processively along microtubules in a hand-over-hand manner by alternately moving the two heads in an 8-nm step toward the plus-end of the microtubule" is inaccurate. The kinesin heads take ~16 nm steps, while the center of mass advances in ~8 nm increments. Please adjust the wording accordingly.
- Introduction, page 5: In the sentence "These results are consistent with the closed and open conformations of the nucleotide-binding pocket in the rear and front heads of microtubule-bound kinesin dimers observed in cryo-electron microscopy (cryo-EM) studies," I recommend changing the order to align with the previous sentence. The correct order would be "These results are consistent with the open and closed conformations of the nucleotide-binding pocket in the front and rear heads."
Significance
All conclusions are well-supported by the provided data. The findings address a critical gap in our understanding of how kinesin's two motor domains coordinate their movements, offering insights into the molecular basis of its stepping mechanism. This work should be of significant interest to the cytoskeletal research community.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer 1
Major issue #1. Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities (https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S. cerevisiae, do the authors observe HAC1 mRNA splicing?
We have not tested whether HAC1 mRNA is processed in S. cerevisiae. To address this question, we will perform RT-PCR to test it.
In addition, as requested by the reviewers, we will further test the involvement of Ire1 in the HU/DIA-induced phenotype in S. pombe. For that, we will reassess our RNA-seq data and compare it with data from (Kimmig et al., 2012) (UPR activation in S. pombe). We will test the levels and splicing of mRNA of Bip1 upon HU/DIA treatments by RT-PCR and finally we will test the levels of Gas2p which has been described to decrease upon Ire1/UPR activation in S. pombe.
We are confident in that the results of these experiments and the re-analysis of our RNA-Seq data will help us to infer the mechanisms that modulate the ER response to HU or DIA treatment.
Major issue #2. The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S. pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status.
We agree with the reviewer that it is important to determine the redox and the functional state of the secretory pathway in our conditions to fully understand the cellular consequences of these treatments, especially in the case of HU, as it is routinely used in clinics.
In this context, we have already included new data showing that HU or DIA treatment leads to alterations in the Golgi apparatus and in the distribution of secretory proteins (Figures 3A-B).
In addition, we plan to perform mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We will test specifically the redox state of Bip1, Pdi and/or Ero1 by immunoprecipitation and western blot.
Finally, we plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (See below, Reviewer 2, Major issue #1).
What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?
We have tested whether the addition of this antioxidant could prevent and/or revert the N-Cap phenotype. We found that NAC in combination with HU increased N-Cap incidence (Figure 5H). As NAC is a GSH precursor and we find that GSH is required to develop the phenotype of N-Cap (Figure 5A-B, D, G), this result further supports that the HU-induced cellular damage might involve ectopic glutathionylation of proteins.
Unfortunately, we have not tested NAC in combination with DIA, as NAC seems to reduce DIA as soon as they get in contact, as judged by the change in the characteristic orange color of DIA, the same that happens when we combine GSH and DIA (Supplementary Figure 5A-B).
In this regard, the following information has been added to the manuscript (page 32-33, highlighted in blue):
"We also tested GSH addition to the medium in combination with either HU or DIA. When mixed with DIA, we noticed that the color of the culture changed after GSH addition (Figure S5A), which suggests that GSH and DIA can interact extracellularly, thus preventing us from being able to draw conclusions from those experiments. On the other hand, combining GSH with HU increased N-Cap incidence (Figure 5G), as expected based on our previous observations. Additionally, we checked whether the addition of the antioxidant N-acetyl cysteine (NAC), a GSH precursor, impacted upon the N-Cap phenotype. The results were the same as with GSH addition: when combined with HU, NAC increased N-Cap incidence (Figure 5H), whereas in combination, the two compounds interacted extracellularly (Figure S5B). These data align with NAC being a precursor of GSH, as incrementing GSH levels augments the penetrance of the HU-induced phenotype".
Major issue #3. The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates?
DIA is a strong oxidant, and HU treatment results in the production of reactive oxygen species (ROS). Therefore, one hypothesis would be that cytoplasmic chaperone foci represent oxidized and/or misfolded soluble proteins. Indeed, this hypothesis is supported by the appearance of cytoplasmic foci containing the guk1-9-GFP and Rho1.C17R-GFP soluble reporters of misfolding upon HU or DIA treatment (Figure 4I-J). We have already tested if they contain Vgl1, which is one of the main components of heat shock induced stress granules in S. pombe (Wen et al., 2010). However, we found that HU or DIA-induced foci lacked this stress granule marker, and indeed Vgl1 did not form any foci in response to these treatments. Therefore, our aggregates differ from the canonical stress-induced granules. We have yet to include this data in the manuscript, but we plan to do that for the final version.
To further explore the nature of the cytoplasmic aggregates induced by HU and DIA, we will test whether Hsp104-containing foci colocalize with guk1-9-GFP and/or Rho1.C17R-GFP foci.
Are those resulting from proficient retrotranslocation or reflux of misfolded proteins from the ER?
To test whether these cytosolic aggregates result from retrotranslocation from the ER, we plan to use the vacuolar Carboxipeptidase Y mutant reporter CPY*, which is misfolded. This misfolded protein is imported into the ER lumen but does not reach the vacuole. Instead, it is retrotranslocated to the cytoplasm, where it is ubiquitinated and degraded by the proteasome (Mukaiyama et al., 2012). We will analyze by fluorescence microscopy the localization of CPY*´-GFP and Hsp104-containing aggregates upon HU or DIA treatment and with or without proteasome inhibitors. We can also test the levels, processing and ubiquitination of CPY*-GFP by western blot, as ubiquitination of retrotranslocated proteins occurs once they are in the cytoplasm.
Are those aggregates membrane bound or do they correspond to aggresomes as initially defined? The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol.
Our results suggest that these aggregates are not bound to ER membranes, as they do not appear in close proximity to the ER area marked by mCherry-AHDL in fluorescence microscopy images.
To fully rule out this possibility, we will test whether these Hsp104-aggregates colocalize with ER transmembrane proteins such as Rtn1 or Yop1, with Gma12-GFP that marks the Golgi apparatus and with the dye FM4-64 that stains endosomal-vacuole membranes.
We have tested whether deletion of key genes involved in autophagy affected the N-Cap phenotype. To this end, we used deletions of ypt1, vac8 and atg8 in strains expressing Cut11-GFP and/or mCherry-AHDL and found that none of them affected N-Cap formation. These data suggest that the core machinery of autophagy is not critical for HU/DIA-induced ER expansion. We plan to include this data in the final version of the manuscript along with the rest of experiments proposed.
To get deeper insights and to fully rule out a possible contribution of macro-autophagy to the HU- and DIA-induced phenotypes, we plan to analyze by western blot whether GFP-Atg8 is induced and cleaved upon HU or DIA treatments which would be indicative of macroautophagy activation.
To test whether the cytoplasmic aggregates are the result of an imbalance between ER-expansion and ER-phagy we plan to analyze the localization of GFP-Atg8 and Hsp104-RFP in the atg7Δ mutant, impaired in the core macro-autophagy machinery. In these conditions, the number or size of the cytoplasmic aggregates might be impacted.
On the other hand, it has been recently shown that an ER-selective microautophagy occurs in yeasts upon ER stress (Schäfer et al., 2020; Schuck et al., 2014). This micro-ER-phagy involves the direct uptake of ER membranes into lysosomes, is independent of the core autophagy machinery and depends on the ESCRT system and is influenced by the Nem1-Spo7 phosphatase. ESCRT directly functions in scission of the lysosomal membrane to complete the uptake of the ER membrane. Interestingly, N-Caps are fragmented in the absence of cmp7 and specially in the absence of vps4 or lem2, the nuclear adaptor of the ESCRT (Figure 3E), We had initially interpreted these results as the need to maintain nuclear membrane identity during the process of ER expansion (Kume et al., 2019); however, the appearance of fragmented ER upon HU treatment in the absence of ESCRT might also be due to an inability to complete microautophagic uptake of ER membranes. To test this hypothesis, we plan to analyze whether the fragmented ER in these conditions co-localize with lysosome/vacuole markers.
Major issue #4. Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (https://academic.oup.com/nar/article/29/14/3030/2383924), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.
As stated in (Taricani et al., 2001), hsp16 expression is strongly induced in a cdc22-M45 mutant background. We performed experiments in this mutant that were included in the original version of the manuscript and remain in the current version (Sup. Fig. 2C) and, under restrictive conditions, we do not see spontaneous N-Cap formation. If Hsp16 overexpression and nucleotide depletion were key to the mechanism triggering N-Cap appearance, we would expect this mutant to eventually form N-Caps when placed at restrictive temperature. Furthermore, Taricani et al. show that Hsp16 expression was abolished in a Δatf1 mutant background in the presence of HU, and we found that this mutant is still able to produce N-Caps in HU; therefore, our results strongly suggest that the phenotype of N-cap is independent on the MAPK pathway and on the expression of hsp16.
Minor issues
- __P1 - UPR = Unfolded Protein Response: __Corrected in the manuscript
- 2__. P22 - HSP upregulation "might" be indicative of a folding stress:__ Corrected in the manuscript
- __ The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors revise the storytelling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.__ We have modified the abstract to better reflect our findings and will further revise our arguments in the final version of the manuscript once we have the results of the experiments proposed
Reviewer 2
Major issue #1. The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?
We have addressed the status of secretion in cells treated with HU or DIA by assessing the morphology of the Golgi apparatus and the localization of several secretory proteins by fluorescence microscopy and found that both HU and DIA treatments impact the secretion system. In addition, we plan on addressing the redox status of ER proteins (Bip1, Pdi or Ero1) by biochemical approaches. Please see the answer to major issue #2 from reviewer 1.
We will also analyze by western blot the biogenesis and processing of the wildtype vacuolar Carboxypeptidase Y (Cpy1-GFP) and alkaline phosphase (Pho8-GFP), two widely used markers to test the functionality of the ER/endomembrane system.
Major issue #2. Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.
This same issue arose with reviewer 3, so we decided to change the image of the western blot showing another one with less exposure and added a quantification showing that Bip1-GFP levels remain mostly constant between control conditions and treatments with HU and DIA.
We have also performed the suggested photobleaching experiment to analyze potential changes in crowding and mobility in Bip1-GFP upon HU treatment. We found that Bip1-GFP signal recovers after photobleaching the perinuclear ER in HU-treated cells that had not yet expanded the ER, showing that Bip1-GFP is dynamic in these conditions. However, Bip1-GFP signal did not recover after photobleaching the whole N-Cap in cells that had fully developed the expanded perinuclear ER phenotype, whereas it did recover when only half of the N-Cap region was bleached. This suggests that Bip1-GFP is mobile within the expanded perinuclear ER but cannot freely diffuse between the cortical and the perinuclear ER once the N-Cap is formed.
These data have been included in the revised version of the manuscript, in figure 4B, sup. figures 4A-B, and in page 23.
Major issue #3. It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?
As all three reviewers had comments about the CHX and Pm-related data, we revised those experiments and noticed a phenotype occurring upon HU+CHX treatment that had gone unnoticed previously and that changed our understanding about the effect of these drugs on the ER. Briefly, we noticed that, although CHX treatment decreases the HU-induced expansion of the perinuclear ER, it indeed induced expansion but in this case in the cortical area of the ER. This means that the phenotype of ER expansion in HU is not being suppressed by addition of CHX, but rather taking place in another area of the ER (cortical ER). We do not understand why this happens; however, these results show that ER expansion is exacerbated both in DIA and HU when combined with CHX. We have included this data in Figures 3C-D and in page 22.
We also examined the trafficking of secretory proteins that go from the ER to the cell tips and noticed that this transit was affected under both drugs (Figures 3A-B). This suggests that, although there is still protein synthesis when cells are exposed to the drugs (as can be seen by the higher levels of chaperones induced by both stresses (Figure 4C-E)), their protein synthesis capacity is possibly impinged on to certain degree. All this information is now included in the manuscript (page 19).
Major issue #4. While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).
Although we have only included experiments using one redox sensor in the manuscript, we had tested the oxidation of several biosensors during HU and DIA exposure monitoring cytoplasmic, mitochondrial and glutathione-specific probes. We have tried to use ER directed probes however, we have not been successful due to oversaturation of the probe in the highly oxidative environment of the ER lumen.
Although so far we have not been able to directly test the redox status of the ER with optical probes, we plan to test the folding and redox status of several ER proteins and secretory markers by biochemical approaches, so hopefully these experiments will give us more information on this question (See answer to Reviewer 1, Main Issue #2 and Reviewer 2, Main issue #1).
Major Issue #5. What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci?
Pm causes premature termination of translation, leading to the release of truncated, misfolded, or incomplete polypeptides into the cytosol and the re-engagement of ribosomes in a new cycle of unproductive translation, as puromycin does not block ribosomes (Aviner, 2020; Azzam & Algranati, 1973). This is likely to decrease the number of peptides entering the ER that can be targeted by either HU or DIA, decreasing in turn ER expansion. Indeed, we have found that Pm treatment alone results in the formation of multiple cytoplasmic protein aggregates marked by Hsp104-GFP (Figure 4K), consistent with a continuous release of incomplete and misfolded nascent peptides to the cytoplasm. This would explain why Pm treatment suppresses N-Cap formation when cells are treated with either HU or DIA.
To further test this idea, we plan to carefully analyze the number, size and dynamics of Hsp104-containing cytoplasmic aggregates in cells treated with HU or DIA and Pm, where N-Caps are suppressed. We expect to find an increase in the accumulation of proteotoxicity in the cytoplasm in these conditions.
On the other hand, CHX inhibits translation elongation by stalling ribosomes on mRNAs, preventing further peptide elongation but leaving incomplete polypeptides tethered to the blocked ribosomes. This reduces overall protein load entering the ER by blocking new protein synthesis and stabilizes misfolded proteins bound to ribosomes. Accordingly, it has been shown previously that blocking translation with CHX abolishes protein aggregation (Cabrera et al., 2020; Zhou et al., 2014). Similarly, we have found that Hsp104 foci are not observed when we add CHX alone or in combination with HU or DIA (Figures 4K-L). These results suggest that cytoplasmic foci that we observe upon HU or DIA treatment likely contain misfolded proteins derived from ongoing translation.
As this question has also been raised by reviewer 1, we have decided to further explore the nature of these cytoplasmic foci (please see answer to Reviewer1, Issue 3). Briefly:
- We plan to test whether they colocalize with the foci of Guk1-9-GFP and Rho1.C17R-GFP reporters of misfolding that appear upon HU or DIA treatments.
- We will test whether these foci are membrane bound.
- We plan to test whether the cytoplasmic foci represent proteins retro-translocated from the ER.
- We will also test whether autophagy or an imbalance between ER expansion and ER-phagy might contribute to the accumulation of cytoplasmic protein foci. The new data regarding the suppression of cytoplasmic foci by CHX treatment has already been included in the current version of the manuscript in Figure 4K and in the text (page 30).
The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.
We agree with the reviewer. We have toned down our statements about the relationship between thiol stress, the cytoplasmic chaperone foci and their relationship with ER expansion. We have removed from the text the statement that cytoplasmic foci are independent from ER expansion and thiol stress and have further revised our claims about CHX and Pm in the main text and the discussion to address these and the other reviewers' concerns.
Major Issue #6. Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.
To address this issue, we plan to analyze the localization of proteins involved in iron-sulfur cluster assembly and/or containing iron-sulfur clusters by in vivo fluorescence microscopy, such as DNA polymerase Dna2 or Grx5, during HU or DIA treatments.
Related to this, we have found that a subunit of the ribonucleotide reductase (RNR) aggregated in the cytoplasm upon HU exposure (Figure S2B). It is worth noting that RNR is an iron-containing protein whose maturation needs cytosolic Grxs (Cotruvo & Stubbe, 2011; Mühlenhoff et al., 2020). The catalytic site, the activity site (which governs overall RNR activity through interactions with ATP) and the specificity site (which determines substrate choice) are located in the R1 (Cdc22) subunits, which are the ones that aggregate, while the R2 subunits (Suc22) contain the di-nuclear iron center and a tyrosyl radical that can be transferred to the catalytic site during RNR activity (Aye et al., 2015). The fact that a subunit of RNR aggregates could be related to an impingement on its synthesis and/or maturation due to defects in iron-sulfur cluster formation, as it has been recently published that RNR cofactor biosynthesis shares components with cytosolic iron-sulfur protein biogenesis and that the iron-sulfur cluster assembly machinery is essential for iron loading and cofactor assembly in RNR in yeast (Li et al., 2017). This information has been added to the discussion.
Major Issue #7. The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.
We modified the language used to describe the experiment in the manuscript, as suggested by the reviewer, to clarify that while DTT is kept in the medium, N-Caps never form. In addition, we have also performed a pre-treatment with DTT; adding 1 mM DTT one hour before, washing the reducing agent out and adding HU to the medium then. The result indicates that pre-treating cells with DTT significantly reduces N-Cap formation after a 4-hour incubation with HU, which suggests that triggering reducing stress "protects" cells from the oxidative damage induced by HU and DIA. This information has been also added to the manuscript (Figure 2J).
Major Issue #8. For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).
We have revised and expanded our discussion. In addition, in the final revision of our work we will increase the discussion in the context of the new results obtained.
Minor points
- __ Figure numbering goes from figure 4 to S6 to 5.__ We have updated the numbering of the figures after merging several supplementary figures, so now this issue is fixed.
__ It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters__.
Both the Guk1-9-GFP and Rho1.C17R-GFP are two thermosensitive mutants in guanylate kinase and Rho1 GTPase respectively, that have been previously used in S. pombe as soluble reporters of misfolding in conditions of heat stress. During mild heat shock, both mutants aggregate into reversible protein aggregate centers (Cabrera et al., 2020). This information has now been added to the manuscript.
__ Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?__
We thank the reviewer for pointing out this mistake. The experiment was performed in 75 mM HU, the legend was correct. It has now been corrected in the manuscript.
__ The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signaling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.__
We have included pertinent clarification in the new discussion.
Reviewer 3
Major issue #1. It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.
Regarding the levels of Bip1, we now show in Figure 4 a less exposed image of the western blot, and a quantification of Bip1-GFP intensity from three independent experiments. We find that, in our experimental conditions, neither HU nor DIA treatments significantly altered Bip1 levels.
With respect to the RNA-Seq, as we mentioned in the major issue 1 from reviewer 1, we plan to reassess our data to further clarify and add information about ER stress markers induced or repressed by HU and DIA. We also will test the levels of Bip1 and several UPR targets by RT-PCR and by western blot.
Major issue #2. The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all.
We have found that puromycin treatment alone results in the formation of cytoplasmic foci containing Hsp104, suggesting that puromycin indeed increases folding stress in the cytoplasm. We have now included this data in Figure 4K (please see Main Issue #5 from Reviewer 2). Pm suppresses the formation of N-caps induced by HU or DIA; however, we have not addressed cell survival or fitness in these conditions and therefore we cannot conclude about being protective.
In addition, upon the reevaluation of our data, we have realized that CHX treatment suppresses HU-induced perinuclear expansion, although it does not suppress but instead enhances ER expansion in the cortical region. This data has been added to the present version of the manuscript in Figure 3C-D (page 22).
Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.
As the reviewer requested, we plan to test the effect of anisomycin (thapsigargin has been described to not work in yeast, as they lack a (SERCA)‐type Ca2+ pump (Strayle et al., 1999), which this drugs targets.
Regarding the downstream effects of HU or DIA treatment on ER proteostasis, we plan to further explore the effect of these drugs on the secretory system (please see major issue #2 from Reviewer 1) and to evaluate the redox state and processing of several key ER and secretory proteins. We will further explore the nature of the aggregates that appear in the cytoplasm in our experimental conditions, which will also shed light into the downstream effects of these drugs in cytoplasmic proteostasis (please see answer to issue #5 from Reviewer 2).
Major issue #3. The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction?
We plan on readdressing this topic by analyzing the genes that have been described to be differentially expressed during UPR activation in S. pombe and comparing them with our data, first by reevaluating our transcriptomic data and second by choosing Bip1 and some other of the differentially expressed genes in (Kimmig et al., 2012) (for example, Gas2, Pho1 or Yop1) and assessing by RT-PCR their mRNA levels in our experimental conditions. As an alternative approach, we will also analyse the levels of UPR targets by western blot upon HU or DIA treatment.
We are confident that the results of these experiments and the re-analysis of our RNA-Seq data will allow us to infer the mechanisms that modulate the ER response to HU or DIA treatment.
Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.
We thank the reviewer for pointing this out. We forgot to include this information which now appears in the M&M section as follows:
"A gene was considered as differentially expressed when it showed an absolute value of log2FC(LFC){greater than or equal to}1 and an adjusted p-valueIn this regard, we plan to perform proteome-wide mass spectrometry experiments to detect protein glutathionylation in our conditions, as it has been previously shown that DIA treatment leads to glutathionylation of key ER proteins such as Bip1, Pdi or Ero1 (Lind et al., 2002; Wang & Sevier, 2016), which might by reproduced upon HU treatment. We will also test specifically the redox state of Bip1, Pdi and/or Ero1 by immunoprecipitation and western blot. We also plan to test the folding and processing of specific secretory cargoes by western blot in our experimental conditions (see below, and Reviewer 2, Major issue #1).
We have already tested whether mutant strains with deletions of key enzymes in both cytoplasmic and ER redox systems are able to expand the ER upon HU or DIA treatment. We have found that only pgr1Δ (glutathione reductase), gsa1Δ (glutathione synthetase) and gcs1Δ (glutamate-cysteine ligase) mutants fully suppressed N-Cap formation, which suggests that glutathione has an important role in the phenotype of ER expansion. We have now added the pgr1Δ mutant strain to the main text of the manuscript (Figure 5C, page 31).
Major issue #5. Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.
We not only investigated the effects of HU on the ER in mammalian cells, but also of DIA. The results from this experiment mimicked the effect of HU (an increase in ER-ID fluorescence intensity in DIA). We merely excluded this information from the manuscript because we were focusing on HU at that point due to its importance as it is used currently in clinics. In this new version of the manuscript, we have included an extra panel in supplementary figure 5 to show the results from DIA in mammalian cells.
Minor concerns
1) Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.
Although we initially changed the graph, we believe the bar plot disposition facilitates its comprehension and went back to the initial one. Also, as the rest of the graphs similar to 1A are all expressed as bar plots, changing one would mean that, to avoid visual noise, we should change all. Therefore, we preferred keeping the figure as it was in the original version. However, we include here the graph with each of the averages of the independent experiments.
2) It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.
We have changed the image to show a whole population of cells, with several of them having NPC clusters, and we have indicated the position of SPB in each of them (all colocalizing with the N-Cap).
3) Figures 1B through 1D do not indicate the HU concentration.
We thank the reviewer for pointing out this mistake. Figures 1B and 1C represent cells exposed to 15 mM HU for 4 hours, while the graph in 1D shows the results from cells exposed to 75 mM HU over a 4-hour period. This information has been now added to the corresponding figure legend.
4) I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.
Our control is the background of each microscopy image; we make sure that after the laser bleaches a cell, the bleached area coincides with the background noise. That way, we make sure that fluorescence from any remaining GFP is completely removed from the bleached area.
5) On page 8, they say "exposure to DIA" when they intend HU.
This has been corrected in the manuscript.
6) In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.
We have added an explanation in the main text to clarify the main conclusions derived from this figure. We think that NPCs localize in a section of the nucleus where the two membranes (INM and ONM) are still bound together.
7) I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?
20 mM HU was used in this experiment to provide a time frame suitable for analysis after HU addition, as a higher HU concentration increases the repositioning time. We found that both HU (75mM 4h) and DIA (3mM 4h)-induced ER expansions are reversible upon drug washout. If HU is kept in the media, ER expansions are eventually resolved. However, DIA is a strong oxidant and if it is kept in the media ER expansions are not resolved and cells do not survive.
8) Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.
Thanks to this comment, we realized the notation underneath Figure 1D (1E in the new version of the manuscript) could lead to misunderstandings, as the timings there were "random". We have now made a clarification for this panel to be clearer: the timings are normalized to the moment when NPCs cluster. The fact that, before, that moment coincided with "40 minutes" does not mean N-Caps appear at that time point-quite the opposite, as most of them start to appear after >2 hours have passed in HU. We hope this can be better understood now.
9) Figure S4 is missing the asterisk on the lower left cell.
Fixed in the corresponding figure.
10) How is roundness determined in Figure S4B?
Roundness in Figure S4B (now S2E) is determined the same way as in Figure 1D, and as is described in the Method section (copied below). A clarification has been added to the legend to address that.
The 'roundness' parameter in the 'Shape Descriptors' plugin of Fiji/ImageJ was used after applying a threshold to the image in order to select only the more intense regions and subtract background noise (Schindelin et al., 2012). Roundness descriptor follows the function:
Round=4 X [Area]/π X [Major axis]2
where [Area] constitutes the area of an ellipse fitted to the selected region in the image and [Major axis] is the diameter of the round shape that in this case would fit the perimeter of the nucleus.
11) What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?
Large ER are considered when their area along the projection of a 3-Z section is over 4 μm2 (more than twice the mean area of the ER in cells with N-Caps in milder conditions). This has now been clarified in the legend of the corresponding figure.
__12) The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. __
We agree with the reviewer and have modified the interpretation of the indicated figure accordingly (page 30).
The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.
To address this suggestion, we plan to analyze the localization of other chaperones and components of the protein quality control such as the ER Hsp40 Scj1 or the ribosome-associated Hsp70 Sks2.
13) Figure 4L is cited on page 28 when Figure 4K is intended.
This has been corrected in the text, although new panels have been added and now it is 4N.
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Referee #3
Evidence, reproducibility and clarity
This article makes the following claims, using S. pombe as their model system. Hydroxyurea (HU) and diamide (DIA) induce ER stress, an atypical UPR, and cytoplasmic protein aggregation. HU and DIA induce IRE1-independent and GSH-dependent reversible ER perinuclear expansion which causes nuclear pore clustering with no effect on protein trafficking, and can be reversed by DTT.
Major concerns:
- It is hard to see how the claim of ER stress can be supported if BiP levels do not change (Fig. 4B). Also, this figure is overexposed. The RNA-seq data should be able to establish ER stress as well, but no rigorous analysis of ER stress markers is presented.
- The interpretation of the CHX and puromycin experiments of Figure 3A-B is hard to follow. My best guess is that the authors argue that CHX decreases misfolded protein load and that puromycin increases misfolded protein load, and that since DIA is a stronger oxidative stress than HU hence CHX is only protective under HU and not DIA. However, while CHX decreases misfolded protein load, puromycin hasn't been show directly to increase it and I don't see how this explains puromycin being protective at all. Furthermore, puromycin causes Ca leakage from the ER (which can be recapitulated with thapsigargin and blocked with anisomycin; easy experiments), which could be responsible for the differences from CHX, and the model does not address the effects on downstream stress signaling. The authors should be much more clear regarding their argument, since this data is used to support the argument of disrupted ER proteostasis.
- The claim that a canonical UPR is not induced is weak. First, the transcriptional program of S. cerevisiae from Travers et al is used as the canonical UPR, and compared to HU/DIA induced stress in S. pombe. These organisms may not be similar enough to assume that they have transcriptionally identical UPRs. Second, no consideration is given to the mechanism by which the different transcripts are modulated between "canonical" and HU/DIA induced UPR. Is it solely through RIDD, or does it point to differences in sensing or signaling transduction? Finally, the p-values used are unadjusted (e.g. by Bonferroni's method or by ANOVA or at least controlled by an FDR approach) and unmodulated (extremely important when n = 3 and variance is poorly sampled), which makes them not dependable. It looks like HSF1 targets are induced, which should be addressed.
- Mechanistically, one would expect effects to be mediated by PDIs and oxidoreductases. No effort is made to characterize the redox state of these molecules, nor how that relates to the kinetics of ER expansion and resolution under HU/DIA treatment. No discussion is made of the existing literature on oxidants and ER stress. A few papers: PMID: 29504610, PMID: 31595201.
- Figure S5 presents weak ER expansion in fribrosarcoma cells in response to HU (at very low concentrations and DIA is not included). The lack of any other phenotypes being presented could suggest that such experiments were done but didn't show any effect. The authors should straightforwardly discuss whether they performed experiments looking for perinuclear ER expansion or NPC clustering, and if not, what challenges precluded such experiments. Given how important this line of experimentation is for establishing generality, much more discussion is needed here.
Minor concerns:
- Figure 1A should show individual data points (i.e. 3 averages of independent experiments) in the bar graph.
- It is argued that Figure 1B demonstrates that the SPB is clustered with the NPC cluster. However, a single image is not enough to support this claim, as the association could be coincidental.
- Figures 1B through 1D do not indicate the HU concentration.
- I was confused by the photobleaching experiments of Figure S1. How do the authors know that there is complete photobleaching of the cytoplasm or nucleus in the absence of a positive control? If photobleaching is incomplete, they could be measuring motility without compartments rather than transport between compartments, and hence the conclusion that trafficking is unaffected could be wrong.
- On page 8, they say "exposure to DIA" when they intend HU.
- In Figure S3A, the colocalization of INM proteins with the ER are presented. It is not clearly explained what conclusions are meant to be drawn from this figure, but it seems it would have been more useful to compare INM and Cut11, to see whether the NPCs are localizing at the INM or ONM.
- I had to read Figure 2C's description and caption several times to understand the experiment. A schematic would be helpful. 20 mM HU is low compared to most conditions used. Does repositioning eventually take place for 75 mM HU or 3 mM DIA treatment, or do the cells just die before they get a chance?
- Figure 2D shows little oxidative consequence from 75 mM HU treatment until 40 min., the same time that phenotypes are observed (Figure 1D). Is this relationship consistent with the kinetics of other concentrations of HU, or of DIA? Seems like a pretty important mechanistic consideration that can rationalize the effects of the two oxidants.
- Figure S4 is missing the asterisk on the lower left cell.
- How is roundness determine in Figure S4B?
- What threshold is used to determine whether cells analyzed in Figures S4C have "small ER" or "large ER"?
- The authors interpret Figure 4K as indicating that ER expansion is not involved in the generation of punctal misfolded protein aggregates. However, the washout occurs only after the proteins have already aggregated. The proper interpretation is that the aggregates are not reversible by resolution of the stress, and hence are not physically reliant on disulfide bonds. The speculation that these proteins are iron dependent is a stretch; there is no reason to believe that losses of iron metabolism are the most important stress in these cells. It seems at least as likely that oxidizing cysteine-containing proteins in the cytosol or messing with the GSH/GSSG ratio in the cytosol would make plenty of proteins misfold; oxidative stress in budding yeast does activate hsf1. However, this point could be addresses by centrifugation and mass spectrometry to identify the aggregated proteome. It is also surprising that the authors did not investigate ER protein aggregation, perhaps by looking at puncta formation of chaperones beyond BiP. By contrast, the fact that gcs1 deletion prevents ER expansion but does not prevent Hsp104 puncta does support the idea that cytoplasmic aggregation is not dependent on ER expansion.
- Figure 4L is cited on page 28 when Figure 4K is intended.
Significance
This paper is for the most part well-written, presenting a logical chain of experiments that fully support the most important claims that have been made. Specifically, they show that HU and DIA induce reversible perinuclear expansion and nuclear pore clustering in an IRE1-independent and GSH-dependent manner, and that DTT can prevent and accelerate recovery of this phenotype. Both oxidants clearly induce protein aggregation in the cytosol. The evidence that perinuclear expansion is responsible for nuclear pore clustering is compelling, with strong support from the kinetics and the nup120 deletion experiments. Some conclusions are not supported, including the claim of an atypical UPR and of ER stress, but the validity of these claims does not substantively affect the overall importance of the paper and could be handled by withdrawal or tempering of the claims. The lack of a molecular mechanism connecting oxidation with ER expansion moderately detracts from the potential impact. Adequate experimental detail is provided unless otherwise noted
This paper is likely to be important for cell biologists interested in interorganelle communication and how the cell responds to oxidative stress. Modulating ER oxidoreductase activity has been shown to be a powerful way to regulate ER stress and proteostasis, and this paper shows how specific oxidative stresses that have not widely been investigated in this context, as opposed to the more commonly studied reductive and electrophilic stresses, can remodel the ER with cell-wide consequences. More specifically, the nuclear pore and nuclear morphology phenotypes, while not yet functionally significant in yeast, could be significant in other unexplored ways identified in the future. Towards that end, it would be valuable to see if these gross phenotypes reproduce in any metazoan cell or tissue, rather than just looking at ER expansion as in the current manuscript. My expertise is centered around ER proteostasis and chaperones, and as such I consider this paper important to my field.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Sánchez-Molina et al describes a striking time and dose-dependent clustering of nuclear pores and perinuclear ER expansion in response to hydroxyurea (HU) or diamide (DIA) treatment in S. pombe. Using microscopy, the authors establish clustering is reversible upon drug washout or extended drug treatment. Pretreatment or post-treatment with the reductant DTT prevents or reverses the clustering and expansion effects, as does the release of translating polypeptides from ribosomes (with puromycin). The phenotypes were established to occur independent of the established impact of HU on RNR activity and the cell cycle. The authors suggest instead that the phenotypes (referred to as nuclear-cap (N-Cap) formation) are associated with disulfide-based folding stress. Overlapping transcriptional responses for HU and DIA treatment suggest that cells are experiencing folding stress (based on chaperone induction) and/or a disruption in iron homeostasis (induction of genes involved in iron homeostasis). The observed clustering, ER expansion, and transcriptional profiles are independent of the well-established ER stress response pathway: the UPR.
The manuscript outlines several interesting phenotypic observations, and they establish the potential for conserved of this ER expansion and nuclear pore clustering from yeast (S. cerevisiae) and mammals (HT1080 fibrosarcoma cells). Data clearly establish the time and dose-dependent formation of these interesting structures. Additional experiments with combined drug treatments points towards a role for changes in the redox environment in cells, an impact on cytoplasmic protein aggregation, and a potential impact on the ER folding environment / ER redox environment.
Data obtained with thiol oxidants and reductants, alongside translation inhibitors, suggest a potential connection between the N-Cap phenotype and oxidative folding within the ER. Yet, this latter observation remains a suggestive link with less clear mechanistic connections. Some experiments that would more directly assess the suggested changes within the nuclear/ER region are outlined below.
- The authors state the cytoplasmic and ER folding are both disrupted. The impact on ER protein biogenesis would be bolstered with some biochemical data focused on the folding of one or more nascent secretory proteins. Is disulfide bond formation and/or protein folding indeed disrupted?
- Increased signal of Bip1 in the expanded perinuclear ER is shown and is suggested as consistent with immobilization of BiP upon binding of misfolded proteins. The authors suggest that this increased signal must reflect Bip1 redistribution because "Bip1 levels are constant". Yet, the western image (Figure 4B) looks to show increased level of Bip1 protein up HU treatment. Given the abundance of Bip1 in cells, it seems possible that a two-fold increase in newly synthesized proteins in the perinuclear region may account for the increased signal. These original data cited by the authors uses photobleaching (not just fluorescence intensity) to show a change in crowding / mobility, which the authors should consider to support their conclusion. Alternatively, a detected increased engagement of Bip1 with substrates (e.g. pulldown experiment) would be similarly strengthening.
- It is curious that cycloheximide (CHX) has a distinct impact on HU versus DIA treatment. Blocking protein synthesis with CHX exacerbates the phenotype with DIA, but not HU. The authors use the data with CHX to argue that their drug treatments are interfering with folding during synthesis and translation into the ER. If so, what is the rationale as to why CHX treatment decreases expansion upon HU treatment? Relatedly, is protein synthesis and/or ER import impacted upon treatment with HU and/or DIA?
- While the authors suggest that there is disulfide stress in the ER / nucleus, the redox environment in these compartments is not tested directly (only cytoplasmic probes).
Addition suggestions / comments:
- What do the authors envision is the role of the cytoplasmic chaperone foci? Do CHX / Pm treatment with HU/DIA reverse the chaperone foci? The authors argue that cytoplasmic foci are "independent" from ER expansion and are "not a direct consequence of thiol stress" based on the observation that DTT does not reverse these foci. This seems like a strong statement based on the limited analysis of these foci.
- Based on the transcriptional data, the authors speculate a potential role on role on iron-sulfur cluster protein biogenesis. This would seem to be rather straightforward to test.
- The authors suggest that "pre-treatment" with DTT before HU addition suppresses formation of the N-Caps. However, these samples (Figure 2J) contain DTT coincident with the treatment as well. To say it is the effect of pre-treatment, the DTT should be added and then washed out prior to HU or DIA addition. Alternatively, the language used to describe these experiments and their outcomes could be revised.
- For a manuscript with 128 references there is rather limited discussion of the data in the context of the wider literature. The discussion primarily focuses on a recap of the results. The authors do cite several prior works focused on redox-dependent nuclear expansion. However, while cited, there is no real discussion of the relationship between this work in the context of that previously published (including several known disulfide bonded proteins that are involved in nuclear/ER architecture).
Minor points
- Figure numbering goes from figure 4 to S6 to 5.
- It would be helpful to the reader to explain what some of the reporters are in brief. For example, Guk1-9-GFP and Rho1.C17R-GFP reporters.
- Supplementary Figure 3. The main text suggests panel 3A is focused on diamide treatment. The figure legend discusses this in terms of HU treatment. Which is correct?
- The authors use ref 110 and 111 to suggest the importance of UPR-independent signaling. However, they do not point out that this UPR-independent signalling referred to in these papers is dependent on the UPR transmembrane kinase IRE1.
Significance
An interesting finding that is well-supported as a phenotype. What would raise the impact would be data that connect these observations more directly with a mechanism. In particular, there are suggestions of a disruption in ER folding and/or the ER redox environment that are logical but not directly tested. How one viewed these additional experiments will depend on what journal is considering the manuscript.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Sanchez-Molina describe the impact of hydroxyurea on the remodeling of the nuclear pore complex (clustering) and the expansion of both cortical and perinuclear ER. The study is carried out in S. pombe, and the observations confirmed in S. cerevisiae. Results are clear and analyzed properly, however considering the differences in UPR signaling in both yeast strains the conclusions raised may remain to be fully documented.
Major issues
Regarding the conclusions on IRE1 signaling, both yeast species have different IRE1 activities https://elifesciences.org/articles/00048), the total deletion of IRE1 in S pombe appears to indicate that expansion of perinuclear ER is independent of IRE1, however since IRE1 signaling has exclusively a negative impact on mRNA expression, it might be relevant to identify mRNA whose expression is stabilized under those circumstances and evaluate whether those could confer a mechanism which would also yield perinuclear ER expansion (eg differential deregulation of ER stress controlled lipid biosynthesis required for lipid membrane synthesis). In S cerevisiae, do the authors observe HAC1 mRNA splicing?
The authors indicate that HU and DIA lead to thiol stress, it might be relevant to evaluate the thiol-redox status of major secretory proteins in S pombe (or even cargo reporters if necessary) to fully document the stress impact on global protein redox status. What happens if HU-treated yeast cells are grown in the presence of n-acetyl cysteine?
The appearance of cytosolic aggregates is intriguing, do the authors have any idea on the nature of the protein aggregates? Are those resulting from proficient retrotranslocation (or reflux of misfolded proteins from the ER? Are those aggregates membrane bound or do they correspond to aggresomes as initially defined?
The Walter lab has demonstrated a tight balance between ER phagy and ER membrane expansion (https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.0040423), which could also impact on the presence of protein aggregates in the cytosol. Does HU impact on the regulation of autophagy?
Nucleotide depletion was previously shown to lead to HSP16 expression through activation of the spc1 MAPK pathway (10.1093/nar/29.14.3030), one might think that HU (or diamide) could lead to this through a nucleotide dependent mechanism and not necessary through a thiol-redox protein misfolding stress. This issue has to be sorted out to ensure that the HSP effect is independent of nucleotide depletion.
Minor issues
P1 - UPR = Unfolded Protein Response
P22 - HSP upregulation "might" be indicative of a folding stress
The abstract does not reflect the findings presented in the manuscript. In addition, I would recommend the authors to revise the story telling in their manuscript to push forward the message on either the specific phenotype associated with perinuclear ER or on the characterization of protein misfolding stress.
Significance
This is a nice manuscript describing the likely effects of HU on protein misfolding and several consequences including the remodeling of the nuclear pore complex, the expansion of both cortical and perinuclear ER. The underlying mechanisms remain however unclear (for each parameter evaluated) and the manuscript would definitely benefit from the elucidation of one of those (if not more).
The work in yeast is novel and might bring light on mechanisms existing in mammalian systems. Since HU is used as a therapeutic, the characterization of the molecular mechanisms associated with its mode(s) of action will definitely be useful for better (targeted) efficiency.
The audience for this work is more targeted towards people working on yeast cell biology, however, the authors could expand the discussion section to make it of a broader scope.
I am expert on ER stress signaling
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #2
Evidence, reproducibility and clarity
This study describes genome-wide, FACS-based, pooled CRISPR knock-out screens carried out in human cortical neurons, to determine the cellular factors that are required for endocytosis of monomeric and fibrillar tau protein. The screens combined fluorescent tau species uptake with labelled transferrin endocytosis (which is predominantly clathrin-dependent). This allowed identification of genes that had specific effects on tau endocytosis versus general endocytosis.
The study identified a plethora of genes/proteins that are required for tau endocytosis. Bioinformatics analysis convincingly demonstrated that the genes required for uptake of both forms of tau are enriched for various endocytic machineries; there was a partial overlap, as well as some important differences, in the classes of machinery involved for monomeric versus fibrillar tau. Reassuringly, the screen for monomeric tau identified LPR1 as important for its endocytosis, consistent with the previous literature, and individual validation results for several other genes confirmed their effect.<br /> Importantly, the study also identified LRRK2 as being important for the uptake of monomeric tau. Further experiments were carried out with gene edited neurons lacking LRRK2, or expressing mutated LRRK2, to characterise this finding in more detail. These identified morphological abnormalities in the endolysosomal system, and also validated that LRRK2 regulates neuronal endocytosis of other key molecules that have been linked to neurodegenerative diseases, such as alpha-synuclein and Abeta. The precise mechanism of this effect of LRRK2 is not clear, and I'm sure will be a fruitful topic for additional studies; it is beyond the scope of the present study.
Overall, I think this is a well-conducted study that is nicely written with well-presented data. The data are largely convincing. The strengths of this study include that:
- the studies are carried out in human neurons, important target cells of tauopathies.
- the screen is nicely designed and the QC presented is thorough.
- it defines the landscape of cellular processes that are involved in tau endocytosis, a process that is highly likely to be of pathological relevance to major neurological disorders.
- an important mechanistic link between LRRK2 mutations and tau uptake is identified and further characterised.
- in virtually all cases (apart from a few experiments, e.g. Figure 6f), the studies are carried out with sufficient replicates and the statistical analysis is, as far as I can tell, appropriate (I do not have detailed experience in the statistical analysis of functional genomics datasets).
My criticisms of this study are all minor:
- in the initial QC of the screen, it would be interesting to see immunofluorescence microscopy assays with labelled tau species to further validate the FACS-based uptake assay is behaving as expected. At the time-point examined by FACS, is most tau in an endosomal compartment (as would be expected)? Furthermore, as an optional point for the authors to consider, in general I think the paper would be enhanced by inclusion of representative immunofluorescence images (as extended information) to supplement the FACS data in some of the subsequent figures, for example those in Figure 4a-d and Figure 6; although I think the conclusions of the paper are supported without such images, they would provide a nice visual representation of the effects. -in Figure 2f there is validation of a selection of screen hits by targeted CRISRP knock-out of the genes involved and FACS-based assays. Was this done with different CRISPR guides to those used in the initial screen, to provide further reassurance that there are no off-target effects? In addition, depletion of the mRNA/protein of interest is not confirmed in these validation experiments and this should be shown.
- in figure 4, the LAMP1 labelling is poorly resolved and it is difficult to see how the surface area of individual pucta could have been accurately measured. In addition, LAMP1 labelling is used as a proxy for the lysosomal compartment and I'm sure the authors appreciate that LAMP1 also labels late endosomal and autophagic compartments. I would suggest additional labelling for a lysosomal enzyme (e.g. cathepsin B or D) to provide additional specificity. This also tends to allow better delineation of individual vesicles than LAMP1, allowing easier measurement of lysosomal size.
- on page 12, regarding the vacuolar ATPase hits from the screen, referring to Figures 4c,d, it is stated that the results indicate "both forms of tau protein are trafficked via intracellular acid compartments of neurons". However, the function of the vacuolar ATPase has also been linked to effects on clathrin-mediated endocytosis (see PMID: 23263279) and this could provide a more direct explanation for the effect seen. This possibility should be mentioned. In addition, I think the authors overstate the case that the Brefeldin experiments "confirm" the dependency of tau uptake on ER-Golgi transport. Brefeldin was used for 24 hours and so there could be many knock-on effects of this treatment. The authors should either soften this statement or provide additional evidence (e.g. through other methods of blocking ER-Golgi and Golgi traffic such as depletion of individual key proteins involved in the process - which could be selected from the screen hits ) to support it.
- in certain figures bar graphs are shown, and these would be improved if they also showed the individual replicate data points.
Referees cross-commenting
Re Reviewer 1's comments:
- Since all results rely on isogenic iPSC lines from only one donor, authors need to confirm their finding using iPSC lines form another donor.
- Although the authors could consider this, I don't think this is strictly necessary. To my mind one of the key strengths of the study is that the lines used are isogenic, meaning that genetic background effects are controlled for. Perhaps the authors could deal with this by recognising this limitation of the study in the text.
- There are no sufficient attempts to assess the effects on synaptic functions and neurotoxicity.
- I think that this is beyond the scope of the current study.
- It is unclear how many technical replicates and how many independent experiments are performed in each experiment.
- This is a fair point. It can sometimes be a little moot as to what constitutes a replicate for a biological repeat in such cell biology experiments, and the authors should clarify more clearly what they have done, and whether they consider it a replicate or biological repeat.
- Since FACS may detect tau uptake in only soma, the effects of tau uptake should be evaluated by imaging entire neurons including axon and dendrites.
- I made a similar point in my review.
- In addition to RAP and LRP1 domain 4, it should be considered validating the results using LRP1 KO models or knockdown approaches.
- The authors could consider this. My opinion was that two orthogonal approaches was sufficient.
- Detailed descriptions in the Methods section for the neuronal differentiation, reagent catalog numbers, reagent concentrations, experimental procedures, and analytical methods should be provided.
- Agreed
- The concentrations and catalog numbers of RAP chaperone and LRP1 domain 4 is unclear
- Agreed
- Individual data should be included as dots in all bar graphs.
- Agreed
Significance
In conclusion, I feel that this is an important study that provides a conceptual advance to the field, especially in delineating the landscape of cellular functions involved in tau endocytosis and in providing a mechanistic linkage between LRRK2 function and tau endocytosis, as well as the endocytosis of other key neurodegeneration-associated molecules. I think that it will be of interest to a broad readership, including basic and translational scientists in the fields of Alzheimer's and Parkinson diseases and other prevalent neurodegenerative disorders. I anticipate that this paper will provide information that stimulates many subsequent studies.
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Referee #1
Evidence, reproducibility and clarity
The authors investigated the cellular uptake of tau in neurodegenerative diseases. Using a genome-wide CRISPR loss-of-function screening in human iPSC-derived excitatory neurons, they identified distinct cellular pathways involved in the uptake of extracellular monomeric and fibrillar tau. The screening results revealed that LRRK2, along with the previously recognized LRP1, plays a role in the uptake of monomeric tau. While LRP1 was critical for the uptake of monomeric tau, it did not contribute to the uptake of fibrillar tau. Similarly, the endocytosis of monomeric tau was dependent on the familial Parkinson's disease gene LRRK2, but LRRK2 was not required for the endocytosis of fibrillar tau. These findings suggest that LRP1 and LRRK2 are involved in the pathogenesis of tauopathies and Parkinson's disease, highlighting LRRK2 as a potential therapeutic target for these diseases.
- Since all results rely on isogenic iPSC lines from only one donor, authors need to confirm their finding using iPSC lines form another donor.
- There are no sufficient attempts to assess the effects on synaptic functions and neurotoxicity.
- It is unclear how many technical replicates and how many independent experiments are performed in each experiment.
- Since FACS may detect tau uptake in only soma, the effects of tau uptake should be evaluated by imaging entire neurons including axon and dendrites.
- In addition to RAP and LRP1 domain 4, it should be considered validating the results using LRP1 KO models or knockdown approaches.
- Detailed descriptions in the Methods section for the neuronal differentiation, reagent catalog numbers, reagent concentrations, experimental procedures, and analytical methods should be provided.
- The concentrations and catalog numbers of RAP chaperone and LRP1 domain 4 is unclear
- Individual data should be included as dots in all bar graphs.
Significance
While their findings are interesting, there are several concerns which should be further addressed.
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Reply to the reviewers
Reviewer #1
Evidence, reproducibility and clarity
The manuscript describes the tracking of individual mesoderm cells through live imaging. Through a combination of reporters including a novel cardiomyocyte reporter and a combined nuclear GFP-inducible Cre reporter under the dependance of the Brachyury promoter, the authors label mesoderm cells at different stages of gastrulation then perform long term (>30h) live imaging of late gastrulation embryo up to the cardiac crescent and heart tube stages. They use elaborate analysis tools as well as manual tracking to reconstruct cells' trajectory, lineage trees, and various behavioral traits.
The study is well designed. Experiments are technically challenging, well executed, and carefully analysed.
Methods are clear and complete so that experiments should be faithfully reproduced provided availability of an appropriate microscope.
The description of the results of the live imaging experiments is not easy to read and understand, but I believe this is inherent to the complexity of the results themselves and due to the high diversity of behaviors observed. Similarly the figures are extremely dense ans some graphs would benefit from a more didactic legend.
I realize the difficulty of being more concise due to the large amount of information and its diversity. If possible, I would suggest integrating tables within the results section that may help shorten the text, and may be easier to grasp.
We will add tables describing the numbers of uni-fated and multi-potent mothers, cell speeds, and dispersion. We will also split the figures to reduce the amount of information in each figure; and improve the legends by providing more detailed explanations.
The interpretation of the results is fair and in line with previous studies, which are adequately cited.
A discussion on the reasons why a large proportion of cells could not determined as uni or multipotent might be useful. Instinctively I would imagine that a majority of those are multipotent and therefore garder to track, so if the authors do not agree with this interpretation it may be useful to detail technical reasons why those cells cannot be fully interpreted.
We have discussed further reasons why a large proportion of cells could not be classified as uni-fated or multipotent. Indeed, while our analysis revealed a predominance of uni-fated progenitors (n=98, generating 728 descendants) over bifated/trifated progenitors (n=18, generating 302 descendants), a significant number of mother cells (n=111) produced progeny whose fates could not be determined. This is due to multiple factors, as explained below.
First, we were unable to fully track a large proportion of cells that generate short tracks. This limitation hindered our ability to determine their final fates. One key reason for these shorter tracks was the occasional high density of labeling, which, coupled with the spatiotemporal resolution of our imaging setup (0.347 x 2 µm z-stacks acquired every 2 minutes), was insufficient to consistently and unambiguously curate some cell tracks. We agree with the reviewer that the difficulty in tracking was probably exacerbated by the high dispersion of cells during the earliest stages, which is particularly high for multipotent mother cells. To avoid introducing erroneous lineage assumptions, we opted to stop tracking under such conditions.
Another contributing factor is related to cells migrating to deeper regions of the heart tube. Over extended timeframes, these cells often relocated towards the more dorsal regions of the forming heart tube, where they became dimmer due to their position along the z-axis. Consequently, many daughter cells did not meet the GFP intensity threshold required to classify them as myocytes and were thus labeled as mesodermal (line 194 and see Fig. 7C for an example). Additionally, some cells could not be tracked for prolonged periods, especially as they moved dorsally during the transformation of the cardiac crescent into the heart tube. A limitation of light-sheet imaging is its reduced capacity to capture high-quality images in deeper tissues due to light scattering. Addressing this limitation and improving imaging depth will be critical in future studies.
We also acknowledge the graded expression pattern of cTnnT2-GFP in the forming heart tube, with early and higher levels in LV/AVC myocytes and later, lower levels in inflow myocytes. To maintain consistency, we refrained from using different thresholds to account for these regional intensity differences. While this choice could have led to false negatives (e.g., inflow cells not meeting the GFP threshold), we believe this approach minimises the risk of false positives. Any daughter cells failing to meet the threshold were conservatively classified as mesodermal (meso GFP-), even though they may have been myocyte progenitors.
Additionally, some cells contributing to the inflow/atria regions may not have passed the GFP threshold during the imaging period but could have done so at later developmental stages. These cells were also classified as mesodermal, as their myocyte progenitor status could not be determined. This conservative approach prioritises accuracy over overestimation. We have included all these explanations in the main text and Materials and Methods.
Significance
Strengths: novel transgenic tools, powerful imaging technique, thorough quantified nalysis. Limitations: the development of embryos after E7.75-E8 is never completely normal ex vivo, particularly when there is no rotation. This is visible in the pictures of the embryos post culture (ballooned yolk sac, unattached allantois). It is probably not a limitation regarding cardiac development but may influence other mesoderm lineages notably ExE. Advance: It is a unique study dur to the labelling strategy, the length of imaging, and thereby the faithful tracking of cell lineages across several rounds of division. The information provided corroborates what previous hypothesis in the field based on less direct assessment, and is here very strong and unbiased. The research is of great interest for developmental biologists (including but not limited to the heart field), cell biologists (notably those working on stem cells and organoids as it provides a ground truth), microscopy and image analysis experts.
Reviewer #2
Evidence, reproducibility and clarity
The authors perform an elegant "tour de force" lineage relationships during mouse heart development. They perform long-term live imaging and single-cell tracking in mouse embryos from early gastrulation to stages of heart tube formation. They then track the progeny of individual cells and reconstruct the lineage tree of tracked cells. They analyze how their migratory paths of cells correlate with cell fate in the heart. Altogether, the manuscript presents a highly detailed live-imaging lineage tracing study of a subset of cells in the cardiac crescent in mouse. This presents a nice contribution to the literature, but would be improved by the suggestions below.
Major comments:
- Can the authors be sure they can track all of the derivatives of labeled cells? They are claiming to be able to follow complete lineages, but I worry if they may lose progeny in their tracking or incorrectly conclude that cells are lineally related. wonder how you could show how accurate it really is. Perhaps if the authors could include a movie where they trace what they claim as an entire lineage of a single cell and show this with the mother and daughter cells labelled throughout the movie, that would at least provide an example for readers to make their own decisions about how reliable the lineage tracing is. Would it be feasible to include an interactive movie where the reader can move the embryo around in 3D at each time point?
We have not tracked all the derivatives of labeled cells, as explained in our response to Reviewer 1. A number of mother cells (n=111) produced progeny whose fates could not be determined. Each cell track (up to 1,000 time points) required manual curation and verification, as even a single linkage error would compromise conclusions. When a track could not be unambiguously determined, we stopped tracking those cells. We have acknowledged this limitation in the manuscript.
We also agree with the reviewer that it is important to show the tracks, and we will therefore include supplementary movies displaying all the cells tracks. Furthermore, we are submitting all our datasets to the Image Data Resource (IDR) (https://idr.openmicroscopy.org/). Our datasets have been accepted, and the IDR team is currently assessing our track data, cell annotations, and metadata. This will enable users to download the data and fully assess them interactively in 3D using MaMuT or Mastodon (https://mastodon.readthedocs.io/en/latest/index.html) for cell tracking, as well as to generate their own tracking data. The availability of our data through this resource will significantly enhance its value to the community.
The authors describe the lineage labeled cells as unipotent, bipotent, etc. But they cannot really say anything about developmental potential as they are only looking at normal fate which is less that their potential. Without manipulation of the cells through transplantation etc., the use of the term 'potential' or 'potent' is not appropriate except when they find cells that are multipotent. Rather than calling cells unipotent, I would suggest using the phrase 'assume a single fate'.
We have replaced all instances of unipotent with uni-fated.
Lines 112-115, the authors state that variability in embryonic stages likely explains differences in labelling. Are there any morphological characteristics across the embryos that support this variability in stages? For example, any characteristics that suggest that the n=3 embryos are slightly older, and the n=7 embryos are slightly younger (line 111)?
We thank the reviewer for this excellent suggestion. Unfortunately, as the embryos were collected at different times, it is not possible to directly compare embryos from different litters. To address this, we would need to repeat the lineage tracing experiments by collecting embryos at fixed time points. This approach would allow us to compare variability in developmental stages at the time of collection while accounting for differences in labeling. Our live analysis shows that the early and late mesoderm contribute to the cardiac crescent and heart tube inflows, respectively, supporting our interpretation of the lineage tracing results.
Paragraph beginning on line 116: Please clarify how cells were counted, from the wholemount/across sections?
We counted the tdTomato+ cells across sections in wholemount embryos using the Cell Counter plugin in Fiji. We added this information to the Methods section.
- Line 165: Authors state that in the absence of tamoxifen, tdTomato-positive cells were identified in one embryo. Please state here the total number of embryos out of which this one embryo was counted.
Done.
- Line 190: 'Figure 2-Supplementary Figure 3A-F' doesn't exist. Do they mean Fig.3 supplementary 3A-F?
Yes, thank you, we corrected. Fig.3 supplementary 3A-F is now Fig.4 supplementary 3A-F.
Figure 1F-G: For cross sections in 'G' please show the level they were taken from in 'F'.
The cross-section shown in panel G (now Figure 2B) was not taken from the same embryo depicted in panel F (now Figure 2C). We apologize for the confusion and have clarified this point in the text.
Figure 4I: There is a large disparity in cell dispersion across movies. Please comment on why this could be. Is there a difference in stage/morphology etc..
Movies 1 and 2 depict embryos cultured at earlier stages, while Movies 3 to 5 show embryos cultured at later stages. The later the embryonic stage at the start of culture, the less dispersed along the anterior-posterior (AP) and dorsal-ventral (DV) axes of the heart tube the clones were. This is consistent with the idea that cell dispersion was more prominent during the earliest phases of migration taking place in the earlier embryos, consistent with the results from Dominguez et al. 2022. We will add a graph comparing the stages at which the cells were tracked (based on the alignment of the movies shown in Figure 5B) to cell dispersion to illustrate this point and have clarified in the manuscript.
Figure 4K-L: The arrowhead color is too similar to the cell fluorescence color, making the visualization a little confusing. Changing the color of the arrowheads may be helpful. This is also true for some of the other figures (red arrowheads).
We have changed all the red arrows to white arrows.
Significance
This is a well-done study that will be useful to developmental biologists as well as cardiologists. The experiments seem very well done and beautifully executed. With the proposed modifications, it will make a very nice contribution to the literature.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In their manuscript, Abukar et al. investigate the origins and migratory behaviors of cardiac progenitor cells, in mice, from gastrulation to early heart tube formation. They use sophisticated live imaging to tracks individual mesodermal cells, reconstructing their lineage and fate over several generations. The findings reveal distinct unipotent progenitors that contribute exclusively to specific cardiac regions, such as the left ventricle/atrioventricular canal (LV/AVC) or atrial cardiomyocytes. LV/AVC progenitors differentiate early, forming the cardiac crescent, while atrial progenitors differentiate later, contributing to the venous poles of the heart tube. Additionally, the study identifies multipotent mesodermal progenitors contributing to various mesodermal cell types, including the endocardium, pericardium and extraembryonic tissues.
Major comments: 1. Important conclusions of the manuscript rely on the expression of a reporter line (cTnnt2-2a-eGFP) as well as on the position of tdTomoto+ cells in relation to the reporter. We feel that markers of non-myocardial lineages should have been used to better characterize these populations. We acknowledge the technical challenge of live imaging, which may not allow labeling of all lineages. We believe that a better description of the final stages of investigation with markers of endocardium, pericardium, extra-embryonic mesoderm together with the eGFP of the reporter will strengthen the conclusions drawn on the multipotency of the progenitors. If not addressed, some claims may appear more speculative and would benefit from being toned down.
We agree that the use of additional specific reporters and endogenous marker gene expression data would provide further insights and have now acknowledge this point in the Discussion. For example, the extra-embryonic mesoderm is situated in the extra-embryonic space, and additional markers would help identify which cell types within the ExEm compartment were traced. Similarly, many cells were classified as meso but could not be defined further in the absence of suitable markers in our live imaging experiments.
However, we stand by our assertion that the spatial distribution of progenitors in the heart tube regions, as observed in our live-imaging data-particularly within the somatic and inner endocardial layers surrounding the cTnnT-2a-GFP+ myocardial layer-provides the most compelling evidence.
Gene expression is not always a perfect proxy for assigning cell fates without carefully documented spatial context, as transcription factors (TFs) are often expressed in multiple cell types. For example, Hand1 is expressed in the pericardium, ExEm, and left ventricle myocardium, while Nr2f2 is expressed throughout the posterior mesoderm and not exclusively in myocytes (as shown in Fig. 1H). Similarly, Tal1 is expressed in hemogenic endothelial/blood progenitors located in the ExEm and endocardial lineages.
Therefore, we stand by our cell annotations. This approach, based on cell location, aligns with well-established lineage mapping studies that have long demonstrated the predictive power of spatial and morphological information in early development. For instance, Wei et al. (2000) successfully predicted early segregation between myocardial and endocardial lineages solely based on cell location within these layers of the heart tube. Decades-old research has provided clear evidence that the pericardial (somatic), myocardial (splanchnic), and endocardial layers are distinguishable in E7.5 mouse embryos (see DeRuiter et al., 1992, PMID: 1567022, Figure 2A-F). In fact, cell types were often defined through morphological observation long before gene expression techniques became available. Such approaches remain relevant for elucidating cell fates, particularly in early embryogenesis, when spatial information plays a crucial role in defining progenitors.
- Similarly, since all the results of the manuscript derive from five movies of five independent embryos, it would be important to provide a more detailed description (for example, in a table) of the experimental setup. This could include the timing of tamoxifen induction (+7h or +21h?), the stage of dissection (based on anatomical landmarks rather than dissection stage - see atlas of gastrulation), the duration of the movies, and the stage at the final time point. Providing this information would greatly enhance the ability to robustly compare each movie and ensure reproducibility. Of note, the methods section could benefit from additional clarity. For example, in line 594, the embryo from Movie1 is described as being dissected in the morning, while the next sentence states it was dissected in the afternoon, similar to the embryo in Movie5. To avoid confusion and ensure greater rigor, describing the developmental stage of the embryos rather than the time of dissection would be more precise and biologically meaningful.
We thank the reviewer for this suggestion. While we have already temporally aligned our movies based on the timing of the first LV/AVC progenitors and atrial progenitors passing the threshold to be considered as myocytes (Fig. 5B), we will provide additional staging of the embryos based on morphological landmarks at T0. This will include the extent of the nGFP+ primitive streak and the normalized intensity of the nGFP signal. Additionally, the duration of the movies and the timing of tamoxifen induction will be indicated in the table, as suggested by the reviewer. We removed the statement on the dissection in the morning and afternoon since it was clumsy.
- This manuscript focuses primarily on LV/AVC progenitors and likely a subpopulation of atrial cardiomyocytes, leaving other cardiac progenitor populations unaddressed. While it is understandable that the study focuses on specific populations, the authors should further discuss the limitations of their approach and explain why not all cardiac progenitors were targeted. A discussion of how these limitations might impact the broader interpretation of their findings would also be valuable.
We agree with the reviewer that our analysis focuses mainly on the LV/AVC and atrial progenitors and have now mentioned these limitations in our Discussion. However, the HCN4+ inflow structures of the heart tube we are analysing likely contribute to most (if not all) of the atria later in development, rather than constituting a subpopulation. Published lineage tracing of HCN4+ cells using a tamoxifen inducible system suggests that these cells contribute to most of E19.5 atria (Fig. 2b in Später et al., 2013), raising the question of the extent of the contribution from an additional HCN4- population to the atria. However, we agree that this question warrants further investigation.
Regarding the progenitors contributing to the RV and OFT, we agree with the reviewer that our analysis does not fully address these progenitors. While we did analyse a subset of distal mesodermal cells contributing to the pharyngeal mesoderm (labeled in red in Fig.), the absence of a live marker prevented us from determining whether these cells localized in this part of the embryo were part of the cardiopharyngeal mesoderm. Consequently, we labeled these cells as meso GFP- in our results.
We suspect that mesodermal cells contributing to the pharyngeal mesoderm may arise earlier than atrial progenitors and are currently investigating their origin using a new Tbx1-2a-tdTomato reporter line (Figure 1). However, as these findings are still preliminary and require further work, which is beyond the scope of this manuscript, we prefer not to include these data at this stage.
More broadly, we fully agree with the reviewer that the inclusion of additional markers in future studies will provide a more comprehensive understanding of cardiac development, and we are excited to pursue this work in the coming years.
- Since a recent preprint (Sendra et al.), already cited in the manuscript, used complementary approaches to investigate endothelial/endocardial cell fate during gastrulation, we feel that a more in-depth discussion is warranted. In particular, how the results presented here align with the early segregation between endocardial and myocardial lineages observed by Sendra et al. could be clarified. Additionally, it is unclear how these findings correlate with Foxa2 lineage tracing. Addressing these points could further strengthen the contextualization and impact of the manuscript.
We agree with the reviewer and have highlighted in our Discussion how our findings align with the Sendra et al. study. Specifically, our observation of short-lived multipotent progenitors supports the hypothesis that mesodermal lineages, including endocardial lineage, are rapidly established during gastrulation. Our observation of rare endo-myo bipotent progenitors is consistent with these findings and aligns with clonal analyses by Devine et al., which identified a shared mesodermal progenitor between these two lineages (Figure 1J in Devine et al., 2014).
However, we believe that the scATAC-seq evidence for an earlier lineage bias specifically toward the endocardial lineage warrants further investigation. In our opinion, it remains unclear whether the nuclei analyzed in their study represent prospective endocardium equivalent to the cells we observed in the live-imaging experiments. Notably, both Nfatc1 and Notch1 exhibit broader expression patterns beyond the endocardium, including in yolk sac endothelial cells and the allantois (see J Cell Biol (2022) 221 (6): e202108093, and doi.org/10.1002/dvdy.21246). Thus, it is plausible that the first mesodermal lineage decision observed in the Sendra et al. scATAC-seq analysis corresponds to the establishment of ExEm hemato/endothelial cells, which are the first mesoderm to ingress in the primitive streak at E6.5 (Development (1999) 126 (21): 4691-4701). Moreover, the scATAC-seq analysis does not demonstrate that the cells analysed are irreversibly excluded from a myocardial fate at these early stages. Instead, their data likely reflect chromatin reconfiguration within a subset of posterior epiblast cells in response to signaling.
We have clarified our mention of Foxa2 lineage tracing. In a previous manuscript (Ivanovitch et al. 2021), we identified a Foxa2+/T+ primitive streak (PS) region that contributes to the LV myocardium but not to the endocardial lineage at the midstreak stage, further supporting the finding that a population of uni-LV/AVC-fated progenitors exists.
Minor comments: 1. For all figures, annotations, axes and/or schematics would greatly help readers outside the field to locate the regions of interest within the embryo.
We have added axes on all our figures and added annotated.
- Interesting questions that could be easily addressed and added in the manuscript: are mother cells T-nGFP positives? If so, do they have different levels of GFP expression? From the different movies, is there a hot spot of cell division? What is the frequency of progenitors that adopt a sustained interaction with their sister cells?
We thank the reviewer for these great suggestions. We will analyse the nGFP signals in mother cells and test whether those that are nGFP+ exhibit different levels of GFP expression. We are particularly interested on this question since we hypothesised in our previous manuscript (Ivanovitch et al., 2021, Figure 1J-K and S4 Fig) that LV progenitors express lower levels of T/Bra and, consequently, lower levels of nGFP expression compared to Atria progenitors. Furthermore, we will analyse the frequency of progenitors that adopt sustained interactions with their sister cells.
We also explored the reviewer's suggestion to analyse whether there is a hotspot of cell division. However, we found this analysis to be complex and will require spatial and temporal registration of the embryos. We feel this falls outside the scope of the present manuscript. That said, we fully agree with the reviewer that this is an intriguing question.
Reviewer #3 (Significance (Required)):
The manuscript presents a technically original study, offering one of the first prospective clonal analyses of cardiac progenitors during mouse gastrulation. While previous studies have addressed the fate of cardiac progenitors using retrospective clonal analysis or lineage tracing (e.g., Meilhac et al., 2004; Devine et al., 2014; Lescroart et al., 2014; Bardot et al., 2017; Ivanovitch et al., 2021; Tyser et al., 2021; Zhang et al., 2021), this work provides new insights into the temporal and spatial dynamics of cardiac progenitor migration and fate allocation. Notably, the study's investigation of the pericardium-a rarely studied cardiac mesodermal fate-adds significant novelty.
However, a limitation of the study is its focus on a relatively small region of the heart, primarily the left ventricle, atrioventricular canal, and atrium, which may not fully represent the broader diversity of cardiac progenitor behaviors across other regions of the developing heart. Additionally, the lack of markers for non-myocardial cell lineages leaves open questions regarding the full spectrum of progenitor fates. These aspects could be addressed in future studies to provide a more comprehensive understanding of cardiac development.
A complementary preprint by the Torres group (Sendra et al., 2024) combines retrospective and prospective clonal analyses and highlights the multipotency of early mesodermal progenitors, particularly those contributing to non-cardiac fates. While both studies reveal the plasticity of early mesoderm, this manuscript by Abukar et al. focuses specifically on cardiac progenitors, offering unique insights into their behaviors and fate decisions.
The study is poised to have a broad impact on the fields of cardiac development and early mouse development. The tools and concepts developed here could also find applications in broader developmental biology studies. This review is written with expertise in cardiac development. I do not have sufficient expertise to evaluate computational modeling within the manuscript.
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Referee #3
Evidence, reproducibility and clarity
In their manuscript, Abukar et al. investigate the origins and migratory behaviors of cardiac progenitor cells, in mice, from gastrulation to early heart tube formation. They use sophisticated live imaging to tracks individual mesodermal cells, reconstructing their lineage and fate over several generations. The findings reveal distinct unipotent progenitors that contribute exclusively to specific cardiac regions, such as the left ventricle/atrioventricular canal (LV/AVC) or atrial cardiomyocytes. LV/AVC progenitors differentiate early, forming the cardiac crescent, while atrial progenitors differentiate later, contributing to the venous poles of the heart tube. Additionally, the study identifies multipotent mesodermal progenitors contributing to various mesodermal cell types, including the endocardium, pericardium and extraembryonic tissues.
Major comments:
- Important conclusions of the manuscript rely on the expression of a reporter line (cTnnt2-2a-eGFP) as well as on the position of tdTomoto+ cells in relation to the reporter. We feel that markers of non-myocardial lineages should have been used to better characterize these populations. We acknowledge the technical challenge of live imaging, which may not allow labeling of all lineages. We believe that a better description of the final stages of investigation with markers of endocardium, pericardium, extra-embryonic mesoderm together with the eGFP of the reporter will strengthen the conclusions drawn on the multipotency of the progenitors. If not addressed, some claims may appear more speculative and would benefit from being toned down.
- Similarly, since all the results of the manuscript derive from five movies of five independent embryos, it would be important to provide a more detailed description (for example, in a table) of the experimental setup. This could include the timing of tamoxifen induction (+7h or +21h?), the stage of dissection (based on anatomical landmarks rather than dissection stage - see atlas of gastrulation), the duration of the movies, and the stage at the final time point. Providing this information would greatly enhance the ability to robustly compare each movie and ensure reproducibility. Of note, the methods section could benefit from additional clarity. For example, in line 594, the embryo from Movie1 is described as being dissected in the morning, while the next sentence states it was dissected in the afternoon, similar to the embryo in Movie5. To avoid confusion and ensure greater rigor, describing the developmental stage of the embryos rather than the time of dissection would be more precise and biologically meaningful.
- This manuscript focuses primarily on LV/AVC progenitors and likely a subpopulation of atrial cardiomyocytes, leaving other cardiac progenitor populations unaddressed. While it is understandable that the study focuses on specific populations, the authors should further discuss the limitations of their approach and explain why not all cardiac progenitors were targeted. A discussion of how these limitations might impact the broader interpretation of their findings would also be valuable.
- Since a recent preprint (Sendra et al.), already cited in the manuscript, used complementary approaches to investigate endothelial/endocardial cell fate during gastrulation, we feel that a more in-depth discussion is warranted. In particular, how the results presented here align with the early segregation between endocardial and myocardial lineages observed by Sendra et al. could be clarified. Additionally, it is unclear how these findings correlate with Foxa2 lineage tracing. Addressing these points could further strengthen the contextualization and impact of the manuscript.
Minor comments:
- For all figures, annotations, axes and/or schematics would greatly help readers outside the field to locate the regions of interest within the embryo.
- Interesting questions that could be easily addressed and added in the manuscript: are mother cells T-nGFP positives? If so, do they have different levels of GFP expression? From the different movies, is there a hot spot of cell division? What is the frequency of progenitors that adopt a sustained interaction with their sister cells?
Significance
The manuscript presents a technically original study, offering one of the first prospective clonal analyses of cardiac progenitors during mouse gastrulation. While previous studies have addressed the fate of cardiac progenitors using retrospective clonal analysis or lineage tracing (e.g., Meilhac et al., 2004; Devine et al., 2014; Lescroart et al., 2014; Bardot et al., 2017; Ivanovitch et al., 2021; Tyser et al., 2021; Zhang et al., 2021), this work provides new insights into the temporal and spatial dynamics of cardiac progenitor migration and fate allocation. Notably, the study's investigation of the pericardium-a rarely studied cardiac mesodermal fate-adds significant novelty.
However, a limitation of the study is its focus on a relatively small region of the heart, primarily the left ventricle, atrioventricular canal, and atrium, which may not fully represent the broader diversity of cardiac progenitor behaviors across other regions of the developing heart. Additionally, the lack of markers for non-myocardial cell lineages leaves open questions regarding the full spectrum of progenitor fates. These aspects could be addressed in future studies to provide a more comprehensive understanding of cardiac development.
A complementary preprint by the Torres group (Sendra et al., 2024) combines retrospective and prospective clonal analyses and highlights the multipotency of early mesodermal progenitors, particularly those contributing to non-cardiac fates. While both studies reveal the plasticity of early mesoderm, this manuscript by Abukar et al. focuses specifically on cardiac progenitors, offering unique insights into their behaviors and fate decisions.
The study is poised to have a broad impact on the fields of cardiac development and early mouse development. The tools and concepts developed here could also find applications in broader developmental biology studies. This review is written with expertise in cardiac development. I do not have sufficient expertise to evaluate computational modeling within the manuscript.
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Referee #2
Evidence, reproducibility and clarity
The authors perform an elegant "tour de force" lineage relationships during mouse heart development. They perform long-term live imaging and single-cell tracking in mouse embryos from early gastrulation to stages of heart tube formation. They then track the progeny of individual cells and reconstruct the lineage tree of tracked cells. They analyze how their migratory paths of cells correlate with cell fate in the heart. Altogether, the manuscript presents a highly detailed live-imaging lineage tracing study of a subset of cells in the cardiac crescent in mouse. This presents a nice contribution to the literature, but would be improved by the suggestions below.
Major comments:
- Can the authors be sure they can track all of the derivatives of labeled cells? They are claiming to be able to follow complete lineages, but I worry if they may lose progeny in their tracking or incorrectly conclude that cells are lineally related. wonder how you could show how accurate it really is. Perhaps if the authors could include a movie where they trace what they claim as an entire lineage of a single cell and show this with the mother and daughter cells labelled throughout the movie, that would at least provide an example for readers to make their own decisions about how reliable the lineage tracing is. Would it be feasible to include an interactive movie where the reader can move the embryo around in 3D at each time point?
- The authors describe the lineage labeled cells as unipotent, bipotent, etc. But they cannot really say anything about developmental potential as they are only looking at normal fate which is less that their potential. Without manipulation of the cells through transplantation etc., the use of the term 'potential' or 'potent' is not appropriate except when they find cells that are multipotent. Rather than calling cells unipotent, I would suggest using the phrase 'assume a single fate'.
- Lines 112-115, the authors state that variability in embryonic stages likely explains differences in labelling. Are there any morphological characteristics across the embryos that support this variability in stages? For example, any characteristics that suggest that the n=3 embryos are slightly older, and the n=7 embryos are slightly younger (line 111)?
- Paragraph beginning on line 116: Please clarify how cells were counted, from the wholemount/across sections?
- Line 165: Authors state that in the absence of tamoxifen, tdTomato-positive cells were identified in one embryo. Please state here the total number of embryos out of which this one embryo was counted.
- Line 190: 'Figure 2-Supplementary Figure 3A-F' doesn't exist. Do they mean Fig.3 supplementary 3A-F?
- Figure 1F-G: For cross sections in 'G' please show the level they were taken from in 'F'.
- Figure 4I: There is a large disparity in cell dispersion across movies. Please comment on why this could be. Is there a difference in stage/morphology etc..
- Figure 4K-L: The arrowhead color is too similar to the cell fluorescence color, making the visualization a little confusing. Changing the color of the arrowheads may be helpful. This is also true for some of the other figures (red arrowheads).
Significance
This is a well-done study that will be useful to developmental biologists as well as cardiologists. The experiments seem very well done and beautifully executed. With the proposed modifications, it will make a very nice contribution to the literature.
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Referee #1
Evidence, reproducibility and clarity
The manuscript describes the tracking of individual mesoderm cells through live imaging. Through a combination of reporters including a novel cardiomyocyte reporter and a combined nuclear GFP-inducible Cre reporter under the dependance of the Brachyury promoter, the authors label mesoderm cells at different stages of gastrulation then perform long term (>30h) live imaging of late gastrulation embryo up to the cardiac crescent and heart tube stages. They use elaborate analysis tools as well as manual tracking to reconstruct cells' trajectory, lineage trees, and various behavioral traits.
The study is well designed. Experiments are technically challenging, well executed, and carefully analysed.
Methods are clear and complete so that experiments should be faithfully reproduced provided availability of an appropriate microscope.
The description of the results of the live imaging experiments is not easy to read and understand, but I believe this is inherent to the complexity of the results themselves and due to the high diversity of behaviors observed. Similarly the figures are extremely dense ans some graphs would benefit from a more didactic legend.
I realize the difficulty of being more concise due to the large amount of information and its diversity. If possible, I would suggest integrating tables within the results section that may help shorten the text, and may be easier to grasp.
The interpretation of the results is fair and in line with previous studies, which are adequately cited.
A discussion on the reasons why a large proportion of cells could not determined as uni or multipotent might be useful. Instinctively I would imagine that a majority of those are multipotent and therefore garder to track, so if the authors do not agree with this interpretation it may be useful to detail technical reasons why those cells cannot be fully interpreted.
Significance
Strengths: novel transgenic tools, powerful imaging technique, thorough quantified nalysis.
Limitations: the development of embryos after E7.75-E8 is never completely normal ex vivo, particularly when there is no rotation. This is visible in the pictures of the embryos post culture (ballooned yolk sac, unattached allantois). It is probably not a limitation regarding cardiac development but may influence other mesoderm lineages notably ExE.
Advance: It is a unique study dur to the labelling strategy, the length of imaging, and thereby the faithful tracking of cell lineages across several rounds of division. The information provided corroborates what previous hypothesis in the field based on less direct assessment, and is here very strong and unbiased. The research is of great interest for developmental biologists (including but not limited to the heart field), cell biologists (notably those working on stem cells and organoids as it provides a ground truth), microscopy and image analysis experts.
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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 #4
Evidence, reproducibility and clarity
In this study Ermanoska and Rodal explored how the presynaptic actomyosin and its subcellular organization and function are assembled and how they respond to mechanical forces. In particular, the authors describe a new type of actin assembly that extends as a continuum through the Drosophila NMJ: this linear actin assembly is in part co-localized with NMII and with Tropomyosin, which led the authors to hypothesize that it may have contractile properties. They follow with knock down (KD) experiments of NMII in motor neurons and show that this KD changes linear actin and also reduces postsynaptic NMII and Integrin receptor levels (pre- and post-synaptically). This data suggests an intricate trans-synaptic molecular interplay between motor neurons and the muscle. Finally, in Figure 6 the authors manipulate axonal mechanical tension through the cutting or not cutting of the nerve bundle and argue that mechanical tension is also required to maintain this type of linear actin core. Altogether, this manuscript describes a potentially very interesting phenomenon whereby mechanical forces contribute to neuronal structure, namely through the control of actin types of assembly and provides some data supporting that actin/NMII/Integrins interact trans-synaptically to transmit force information between cells.
However, in its current format this study is a bit preliminary and mechanistically incomplete. The data regarding the description of 2 distinct types of actin assemblies, with distinct half-lives and stability is convincing, and well-documented but the remainder of the manuscript is more preliminary and not fully sustained by the data presented. The data regarding mechanical forces is particularly unprecise, but it can potentially unveil a novel mechanism that (at least in part) explains how force and biochemical signaling are integrated by neurons. In sum, this manuscript describes an interesting topic but the current version can be significantly improved with additional experiments and/or controls.
Below are my specific comments. If addressed, this manuscript should be published as it significantly adds to the emerging field of mechanobiology and intercellular communication. It provides a new way to look at the effect of mechanical forces in the context of synaptic biology.
Major comments and suggestions for experiments:
- In the images presented on Fig. 2A and 2B, both Arp2-3-GFP and Dia-GFP seem to co-localize with the filamentous F-actin signal, and the authors state this. However, the Pearson correlation is weak, leading the authors to "remove" this claim. On the contrary, the Tm signal is said to have a strong Pearson Correlation. However, looking at the images, it is very hard to understand why the signals are not correlated. Can the authors explain how they quantified the correlation? If Arp2-3-GFP and Dia-GFP are not enriched on linear F-actin, the chosen images are not appropriate.Alternatively, can the authors find a better way to assess colocalization? % of puncta colocalized? Also, I suggest that the quantification of these data, which is currently on Fig. S3 to be moved to the main figure 2.
- Also on Figure 2D, the Lifeact::Halo is a lot smoother than on the other panels with the same marker, and is very much alike the QmN-Tm signal, raising the possibility of a bleed-through artifact. Given that the authors have an antibody against Tm1, can they use it on larvae that express Lifeact::Halo (without QmN-Tm1) to confirm the degree of co-localization (which based on Figure 2E appears as the authors claim, but that is not very convincing on Fig.2D, where it looks like there may be some bleed-through of the channels).
- In figure 3, for consistency, can the authors use Lifeact in zip KD rather than GMA? Or is there a specific reason for this change relative to Fig. 1 and 2? Alternatively, it would be important to show that GMA and Lifeact have similar expression patterns, by co-expressing them simultaneously.
- Figures 2 and 3 raise the idea that there are contractile actin fibers, and this is an important message of this paper. Therefore, it would help to have additional data regarding the manipulation of NMII. Namely, 1) whether expressing RNAi against Sqh gives rise to the same effects as the KD of Zip, and 2) what is the effect of expressing UAS-Sqh CA (phosphomimetic) and UAS-Sqh DN (non phosphorylatable) on linear actin and on the levels of postsynaptic NMII, and pre- and post-synaptic Integrin receptor levels.
- The idea of NMII neuronal KD influencing postsynaptic NMII levels is rather intriguing and potentially very interesting. Is this interaction reciprocal? What happens if Zip is KD in the muscle? Does it influence presynaptic NMII levels? Same comment for Integrin staining. Also, can the authors comment on how they envision that NMII KD can lead to a generalized reduction in the whole muscle? NMII and Integrin should be quantified in non-synaptic and synaptic areas of the muscle.
- The difference in intensity of NMII and Integrins is quite striking and meaningful in terms of trans-synaptic signaling. To validate the quantifications shown in Figures 4 and 5, it is critical to be confident that the larvae analyzed are both time and size matched. Because the authors don't state it clearly, it is a formal possibility that the developmental timing is slightly different between controls and KDs, which could lead to lower levels of NMII and Integrins due to timing rather than manipulation or genotype. If this is the case, the two situations (time and size matching) should be analyzed for post-synaptic reductions of NMII and Integrins. To further confirm a direct effect of NMII KD leading to pre- and post-synaptic alterations of NMII and Integrins, it would be important to use a neuronal line that is expressed in a subset of motor neurons and compare with non-expressing NMJs in the same larvae. This would remove possible effects of the developmental timing. Additionally, since every marker analyzed is reduced, it would be important to find a marker that is unaltered by the KD of Zip (FasII?). Without these controls/extra experiments, the claims regarding NMII and Integrin reduction are not well supported.
- Figure 6: in this figure the authors cut the nerve and then measure actin intensity, and types of actin assemblies. This data is used to conclude that axonal severing impacts mechanical properties of axons and changes actin distribution and types of assemblies. Even though the concept is novel and interesting, the data is not sufficient for the claims. Ideally, it would be important to be able to control and quantify the stretch force applied and the level that is required to promote the distinct types of actin structure. I do understand that these experiments may be difficult to perform, and may require methodologies that are not standard. However, there are ways to improve this data. For example, since these measurements of actin levels and distribution are performed live, it would be important to do a time-lapse movie to understand how linear actin is lost and puncta of actin increase, followed by a quantification of these parameters.
Even though it is hard to provide a "force number", it is relatively simple to repeat the experiment from Figure 6 in conditions of cut and uncut nerve, but adding a stretched nerve condition. Does stretch promote linear actin? To perform this experiment, the authors can pull the brain and its nerves up and glue it in a way that the nerve bundles are connected to the NMJ but are more stretched than in the dissected "loose" condition. Additionally, the authors should analyze how manipulation of actin polymerization (LatA and JASPA) impact this process. Finally, since the authors show in Figures 4 and 5 that manipulations that result in the decrease of linear actin leads to reductions of Integrins and NMII, they should assess if changing the mechanical tension of the nerve also impacts these signaling pathways. - Perhaps a bit out of scope, but very much related: what happens to actin structure after muscle contraction? In other words, does mechanical pressure at the NMJ also alter actin?
Minor comments:
- In all Figures, it is not stated from how many independent experiments/crosses are the data derived from. In most experiments, the number of larvae analyzed is on the low end.
- In Figure 3 and Figure S5, in the zip KD (at least by eye) bouton size looks increased. Is there a difference? Since it looks obvious by eye, can the authors quantify this morphological feature, that can also be related with an actomyosin cortex?
- Can the authors specify that the control UAS-BL35785 is and RNAi against mCherry (in the Tables and perhaps also in the legend)?
- In the discussion, the authors state that they "We took advantage of the Drosophila model and targeted NMII directly by neuronal depletion of both the heavy chain and light chain of NMII. Interestingly, we observed major perturbations of presynaptic actin subpopulations, including of the linear presynaptic actin core." Unless I am missing some Figure, I could not find this data regarding Sqh. The KD of Sqh appears only in Supp Figure 4, to validate the efficacy of KD and not actin. This should be corrected.
Methods:
- Can the authors say if the crosses were performed in vials or cages? This can significantly change some NMJ parameters.
- Extra information regarding the mounting of the larvae for live imaging can be provided: if the larvae is not fixed, how do the authors control the positioning in the drop of HL3.1? How is the stretching/non-stretching of the nerve controlled for? Or are the larvae glue on the side with the double-sided sticky tape? These details can be provided to assure reproducibility by other labs.
- If I understood correctly, in the LatA experiment, the larvae are imaged in the absence of LatA. This is not clear in the results section and should be corrected.
- Please provide more details on how were the correlations performed?
Significance
This study describes the existence of an new actin assembly, linear actin, that extends through the Drosophila larval NMJ. To my knowledge this is reported for first time and has functional implications, since the authors hypothesize that this structure has contractile properties. This study also proposes that mechanical forces can directly be sensed by actin, which modifies its structure and alters signaling molecules at the synapse, namely through transsynaptic signaling, via Integrins. Altogether, the idea represents a novel concept, with an attempt to provide some mechanistic detail (even though it lacks data to support some of the hypothesis).
This study is of interest to both specialized and broad audiences, interested in basic research.
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Referee #3
Evidence, reproducibility and clarity
The advent of super-resolution microscopy has dramatically increased our understanding of the organization and function of the cytoskeleton in neurons. However, there are still areas which remain poorly understood, particularly in neuronal subtypes that are not conventional models for studying the neuronal cytoskeleton. Here Ermanoska and Rodal use super-resolution microscopy and improved probes for imaging actin in Drosophila motor neurons and have identified a novel linear actin structure in the presynaptic terminal of motor neurons. This linear structure appears to be regulated by non-muscle myosin 2 and is important in maintaining the integrity of the neuromuscular connection. For example, the authors show that depleting NM2 in the neurons alters the amount of linear F-actin and the distribution of integrins at the presynaptic terminal. Additionally, performing an axotomy also reduces these linear structures at the nerve terminal, presumably due to decreased tension along the neuron.
Since this is a review of a preprint, I will limit my assessment of the manuscript to what I feel are the major issues in the hopes that it will be helpful to the authors in reworking the manuscript for submission. Most of these points could be addressed in multiple ways.
Major issues and outstanding questions:
- Axonal actin bundles have been previously identified, though that would not have been clear from reading this paper. The work of Ganguly et. al, JCB 2015; Chakrabarty et al, JCB 2019; Phillips et al., J Neurosci Methods; Gallo J Cell Sci 2006; Brown and Bridgeman Dev. Neurobiol 2009; Orlava et al. Dev. Neurobiol 2007; and Ketshek et al eLife 2021 should be cited and discussed in the context of this work. Interestingly, many of the linear bundles of actin filaments described above are associated with NM2-dependent axonal retraction. The works should be cited and discussed in the context of the results found in this manuscript.
- Are there similar bundles along the axons of these motor neurons, or do they only occur at the presynaptic terminal? Or does the type of imaging and model system being used only allow for these structures to be visualized at the presynaptic terminal?
- The term "Molecular composition of linear actin structures" is being overused here- you are only showing the colocalization of tropomyosin 1.
- If Tm1 is important for these structures, why are they still present when it is deleted? I do not see the quantification of linear actin when Tm1 is depleted. Additionally, when integrin redistribution is being measured in Sup. Fig 6, I do not see the Tm1 depleted data despite Tm1 being in the title of the figure.
- Is there an increase in activated NM2 at the presynaptic terminal? What happens if you increase NM2 activity in these neurons?
- There is a depletion of NM2 particles in the postsynaptic terminal when NM2 is being depleted in only the neurons- but is NM2 expression being affected in the muscle cells or only localization of puncta to the nerve terminals?
- What is the functional consequence when linear actin structures are depleted- Denervation? Decreased synaptic activity? Anything?
- It would really help to strengthen the conclusions of this paper if NM2 could be locally and acutely activated or inactivated at the nerve terminal. Nearly all the phenotypes observed are due to global perturbations that may have broad consequences.
- Are these structures present at the presynaptic nerve terminal in other species? If not, or if you do not want to look into it, then it might be more appropriate to add "in Drosophila" to the title.
Significance
This manuscript presents an exciting concept that will be of high interest to cellular neuroscientists and cytoskeletal biologists. There are also interesting implications that could be made with aging and neurodegenerative diseases of the neuromuscular system. The manuscript is well written and contains rigorous experimentation and analysis of the data. My main issue with it, however, is that the conclusions seem preliminary and are heavily reliant on correlation. Additionally, there is a complete lack of discussion of similar structures that have been seen in axons. Finally, all of the data is from one cell type from a single species, which limits how broadly the results can be interpreted and whether this data has potential relevance to human aging/disease, which would help it reach a larger audience. Basically, I am confident that the data that is presented is correct, though it is potentially being overinterpreted when being put into a broader context.
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Referee #2
Evidence, reproducibility and clarity
Summary: In this study, Drosophila larval NMJs were used to investigate the very interesting and innovative hypothesis that actomyosin-mediated contractility generates and responds to cellular forces at the neuron-muscle interface. In summary, the authors identified a new presynaptic actomyosin subpopulation that transmits signals to adjacent muscle tissue that together with with integrin receptors governs the mechanobiology of the neuromuscular junction.
While this study presents exciting evidence supporting the existence of a cable-like actomyosin structure traversing the NMJ, some of the conclusions are not fully supported by the data provided. It is unclear how this actomyosin arrangement differs (or not) from other longitudinal myosin arrangements found in the axon shaft. In this respect, it would be informative to provide images of the axon shaft to further verify the possible presynaptic specificity of this actomyosin arrangement, and check whether alternatively it might exist as a continuum of actin cables already present in the axon shaft.
The data presented in Figure 2F is insufficient to claim that a presynaptic actomyosin core exists. As it is, the myosin puncta shown do not definitely support that such a structure exists. Alternative approaches such as using fluorescent NMII fusions that allow visualizing simultaneously the N- and C-terminal domains of the NMII heavy chain could be used.
Claims on the effect of the neuronal actomyosin assemblies on tension, in the absence of experiments directly assessing tension, should be down toned.
Also, the data provided in the axotomy experiments is not sufficient to claim that axonal severing is sensed specifically at the presynaptic terminal in a similar manner to neuronal NMII depletion. Axotomy is certainly followed by degeneration and dismantling of different axonal cytoskeleton compartments including the formation of altered actin arrangements, including those of the presynaptic terminal.
Significance
This is a very interesting study that raises a novel hypothesis on how neuronal mechanobiology is governed. If complemented with additional experiments further supporting the existence of a specific actomyosin arrangement in presynaptic terminals, this study will certainly be of high significance to the field and of broad interest to readers that are not experts on the topic.
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Referee #1
Evidence, reproducibility and clarity
In this study, Ermanoska and Rodal describe the features of a recently described (by the same group) presynaptic entity in the NMJ. The authors find evidence of diverse types of actin assemblies along the presynaptic contact, patches, and cables (similar to structures observed during fission yeast division). Among these proteins, NMII (Sqh) seems prominently featured. Zip mutations apparently alter the distribution of the actin, albeit modestly, and also affect integrin patching at the synapse. Finally, the authors provide evidence that mechanical severing induces specific actin remodeling.
The study is provocative, but some of the conclusions of the study are quite evident and predictable. Also, the localization of the proteins at presynaptic cables is not as clear as the authors describe them. Finally, the effects of NMII depletion using siRNA are compounded by possible off-targets effects that the authors shrewdly attribute to presynaptic-specific phenotypes. Proof of this is quite weak and it seems likely that some neuron-specific promoters are leaking beyond neurons.
Major issues:
- The authors have made a large effort to characterize the presynaptic actin structures in as much detail as possible, but this reviewer is apprehensive regarding the validity of the observations made in the presence of highly perturbing probes. It is well-known in the field that most actin-binding probes, including moesin-actin BD, Lifeact, utrophin, etc., have no perturbing effects... except in neurons. In their previous publication (eLife 2017), the authors used GFP-actin (which display binding kinetic alterations), MA and Lifeact, and got away with it. They never stained with phalloidin, which is the gold standard for unperturbed F-actin visualization. Given the level of structural detail the authors are getting into, they need to address the visualization of these structures in a totally unperturbed manner.
- Sqh:GFP does not really localize in the structures, but everywhere (Fig. 2F). Again, Sqh:GFP is a notoriously flaky probe (DOI: 10.1002/cm.21212) that makes this reviewer nervous in the absence of additional validation, which in this case may take the form of HA/myc/FLAG-tagging (which require staining but does not interfere with Zip:Sqh binding) or endogenous staining, particularly with phospho-specific antibodies (for use in Drosophila samples, see for example DOI: 10.1038/emboj.2010.338).
- What is the actual efficiency of NMII depletion? This is a stubborn molecule difficult to deplete efficiently in most systems.
- The authors observed that NMII depletion driven by RNAi under a neuronal specific promoter also reduces NMII expression in the post-synaptic region and the muscle. The authors claim that this is specific and not leaky by examining NMII expression in the absence of C155-Gal4. To the extent of this reviewer's knowledge, this is thus based on the specificity of C155. However, it has been well documented and explicitly stated that Drosophila enhancer-Gal4 lines show ectopic expression during development (paper by this title, using C155-Gal4 among other promoters, DOI: 10.1098/rsos.170039). Those authors observed expression in wing cells, for example, which casts severe doubt on this particular conclusion.
- What would be the effect of severing in NMII-depleted presynaptic assemblies?
Referees cross-commenting
I concur with the comments of my esteemed colleagues. Still, I am concerned regarding the use of the C155-Gal4 promoter and its effects outside of neurons. The conclusion that that NMII depletion driven by RNAi under a neuronal specific promoter also reduces NMII expression in the post-synaptic region and the muscle is potentially the most striking finding of the paper, but the fact that this promoter (which is potentially leaky) is used dampens my enthusiasm. Also, the use of the actin probes is a problem, and one I don't see fixed by the fact they published a previous paper before using them. Maybe the reviewers then had less or no experience with these probes. I have in the past, and I cannot let this slide
Significance
As described in the previous section, the study has several built-in limitations that dampen this reviewer's enthusiasm for the overall story, including the limitations of the molecular tools used, which are quite-artifact prone (this reviewer has plenty of firsthand experience with all these tools in mammalian models, and has suffered some of them to become big, months-consuming artifacts). Also, the authors use fly lines that either are leaky; or they elect not to explore the most interesting piece of data in the paper, which is the transsynaptic effect on NMII expression. This reviewer suspects that the authors have not pursued this vigorously because they have their own suspicions in this regard.
If properly carried out, this study would have filled an important gap, since most existing studies have so far focused on the post-synaptic region, hence it'd be important to find out precisely what is happening on the other side. But this study does not clarify this.
The audience would have been mainly cell biologists, cellular neurobiologists and "fly people", with some transversal interest from the budding mechanobiology community. But the story is quite flawed, beyond revision given the approaches used (and trusted) by the authors. I cannot recommend publication of this manuscript if the issues raised here are not addressed.
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Reply to the reviewers
The authors do not wish to provide a response at this time when we only have incorporated the reviewers' suggestions partially and are presenting here a revision plan.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Reported here is an elegant study on the role of GLE1 and its most common pathological variant through carefully constructed mouse models. GLE1 has been studied in cellular and zebrafish models as an important co-factor that regulates RNA processing and response to stress but investigations into the impact of the FinMajor mutation of GLE1 in mammalian in vivo models is lacking. Zárybnický et al. establish GLE1 KO and FinMajor variant mouse models through CRISPR/Cas9 gene editing and replicate early lethality of GLE1 KO models. The authors demonstrate this is due to augmented polarisation of blastocysts pre-gastrulation. The knock-in FinMajor mouse survives until mid-adulthood without complication but die suddenly. The rest of the study characterises the FinMajor mouse by examining known phenotypes of this model and more. Cell cycle arrest, augmented stress granule response and DNA damage repair are successfully replicated in MEFs. The authors reveal that MEFs display a prominent senescent state. Whilst polyA mRNA localisation is surprisingly unchanged, RNA and protein translation is disrupted as expected. In vivo, motor neuron number, organisation and branching is impaired, mirroring other studies, but the functional consequences of this in PFQ KI mice is unclear. The authors break ground by examining sympathetic nervous system development and identify neural crest-derived tissue as being selectively sensitive to the GLE1 mutation where increased mitotic arrest was apparent in mutant mice. Consequently, the authors identify cardiac innervation by sympathetic neurons, which are derived from neural crest tissue, to be augmented in FinMajor mice. It is unclear whether this is the cause of sudden death in mid-adulthood. The two mouse models presented here provide opportunity to study GLE1 absence or mutation in mammalian development at multiple levels. Overall, the FinMajor KI mouse model presents with milder phenotype than predicted but do display disease relevant phenotypes and the study has uncovered novel areas of research to pursue.
Major comments:
none
Minor comments:
- Based on the RNA sequencing data, there appear to be issues with high variance and data normalization that need attention. The PCA results are a little concerning and the volcano plot shows an unusual shape-with massive fold changes dominating-suggesting that low-count genes may not have been adequately filtered out, potentially skewing the analysis. It's recommended to set a minimum count threshold (e.g., 5 or 10 counts) to exclude low-expression genes and to consider log₂ fold change shrinkage methods like apeglm to adjust for variability in low-count genes. Performing exploring methods like RUVSeq could help regress out unwanted variance, especially given the inherent variability in E3.5 embryos and if increasing replicates isn't feasible.
- Do the gene expression changes identified in GLE1 KO blastocysts hold significance in GLE1 KI mice? Augmented function of GLE1 may induce both loss of function as well as gain of toxic function and so transcriptionally they may appear as separate disorders. However, it would be worthwhile testing by qPCR the expression levels of the most differentially regulated genes.
- What is the expression profile of Kcnv2 in the developing spinal cord of PFQ KI mice? Or in the heart? Is the MN organisation / cardiac innervation a feature of neurotransmitter receptor misexpression or an issue of morphogen gradient as is mentioned in the discussion.
- MN disorganisation is seen in LCCS1 patients and in zebrafish model of GLE1FinMajor with dramatic consequences on development. MN organisation is changed in FinMajor KI mice but the functional consequences of these changes are not addressed. Do the mice display motor impairment?
- It is surprising that polyA mRNA localisation is not affected in PFQ KI cells. I'm glad the authors performed oligoDT FISH on embryonic spinal cords in addition to MEFs. However, in keeping with the selective vulnerabilities of TH+ chromaffin cells to cell cycle disruption, I am curious whether these cells would demonstrate RNA dysregulation. In addition to analysis of global mRNA localisation with oligoDT, it would be good to explore selective mRNA localisation-perhaps those genes implicated in GLE1 KO eg Vimentin, or genes implicated in cell cycle arrest.
- Please include a description of how PFQ knock in is predicted to impact oligomerisation of GLE1? Differential attributes have been given to the various GLE1 domains (PMID: 32981894). Are the specific phenotypes observed in-keeping with predicted changes to GLE1 function?
- Is there a sex bias to the sudden death phenotype observed in PFQ KI mice? Given the deficit of cardiac stroke volume in female mice, does this explain the trend for premature death? Additionally, please use 'sex' instead of 'gender' when referring to male and female mice.
- Other minor issues:
a. Figure 1:
i. Typo in figure 1f (OCT3/4 not /44).
ii. What do white arrowheads indicate?
b. Figure 2:
i. The padjusted heat map is from 0 to 1. Please only include GO terms that were significant.
c. Supp figure 4:
i. why was adult heart chosen to measure protein expression of GLE1? What are the expression differences of GLE1 between heart and SC?
Significance
This is a carefully constructed study with thorough examination and well-presented data. The PFQ knock-in mouse model is an elegant solution to study the FinMajor variant of GLE1 that will be a useful resource for the community. The paper is of broad interest due the breadth and strength of experimentation including characterisation of blastocysts, MEFs, developing nervous system and of cardiac functions. The mouse model phenocopies many of the known phenotypes of GLE1 dysfunction and builds upon these thereby providing an excellent platform from which to undertake further examination. However, I feel that the manuscript is disconnected in parts (how does GLE1 KO signatures relate to GLE1 KI? How do MEF phenotypes relate to in vivo phenotypes?) and does not go far enough to describe how PFQ knock-in affects GLE1 function or how disrupted GLE1 function leads to the observed phenotypes in nervous system development. These questions may be beyond the scope of this paper, which successfully establishes the first mammalian model to study GLE1 dysfunction. As such, I have made minor comments that I hope can be addressed. Furthermore, given that this is a descriptive study and that the key phenotypes used in the current title have mostly been described before, I suggest that the authors use their running title of 'Modeling LCCS1 in mouse', or similar, to reflect the scope of this paper. The paper fills a gap in our understanding of mammalian GLE1 dysfunction, demonstrating that PFQ knock-in likely leads to augmented GLE1 function rather than loss of function and provides novel areas for exploring sympathetic nervous system development and cardiac innervation in the context of LCCS1. As such, it provides an incremental and methodological advance. The paper will be of interest to a broad audience of basic and clinical researchers.
This reviewer's expertise is based in stem cell modelling of neurodevelopment and of neurodegenerative diseases.
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Referee #2
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, Zarybnicky et al characterize their two mouse models of GLE1 knockout, or knock-in of a disease-causing variant of GLE1. The authors show that knockout causes early embryonic lethality, while the GLE1 variant causes disturbed tissue and organ organization with induced mid-adulthood sudden death. In particular effects on neural crest-derived tissues, such as innervation of the heart, are detected. The authors use mainly immunocytochemistry to visualize their findings, complemented with bulk RNA sequencing, quantitative PCR, western blot, and electron microscopy. The study is extremely comprehensive and technically sound.
Major comments:
- Are the key conclusions convincing?
Key conclusions and data are convincing. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
The authors' fifth 'Highlight' reads "Neural crest-derived tissues represent a new target of GLE1FinMajor and GLE1-related disorders". This is not shown or clearly touched upon in the manuscript and should be removed altogether.
On page 6 (Last part of "Gle1-/- blastocyst show transcriptional...", the authors claim "This together with altered cellular adhesion suggests...". I cannot find that cellular adhesion is addressed or experimentally investigated in the present manuscript. Hence, this needs to be shown, or text needs to be re-written. - 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.
There are some quantifications as well as improved image quality that are needed to support their claims. - First of all, it looks like the adrenal medulla is smaller in GlePFQ/PFQ mice in Fig. 7A. Is this correct? The authors should quantify the size, since this could be of importance to understand Gle1, but also to determine if the decrease in number of chromaffin cells is due to the size of the medulla, or an actual decrease in the fraction of chromaffin cell number. Since the authors display their results as area or cell count per section, the size/total number of cells are not taken into account. - Is there a rationale for only investigating the chromaffin cells, and not the sympathoblasts in the adrenal gland? Albeit the population of sympathoblasts are profoundly smaller than that of chromaffin cells, it would be good to see if also this neural crest-derived cell population is affected. - Did the authors investigate the hearts of the mice that died suddenly during mid-adulthood? Since the sympathetic patterning of the heart is severely affected during development, analyzing the hearts to find the possible cause of death could improve the conclusions and add to the biology of this manuscript. I do understand that if hearts have not been investigated or preserved from experiments already done, it would be major work to do so, and not required. - 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.
Most edits suggested here are discussion points, image reconstructions and quantifications, hence, should not be particularly time consuming. - Are the data and the methods presented in such a way that they can be reproduced?
There are a few places where some clarification is needed. However, for the majority of the manuscript, the data and methods are adequately presented.
Figure 1E: "Individual data points generated from each embryo" - is n=4 from four different embryos? Or four blasts from one embryo? Or something else? Please clarify.
Figure 3G: Is n=6 for both groups together? Or n=6 per group? - Are the experiments adequately replicated and statistical analysis adequate?
To my ability to judge these points, experiments and statistical analysis are adequately replicated.
Minor comments:
- Specific experimental issues that are easily addressable.
Fig. 2: It would be good with a heat map with top hits marked, to quickly visualize the core data and genes for the reader, without the need to open the supplemental file with full data list.
Figure 4E: Why are there no p-values for the three F-action graphs, when such values are presented for all other markers?
Figure 1E-F: For quantification, how many cells are double positive for CDX2/GATA6 and OCT3/4/GATA6/NANOG?
Figure 5B: It is very difficult to assess the difference in perinuclear stress granules when presented in the same graph together with cytoplasmic and total. Would it be good to present separately to enable this, or is the difference so small that it is 'biologically irrelevant'?
Figure 7C: T1-T4/SG quantification shows a big difference between the groups. This is not visible at all in the images. Are they not representative?
Figure 6E: Are the arrowheads correct? Should they be at the exact same spot in both images? It is also extremely difficult to understand what the authors have assessed and measured. To my eye, there are no differences in appearance between the two groups/images.
Figure 8 and corresponding text: In Fig. 8A, only results for the males are shown, however, in the text, only differences in the females are discussed. Could this be expanded/edited/clarified? - Are prior studies referenced appropriately?
Yes. - Are the text and figures clear and accurate?
A number of figure references are wrong. Double check and edit.
Figure 7A: Are the SOX10+ cells quantified from a specific axis level of the embryo, i.e., is it a specific crest derivative?
Text related to Figure 4E: The authors write "...slightly downregulated while Pai-1...". Since expression of the SASP genes are virtually absent in the mutant MEFs, the authors should remove or re-phrase from using 'slightly'.
The authors write: "The cell morphology of Gle1PFQ/PFQ MEFs differed by eye from WT...". Clarify if this is referring to size, and/or something else?
In Figure 3E, the authors show ZO-1. This marker is not mentioned or explained either in Figure legend or text. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Figure 4D: The images are poor and it is very difficult to see the results.
Supplementary Fig. 11 would benefit from providing some magnified/zoomed in images as well.
Supplementary Figure 8: It is impossible to see which cells are positive and how they look. Magnifications/zoom is necessary.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
The authors create new models to study the biology of wild-type Gle1 as well as its disease-causing variant. These add to existing models and enables experiments and studies not previously possible to perform. Conceptuallt and biologically, the authors provide findings on Gle1 not previously presented. Since this gene is disease-causing, the knowledge provided in this manuscript can be used to build upon. - Place the work in the context of the existing literature (provide references, where appropriate).
To my knowledge, the authors use existing literature and data to design their work, and their results add and complement. - State what audience might be interested in and influenced by the reported findings.
This work is interesting for scientists working on Gle1-related biology, the very earliest time points of embryogenesis, heart development, neural crest and LCCS1. - 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.
Neural crest biology from delamination and onwards, and disease modeling using ex vitro methods.
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Referee #1
Evidence, reproducibility and clarity
Several severe diseases, in particular lethal congenital contracture syndrome type 1 (LCCS1), are caused by a mutation in the GLE1 gene that leads to the inclusion of three additional amino acids (PFQ) into the mutant gene product. GLE acts as a co-factor for RNA-dependent DEAD-box ATPases, and its function in nuclear RNA export and protein translation has been characterized by previous studies. However, the mechanisms how dysfunction of GLE1 leads to the specific cellular defects in diseases associated with GLE mutations are not well understood. The authors have generated both GLE1 full knockout and GLE1 knockin mice, in which the PFQ expansion is included. GLE1 knockout mice are embryonic lethal, and the knockin mice do not show major defects but die suddenly between 15 and 40 weeks of age.
Specific comments:
- The data shown in Fig. 4D are not convincing. SA-β-Gal-expression is hardly detectable, the number of investigated cultures is low and it is unclear whether these data allow conclusions on pathological cellular senescence. This should be based on additional parameters such as altered proteasome function and altered protein turnover.
- Most of the cellular assays for investigating cellular defects in the GLE-PFQ/PFQ cells have been done with mouse embryonic fibroblasts, and unfortunately not with primary neuronal cells such as motoneurons or sympathetic neurons that seem to be prominently affected in human patients with the same mutation. Since the GLE1-PFQ/PFQ mice do not show defects in body size or any severe symptoms pointing to defects in connective tissue, it is unclear whether the alterations observed in MEFs are representative on the disease phenotype. In Fig. 5, the authors show that there are apparently no defects in nuclear export of mRNAs in the MEFs. This does not exclude the possibility that such defects occur in primary neurons. This should be adequately addressed, ideally with additional analyses using primary spinal motoneurons derived from this mouse model.
- The authors show by puromycinilation that protein synthesis is altered (Fig. S6A and B). The gel shown in Fig. S6B is not convincing, it appears as if differential blotting efficacies contribute to the appearance of the blot. This needs to be performed in a more convincing manner with higher n. Moreover, such analyses should also be done with primary neuronal cultures, in order to test whether neuronal cells are more severe or diffentially affected in comparison to fibroblasts by the GLE1 mutation.
- Previous studies, for example the manuscript by Bresson et al., Molecular Cell 80: 470-484, 2020, have shown that stress-induced translation inhibition involves DEAD-box translation initiation factor, in particular DED1, which interacts with eIF4A. The translation inhibition phenotype observed in this study appears very similar to that observed in these previous studies, and the question arises whether GLE1 can also modify the interaction with DED1 and its role in modulating translation via eIF4A. A clear analysis of the mechanisms how GLE1 is involved in modulating protein synthesis in fibroblasts and also in cells that seem to be more severely affected, such as neurons, would highly strengthen the impact of this paper.
- The data shown on the quantification of motoneurons and sympathetic neurons are not convincing. In Fig. S7B, a minor reduction of ISL1/2 positive cells is observed at E11.5, before the period of physiological cell death starts in mice. Since these mice day at postnatal stages, it appears essential that the authors quantify motoneuron numbers in the adult spinal cord in 20 week old mutant mice. Previous studies have shown that mice can live normally with loss of more than 50% of spinal motoneurons (for example Jablonka et al., HMG 9, 341-46, 2000), and it is unclear whether the mild loss of motoneurons is a cause of death in these animals.
- The same is true for the loss of the sympathetic fibers. Changes shown in Fig. 7C and D appear very low, and clear conclusions can only be drawn when the number of neurons are quantitated. Moreover, the data shown in Fig. 7 are from embryonic stages, whereas death of these mice occurs at postnatal stages between 15 and 40 weeks. Therefore, the morphology and cell numbers in sympathetic neurons at these critical postnatal stage might be much more relevant than the number of embryonic neurons before the stage of physiological cell death.
- The data shown on altered arborization in the periphery are not convincing. Such data have to be based on highly systematic 3D analyses, which have not been performed in this study. In addition, denervation of skeletal muscle or loss of sympathetic terminals in the heart ventricles appear much more relevant. These analyses need to be expanded. It would also be interesting to know how these mice at a stage when sudden death occurs, react to sympathomimetic drugs or inhibitors of noradrenergic receptors. I also do not understand which the authors have not measured adrenaline levels in the circulation of these mice, in order to find out whether the morphological changes in the adrenal gland, as shown in Fig. 7A, are of functional significance.
Minor point: The data shown in Fig. S6A and B are highly important and should be shifted to the main part of the manuscript.
Referees cross-commenting
I agree with all comments that have been made by reviewer 2 and 3.
Significance
These mouse models will certainly be very useful for this research field, and the characterization of the knockout mouse and, at least in part, also of the knockin mouse model is well done and a significant contribution to understand the physiological function of GLE1. However, the manuscript falls short in explaining the cell type specificity of the disease mechanisms for several types of neurons. Moreover, some of the analyses on the losses of specific cell populations, in particular motoneurons and sympathetic neurons, are technically not sound and not convincing. The paper would highly benefit from revisions.
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Reply to the reviewers
Reviewer #1
Evidence, reproducibility and clarity
Summary: The manuscript by Yang et al. describes a new CME accessory protein. CCDC32 has been previously suggested to interact with AP2 and in the present work the authors confirm this interaction and show that it is a bona fide CME regulator. In agreement with its interaction with AP2, CCDC32 recruitment to CCPs mirrors the accumulation of clathrin. Knockdown of CCDC32 reduces the amount of productive CCPs, suggestive of a stabilisation role in early clathrin assemblies. Immunoprecipitation experiments mapped the interaction of CCDC42 to the α-appendage of the AP2 complex α-subunit. Finally, the authors show that the CCDC32 nonsense mutations found in patients with cardio-facial-neuro-developmental syndrome disrupt the interaction of this protein to the AP2 complex. The manuscript is well written and the conclusions regarding the role of CCDC32 in CME are supported by good quality data. As detailed below, a few improvements/clarifications are needed to reinforce some of the conclusions, especially the ones regarding CFNDS.
Response: We thank the referee for their positive comments. In light of a recently published paper describing CCDC32 as a co-chaperone required for AP2 assembly (Wan et al., PNAS, 2024, see reviewer 2), we have added several additional experiments to address all concerns and consequently gained further insight into CCDC32-AP2 interactions and the important dual role of CCDC32 in regulating CME.
Major comments:
1) Why did the protein could just be visualized at CCPs after knockdown of the endogenous protein? This is highly unusual, especially on stable cell lines. Could this be that the tag is interfering with the expressed protein function rendering it incapable of outcompeting the endogenous? Does this points to a regulated recruitment?
Response: The reviewer is correct, this would be unusual; however, it is not the case. We misspoke in the text (although the figure legend was correct) these experiments were performed without siRNA knockdown and we can indeed detect eGFP-CCDC32 being recruited to CCPs in the presence of endogenous protein. Nonetheless, we repeated the experiment to be certain.
2) The disease mutation used in the paper does not correspond to the truncation found in patients. The authors use an 1-54 truncation, but the patients described in Harel et al. have frame shifts at the positions 19 (Thr19Tyrfs*12) and 64 (Glu64Glyfs*12), while the patient described in Abdalla et al. have the deletion of two introns, leading to a frameshift around amino acid 90. Moreover, to be precisely test the function of these disease mutations, one would need to add the extra amino acids generated by the frame shift. For example, as denoted in the mutation description in Harel et al., the frameshift at position 19 changes the Threonine 19 to a Tyrosine and ads a run of 12 extra amino acids (Thr19Tyrfs*12).
Response: The label of the disease mutant p.(Thr19Tyrfs∗12) and p.(Glu64Glyfs∗12) is based on a 194aa polypeptide version of CCDC32 initiated at a nonconventional start site that contains a 9 aa peptide (VRGSCLRFQ) upstream of the N-terminus we show. Thus, we are indeed using the appropriate mutation site (see: https://www.uniprot.org/uniprotkb/Q9BV29/entry). The reviewer is correct that we have not included the extra 12 aa in our construct; however as these residues are not present in the other CFNDS mutants, we think it unlikely that they contribute to the disease phenotype. Rather, as neither of the clinically observed mutations contain the 78-98 aa sequence required for AP2 binding and CME function, we are confident that this defect contributed to the disease. Thus, we are including the data on the CCDC32(1-54) mutant, as we believe these results provide a valuable physiological context to our studies.
3) The frameshift caused by the CFNDS mutations (especially the one studied) will likely lead to nonsense mediated RNA decay (NMD). The frameshift is well within the rules where NMD generally kicks in. Therefore, I am unsure about the functional insights of expressing a disease-related protein which is likely not present in patients.
Response: We thank the reviewer for bringing up this concern. However, as shown in new Figure S1, the mutant protein is expressed at comparable levels as the WT, suggesting that NMD is not occurring.
4) Coiled coils generally form stable dimers. The typically hydrophobic core of these structures is not suitable for transient interactions. This complicates the interpretation of the results regarding the role of this region as the place where the interaction to AP2 occurs. If the coiled coil holds a stable CCDC32 dimer, disrupting this dimer could reduce the affinity to AP2 (by reduced avidity) to the actual binding site. A construct with an orthogonal dimeriser or a pulldown of the delta78-98 protein with of the GST AP2a-AD could be a good way to sort this issue.
Response: We were unable to model a stable dimer (or other oligomer) of this protein with high confidence using Alphafold 3.0. Moreover, we were unable to detect endogenous CCDC32 co-immunoprecipitating with eGFP-CCDC32 (Fig. S6C). Thus, we believe that the moniker, based solely on the alpha-helical content of the protein is a misnomer. We have explained this in the main text.
Minor comments:
1) The authors interchangeably use the term "flat CCPs" and "flat clathrin lattices". While these are indeed related, flat clathrin lattices have been also used to refer to "clathrin plaques". To avoid confusion, I suggest sticking to the term "flat CCPs" to refer to the CCPs which are in their early stages of maturation.
Response: Agreed. Thank you for the suggestion. We have renamed these structures flat clathrin assemblies, as they do not acquire the curvature needed to classify them as pits, and do not grow to the size that would classify then as plaques.
Significance
General assessment: CME drives the internalisation of hundreds of receptors and surface proteins in practically all tissues, making it an essential process for various physiological processes. This versatility comes at the cost of a large number of molecular players and regulators. To understand this complexity, unravelling all the components of this process is vital. The manuscript by Yang et al. gives an important contribution to this effort as it describes a new CME regulator, CCDC32, which acts directly at the main CME adaptor AP2. The link to disease is interesting, but the authors need to refine their experiments. The requirement for endogenous knockdown for recruitment of the tagged CCDC32 is unusual and requires further exploration.
Advance: The increased frequency of abortive events presented by CCDC32 knockdown cells is very interesting, as it hints to an active mechanism that regulates the stabilisation and growth of clathrin coated pits. The exact way clathrin coated pits are stabilised is still an open question in the field.
Audience: This is a basic research manuscript. However, given the essential role of CME in physiology and the growing number of CME players involved in disease, this manuscript can reach broader audiences.
Response: We thank the referee for recognizing the 'interesting' advances our studies have made and for considering these studies as 'an important contribution' to 'an essential process for various physiological processes' and able 'to reach broader audiences'. We have addressed and reconciled the reviewer's concerns in our revised manuscript.
Field of expertise of the reviewer: Clathrin mediated endocytosis, cell biology, microscopy, biochemistry.
Reviewer #2
Evidence, reproducibility and clarity
In this manuscript, the authors demonstrate that CCDC32 regulates clathrin-mediated endocytosis (CME). Some of the findings are consistent with a recent report by Wan et al. (2024 PNAS), such as the observation that CCDC32 depletion reduces transferrin uptake and diminishes the formation of clathrin-coated pits. The primary function of CCDC32 is to regulate AP2 assembly, and its depletion leads to AP2 degradation. However, this study did not examine AP2 expression levels. CCDC32 may bind to the appendage domain of AP2 alpha, but it also binds to the core domain of AP2 alpha. Overall, while this work presents some interesting ideas, it remains unclear whether CCDC32 regulates AP2 beyond the assembly step.
Response: We thank the reviewer for drawing our attention to the Wan et al. paper, that appeared while this work was under review. However, our in vivo data are not fully consistent with the report from Wan et al. The discrepancies reveal a dual function of CCDC32 in CME that was masked by complete knockout vs siRNA knockdown of the protein, and also likely affected by the position of the GFP-tag (C- vs N-terminal) on this small protein. Thus:
- Contrary to Wan et al., we do not detect any loss of AP2 expression (see new Figure S3A-B) upon siRNA knockdown. Most likely the ~40% residual CCDC32 present after siRNA knockdown is sufficient to fulfill its catalytic chaperone function but not its structural role in regulating CME beyond the AP2 assembly step.
- Contrary to Wan et al., we have shown that CCDC32 indeed interacts with intact AP2 complex (Figure S3C and 6B,C) showing that all 4 subunits of the AP2 complex co-IP with full length eGFP-CCDC32. Interestingly, whereas the full length CCDC32 pulls down the intact AP2 complex, co-IP of the ∆78-98 mutant retains its ability to pull down the b2-µ2 hemicomplex, its interactions with α:σ2 are severely reduced. While this result is consistent with the report of Wan et al that CCDC32 binds to the α:σ2 hemi-complex, it also suggests that the interactions between CCDC32 and AP2 are more complex and will require further studies.
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Contrary to Wan et al., we provide strong evidence that CCDC32 is recruited to CCPs. Interestingly, modeling with AlphaFold 3.0 identifies a highly probably interaction between alpha helices encoded by residues 66-91 on CCDC32 and residues 418-438 on a. The latter are masked by µ2-C in the closed confirmation of the AP2 core, but exposed in the open confirmation triggered by cargo binding, suggesting that CCDC32 might only bind to membrane-bound AP2. Thus, our findings are indeed novel and indicate striking multifunctional roles for CCDC32 in CME, making the protein well worth further study.
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Besides its role in AP2 assembly, CCDC32 may potentially have another function on the membrane. However, there is no direct evidence showing that CCDC32 associates with the plasma membrane.
Response: We disagree, our data clearly shows that CCDC32 is recruited to CCPs (Fig. 1B) and that CCPs that fail to recruit CCDC32 are short-lived and likely abortive (Fig. 1C). Wan et al. did not observe any colocalization of C-terminally tagged CCDC32 to CCPs, whereas we detect recruitment of our N-terminally tagged construct, which we also show is functional (Fig. 6F). Further, we have demonstrated the importance of the C-terminal region of CCDC32 in membrane association (see new Fig. S7). Thus, we speculate that a C-terminally tagged CCDC32 might not be fully functional. Indeed, SIM images of the C-terminally-tagged CCDC32 in Wan et al., show large (~100 nm) structures in the cytosol, which may reflect aggregation.
CCDC32 binds to multiple regions on AP2, including the core domain. It is important to distinguish the functional roles of these different binding sites.
Response: We have localized the AP2-ear binding region to residues 78-99 and shown these to be critical for the functions we have identified. As described above we now include data that are complementary to those of Wan et al. However, our data also clearly points to additional binding modalities. We agree that it will be important and map these additional interactions and identify their functional roles, but this is beyond the scope of this paper.
AP2 expression levels should be examined in CCDC32 depleted cells. If AP2 is gone, it is not surprising that clathrin-coated pits are defective.
Response: Agreed and we have confirmed this by western blotting (Figure S3A-B) and detect no reduction in levels of any of the AP2 subunits in CCDC32 siRNA knockdown cells. As stated above this could be due to residual CCDC32 present in the siRNA KD vs the CRISPR-mediated gene KO.
If the authors aim to establish a secondary function for CCDC32, they need to thoroughly discuss the known chaperone function of CCDC32 and consider whether and how CCDC32 regulates a downstream step in CME.
Response: Agreed. We have described the Wan et al paper, which came out while our manuscript was in review, in our Introduction. As described above, there are areas of agreement and of discrepancies, which are thoroughly documented and discussed throughout the revised manuscript.
The quality of Figure 1A is very low, making it difficult to assess the localization and quantify the data.
Response: The low signal:noise in Fig. 1A the reviewer is concerned about is due to a diffuse distribution of CCDC32 on the inner surface of the plasma membrane. We now, more explicitly describe this binding, which we believe reflects a specific interaction mediated by the C-terminus of CCDC32; thus the degree of diffuse membrane binding we observe follows: eGFP-CCDC32(FL)> eGFP-CCDC32(∆78-98)>eGFP-CCDC32(1-54)~eGFP/background (see new Fig. S7). Importantly, the colocalization of CCDC32 at CCPs is confirmed by the dynamic imaging of CCPs (Fig 1B).
In Figure 6, why aren't AP2 mu and sigma subunits shown?
Response: Agreed. Not being aware of CCDC32's possible dual role as a chaperone, we had assumed that the AP2 complex was intact. We have now added this data in Figure 6 B,C and Fig. S3C, as discussed above.
Page 5, top, this sentence is confusing: "their surface area (~17 x 10 nm2) remains significantly less than that required for the average 100 nm diameter CCV (~3.2 x 103 nm2)."
Response: Thank you for the criticism. We have clarified the sentence and corrected a typo, which would definitely be confusing. The section now reads, "While the flat CCSs we detected in CCDC32 knockdown cells were significantly larger than in control cells (Fig. 4D, mean diameter of 147 nm vs. 127 nm, respectively), they are much smaller than typical long-lived flat clathrin lattices (d{greater than or equal to}300 nm)(Grove et al., 2014). Indeed, the surface area of the flat CCSs that accumulate in CCDC32 KD cells (mean ~1.69 x 104 nm2) remains significantly less than the surface area of an average 100 nm diameter CCV (~3.14 x 104 nm2). Thus, we refer to these structures as 'flat clathrin assemblies' because they are neither curved 'pits' nor large 'lattices'. Rather, the flat clathrin assemblies represent early, likely defective, intermediates in CCP formation."
Significance
Please see above.(from above: Overall, while this work presents some interesting ideas, it remains unclear whether CCDC32 regulates AP2 beyond the assembly step)
Response: Our responses above argue that we have indeed established that CCDC32 regulates AP2 beyond the assembly step. We have also identified several discrepancies between our findings and those reported by Wan et al., most notably binding between CCDC32 and mature AP2 complexes and the AP2-dependent recruitment of CCDC32 to CCPs. It is possible that these discrepancies may be due to the position of the GFP tag (ours is N-terminal, theirs is C-terminal; we show that the N-terminal tagged CCDC32 rescues the knockdown phenotype, while Wan et al., do not provide evidence for functionality of the C-terminal construct).
__Reviewer #3 __
Evidence, reproducibility and clarity (Required):
In this manuscript, Yang et al. characterize the endocytic accessory protein CCDC32, which has implications in cardio-facio-neuro-developmental syndrome (CFNDS). The authors clearly demonstrate that the protein CCDC32 has a role in the early stages of endocytosis, mainly through the interaction with the major endocytic adaptor protein AP2, and they identify regions taking part in this recognition. Through live cell fluorescence imaging and electron microscopy of endocytic pits, the authors characterize the lifetimes of endocytic sites, the formation rate of endocytic sites and pits and the invagination depth, in addition to transferrin receptor (TfnR) uptake experiments. Binding between CCDC32 and CCDC32 mutants to the AP2 alpha appendage domain is assessed by pull down experiments. Together, these experiments allow deriving a phenotype of CCDC32 knock-down and CCDC32 mutants within endocytosis, which is a very robust system, in which defects are not so easily detected. A mutation of CCDC32, known to play a role in CFNDS, is also addressed in this study and shown to have endocytic defects.
Response: We thank the reviewer for their positive remarks regarding the quality of our data and the strength of our conclusions.
In summary, the authors present a strong combination of techniques, assessing the impact of CCDC32 in clathrin mediated endocytosis and its binding to AP2, whereby the following major and minor points remain to be addressed:
- The authors show that CCDC32 depletion leads to the formation of brighter and static clathrin coated structures (Figure 2), but that these were only prevalent to 7.8% and masked the 'normal' dynamic CCPs. At the same time, the authors show that the absence of CCDC32 induces pits with shorter life times (Figure 1 and Figure 2), the 'majority' of the pits. Clarification is needed as to how the authors arrive at these conclusions and these numbers. The authors should also provide (and visualize) the corresponding statistics. The same statement is made again later on in the manuscript, where the authors explain their electron microscopy data. Was the number derived from there?
These points are critical to understanding CCDC32's role in endocytosis and is key to understanding the model presented in Figure 8. The numbers of how many pits accumulate in flat lattices versus normal endocytosis progression and the actual time scales could be included in this model and would make the figure much stronger.
Response: Thank you for these comments. We understand the paradox between the visual impression and the reality of our dynamic measurements. We have been visually misled by this in previous work (Chen et al., 2020), which emphasizes the importance of unbiased image analysis afforded to us through the well-documented cmeAnalysis pipeline, developed by us (Aguet et al., 2013) and now used by many others (e.g. (He et al., 2020)).
The % of static structures was not derived from electron microscopy data, but quantified using cmeAnalysis, which automatedly provides the lifetime distribution of CCPs. We have now clarified this in the manuscript and added a histogram (Fig. S4) quantifying the fraction of CCPs in lifetime cohorts 150s (static).
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In relation to the above point, the statistics of Figure 2E-G and the analysis leading there should also be explained in more detail: For example, what are the individual points in the plot (also in Figures 6G and 7G)? The authors should also use a few phrases to explain software they use, for example DASC, in the main text.
Response: Each point in these bar graphs represents a movie, where n{greater than or equal to}12. These details have been added to the respective figure legend. We have also added a brief description of DASC analysis in the text.
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There are several questions related to the knock-down experiments that need to be addressed:
Firstly, knock-down of CCDC32 does not seem to be very strong (Figure S2B). Can the level of knock-down be quantified?
Response: We have now quantified the KD efficiency. It is ~60%. This turns out to be fortuitous (see responses to reviewer 2), as a recent publication, which came out after we completed our study, has shown by CRISPR-mediated knockout, that CCD32 also plays an essential chaperone function required for AP2 assembly. We do not see any reduction in AP2 levels or its complex formation under our conditions (see new Supplemental Figure S3), which suggests that the effects of CCDC32 on CCP dynamics are more sensitive to CCDC32 concentration than its roles as a chaperone. Our phenotypes would have been masked by more efficient depletion of CCDC32.
In page 6 it is indicated that the eGFP-CCDC32(1-54) and eGFP-CCDC32(∆78-98) constructs are siRNA-resistant. However in Fig S2B, these proteins do not show any signal in the western blot, so it is not clear if they are expressed or simply not detected by the antibody. The presence of these proteins after silencing endogenous CCDC32 needs to be confirmed to support Figures 6 and Figures 7, which critically rely on the presence of the CCDC32 mutants.
Response: Unfortunately, the C-terminally truncated CCDC32 proteins are not detected because they lack the antibody epitope, indeed even the D78-98 deletion is poorly detected (compare the GFP blot in new S1A with the anti-CCDC32 blot in S1B). However, these constructs contain the same siRNA-resistance mutation as the full length protein. That they are expressed and siRNA resistant can be seen in Fig. S2A (now Fig. S1A) blotting for GFP.
In Figures 6 and 7, siRNA knock-down of CCDC32 is only indicated for sub-figures F to G. Is this really the case? If not, the authors should clarify. The siRNA knock-down in Figure 1 is also only mentioned in the text, not in the figure legend. The authors should pay attention to make their figure legends easy to understand and unambiguous.
Response: No, it is not the case. Thank you for pointing out the uncertainty. We have added these details to the Figure legends and checked all Figure legends to ensure that they clearly describe the data shown.
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It is not exactly clear how the curves in Figure 3C (lower panel) on the invagination depth were obtained. Can the authors clarify this a bit more? For example, what are kT and kE in Figure 3A? What is I0? And how did the authors derive the logarithmic function used to quantify the invagination depth? In the main text, the authors say that the traces were 'logarithmically transformed'. This is not a technical term. The authors should refer to the actual equation used in the figure.
Response: This analysis was developed by the Kirchhausen lab (Saffarian and Kirchhausen, 2008). We have added these details and reference them in the Figure legend and in the text. We also now use the more accurate descriptor 'log-transformed'.
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In the discussion, the claim 'The resulting dysregulation of AP2 inhibits CME, which further results in the development of CFNDS.' is maybe a bit too strong of a statement. Firstly, because the authors show themselves that CME is perturbed, but by no means inhibited. Secondly, the molecular link to CFNDS remains unclear. Even though CCDC32 mutants seem to be responsible for CFNDS and one of the mutant has been shown in this study to have a defect in endocytosis and AP2 binding, a direct link between CCDC32's function in endocytosis and CFNDS remains elusive. The authors should thus provide a more balanced discussion on this topic.
Response: We have modified and softened our conclusions, which now read that the phenotypes we see likely "contribute to" rather than "cause" the disease.
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In Figure S1, the authors annotate the presence of a coiled-coil domain, which they also use later on in the manuscript to generate mutations. Could the authors specify (and cite) where and how this coiled-coil domain has been identified? Is this predicted helix indeed a coiled-coil domain, or just a helix, as indicated by the authors in the discussion?
Response: See response to Reviewer 1, point 4. We have changed this wording to alpha-helix. The 'coiled-coil' reference is historical and unlikely a true reflection of CCDC32 structure. AlphaFold 3.0 predictions were unable to identify with certainly any coiled-coil structures, even if we modelled potential dimers or trimers; and we find no evidence of dimerization of CCDC32 in vivo. We have clarified this in the text.
Minor comments
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In general, a more detailed explanation of the microscopy techniques used and the information they report would be beneficial to provide access to the article also to non-expert readers in the field. This concerns particularly the analysis methods used, for example: How were the cohort-averaged fluorescence intensity and lifetime traces obtained? How do the tools cmeAnalysis and DASC work? A brief explanation would be helpful.
Response: We have expanded Methods to add these details, and also described them in the main text.
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The axis label of Figure 2B is not quite clear. What does 'TfnR uptake % of surface bound' mean? Maybe the authors could explain this in more detail in the figure legend? Is the drop in uptake efficiency also accessible by visual inspection of the images? It would be interesting to see that.
Response: This is a standard measure of CME efficiency. 'TfnR uptake % of surface bound' = Internalized TfnR/Surface bound TfnR. Again, images may be misleading as defects in CME lead to increased levels of TfnR on the cell surface, which in turn would result in more Tfn uptake even if the rate of CME is decreased.
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Figure 4: How is the occupancy of CCPs in the plasma membrane measured? What are the criteria used to divide CCSs into Flat, Dome or Sphere categories?
Response: We have expanded Methods to add these details. Based on the degree of invagination, the shapes of CCSs were classified as either: flat CCSs with no obvious invagination; dome-shaped CCSs that had a hemispherical or less invaginated shape with visible edges of the clathrin lattice; and spherical CCSs that had a round shape with the invisible edges of clathrin lattice in 2D projection images. In most cases, the shapes were obvious in 2D PREM images. In uncertain cases, the degree of CCS invagination was determined using images tilted at {plus minus}10-20 degrees. The area of CCSs were measured using ImageJ and used for the calculation of the CCS occupancy on the plasma membrane.
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Figure 5B: Can the authors explain, where exactly the GFP was engineered into AP2 alpha? This construct does not seem to be explained in the methods section.
Response: We have added this information. The construct, which corresponds to an insertion of GFP into the flexible hinge region of AP2, at aa649, was first described by (Mino et al., 2020) and shown to be fully functional. This information has been added to the Methods section.
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Figure S1B: The authors should indicate the colour code used for the structural model.
Response: We have expanded our structural modeling using AlphaFold 3.0 in light of the recent publication suggesting the CCDC32 interacts with the µ2 subunit and does not bind full length AP2. These results are described in the text. The color coding now reflects certainty values given by AlphaFold 3.0 (Fig. S6B, D).
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The list of primers referred to in the materials and methods section does not exist. There is a Table S1, but this contains different data. The actual Table S1 is not referenced in the main text. This should be done.
Response: We apologize for this error. We have now added this information in Table S2.
__ Significance (Required):__
In this study, the authors analyse a so-far poorly understood endocytic accessory protein, CCDC32, and its implication for endocytosis. The experimental tool set used, allowing to quantify CCP dynamics and invagination is clearly a strength of the article that allows assessing the impact of an accessory protein towards the endocytic uptake mechanism, which is normally very robust towards mutations. Only through this detailed analysis of endocytosis progression could the authors detect clear differences in the presence and absence of CCDC32 and its mutants. If the above points are successfully addressed, the study will provide very interesting and highly relevant work allowing a better understanding of the early phases in CME with implication for disease.
The study is thus of potential interest to an audience interested in CME, in disease and its molecular reasons, as well as for readers interested in intrinsically disordered proteins to a certain extent, claiming thus a relatively broad audience. The presented results may initiate further studies of the so-far poorly understood and less well known accessory protein CCDC32.
Response: We thank the reviewer for their positive comments on the significance of our findings and the importance of our detailed phenotypic analysis made possible by quantitative live cell microscopy. We also believe that our new structural modeling of CCDC32 and our findings of complex and extensive interactions with AP2 make the reviewers point regarding intrinsically disordered proteins even more interesting and relevant to a broad audience. We trust that our revisions indeed address the reviewer's concerns.
The field of expertise of the reviewer is structural biology, biochemistry and clathrin mediated endocytosis. Expertise in cell biology is rather superficial.
References:
Aguet, F., Costin N. Antonescu, M. Mettlen, Sandra L. Schmid, and G. Danuser. 2013. Advances in Analysis of Low Signal-to-Noise Images Link Dynamin and AP2 to the Functions of an Endocytic Checkpoint. Developmental Cell. 26:279-291.
Chen, Z., R.E. Mino, M. Mettlen, P. Michaely, M. Bhave, D.K. Reed, and S.L. Schmid. 2020. Wbox2: A clathrin terminal domain-derived peptide inhibitor of clathrin-mediated endocytosis. Journal of Cell Biology. 219.
Grove, J., D.J. Metcalf, A.E. Knight, S.T. Wavre-Shapton, T. Sun, E.D. Protonotarios, L.D. Griffin, J. Lippincott-Schwartz, and M. Marsh. 2014. Flat clathrin lattices: stable features of the plasma membrane. Mol Biol Cell. 25:3581-3594.
He, K., E. Song, S. Upadhyayula, S. Dang, R. Gaudin, W. Skillern, K. Bu, B.R. Capraro, I. Rapoport, I. Kusters, M. Ma, and T. Kirchhausen. 2020. Dynamics of Auxilin 1 and GAK in clathrin-mediated traffic. J Cell Biol. 219.
Mino, R.E., Z. Chen, M. Mettlen, and S.L. Schmid. 2020. An internally eGFP-tagged α-adaptin is a fully functional and improved fiduciary marker for clathrin-coated pit dynamics. Traffic. 21:603-616.
Saffarian, S., and T. Kirchhausen. 2008. Differential evanescence nanometry: live-cell fluorescence measurements with 10-nm axial resolution on the plasma membrane. Biophys J. 94:2333-2342.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, Yang et al. characterize the endocytic accessory protein CCDC32, which has implications in cardio-facio-neuro-developmental syndrome (CFNDS). The authors clearly demonstrate that the protein CCDC32 has a role in the early stages of endocytosis, mainly through the interaction with the major endocytic adaptor protein AP2, and they identify regions taking part in this recognition. Through live cell fluorescence imaging and electron microscopy of endocytic pits, the authors characterize the lifetimes of endocytic sites, the formation rate of endocytic sites and pits and the invagination depth, in addition to transferrin receptor (TfnR) uptake experiments. Binding between CCDC32 and CCDC32 mutants to the AP2 alpha appendage domain is assessed by pull down experiments. Together, these experiments allow deriving a phenotype of CCDC32 knock-down and CCDC32 mutants within endocytosis, which is a very robust system, in which defects are not so easily detected. A mutation of CCDC32, known to play a role in CFNDS, is also addressed in this study and shown to have endocytic defects.
In summary, the authors present a strong combination of techniques, assessing the impact of CCDC32 in clathrin mediated endocytosis and its binding to AP2, whereby the following major and minor points remain to be addressed:
- The authors show that CCDC32 depletion leads to the formation of brighter and static clathrin coated structures (Figure 2), but that these were only prevalent to 7.8% and masked the 'normal' dynamic CCPs. At the same time, the authors show that the absence of CCDC32 induces pits with shorter life times (Figure 1 and Figure 2), the 'majority' of the pits.
Clarification is needed as to how the authors arrive at these conclusions and these numbers. The authors should also provide (and visualize) the corresponding statistics. The same statement is made again later on in the manuscript, where the authors explain their electron microscopy data. Was the number derived from there?
These points are critical to understanding CCDC32's role in endocytosis and is key to understanding the model presented in Figure 8. The numbers of how many pits accumulate in flat lattices versus normal endocytosis progression and the actual time scales could be included in this model and would make the figure much stronger. - In relation to the above point, the statistics of Figure 2E-G and the analysis leading there should also be explained in more detail: For example, what are the individual points in the plot (also in Figures 6G and 7G)? The authors should also use a few phrases to explain software they use, for example DASC, in the main text. - There are several questions related to the knock-down experiments that need to be addressed:
Firstly, knock-down of CCDC32 does not seem to be very strong (Figure S2B). Can the level of knock-down be quantified?
In page 6 it is indicated that the eGFP-CCDC32(1-54) and eGFP-CCDC32(∆78-98) constructs are siRNA-resistant. However in Fig S2B, these proteins do not show any signal in the western blot, so it is not clear if they are expressed or simply not detected by the antibody. The presence of these proteins after silencing endogenous CCDC32 needs to be confirmed to support Figures 6 and Figures 7, which critically rely on the presence of the CCDC32 mutants.
In Figures 6 and 7, siRNA knock-down of CCDC32 is only indicated for sub-figures F to G. Is this really the case? If not, the authors should clarify. The siRNA knock-down in Figure 1 is also only mentioned in the text, not in the figure legend. The authors should pay attention to make their figure legends easy to understand and unambiguous. - It is not exactly clear how the curves in Figure 3C (lower panel) on the invagination depth were obtained. Can the authors clarify this a bit more? For example, what are kT and kE in Figure 3A? What is I0? And how did the authors derive the logarithmic function used to quantify the invagination depth? In the main text, the authors say that the traces were 'logarithmically transformed'. This is not a technical term. The authors should refer to the actual equation used in the figure. - In the discussion, the claim 'The resulting dysregulation of AP2 inhibits CME, which further results in the development of CFNDS.' is maybe a bit too strong of a statement. Firstly, because the authors show themselves that CME is perturbed, but by no means inhibited. Secondly, the molecular link to CFNDS remains unclear. Even though CCDC32 mutants seem to be responsible for CFNDS and one of the mutant has been shown in this study to have a defect in endocytosis and AP2 binding, a direct link between CCDC32's function in endocytosis and CFNDS remains elusive. The authors should thus provide a more balanced discussion on this topic. - In Figure S1, the authors annotate the presence of a coiled-coil domain, which they also use later on in the manuscript to generate mutations. Could the authors specify (and cite) where and how this coiled-coil domain has been identified? Is this predicted helix indeed a coiled-coil domain, or just a helix, as indicated by the authors in the discussion?
Minor comments:
- In general, a more detailed explanation of the microscopy techniques used and the information they report would be beneficial to provide access to the article also to non-expert readers in the field. This concerns particularly the analysis methods used, for example: How were the cohort-averaged fluorescence intensity and lifetime traces obtained? How do the tools cmeAnalysis and DASC work? A brief explanation would be helpful.
- The axis label of Figure 2B is not quite clear. What does 'TfnR uptake % of surface bound' mean? Maybe the authors could explain this in more detail in the figure legend? Is the drop in uptake efficiency also accessible by visual inspection of the images? It would be interesting to see that.
- Figure 4: How is the occupancy of CCPs in the plasma membrane measured? What are the criteria used to divide CCSs into Flat, Dome or Sphere categories?
- Figure 5B: Can the authors explain, where exactly the GFP was engineered into AP2 alpha? This construct does not seem to be explained in the methods section.
- Figure S1B: The authors should indicate the colour code used for the structural model.
- The list of primers referred to in the materials and methods section does not exist. There is a Table S1, but this contains different data. The actual Table S1 is not referenced in the main text. This should be done.
Significance
In this study, the authors analyse a so-far poorly understood endocytic accessory protein, CCDC32, and its implication for endocytosis. The experimental tool set used, allowing to quantify CCP dynamics and invagination is clearly a strength of the article that allows assessing the impact of an accessory protein towards the endocytic uptake mechanism, which is normally very robust towards mutations. Only through this detailed analysis of endocytosis progression could the authors detect clear differences in the presence and absence of CCDC32 and its mutants. If the above points are successfully addressed, the study will provide very interesting and highly relevant work allowing a better understanding of the early phases in CME with implication for disease.
The study is thus of potential interest to an audience interested in CME, in disease and its molecular reasons, as well as for readers interested in intrinsically disordered proteins to a certain extent, claiming thus a relatively broad audience. The presented results may initiate further studies of the so-far poorly understood and less well known accessory protein CCDC32.
The field of expertise of the reviewer is structural biology, biochemistry and clathrin mediated endocytosis. Expertise in cell biology is rather superficial.
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Referee #2
Evidence, reproducibility and clarity
In this manuscript, the authors demonstrate that CCDC32 regulates clathrin-mediated endocytosis (CME). Some of the findings are consistent with a recent report by Wan et al. (2024 PNAS), such as the observation that CCDC32 depletion reduces transferrin uptake and diminishes the formation of clathrin-coated pits. The primary function of CCDC32 is to regulate AP2 assembly, and its depletion leads to AP2 degradation. However, this study did not examine AP2 expression levels. CCDC32 may bind to the appendage domain of AP2 alpha, but it also binds to the core domain of AP2 alpha. Overall, while this work presents some interesting ideas, it remains unclear whether CCDC32 regulates AP2 beyond the assembly step.
- Besides its role in AP2 assembly, CCDC32 may potentially have another function on the membrane. However, there is no direct evidence showing that CCDC32 associates with the plasma membrane.
- CCDC32 binds to multiple regions on AP2, including the core domain. It is important to distinguish the functional roles of these different binding sites.
- AP2 expression levels should be examined in CCDC32 depleted cells. If AP2 is gone, it is not surprising that clathrin-coated pits are defective.
- If the authors aim to establish a secondary function for CCDC32, they need to thoroughly discuss the known chaperone function of CCDC32 and consider whether and how CCDC32 regulates a downstream step in CME.
- The quality of Figure 1A is very low, making it difficult to assess the localization and quantify the data.
- In Figure 6, why aren't AP2 mu and sigma subunits shown?Page 5, top, this sentence is confusing: "their surface area (~17 x 103 nm2) remains significantly less than that required for the average 100 nm diameter CCV (~3.2 x 103 nm2)."
Significance
Overall, while this work presents some interesting ideas, it remains unclear whether CCDC32 regulates AP2 beyond the assembly step.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The manuscript by Yang et al. describes a new CME accessory protein. CCDC32 has been previously suggested to interact with AP2 and in the present work the authors confirm this interaction and show that it is a bona fide CME regulator. I agreement with its interaction with AP2, CCDC32 recruitment to CCPs mirrors the accumulation of clathrin. Knockdown of CCDC32 reduces the amount of productive CCPs, suggestive of a stabilisation role in early clathrin assemblies. Immunoprecipitation experiments mapped the interaction of CCDC42 to the α-appendage of the AP2 complex α-subunit. Finally, the authors show that the CCDC32 nonsense mutations found in patients with cardio-facial-neuro-developmental syndrome disrupt the interaction of this protein to the AP2 complex. The manuscript is well written and the conclusions regarding the role of CCDC32 in CME are supported by good quality data. As detailed below, a few improvements/clarifications are needed to reinforce some of the conclusions, especially the ones regarding CFNDS.
Major comments:
- Why did the protein could just be visualized at CCPs after knockdown of the endogenous protein? This is highly unusual, especially on stable cell lines. Could this be that the tag is interfering with the expressed protein function rendering it incapable of outcompeting the endogenous? Does this points to a regulated recruitment?
- The disease mutation used in the paper does not correspond to the truncation found in patients. The authors use an 1-54 truncation, but the patients described in Harel et al. have frame shifts at the positions 19 (Thr19Tyrfs12) and 64 (Glu64Glyfs12), while the patient described in Abdalla et al. have the deletion of two introns, leading to a frameshift around amino acid 90. Moreover, to be precisely test the function of these disease mutations, one would need to add the extra amino acids generated by the frame shift. For example, as denoted in the mutation description in Harel et al., the frameshift at position 19 changes the Threonine 19 to a Tyrosine and ads a run of 12 extra amino acids (Thr19Tyrfs*12).
- The frameshift caused by the CFNDS mutations (especially the one studied) will likely lead to nonsense mediated RNA decay (NMD). The frameshift is well within the rules where NMD generally kicks in. Therefore, I am unsure about the functional insights of expressing a disease-related protein which is likely not present in patients.
- Coiled coils generally form stable dimers. The typically hydrophobic core of these structures is not suitable for transient interactions. This complicates the interpretation of the results regarding the role of this region as the place where the interaction to AP2 occurs. If the coiled coil holds a stable CCDC32 dimer, disrupting this dimer could reduce the affinity to AP2 (by reduced avidity) to the actual binding site. A construct with an orthogonal dimeriser or a pulldown of the delta78-98 protein with of the GST AP2a-AD could be a good way to sort this issue.
Minor comments:
- The authors interchangeably use the term "flat CCPs" and "flat clathrin lattices". While these are indeed related, flat clathrin lattices have been also used to refer to "clathrin plaques". To avoid confusion, I suggest sticking to the term "flat CCPs" to refer to the CCPs which are in their early stages of maturation.
Significance
General assessment:
CME drives the internalisation of hundreds of receptors and surface proteins in practically all tissues, making it an essential process for various physiological processes. This versatility comes at the cost of a large number of molecular players and regulators. To understand this complexity, unravelling all the components of this process is vital. The manuscript by Yang et al. gives an important contribution to this effort as it describes a new CME regulator, CCDC32, which acts directly at the main CME adaptor AP2. The link to disease is interesting, but the authors need to refine their experiments. The requirement for endogenous knockdown for recruitment of the tagged CCDC32 is unusual and requires further exploration.
Advance:
The increased frequency of abortive events presented by CCDC32 knockdown cells is very interesting, as it hints to an active mechanism that regulates the stabilisation and growth of clathrin coated pits. The exact way clathrin coated pits are stabilised is still an open question in the field.
Audience:
This is a basic research manuscript. However, given the essential role of CME in physiology and the growing number of CME players involved in disease, this manuscript can reach broader audiences.
Field of expertise of the reviewer:
Clathrin mediated endocytosis, cell biology, microscopy, biochemistry.
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Reply to the reviewers
*We are grateful for the overall positive feedback and constructive suggestions. We have been able to experimentally address several of the suggested points and provide here a revision plan addressing all of the reviewers’ additional concerns. *
*In summary, this study is of fundamental novelty and high impact as it: *
- Reveals an unexpected role of ErbB3 in controlling ____Integrin β1 ____trafficking ____and thus epithelial cell motility and extracellular vesicles secretion. This may shed important insights into the role of ErbB3____ in cancer.
- Uncovers the first ligand-independent, non-canonical cellular function for ErbB3 as a scaffold for the Arf6-Rabaptin5-GGA3 endosomal sorting complex.
- Provoking the notion that pseudo-RTKs may have evolved cellular functions beyond receptor signaling, such as by scaffolding endosomal sorting compartments. *We hope that you share our view that these conceptually ground breaking findings will be of interest to a broad cross-disciplinary audience interested in cell signaling, cancer biology, endocytic trafficking and integrin biology. *
1. Point-by-point description of the revisions
Reviewer #1 (Evidence____, reproducibility and clarity (Required)):
ErbB3 is well-known for its significance in cancer, which is dependent on ligand-binding and heterodimerization with other ErbB family members. In the current work, Rodrigues-Junior et al. identified novel, unexpected functions of ErbB3 in promoting early endocytic recycling and restricting exocytic trafficking (extracellular vesicles secretion) of membrane receptors, such as integrin b1 and transferrin receptor, via stabilizing the Arf6-GGA3-Rabaptin5 endosomal sorting complex. Via ErbB3 siRNA knockdown, they observed an impaired recycling of transferrin receptor and integrin b1 back to the cell membrane. The recycling assay condition (growth factor-deprived) provided a very clean result to support that this ErbB3-dependent endocytic trafficking is ligand-binding independent. The trafficking-dependence on ErbB3 (both the endocytic and the exocytic) was further supported by integrin b1 functional assays (scratch closure assay and Matrigel invasion assay). There are still some details that need to be clarified to fully understand the conclusion.
Major points:
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- The manuscript started with a pathological correlation between high ErbB3 level and poor patient survival rate. In Fig.1, the impaired TfR recycling, and the co-localization between ErbB3 and integrin b1 were also performed in the pathological breast cancer cell line, MCF7. While investigating integrin b1 recycling, the authors suddenly switched to another two non-malignant human breast epithelial cell lines, which led to a difficult correlation of ErbB3-mediated recycling back to the disease situation. The authors should state more clearly this point, rather than data not shown. This inconsistency occurred also in other assays, for example, when addressing the trafficking from TGN to cell surface, MCF7 was utilized; while when addressing extracellular vesicle secretion, MCF10A was utilized. Response: we thank the reviewer for the comment. The rationale for using different cell-lines or primary cells is now better explained in the manuscript. We found that depletion of ErbB3 impaired recycling of Integrin β1 in the non-malignant cells, including MCF10A and primary breast epithelial cells, but not in malignant MCF7 cells that overexpress ErbB3 (data not shown). We now speculate in the manuscript that perhaps the dependence on ErbB3 for Integrin b1 recycling is lost at some point during carcinogenesis, although further studies will be needed to address this possibility. MCF7 cells were used to detect endogenous ErbB3 as normal expression levels of ErbB3 (primary MECs and MCF10A) were not detectable by immunofluorescence microscopy in our hands with a range of antibodies we tested. With regard to the transferrin recycling assay, we first attempted to use MCF10A cells for consistency, however we found that transferrin internalized poorly in these cells and the limited pool of transferrin that internalised was retained in these cells for an extended time (3 h), thus rendering them unsuitable for our transferrin experiments. *
*Concerning the data on trafficking from the TGN to cell surface we mistakenly wrote that they were performed in MCF7 cells although they were in fact done in MCF10A cells. This is now corrected in the new version of this manuscript. *
Additionally, based on the constructive comment by this reviewer, we have now extended the analysis of EV secretion in ErbB3, Rab4 and Rabaptin5 silenced cells to MCF7 cells. The new data is in line with our findings in MCF10A and prHMEC cells, that absence of ErbB3 significantly increased EV secretion. Moreover, Rab4 and Rabaptin5 knockdown also enhanced the amount of EVs secreted by MCF7 cells. These results were incorporated in the manuscript as new Supplementary Figure S7F-G and new Supplementary Figure S9F-G, as recommended. Furthermore, we also included in this new version that GGA3 and to a lesser extent Rab GTPase-binding effector protein 1 (Rabaptin5 or RABPT5) shared colocalisation with endogenous ErbB3 in MCF7 cells as the new Supplementary Figure 9A, B. Finally, we also attempted to conduct the Arf6 IP in MCF7 cells, but as opposed to MCF10A cells, the yield of Arf6 in pull down experiments was much lower than in MCF10A cells, and interacting proteins were not detectable.
It was shown before that ErbB3 undergoes constitutive internalization and degradation within several hours that is independent of ligand-binding (ref#13). Can the authors provide experimental evidences to show the correlation of TfR or integrin b1 recycling with this dynamic ErbB3 levels rather than ErbB3 knockdown?
Response: we have performed colocalization of ErbB3, traced Integrin β1 and the recycling endosome marker EHD1, showing triple colocalization in a subset of endosomes, as shown in the new Supplementary Figure S2H. Experimental limitations prevented us from including EEA1 in triple staining for mCherry-ErbB3 or endogenous ErbB3 protein. Furthermore, ectopically expressed ErbB3 in MCF10A cells did not show convincing co-localisation. We hope that the new EHD1 triple colocalization with ErbB3 and Integrin β1 in endosomal compartments satisfies this specific comment.
As mentioned above, regarding the transferrin recycling assay, we first attempted to use MCF10A cells for consistency, however we found that transferrin internalized poorly in these cells and the limited pool of transferrin that internalised was retained in these cells for an extended time (3 h), thus preventing their use.
The efficiency of siRNA knockdown of ErbB3 (both #1 and #2) should support the observed phenotype (Fig. 1I-J, K-L). Is there a correlation between the ErbB3 level with integrin recycling? For example, siRNA#2 led to more efficient knockdown of ErbB3 in MCF10A?
Response: notably, the immunoblots presented here to assess the efficiency of the two different siRNAs are one example and we noted some variability between different experiments but find that both siRNAs work well and yield comparable effects on recycling of Integrin β1. Importantly, the recycling data represents biological repeats of independently performed experiments, and have yielded reproducible and consistent ErbB3 silencing using both siRNAs. This is noted by the lack of significance between ErbB3 knocked down cells in Fig. 1I-J and K-L. Hence, we consider that both siRNAs against ErbB3 worked efficiently with comparable outcome. Please also note our reply to Rev2 #07.
ErbB3 loss led to more extracellular vesicles secretion, but also lysosomal degradation of integrin b1. This conclusion is supported by results shown in Fig.4D-E and Fig. S8A-B, while the analysis from the same cell line (MCF10A, Fig. S3A) results in no change of integrin b1 levels upon ErbB3 depletion. Fig. S3B showed also no change in a second non-malignant cell line (prHMEC). How do the authors explain this conflict?
Response*: we thank the reviewer for this comment. We believe that the increase in EV secretion and lysosomal degradation is compensated by increase in de novo synthesis of Integrin β1 (see data below, from Fig. S3C). In the original manuscript we did not perform the appropriate statistical analysis of the RT-qPCR data. The unpaired two-tail Student’s T-test is only suitable for normally distributed samples, which is not the case here. Instead, we performed the appropriate Mann-Whitney U-test assuming non-normal distribution, yielding an exact p-value of 0.017. The figure S3A and associated text has been modified accordingly. *
Minor points: 1. Is TfR also colocalizing with endogenous ErbB3?
*Response: as mentioned in the major comment #02, we attempted to perform the transferrin recycling assay using MCF10A cells to enable direct comparisons with the integrin b1 recycling, but found that transferrin internalized poorly in these cells. *
Fig. 3J, TSG101, T is masked by 3I
Response: we apologize for this oversight. We have gone through the manuscript in detail and corrected all pointed errors accordingly.
Page 10, the description of the EV secretion in prHMEC cells is annotated to the wrong figure. Fig S5Dà S7D; S5Eà S7E
Response: we apologize for this oversight and have now corrected the mistake.
Fig. 4M: How was the motility/invasion into Matrigel determined? Images? Only quantifications are shown.
Response*: the matrigel invasion assay was described in the Material and Methods section. Accordingly, the data were expressed as the percentage of invasion based on the ratio of the mean number of cells invading through Matrigel matrix per mean number of cells in the uncoated support. For this rebuttal letter, the reviewer can find representative images of invaded MCF10A siCtrl non-treated (Ctrl) or treated with VSF secreted from MCF10A siCtrl or siErbB3. Since this is an established method to measure cell invasion, we hope the reviewer agrees that these images do not add value to the manuscript. *
Fig. 4M: Exosomes collected from ErbB3-depleted cells promotes the migration in MCF10A-wild type cells, how about the effects on ErbB3-depleted cells? This group should be included for analysis.
Response*: as proposed, we have treated both control and ErbB3-silenced MCF10A cells with normalized concentrations of EVs secreted from siCtrl and siErbB3 (1 x 109 nanoparticles/ mL) for 48 hours, followed by cell viability and cell invasion assays. The new data show that both EV pools modestly increased cell viability and substantially increased invasiveness of both wild-type and ErbB3-depleted cells through Matrigel (new Figures 4K and L). Together, our results indicate that while ErbB3-silenced MCF10A cells exhibited lower basal motility, ErbB3 is not required for the observed EV induced motility. The new Figures 4K and L were included and further discussed in this manuscript. *
Quantification of the blots should be provided for Fig. 5A (GGA3), 5B (GGA3, Rabaptin5 and Arf6), 5F (GGA3) and 5G (GGA3, Rabaptin5 and Arf6). What is mock IP in each graph? The mock IP is neither mentioned in methods nor in legends.
Response*: we have now carried out densitometry analysis in all the requested immunoblots shown in Figure 5. We also changed the mock IP term to IgG IP for clarity. The use of non-immunogenic IgG in control IPs is now specified in the methods and respective figure legend. *
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary: In their manuscript, Rodrigues-Junior and colleagues identify a novel ligand-independent function of the tyrosine kinase receptor (RTK) ErbB3 as a regulator of integrin β1 recycling. In particular, the authors demonstrate that ErbB3 depletion reduce β1 integrin surface expression, triggering its lysosomal degradation and increasing its secretion in extracellular vesicles (EVs). Moreover, the authors show that these EVs enhanced the invasive capacity of ErbB3 wild type breast epithelial cells. In addition, the authors evidence the interaction between ErbB3, GGA3 and Rabaptin5. Loss of any of these proteins destabilizes this interaction, which abrogates integrin β1 recycling and leads to its degradation and secretion. The work is potentially interesting; however, there are some aspects that need to be analyzed in a more robust manner.
Major comments:
- The manuscript is mainly focused on β1 integrin endocytic and post-endocytic fate following ErbB3 silencing, describing also a molecular mechanism underlying these observations. Despite the cited manuscript by Deneka, A. and colleagues indicates a similar mechanism for transferrin receptor (TfR) recycling, the Authors only studied the receptor internalization upon ErbB3 silencing. Therefore, this observation does not add any significance to the main topic of the manuscript and its removal should be considered. Response*: we agree with the reviewer the fate of Integrin β1 is the main focus of this manuscript. We would however favour retaining the TfR data as it implies a wider role of ErbB3, beyond trafficking of Integrin β1. We ask for the reviewer’s understanding of our rationale. *
2.Data from Figure S1A seems to be not normally distributed. Have the Authors tested the data for normal distribution? If not, please consider it. If the data is not normally distributed, a non parametric Mann-Whitney U-Test would be more suitable.
Response: we thank the reviewer for the comment. The differential ErbB3 mRNA expression analysis was retrieved from the widely used GEPIA2 portal (to date about 600 manuscripts cite this portal on PubMed), based on the selected datasets (“TCGA tumors vs TCGA normal + GTEx normal” or “TCGA tumors vs TCGA normal”). The method for differential analysis is one-way ANOVA, using disease state (Tumor or Normal) as variable for calculating differential expression, as it considers differential expression among several tumors.
Tang, Z., Kang, B., Li, C., Chen, T., and Zhang, Z. (2019). GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556–W560. https://doi.org/10.1093/nar/gkz430.
- The Authors studied the colocalization of ErbB3, Rab4 and Rab11, observing an increased colocalization between ErbB3 and Rab4 10 minutes following primaquine. However, the Authors previously referred to Sönnichsen, B et al. manuscript, in which TfR colocalized with Rab11 at 30min. It would be interesting to see whether ErbB3 and Rab11 colocalize at later time points in the presence or absence of primaquine. This will reinforce the conclusion that ErbB3 is involved in early Rab4-dependent recycling.
Response: we appreciate the reviewer’s comment. However, we consider that these requested experiments will not add significant value to the novelty of this manuscript and hope that the reviewer accepts that we politely refrain from reproducing them.
In Figure 4C the Authors observed a reduction in β1 integrin levels in ErbB3 silenced cells compared to the control already at the beginning of tracing (0 min), which might be due to accelerated turnover at the internalization step of their experimental design. To confirm this, immunofluorescence of β1 integrin in control and ErbB3 silenced cells could be performed just right after the 15min integrin internalization.
Response: this is likely a misunderstanding as the timepoint (0 min) is defined as the point after the 15 min internalization step when the imaging-based tracing begins, which aligns perfectly with the reviewer’s request.
In the discussion, the Authors indicate that "loss of ErbB3 redirects Integrin β1 towards lysosomes for degradation, mimicking loss of GGA3 that similarly redirects both Integrin β1 and c-Met towards lysosomal degradation, or Rabaptin5 depletion that we find similarly redirects trafficking of internalised Integrin β1 towards lysosomal degradation". However, the involvement of lysosomal degradation was only studied for ErbB3 silencing by employing chloroquine. To further support this statement, the use of chloroquine in Rabaptin5- and GGA3-depleted cells is recommended.
Response: we appreciate the reviewer’s comment, but since these findings have been published earlier, we think that they will not add significant value to the manuscript and hope that the reviewer accepts that we politely refrain from reproducing them.
Minor comments:
6.The Authors should consider shortening the following sentences from the Introduction: "GGA proteins contain several functional domains that...thereby regulating sorting of cargo including Integrin β3 and TfR into recycling endosomes".
Response: we thank the reviewer for the comment. We have now divided this sentence into two for smoother reading.
The Authors do not show ErbB3 silencing efficiency at the protein level until Figure 3G, which should have been shown in Figure 1 or Supplementary Figure 1, as all the research is based on it. Moreover, GGA3 silencing efficiency was never tested.
Response*: we thank the reviewer for this comment. We have included a new immunoblot confirming the silencing of ErbB3 by two independent siRNAs in MCF7 cells, as the new Supplementary Figure S2A. Please, note that GGA3 silencing was shown in the main Figure 6J. *
Figure 1I and Figure 1K may include the representative images for the missing siErbB3 to properly illustrate the associated quantification.
Response: we thank the reviewer for the comment. We have now included the representative images, as suggested.
Consider including a Western blot showing the effect of lapatinib in EGFR, ErbB2 and ErbB3 protein expression, including their phosphorylated forms.
Response: we thank the reviewer for the comment. As requested, we now show that at used concentration, lapatinib efficiently blocked tyrosine phosphorylation of ErbB3 and ERK1/2, without perturbing EGFR or ErbB3 expression levels. We also considered it relevant to show that 1 µM lapatinib used was not cytotoxic to MCF10A and MCF7 cells. We hope that these new results satisfy this specific request.
Some supplementary figures are mislabelled, such as Supplementary Figure S5D and S5E on page 10, which should be S7D and S7E, respectively. Supplementary Figure S7C on page 15 should be S9C.
Response: we apologize for this oversight and have performed the corrections.
The following sentence on page 8 should be revised as a verb is missing: "which corresponds to the reported peak time when colocalization of Rab4 with traced TfR, preceding Rab11 and TfR colocalization that peaks later at 30 minutes".
Response: we apologize for this oversight. It now reads: "which corresponds to the reported peak time of colocalization of Rab4 with traced TfR, which precedes Rab11 and TfR colocalization that peaks later at 30 minutes".
The main text indicates that the amount of VSV-G transported to the cell surface after 30min it is not affected by ErbB3 silencing. However, in Figure 3E seems to slightly decrease following the silencing. The Authors may consider employing another Western blot image to match the main text and the quantification in Figure 3F.
Response: as the reviewer noted the immunoblot showed a slight decrease. It is however a very modest decrease that is also observed in the positive control (MUC1) in the same Streptavidin IP sample. We ask for permission to keep these representative images.
In the main text, a significant difference in the nanoparticles/cell between ErbB3-depleted cells and wild type or control cells were reported. However, Figure 3I only showed the statistics of each siRNA vs the control and not the wild type condition.
Response: we apologize for this oversight. We removed from the text the comparison with the wild-type non-transfected cells to avoid misunderstanding.
The Authors concluded that "chloroquine treatment significantly restored traced Integrin β1 levels". However, this conclusion is not reflected in the statistical analysis reported in Figure 4H, which only showed the differences between control and ErbB3 silenced cells. Thus, the statistics reported for the chloroquine results should be added.
Response: we appreciate the comment by the reviewer. The requested comparison is now included in the new Figure 4H.
The Authors concluded that "loss of either GGA3 or Rabaptin5 mimics the effect of loss of ErbB3 on endocytic trafficking of Integrin β1, consistent with the hypothesis that GGA3 and Rabaptin5 are effectors of ErbB3 in promoting endosomal recycling and impeding EV release". To confirm this conclusion, the inclusion of siRabaptin5 results in Figures 6H and 6J is suggested.
Response*: we thank the reviewer for the comment. We have now included immunoblots of MCF10A cell lysate after silencing ErbB3 or Rabaptin5, as the results shown in the previous Figure 6G. We believe that these new data satisfy the specific request. *
To be consistent with the results presentation:
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The inclusion of Modal size is recommended in Figure 6I.
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Some graphs show the number of cells or biological replicates while other ones no.
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Figure 4E showed different time points for both siRNAs.
Response: we appreciate the comment and we have now included as the new main Figure 6H the modal size for the EVs secreted by MCF10A cells upon Rabaptin5 silencing. We will ensure that all respective Figure legends indicate the number of replicates. The intermediate time points showed in the main Figure 4E are different, however since the final read out at 9 h using two independent siRNAs against ErbB3 are directly comparable we ask permission to maintain the time points with respect to the analysis we performed.
Figure 1E represents the squared regions of Figure 1D, but it is not indicated in the figure legend.
Response: we apologize for this oversight. We have now indicated in Figure 1 legend that Figure 1E represents the squared regions of Figure 1D, as suggested.
In the legend of Figure 1D-G, 30min of integrin internalization is reported, where it should be 15min according to main text and methods.
Response: we apologize for this oversight and we thank the reviewer for this comment. We have now indicated the correct time point in Figure 1 legend.
The addition of representative images in Figure 6A is recommended, as already present in Figure 1I.
Response: we thank the reviewer for the comment. Representative images of Fig. 6A-D were included as the new panel Fig. 6B.
As two different siRNAs for ErbB3 were used and not in all experiments, the employed siRNA should be indicated in each experiment. In the cases where both ErbB3 siRNAs were employed, figures should report them either as main results or supplementary.
Response: we appreciate this meticulous comment. We have now indicated in the figure and in the respective figure legends which siRNA was used in the respective set of experiments (siErbB3 #01 or #02).
Why do the Authors use EVs enriched in the VSF or by UC to show the same result? What is the criteria to choose one or the other one? For example, in Figures 6G and 6K.
Response*: based on the guidelines suggested by MISEV 2018 and 2023, there is no gold standard method for EV isolation. Thus, by using at least two independent methods (i.e., tangential flow filtration, followed by immuno-affinity and ultracentrifugation; UC) we validate the enrichment of EVs in our sample preparations, showing reproducible results among the different EV enrichment protocols (Figure 3). *
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The paper by Dorival Mendes Rodrigues-Junior et al., focuses on a novel ligand-independent role of ErbB3 receptor, modulating Transferrin receptor and integrin beta1 early recycling. Authors perform several in vitro studies where they show how ErbB3 depletion diverts integrin beta1 from recycling towards lysosomal degradation and extracellular vesicle secretion, impairing cell migration. They also provide mechanistic experiments showing the role of ErbB3 on Arf6-GGA3-Rabaptin5 endosomal complex assembly.
Major comments:
- Fig. 1. Authors should co-stain with early endosomal markers (such as EEA1) to clearly show endogenous ErbB3 and Beta1 integrin endosomal co-localization. Including some insets with higher magnifications would also improve visual inspection of such interactions. Response: as requested, we have performed colocalization of ErbB3, traced Integrin β1 and the recycling endosome marker EHD1, showing triple colocalization in a subset of endosomes, as shown in the new Supplementary Figure S2H. Experimental limitations prevented us from including EEA1 in triple staining for mCherry-ErbB3 or endogenous ErbB3 protein. Furthermore, ectopically expressed ErbB3 in MCF10A cells did not show convincing co-localisation with EEA. We believe that the new triple colocalization showing ErbB3 and Integrin β1 in EHD1-positive endosomal compartments satisfies this specific comment.
Fig. 1H and 1I. Authors need to provide TIRF penetration depth to better evaluate the potential cytosolic contribution. Additionally, plasma membrane purification studies would help to validate their live imaging results.
Response: the TIRF penetration depth was 83nm which has now been added to the methods section. Purifications of plasma membrane fractions, following recycling of traced surface-labelled Integrin β1 in control or siErbB3 depleted cells, by cell surface biotinylation and immunoblotting of the recovered proteins is indeed a valuable approach to validate our findings. Nevertheless, we are confident about the results of our confocal imaging results. Thus, including these results might not contribute significantly to the novelty of this manuscript. Hence, we ask permission to publish the paper at this stage, without the plasma membrane purification, as this requires optimizations and will delay the publication of our paper, in addition to exhausting our limited financial resources.
Fig. 1J. Authors should explain better how they calculated normalized fluorescence.
Response: the normalized fluorescence is explained in the Fig. 1J legend and in the respective method section. Alexa488 intensity was normalized between 0-1, with the control as reference where Fnorm=((Fmax-Fmin)/(F-Fmin)). All data points were background corrected, followed by normalization to the pre-stimulatory level (F/F0).
Fig. 2B. Authors should include some plasma membrane markers (such as WGA) to better localize cell surface after beta1 integrin tracing.
Response: we appreciate the reviewer’s comment, and have attempted the suggested experiment, but in our hands, WGA did not give a clear membrane staining but a diffuse faint signal in MCF10A cells for reasons we do not fully understand.
Fig. 1J, 1M-1L: beta1 integrin endocytic recycling should be compared across the same time-points to better evaluate kinetic differences.
Response: the intermediate time points showed in the main Figure 1J, M-L are based on the final read out. We understand that it could be interesting evaluating the kinetic differences but this will generate a substantial number of comparisons that might be difficult for visualization. We ask permission to keep the comparisons among the latest respective time points with respect to the performed analysis.
Fig. 3. Author should consider adding additional experiments with Rab4 and Rab11 dominant negative forms to validate their results.
Response: the experiments proposed have been performed, but the ectopic expression of dominant negative Rab4 and Rab11 had detrimental effects to the cells, with the formation of large endosomal blobs and rounding up of the MCF10A cells. Subsequently we do not feel confident with the possible conclusions from these data. We ask the reviewer to understand this technical detail and accept the fact that we are not able to address this point.
Fig. 4M. To validate authors' claim on the role of integrin Beta1-containing EVs on invasive behaviour, they should repeat the experiment using blocking beta1 antibodies prior to EV addition.
Response*: we thank the reviewer for this comment. As requested, we performed the experiment using the Integrin β1 blocking monoclonal antibody (mAb; clone P4C10). The new data show that P4C10A treatment alone or in combination with EVs derived from MCF10A cells transfected with siCtrl or siErbB3 significantly reduced invasiveness in comparison to IgG treatments, confirming the mechanistic role of Integrin β1 promoting MCF10A invasive behaviour. The new Figure 4M was included and further discussed in this manuscript. *
While authors claim that their results could potentially clarify different aspects of tumour dissemination, most of their experiments are done in MCF10A, a non-tumorigenic epithelial cell line. To better support their conclusion, they should reproduce key experiments in MCF7 or other tumorigenic cell line.
Response: we thank the reviewer for the comment. As explained in response to reviewer 1, the rational for using different cell-lines or primary cells is now better explained in the manuscript. We found that depletion of ErbB3 impaired recycling of Integrin β1 in the normal non-malignant cells including MCF10A and primary breast epithelial cells, but not in malignant MCF7 cells that overexpress ErbB3 (data not shown), which is now discussed in the paper. Moreover, *MCF7 cells were used to detect endogenous ErbB3 as normal expression levels of ErbB3 (primary MECs and MCF10A) were not detectable by immunofluorescence microscopy with a range of antibodies we tested. Furthermore, we also included in this new version that GGA3 and Rab GTPase-binding effector protein 1 (Rabaptin5 or RABPT5) shared colocalisation with endogenous ErbB3 in MCF7 cells as the new Supplementary Figure 9A, B. Finally, we also attempted to conduct the Arf6 IP in MCF7 cells, but as opposed to MCF10A cells, the yield of Arf6 in pull down experiments was much lower than in MCF10A cells, and interacting proteins were not detectable. *
Minor comments:
- Fig. 1D-1F: please explain better if beta1 integrin surface signal was quenched in these specific set of studies. Response: Beta1 Integrin was quenched on ice with an antibody against Alexa488 as described by Arjonen et al. (Traffic, 2012; DOI: 10.1111/j.1600-0854.2012.01327.x), and further outlined in the methods section and results section (page 6 and schematic Fig4A).
Suppl. Fig. 3A: last WB lane should read "siErB2" instead of "siErbB3".
Response: we thank the reviewer and we apologize for this oversight. We corrected the siErbB2 lane in Supplementary Figure 3A, as requested.
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Referee #3
Evidence, reproducibility and clarity
The paper by Dorival Mendes Rodrigues-Junior et al., focuses on a novel ligand-independent role of ErbB3 receptor, modulating Transferrin receptor and integrin beta1 early recycling. Authors perform several in vitro studies where they show how ErbB3 depletion diverts integrin beta1 from recycling towards lysosomal degradation and extracellular vesicle secretion, impairing cell migration. They also provide mechanistic experiments showing the role of ErbB3 on Arf6-GGA3-Rabaptin5 endosomal complex assembly.
Major comments:
- Fig. 1. Authors should co-stain with early endosomal markers (such as EEA1) to clearly show endogenous ErbB3 and Beta1 integrin endosomal co-localization. Including some insets with higher magnifications would also improve visual inspection of such interactions.
- Fig. 1H and 1I. Authors need to provide TIRF penetration depth to better evaluate the potential cytosolic contribution. Additionally, plasma membrane purification studies would help to validate their live imaging results.
- Fig. 1J. Authors should explain better how they calculated normalized fluorescence
- Fig. 1J, 1M-1L: beta1 integrin endocytic recycling should be compared across the same time-points to better evaluate kinetic differences.
- Fig. 2B. Authors should include some plasma membrane markers (such as WGA) to better localize cell surface after beta1 integrin tracing.
- Fig. 3. Author should consider adding additional experiments with Rab4 and Rab11 dominant negative forms to validate their results.
- Fig. 4M. To validate authors' claim on the role of integrin Beta1-containing EVs on invasive behaviour, they should repeat the experiment using blocking beta1 antibodies prior to EV addition.
- While authors claim that their results could potentially clarify different aspects of tumour dissemination, most of their experiments are done in MCF10A, a non-tumorigenic epithelial cell line. To better support their conclusion, they should reproduce key experiments in MCF7 or other tumorigenic cell line.
Minor comments:
- Fig. 1D-1F: please explain better if beta1 integrin surface signal was quenched in these specific set of studies
- Suppl. Fig. 3A: last WB lane should read "siErB2" instead of "siErbB3".
Significance
The paper gathers important observations showing a new role of ErbB3 in vesicular trafficking. While these results provide new mechanistic insights that potentially deepen our understanding of tumor dissemination, most of the experiments are done with a non-tumorigenic cell line, and therefore key results should be validated in a tumor cell line context before considering for publication.
The evidence gathered could be of interest for experts across different biomedical fields, specially within cellular and molecular oncology
My expertise: cell competition, cancer, mechanobiology, integrin recycling
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Referee #2
Evidence, reproducibility and clarity
Summary:
In their manuscript, Rodrigues-Junior and colleagues identify a novel ligand-independent function of the tyrosine kinase receptor (RTK) ErbB3 as a regulator of integrin β1 recycling. In particular, the authors demonstrate that ErbB3 depletion reduce β1 integrin surface expression, triggering its lysosomal degradation and increasing its secretion in extracellular vesicles (EVs). Moreover, the authors show that these EVs enhanced the invasive capacity of ErbB3 wild type breast epithelial cells. In addition, the authors evidence the interaction between ErbB3, GGA3 and Rabaptin5. Loss of any of these proteins destabilizes this interaction, which abrogates integrin β1 recycling and leads to its degradation and secretion. The work is potentially interesting; however, there are some aspects that need to be analyzed in a more robust manner.
Major comments:
- The manuscript is mainly focused on β1 integrin endocytic and post-endocytic fate following ErbB3 silencing, describing also a molecular mechanism underlying these observations. Despite the cited manuscript by Deneka, A. and colleagues indicates a similar mechanism for transferrin receptor (TfR) recycling, the Authors only studied the receptor internalization upon ErbB3 silencing. Therefore, this observation does not add any significance to the main topic of the manuscript and its removal should be considered.
- Data from Figure S1A seems to be not normally distributed. Have the Authors tested the data for normal distribution? If not, please consider it. If the data is not normally distributed, a non parametric Mann-Whitney U-Test would be more suitable.
- The Authors studied the colocalization of ErbB3, Rab4 and Rab11, observing an increased colocalization between ErbB3 and Rab4 10 minutes following primaquine. However, the Authors previously referred to Sönnichsen, B et al. manuscript, in which TfR colocalized with Rab11 at 30min. It would be interesting to see whether ErbB3 and Rab11 colocalize at later time points in the presence or absence of primaquine. This will reinforce the conclusion that ErbB3 is involved in early Rab4-dependent recycling.
- In Figure 4C the Authors observed a reduction in β1 integrin levels in ErbB3 silenced cells compared to the control already at the beginning of tracing (0 min), which might be due to accelerated turnover at the internalization step of their experimental design. To confirm this, immunofluorescence of β1 integrin in control and ErbB3 silenced cells could be performed just right after the 15min integrin internalization.
- In the discussion, the Authors indicate that "loss of ErbB3 redirects Integrin β1 towards lysosomes for degradation, mimicking loss of GGA3 that similarly redirects both Integrin β123 and c-Met towards lysosomal degradation21, or Rabaptin5 depletion that we find similarly redirects trafficking of internalised Integrin β1 towards lysosomal degradation". However, the involvement of lysosomal degradation was only studied for ErbB3 silencing by employing chloroquine. To further support this statement, the use of chloroquine in Rabaptin5- and GGA3-depleted cells is recommended.
Minor comments:
6.The Authors should consider shortening the following sentences from the Introduction: "GGA proteins contain several functional domains that...thereby regulating sorting of cargo including Integrin β3 and TfR into recycling endosomes". 7. The Authors do not show ErbB3 silencing efficiency at the protein level until Figure 3G, which should have been shown in Figure 1 or Supplementary Figure 1, as all the research is based on it. Moreover, GGA3 silencing efficiency was never tested. 8. Figure 1I and Figure 1K may include the representative images for the missing siErbB3 to properly illustrate the associated quantifications 9. Consider including a Western blot showing the effect of lapatinib in EGFR, ErbB2 and ErbB3 protein expression, including their phosphorylated forms. 10. Some supplementary figures are mislabeled, such as Supplementary Figure S5D and S5E on page 10, which should be S7D and S7E, respectively. Supplementary Figure S7C on page 15 should be S9C. 11. The following sentence on page 8 should be revised as a verb is missing: "which corresponds to the reported peak time when colocalization of Rab4 with traced TfR, preceding Rab11 and TfR colocalization that peaks later at 30 minutes". 12. The main text indicates that the amount of VSV-G transported to the cell surface after 30min it is not affected by ErbB3 silencing. However, in Figure 3E seems to slightly decrease following the silencing. The Authors may consider employing another Western blot image to match the main text and the quantification in Figure 3F. 13. In the main text, a significant difference in the nanoparticles/cell between ErbB3-depleted cells and wild type or control cells were reported. However, Figure 3I only showed the statistics of each siRNA vs the control and not the wild type condition. 14. The Authors concluded that "chloroquine treatment significantly restored traced Integrin β1 levels". However, this conclusion is not reflected in the statistical analysis reported in Figure 4H, which only showed the differences between control and ErbB3 silenced cells. Thus, the statistics reported for the chloroquine results should be added. 15. The Authors concluded that "loss of either GGA3 or Rabaptin5 mimics the effect of loss of ErbB3 on endocytic trafficking of Integrin β1, consistent with the hypothesis that GGA3 and Rabaptin5 are effectors of ErbB3 in promoting endosomal recycling and impeding EV release". To confirm this conclusion, the inclusion of siRabaptin5 results in Figures 6H and 6J is suggested. 16. To be consistent with the results presentation: - The inclusion of Modal size is recommended in Figure 6I. - Some graphs show the number of cells or biological replicates while other ones no. - Figure 4E showed different time points for both siRNAs. 17. Figure 1E represents the squared regions of Figure 1D, but it is not indicated in the figure legend. 18. In the legend of Figure 1D-G, 30min of integrin internalization is reported, where it should be 15min according to main text and methods. 19. The addition of representative images in Figure 6A is recommended, as already present in Figure 1I. 20. As two different siRNAs for ErbB3 were used and not in all experiments, the employed siRNA should be indicated in each experiment. In the cases where both ErbB3 siRNAs were employed, figures should report them either as main results or supplementary. 21. Why do the Authors use EVs enriched in the VSF or by UC to show the same result? What is the criteria to choose one or the other one? For example in Figures 6G and 6K.
Significance
Various studies highlight the involvement of the RTK ErbB3 in cancer development, as well as its potential use a biomarker for prognosis and therapy resistance. It is also known that ErbB3 is constitutively internalized and degraded, in a process controlled by PKC (Dietrich, M. et al. 2019, Exp Cell Res). However, the novelty of this manuscript resides in the idea that ErbB3, as other transmembrane receptors, may regulate the endocytosis and post-endocytic fate of different cargoes, such as integrin β1. The discovery and understanding of new molecular mechanisms might help in the identification of new potential targets for cancer treatment, as well as other diseases in which the endocytic pathways are altered.
Field of expertise: integrin-mediated cell adhesion and migration, integrin endocytosis and recylcing
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Referee #1
Evidence, reproducibility and clarity
ErbB3 is well-known for its significance in cancer, which is dependent on ligand-binding and heterodimerization with other ErbB family members. In the current work, Rodrigues-Junior et al. identified novel, unexpected functions of ErbB3 in promoting early endocytic recycling and restricting exocytic trafficking (extracellular vesicles secretion) of membrane receptors, such as integrin b1 and transferrin receptor, via stabilizing the Arf6-GGA3-Rabaptin5 endosomal sorting complex.
Via ErbB3 siRNA knockdown, they observed an impaired recycling of transferrin receptor and integrin b1 back to the cell membrane. The recycling assay condition (growth factor-deprived) provided a very clean result to support that this ErbB3-dependent endocytic trafficking is ligand-binding independent. The trafficking-dependence on ErbB3 (both the endocytic and the exocytic) was further supported by integrin b1 functional assays (scratch closure assay and Matrigel invasion assay). There are still some details that need to be clarified to fully understand the conclusion.
Major points:
- The manuscript started with a pathological correlation between high ErbB3 level and poor patient survival rate. In Fig.1, the impaired TfR recycling, and the co-localization between ErbB3 and integrin b1 were also performed in the pathological breast cancer cell line, MCF7. While investigating integrin b1 recycling, the authors suddenly switched to another two non-malignant human breast epithelial cell lines, which led to a difficult correlation of ErbB3-mediated recycling back to the disease situation. The authors should state more clearly this point, rather than data not shown. This inconsistency occurred also in other assays, for example, when addressing the trafficking from TGN to cell surface, MCF7 was utilized; while when addressing extracellular vesicle secretion, MCF10A was utilized.
- It was shown before that ErbB3 undergoes constitutive internalization and degradation within several hours that is independent of ligand-binding (ref#13). Can the authors provide experimental evidences to show the correlation of TfR or integrin b1 recycling with this dynamic ErbB3 levels rather than ErbB3 knockdown?
- The efficiency of siRNA knockdown of ErbB3 (both #1 and #2) should support the observed phenotype (Fig. 1I-J, K-L). Is there a correlation between the ErbB3 level with integrin recycling? For example, siRNA#2 led to more efficient knockdown of ErbB3 in MCF10A?
- ErbB3 loss led to more extracellular vesicles secretion, but also lysosomal degradation of integrin b1. This conclusion is supported by results shown in Fig.4D-E and Fig. S8A-B, while the analysis from the same cell line (MCF10A, Fig. S3A) results in no change of integrin b1 levels upon ErbB3 depletion. Fig. S3B showed also no change in a second non-malignant cell line (prHMEC). How do the authors explain this conflict?
Minor points:
- Is TfR also colocalizing with endogenous ErbB3?
- Fig. 3J, TSG101, T is masked by 3I
- Page 10, the description of the EV secretion in prHMEC cells is annotated to the wrong figure. Fig S5D S7D; S5E S7E
- Fig. 4M: How was the motility/invasion into Matrigel determined? Images? Only quantifications are shown.
- Fig. 4M: Exosomes collected from ErbB3-depleted cells promotes the migration in MCF10A-wild type cells, how about the effects on ErbB3-depleted cells? This group should be included for analysis.
- Quantification of the blots should be provided for Fig. 5A (GGA3), 5B (GGA3, Rabaptin5 and Arf6), 5F (GGA3) and 5G (GGA3, Rabaptin5 and Arf6). What is mock IP in each graph? The mock IP is neither mentioned in methods nor in legends.
Significance
Strength: The recycling assay condition (growth factor-deprived) provided a very clean result to support that this ErbB3-dependent endocytic trafficking is ligand-binding independent.
Limitations: Constantly change cell lines when addressing different questions
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Reply to the reviewers
We thank the reviews for their thorough assessment of our manuscript and their constructive suggestions for further improvement. We are pleased that the reviewers recognise that “this work represents an important and substantive contribution” to the field of genome organization and gene transcription.
Reviewer 1
1) Does the CTCF degron substantially remove CTCF from the Mnx1/Shh TAD border? In prior AID-CTCF degron studies a considerable fraction of cohesin dependent TAD borders are retained upon CTCF removal. Moreover, CTCF sites at these retained borders still have clear ChIP-seq peaks - even though the protein is >95% depleted and scarcely detectable by western. Thus, while I suspect that the authors are correct that the shorter distance of the 35 kb border deletion contributes substantially to the increased crosstalk between the Mnx1 and Shh-enhancers, I suspect part of the reason for a lack of a similar effect in the CTCF degron is due to the known challenges in removing CTCF from this border. To argue that the border but not the CTCF is important, I think it would be helpful to show the CTCF signal is sufficiently lost in the degron by ChIP-seq and/or show that this TAD border has been lost by Hi-C. Alternatively, the authors could tone down this claim to something more conservative, as I did not find it to be presented as a key conclusion of the paper as a whole.
We used the CTCF-AID mESC line published by Nora et al (2017). In our previous manuscript (Kane et al., 2022) we presented the published Hi-C and CTCF-ChIP-seq data from these cells at the Shh TAD (Fig 2c of Kane et al) – reproduced below for the reviewer’s benefit. This shows the loss of insulation at the Shh/Mnx1 TAD boundary when CTCF is degraded, and the loss of CTCF ChIP-seq signal at this boundary.
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2) In my opinion, the authors' description of existing data for the importance of TAD borders in enhancer promoter regulation is not described in a sufficiently balanced and complete manner, and overall impression given by the text is that CTCF marked borders have little serious evidence for a role in developmental enhancer specificity and are maybe a cancer thing. This is doubly unfortunate, as it undermines the impact of the authors work in expanding our view of what TAD borders are in a regulatory sense, as well as presents an unbalanced view of work in the field. This is of course easily corrected. In particular, I recommend the following revisions: It is " depletion of CTCF has only a small effect on transcription in cell culture (Nora et al., 2017; Hsieh et al., 2022)." It should be clarified that there is only a small *acute * effect on transcription (in the first 6-12 hours), which may tell us more about the timescale at which promoters sample, integrate and respond to changes in their enhancer environment than about the roles of CTCF particularly. Notably, this degradation is *lethal*, it results in massive changes in transcription after 4 days, and I suspect the authors agree that this lethal affect arises from CTCF's role in transcription regulation (if you remove some key cytoskeletal protein or metabolic enzyme the primary cause of cell death is not transcriptional, but almost all the evidence for CTCF's vital role in the cell is linked in one way or another to transcription).
As suggested by the reviewer we have inserted the word “acute” into that sentence.
The discussion of TAD border deletions is more one-sided than ideal. I appreciate the discussion is usually even more unbalanced when presenting the opposite view in the literature - many works only cite the examples where border deletion does lead to ectopic expression and phenotypes. The current text presented a subset of these border deletion data in such a way as to give me the impression the authors are deeply skeptical that CTCF plays a role as an insulator of E-P interactions in a developmental context (rather than just as a weird cancer thing). For example: Pennacchio's lab has analyzed a series of TAD border deletions with more examples of both lethal effects and effects with no apparent phenotype 3
I appreciate that Bickmore and colleagues found quite phenotypically normal mice upon deletion of CTCF sites from Shh, but it might be balanced to still reference the work from Uishiki et al that indicate in humans the CTCF site does play a role in Shh - ZRS communication. As the authors are doubtless aware, Andrey and colleagues show a CTCF dependent enhancement of a sensitized ZRS enhancer. Zuin et al. in an elegant experiment in which an enhancer is mobilized to different distances away from its promoter using transposon induction, reported a complete lack of detection of enhancers mobilizing outside the TAD to activate gene expression. A balanced presentation of the data on CTCF role might include some discussion of the above. In light of these earlier works, the findings the authors report about border bypass are all the more surprising.
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We thank the reviewer for highlighting some of these studies, especially for drawing our attention to the interesting recent preprint from Chakraborty et al. (doi.org/10.1101/2024.08.03.606480), which we now discuss in the revised manuscript. * As suggested by the reviewer, we now also cite Ushiki et al., 2021 in the Introduction in the context of CTCF-associated phenotypes, rather than just in the Discussion as in the original submission. We already cited the work of Andrey and colleagues (Paliou et al). However, we chose not to cite the Pennacchio study, because the deletions used were large – all >10kb and some as large as 80kb. Therefore, we consider it highly likely that other regulatory sequences beyond CTCF sites themselves may have been deleted, complicating conclusions drawn about the function of the TAD boundaries per se. We have also chosen to focus our discussion on studies of enhancers in their native genomic locus, and predominantly in vivo analyses, rather than ectopic enhancer integrations (such as Zuin et al) in cell lines.*
4) By contrast, direct evidence for cross TAD interactions at endogenous loci has not to my knowledge been shown as clearly as described in the current manuscript. Recent work from Rocha and colleagues showed evidence that some enhancers upstream of Sox2 can pass ectopically induced boundaries. While recent work has described examples of 'TAD border bypass' at endogenous loci (e.g. for Pitx1 8, Hoxa regulation 9), these reports really just expand the view of regulatory boundaries rather than provide evidence against it. They invoke a 3D stacking of boundaries that allows boundary proximal enhancers and promoters to stack with (and so bypass) an intervening TAD boundary. Notably, in this view enhancers and promoters that lie away from the border of their respective TADs are still separate, and indeed intervening genes between distal enhancers for Pitx1 and Hoxa appear to follow these rules.2 Mnx1 and the Shh enhancers by contrast do not appear to be an example of border stacking. Given that Sox2 at least is also a TAD border, and the position of the bypassing enhancers is not precisely known in the work from Rocha, it is possible that that case is also an example of boundary stacking, which appears less likely in the case of Mnx1 (which does not appear to be at CTCF marked border, at least in mESCs).
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We thank the reviewer for highlighting some of these studies. We had already discussed the study from Rocha and colleagues (Chakraborty et al., 2023) and we had discussed the boundary stacking paper from Hung et al, (2024). However, based on the reviewer’s comment we now include a specific discussion about TAD boundary stacking and boundary proximal enhancer bypass, noting that Mnx1 is not close to a TAD boundary. This will become even more relevant in our planned revised manuscript where we will investigate possible Mnx1 activation by Shh enhancers (SBE2/3) located even further away from the Shh/Mnx1 TAD boundary.
Statistics: Some of the bar graphs quantifying the %-expressing cells do not obviously have associated n-values, as are some of the violin plots of the distances. I think all these bar graphs could also benefit from adding error bars (e.g. by bootstrapping from the sampled population). This will help the reader more easily appreciate how sampling error and sample size affect the variation seen in the plots.
We will add the n-values to all graphs. Regarding error bars, we think that showing the data from the two biological replicate separately is a better way to show the data reproducibility to the reader, than using boostrapping to estimate error bars.
Figures 2 and 3: I would have preferred the authors zoom in more on the FISH spots to help the reader appreciate the proximity. I do appreciate also seeing a field of more than 1 cell (to give some sense of the variability), but these images mostly have only 1 spot pair per panel, which is exceedingly small as they contain parts of more than 1 nucleus. There is also unnecessary white space in this figure that could have been used to show zoom in panels.
The same applies to the image panels in Figure 3 as for figure 2 - there is considerable unused whitespace, the image panels capture mostly a single nucleus and its pattern of DAPI dense heterochromatin (which isn't particularly relevant to the narrative) while the fluorescent spots that are the focus of the narrative are quite small. It is nice to have an example of the cell to see that this isn't just random background (that there is just one spot per cell) - in that sense though it's equally helpful to show its not just 1 cell in the field that has the signal-to-noise (SNR) shown. For this figure and the panels in figure 2, I'd recommend showing a zoom out showing ~3 nuclei with transcription foci (at least in the regions where the % transcribing is >60% it should be fine to have adjacent nuclei transcribing, for those where it is 10%, 1 of 3 nuclei transcribing in the image selected would also help get the sense of the data). These zoom out images would also give a sense of the SNR in the image, and then a zoom in where the FISH spots are sizable would make it easier to see the neighboring transcripts. Extended Data Fig 3 does a better job showing the context of the limb and then zooming in to an image where the RNA spots are appreciable. It looks like the resolution of the zoom in is lower, such that zooming in further on the spots in this data may not enhance the image.
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In response to the reviewer’s comment, we will present zoomed-out and zoomed-in images as suggested.
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Figure 3 - DNA FISH It would be helpful to include a diagram indicated where the DNA FISH probes are located on the genome and their size in kb as an inset in the figure.
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We will indicate the locations of DNA-FISH probes in a revised version of Figure 1a. Probe sizes are listed in the supplementary tables. We have now made this clearer in the legend to Figure 3.
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Reviewer 2
The authors claim that co-expression of Mnx1 and Shh in the foregut and lung buds is also driven by boundary crossing contacts with the MACS1 enhancer. However, the effect of the boundary deletion on the co-transcription of Shh and Mnx1 is only showed for the ZPA. In this sense I find potentially misleading the statement of the authors in the following paragraph: "In the ZPA, the foregut, and the lung buds, the majority of Mnx1 RNA-FISH signals are at alleles that show simultaneous signal for Shh nascent transcript from the same allele (closely apposed signals) (Fig. 2a, b and Extended Data Fig. 2a). In del 35 embryos, an even higher proportion of Mnx1 transcribing alleles also transcribe Shh (Fig. 2b,Extended Data Fig. 2a, Extended Data Table 3.). These data suggest that both the ZRS and MACS1 enhancers are able to simultaneously activate transcription at two gene loci on the same chromosome". In my opinion this phrasing implicitly extends the increase in Mnx1-Shh co-expressing nuclei observed in the ZPA of 35 del embryos to the expression of these two genes in the foregut and lung buds (driven by the MACS1 enhancer) while this effect has not been specifically addressed. In a previous work, the authors showed that boundary deletion does not impact Mnx1 expression in the foregut and lungs. It would be important to clarify whether more precise analysis in this study have led to different conclusions or, alternatively, appropriately discuss the results. Ideally the authors should analyse the effect of the 35 del allele in the foregut / lung buds or rephrase the statement about the sharing of the MACS1 enhancer. * *
The reviewer is correct that in our previous publication (Williamson et al., 2019) we did not detect Mnx1 expression in the lungs of 35kb del embryos. However, we only examined this by in situ hybridisation so we probably lacked the sensitivity to detect weak Mnx1 expression. In response to the reviewer’s comments, we now propose to do RNA FISH for transcription at Mnx1 in other tissues of 35kb del embryos.
The authors use the quantifications of nuclei co-expressing Mnx1 and Shh from the same allele as an indicator of simultaneous transcription of the two genes by the sharing of the enhancer as opposed to a model of alternate transcriptional bursts. However, I am concerned that the time scale at which looping and transcriptional bursts occur is at odds with the detection of nascent transcription in FISH experiments, thus not excluding that shifting of the enhancer from one promoter to the other could still result in detection of nascent RNA of the two genes in the same allele. In any case, following the argumentation of the authors, the fraction of nuclei expressing Mnx1 alone does not appear to be significantly different from those expressing Mnx1 and Shh, and the increase of Mnx1 expressing nuclei upon boundary deletion seem proportionally similar to the increase of Mnx1+/Shh+ nuclei. In my opinion, this makes it difficult to interpret the detection of Mnx1 alone or both Mnx1-Shh expression as a reflection of alternate looping and transcriptional burst from enhancer sharing. Determining whether the two promoters compete for the interaction with the enhancer or share it would require estimate whether in the 35 del homozygote embryos Shh expression is reduced compared to wts, as a result of the increased interaction of the ZRS with the enhancer. The authors claim that there are no differences in the % of cells expressing Shh upon boundary deletion but in my opinion measurement is not sufficient to estimate a change in transcriptional rate (frequency of bursting). Nascent mRNA level detection in single cells would allow to better asses competition or concomitant activation of the two gene. Not being an expert in the RAN FISH technique it is not clear to me whether fluorescence intensity could be used as an estimator of transcription. From the images of the authors, in some cases it seems that expression of Shh alone is higher than when both Shh and Mnx1 are transcribed from the same allele (Fig. 2a, left panel, Fig 2c left vs right panel. However, in other cases an opposite trend can be observed (Mnx1 intensity in Fig2a central vs right panel). Thus, a single nuclei PCR or RNAseq approach may be more suited for this assessment.
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We respectfully disagree with the reviewer. We argue that nascent RNA FISH, using probe pools that for the most part detect the introns of Shh and Mnx1, is a better measure of transcription bursting/frequency (on or off) than probe signal intensity and therefore is a measurement of transcription rate. Single nuclei PCR or RNAseq would not assay nascent transcription and would not distinguish between alleles.
Minor comments: 3. In the mESC model overexpressing the tZRS-VP64 construct, Shh and Mnx1 seem to be transcribed at similar rates compared to what observed in vivo (where only a minor fraction of Shh+ cells express Mnx1). Thus, despite the fact that TAD boundary deletion increases Mnx1, but not Shh, expression, the ZRS activity seems to more easily overcome the border in this context than in vivo. Could the authors comment on this interesting observation? May it relate to the insulation score of TAD boundaries in the mESCs compared to in vivo? Alternatively, could it reflect that combinatorial TF binding to an enhancer contribute to its directionality?
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*These are interesting speculations by the reviewer, but we would argue that it is hard to compare in vivo and in vitro experiments. For example, in the limb bud, the ZPA region where the ZRS is active cannot be distinguished morphologically from the surrounding mesenchymal cells, therefore it is likely that some nuclei that are just outside the ZPA may be included in the analysis. *
Overall, figure organization and clarity could be improved. For example, enlargement of RNA fish images in Fig. 1 could be enlarged (to the same size than the broad view image) and RNA FISH signal could be highlighted with arrowheads. Panel distribution could also be optimized.
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We will try to clarify these figures – see also response to reviewer 1 (point 6).
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Reviewer 3
There are a couple of claims and conclusions that are not fully supported by the data, and which I think could be resolved by rephrasing them and/or qualify them as preliminary or speculative. The authors often indicate co-expression as suggestive of co-regulation by a single enhancer, when in most cases this is not formally shown; such suggestion remains one among other possibilities. For instance, co-expression of Shh and Mnx1 in the developing bud is attributed to the ZRS enhancer, co-expression of Shh and Mnx1 in the foregut is attributed to MACS1 enhancer. Do the authors have any evidence that when deleting these enhancers, Mnx1 expression is abolished (or reduced) in the respective tissues?
If not, I think the following sentences need revision, because causality is implied by the way it is written but it is not formally shown (and the data could suggest other options too):
"However, we have previously identified that ZRS can also drive low level expression of Mnx1, located 150kb away in the adjacent TAD, in the developing limb bud (Williamson et al., 2019)." No genetic evidence is provided in Williamson et al. 2019
i) It is true that in Williamson et al., we did not provide genetic evidence that ZRS is the enhancer responsible for Mnx1 expression in the limb bud ZPA. However, there is no other known enhancer in biology with activity specific to the ZPA, and when the ZRS is deleted the ZPA no longer functions as a signaling centre for the limb bud. As a compromise, we have rephrased the indicated text to “However, we have previously identified that ZRS also appears to be able to drive low level expression of Mnx1, located 150kb away in the adjacent TAD, in the ZPA of the developing limb bud”.
"However, we also detect nascent transcription from Mnx1 in the Shh expressing portions of the developing ventral foregut and the lung bud of E10.5 embryos, an activity that is driven by the Shh MACS1 enhancer, located a further 100kb into the Shh TAD from ZRS (Sagai et al., 2017) and therefore able to induce transcription at Mnx1 across a TAD boundary from a distance of >260 kb (Fig. 1a)."
ii) We have modified the text to now read “However, we also detect nascent transcription from Mnx1 in the Shh expressing portions of the developing ventral foregut and the lung bud of E10.5 embryos, an activity that is likely to bedriven by the Shh MACS1 enhancer, located a further 100kb into the Shh TAD from ZRS”.
"These data suggest that both the ZRS and MACS1 enhancers are able to simultaneously activate transcription at two gene loci on the same chromosome."
iii) We have modified this statement to now read that these enhancers “may be able to simultaneously activate transcription at two gene loci on the same chromosome”.
"This is the first report of two endogenous mammalian genes transcribed simultaneously under the control of the same enhancer" (can the authors really claim this without genetic evidence, i.e., deleting the enhancer? Isn't that the golden standard in the field?).
iv) We stand by this claim, because we have been able to provide evidence in support of our observations in tissues, by using synthetic enhancer activation in cell culture where we can be absolutely be sure what the enhancer responsible for activation is.
"Therefore, the Shh ZRS enhancer can simultaneously activate transcription at two genes and across an intact, but porous, TAD boundary. See response (iv) above
"This is a consequence of ZRS-driven activation, not Mnx1 transcription per se."
v) We stand by this claim.
The mathematical model, even if simple, is very poorly described. In the results section, it is not easy to understand what the model takes into account, etc; it would be important for non-experts to understand as well what is at stake. In the methods section, it does not seem to be properly described; it is only stated "The association between the transcription of Shh and Mnx1 regulated by the same enhancer was done by linear modelling with binomial link function." Would this be enough to recreate / reproduce the same model? I am not a mathematician, but I suspect more details would be needed. * *
*We apologize if our approach was not clear. We used logistic regression not a mathematical model. We have now expanded the relevant Methods section to now read: *
“
“To test whether or not there is a tendency of coexpression between two loci on the same chromatid, only nuclei with exactly one signal of each locus are informative. For these nuclei, we scored how many had expression in cis and how many in trans. To assess whether there was chromatid-specific coexpression, we tested statistically whether there was an excess of nuclei showing expression in cis. We did this using logistic regression, a form of generalized linear regression model. More specifically, we tested, for each model, whether the model intercept was significantly different from zero by using the z-scaled test statistic returned by these models and converting it to a p-value.”
The authors claim that an enhancer working exclusively on one gene at a time would lead to a preference in individual expression - is this really the case? Could the authors show the expected scenarios for [one enhancer - two common targets] versus [two enhancers - two independent targets] and how this compares to the data?
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Our statistical analysis is restricted to the scenario of one enhancer acting on two genes (either simultaneously, or alternately). We do not test a two enhancers two target genes scenario because it is not relevant to our experimental analyses using synthetic activation of a single enhancer (with tZRS-Vp64, Extended Data Table 4).
- The results obtained with the VP64 activation (activation of ZRS leads to increased expression of Mnx1) are used by the authors as another piece of evidence that ZRS controls Mnx1 - but could VP64 activation be inducing chromatin opening / enhanced accessibility and therefore increased expression across the TAD boundary? I am not sure the authors need to test this, but they should at least acknowledge other possibilities (in relation to point 1).
*We have previously shown (Benabdallah et al., 2019) that tal-VP64 activators alter chromatin structure (H3K27ac) in the Shh TAD only locally at the site of binding and at the Shh gene, and that this does not spread more generally. We have clarified this in the revised text. We also note that the effect of both the 35kb deletion and cohesin degradation on Mnx1 activation from the tZRSVp64 activator would not be consistent with a model of general chromatin opening/accessibility. The same argument applies to the DNA-FISH experiment (Fig 3) showing Mnx1 activation in the limb bud (ZPA) occurs specifically in the context of a compact chromatin conformation. *
"In the nuclei of pre-motor neurons, where Mnx1 expression is driven from its own proximal enhancers (Fig. 1a), Mnx-ZRS and Mnx1-Shh distances are not different between Mnx1 expressing and non-expressing alleles." The authors use this as an argument to claim that Mnx1 expression per se does not explain the distance differences observed in the limb bud - but can such comparisons of expression and distances between loci be made between different cell types? Is there enough evidence for this to be a valid assumption? If not, then the assumption should be explicitly presented.
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We believe that the reviewer is confused here. We are not suggesting that Mnx1 expression per se doesn’t explain the distance differences in the limb bud, rather that these distance differences in the limb bud associated with Mnx1 transcription do not occur in the pre-motor neurons where activation is not dependent on distal enhancers, particularly in the Shh TAD.
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In Fig. 3b the authors show that shorter distances between the loci (Mnx1, Shh, ZRS) were associated with simultaneous transcription at Mnx1 and Shh, implying throughout that this would be associated with common activation by ZRS; but the shorter distances between the three loci are also associated with Mnx1 transcription alone. How is this explained?
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*This is explained by the configuration of the Shh TAD and the general spatial proximity of Shh-ZRS in both expressing and non-expressing tissues due to the CTCF-mediated loop and that is apparent in Hi-C heat maps. *
- The text could be revised to look out for "expression levels" versus "expression frequency" - in several instances the authors mention expression "levels" when they are referring to % of cells expressing a given gene, which would thus be more appropriate called "expression frequency"?
The reviewer makes an important point. In the revised manuscript we have removed all mention of “expression levels” and have replaced these with “ frequency”.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Williamson et al. set out to investigate in further detail the previously described co-expression of Shh and Mnx1 in the same cells of the developing mouse embryo (Williamson et al. 2019). The authors suspect that this co-expression is caused by the activity of a single enhancer (ZRS), which is intriguing because Shh and Mnx1 lie in adjacent TADs, i.e., there is a TAD boundary in between them, which in principle would prevent ZRS (within the same TAD as Shh) from activating Mnx1 expression. Using RNA-FISH in mouse embryo sections, the authors first confirmed the co-expression of Shh and Mnx1 in the developing bud, which was not observed in other tissues where Shh and Mnx1 are normally expressed (e.g., neural tube) - further suggesting the involvement of the limb-specific ZRS enhancer. Using mouse embryos harbouring a 35kb-deletion encompassing the TAD boundary between Shh and Mnx1, the authors observed by RNA-FISH that Mnx1 was more frequently expressed in the mutant limb buds, in accordance to their previous results via RT-qPCR (Williamson et al. 2019). Based on RNA FISH and a mathematical model, the authors conclude that Shh and Mnx1 are frequently expressed from the same allele, also supporting the idea of common cis-regulation. Using a TALE-based system in mESC, the authors induce specific recruitment of VP64 to ZRS and observe increased expression for both Shh and Mnx1. This effect was enhanced for Mnx1 (but not for Shh) when the TAD boundary was missing (in the context of the 35kb-deletion). Using DNA-FISH following RNA-FISH on the same samples, the authors were then able to correlate transcriptional states with distances between the loci. This analysis led them to show that shorter distances between the loci (Mnx1, Shh, ZRS) were associated with simultaneous transcription at Mnx1 and Shh (but also with Mnx1 transcription alone). Finally, the authors combine their synthetic recruitment of VP64 to ZRS with protein-degron systems for CTCF and cohesin. These investigations showed that CTCF degradation did not impact the VP64-induced expression of Shh nor of Mnx1. In contrast, acute depletion of cohesin led to impaired VP64-induced expression of both Shh nor of Mnx1. Using DNA FISH, the authors showed that this was correlated with increased Mnx1-ZRS and Shh-ZRS distances. Overall, the authors conclude that their data support a model by which ZRS regulates Mnx1 across a TAD boundary and in a cohesin-dependent manner.
Major comments:
- There are a couple of claims and conclusions that are not fully supported by the data, and which I think could be resolved by rephrasing them and/or qualify them as preliminary or speculative. The authors often indicate co-expression as suggestive of co-regulation by a single enhancer, when in most cases this is not formally shown; such suggestion remains one among other possibilities. For instance, co-expression of Shh and Mnx1 in the developing bud is attributed to the ZRS enhancer, co-expression of Shh and Mnx1 in the foregut is attributed to MACS1 enhancer. Do the authors have any evidence that when deleting these enhancers, Mnx1 expression is abolished (or reduced) in the respective tissues?
If not, I think the following sentences need revision, because causality is implied by the way it is written but it is not formally shown (and the data could suggest other options too):
"However, we have previously identified that ZRS can also drive low level expression of Mnx1, located 150kb away in the adjacent TAD, in the developing limb bud (Williamson et al., 2019)." No genetic evidence is provided in Williamson et al. 2019
"However, we also detect nascent transcription from Mnx1 in the Shh expressing portions of the developing ventral foregut and the lung bud of E10.5 embryos, an activity that is driven by the Shh MACS1 enhancer, located a further 100kb into the Shh TAD from ZRS (Sagai et al., 2017) and therefore able to induce transcription at Mnx1 across a TAD boundary from a distance of >260 kb (Fig. 1a)."
"These data suggest that both the ZRS and MACS1 enhancers are able to simultaneously activate transcription at two gene loci on the same chromosome."
"This is the first report of two endogenous mammalian genes transcribed simultaneously under the control of the same enhancer" (can the authors really claim this without genetic evidence, i.e., deleting the enhancer? Isn't that the golden standard in the field?)
"Therefore, the Shh ZRS enhancer can simultaneously activate transcription at two genes and across an intact, but porous, TAD boundary.
"This is a consequence of ZRS-driven activation, not Mnx1 transcription per se." 2. The mathematical model, even if simple, is very poorly described. In the results section, it is not easy to understand what the model takes into account, etc; it would be important for non-experts to understand as well what is at stake. In the methods section, it does not seem to be properly described; it is only stated "The association between the transcription of Shh and Mnx1 regulated by the same enhancer was done by linear modelling with binomial link function." Would this be enough to recreate / reproduce the same model? I am not a mathematician, but I suspect more details would be needed. 3. The authors claim that an enhancer working exclusively on one gene at a time would lead to a preference in individual expression - is this really the case? Could the authors show the expected scenarios for [one enhancer - two common targets] versus [two enhancers - two independent targets] and how this compares to the data? 4. The results obtained with the VP64 activation (activation of ZRS leads to increased expression of Mnx1) are used by the authors as another piece of evidence that ZRS controls Mnx1 - but could VP64 activation be inducing chromatin opening / enhanced accessibility and therefore increased expression across the TAD boundary? I am not sure the authors need to test this, but they should at least acknowledge other possibilities (in relation to point 1) 5. "In the nuclei of pre-motor neurons, where Mnx1 expression is driven from its own proximal enhancers (Fig. 1a), Mnx-ZRS and Mnx1-Shh distances are not different between Mnx1 expressing and non-expressing alleles." The authors use this as an argument to claim that Mnx1 expression per se does not explain the distance differences observed in the limb bud - but can such comparisons of expression and distances between loci be made between different cell types? Is there enough evidence for this to be a valid assumption? If not, then the assumption should be explicitly presented. 5.1/ In Fig. 3b the authors show that shorter distances between the loci (Mnx1, Shh, ZRS) were associated with simultaneous transcription at Mnx1 and Shh, implying throughout that this would be associated with common activation by ZRS; but the shorter distances between the three loci are also associated with Mnx1 transcription alone. How is this explained? 6. The text could be revised to look out for "expression levels" versus "expression frequency" - in several instances the authors mention expression "levels" when they are referring to % of cells expressing a given gene, which would thus be more appropriate called "expression frequency"?
Otherwise, I find the experimental and analytical work very solid.
Minor comments:
- The authors claim that their study "is the first report of two endogenous mammalian genes transcribed simultaneously under the control of the same enhancer" - despite the fact that this claim is not formally demonstrated given the absence of genetic data (as mentioned above), it is still an intriguing idea that to date no such case has been formally demonstrated in mammals. I think the authors could emphasise this more.
- Cis-regulation across TAD borders has been suggested/demonstrated before, for both enhancers (two examples cited by the authors) and other types of elements (e.g., silencers). The authors might want to consider mentioning/discussing other previous studies that have either also suggested that TAD boundaries are not "absolute barriers" and/or found cis-regulatory elements working across TAD boundaries, such as those from the Andrey/Mundlos lab (Kragesteen et al. 2020), Heard lab (Galupa et al. 2020 and 2022), Ren lab (Diao et al. 2017), Rinn lab (Groff et al. 2018), Spitz lab (Tsujimura et al. 2015); the last one is cited by the authors but not in the context of inter-TAD communication. Some of these have been recently reviewed in Szalay et al. 2024 (PMID: 39592879).
- The authors state that "Conventional enhancer-promoter looping models predict that transcriptional bursts from two genes under the control of the same enhancer should not be coincident." Could the authors provide references for this statement, or explain this further?
Significance
This study set out to understand whether co-expression of Shh and Mnx1 in the same cells of the developing mouse embryo was due to regulation by a common enhancer (ZRS), located across a TAD boundary from Mnx1, which would strengthen a growing body of studies showing that TAD boundaries are not as impermeable as once thought and postulated. Despite lacking formal genetic evidence to fully support their hypothesis, this study will nevertheless be an important addition to the field of transcriptional regulation and 3D chromosome structure and to its specialized audience. My field of expertise: transcriptional regulation and 3D chromosome structure.
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Referee #2
Evidence, reproducibility and clarity
In this work, Williamson L et al address two intriguing questions in the field of gene regulation and chromatin organization: whether enhancer activity can overcome TAD boundaries and whether regulatory elements- shared between two genes interact with them concomitantly or competitively. For that, they use as a paradigm the well characterized regulatory landscape of the mouse Shh locus. This gene is located in a large TAD containing enhancers for different embryonic structures, including the ZPA of the developing limbs, foregut, lung buds and floor plate of the embryonic neural tube. Mnx1, a gene located in the adjacent TAD near the boundary of the Shh TAD, is mainly expressed in differentiating motor neurons of the spinal cord. However, the authors observed that Mnx1 transcription overlpas that of Shh in the ZPS, lung and foregut.
Combining high resolution RNA and DNA FISH experiments they demonstrate that: (a) Shh enhancers located near (up to few hundreds kb) from the TAD border can overcome TAD insulation and drive Mnx1 transcription, while enhancers located near the Mnx1 and Shh loci or more internally in the Shh TAD specifically activate only their respective loci, (b) Deletion of the TAD boundary increases the fraction of cells coexpressing Shh and Mnx1 in the ZPA, (c) Coactivation of Shh and Mnx1 occurs in cis (i.e. in the same allele) suggesting concomitant activity of the shared ZRS enhancer on the two promoters (d) the co-activation correlates with the tightening of the distances between Shh, Mnx1 and the ZRS enhancer and is dependent on the ZRS activity as shown by the overexpression of tZRS-VP64 in mouse ES cells, (e) Cohesin, but not CTCF activity is required for the looping of the ZRS element with Shh and Mnx1. The experiments are well designed and the findings provide important insights improving our understanding that TAD boundaries constitute somewhat permeable rather than absolute barriers, reinforcing previous evidences in the fields, although the functional significance of this permeability is not addressed in this work. There are two main points which, in my opinion, require to be addressed by the authors to improve the overall quality and clarity of the work:
Major comments:
- The authors claim that co-expression of Mnx1 and Shh in the foregut and lung buds is also driven by boundary crossing contacts with the MACS1 enhancer. However, the effect of the boundary deletion on the co-transcription of Shh and Mnx1 is only showed for the ZPA. In this sense I find potentially misleading the statement of the authors in the following paragraph: "In the ZPA, the foregut, and the lung buds, the majority of Mnx1 RNA-FISH signals are at alleles that show simultaneous signal for Shh nascent transcript from the same allele (closely apposed signals) (Fig. 2a, b and Extended Data Fig. 2a). In del 35 embryos, an even higher proportion of Mnx1 transcribing alleles also transcribe Shh (Fig. 2b,Extended Data Fig. 2a, Extended Data Table 3.). These data suggest that both the ZRS and MACS1 enhancers are able to simultaneously activate transcription at two gene loci on the same chromosome". In my opinion this phrasing implicitly extends the increase in Mnx1-Shh co-expressing nuclei observed in the ZPA of 35 del embryos to the expression of these two genes in the foregut and lung buds (driven by the MACS1 enhancer) while this effect has not been specifically addressed. In a previous work, the authors showed that boundary deletion does not impact Mnx1 expression in the foregut and lungs. It would be important to clarify whether more precise analysis in this study have led to different conclusions or, alternatively, appropriately discuss the results. Ideally the authors should analyse the effect of the 35 del allele in the foregut / lung buds or rephrase the statement about the sharing of the MACS1 enhancer.
- The authors use the quantifications of nuclei co-expressing Mnx1 and Shh from the same allele as an indicator of simultaneous transcription of the two genes by the sharing of the enhancer as opposed to a model of alternate transcriptional bursts. However, I am concerned that the time scale at which looping and transcriptional bursts occur is at odds with the detection of nascent transcription in FISH experiments, thus not excluding that shifting of the enhancer from one promoter to the other could still result in detection of nascent RNA of the two genes in the same allele. In any case, following the argumentation of the authors, the fraction of nuclei expressing Mnx1 alone does not appear to be significantly different from those expressing Mnx1 and Shh, and the increase of Mnx1 expressing nuclei upon boundary deletion seem proportionally similar to the increase of Mnx1+/Shh+ nuclei. In my opinion, this makes it difficult to interpret the detection of Mnx1 alone or both Mnx1-Shh expression as a reflection of alternate looping and transcriptional burst from enhancer sharing. Determining whether the two promoters compete for the interaction with the enhancer or share it would require estimate whether in the 35 del homozygote embryos Shh expression is reduced compared to wts, as a result of the increased interaction of the ZRS with the enhancer. The authors claim that there are no differences in the % of cells expressing Shh upon boundary deletion but in my opinion measurement is not sufficient to estimate a change in transcriptional rate (frequency of bursting). Nascent mRNA level detection in single cells would allow to better asses competition or concomitant activation of the two gene. Not being an expert in the RAN FISH technique it is not clear to me whether fluorescence intensity could be used as an estimator of transcription. From the images of the authors, in some cases it seems that expression of Shh alone is higher than when both Shh and Mnx1 are transcribed from the same allele (Fig. 2a, left panel, Fig 2c left vs right panel ). However, in other cases an opposite trend can be observed (Mnx1 intensity in Fig2a central vs right panel). Thus, a single nuclei PCR or RNAseq approach may be more suited for this assessment.
Minor comments:
- In the mESC model overexpressing the tZRS-VP64 construct, Shh and Mnx1 seem to be transcribed at similar rates compared to what observed in vivo (where only a minor fraction of Shh+ cells express Mnx1). Thus, despite the fact that TAD boundary deletion increases Mnx1, but not Shh, expression, the ZRS activity seems to more easily overcome the border in this context than in vivo. Could the authors comment on this interesting observation? May it relate to the insulation score of TAD boundaries in the mESCs compared to in vivo? Alternatively, could it reflect that combinatorial TF binding to an enhancer contribute to its directionality?
- Overall, figure organization and clarity could be improved. For example, enlargement of RNA fish images in Fig. 1 could be enlarged (to the same size than the broad view image) and RNA FISH signal could be highlighted with arrowheads. Panel distribution could also be optimized.
Significance
Significance. The work presented by Williamson L et al provide interesting insights on how TAD borders contribute to the insulation of topologically domains and restricting enhancer interactions, showing that some enhancers are able to overcome TAD insulation and showing that enhancer looping and TAD border crossing rely on enhancer activity and cohesin loop extrusion. As mentioned above, these findings reinforce and extend previous reports (Chakraborty et al 2023, Kessler S et al 2023, Balasubramanian et al 2024, Tzu-Chiao H. et al 2024,). This work does not specifically address whether the fact that their tested enhancer (really focusing on the ZRS enhancer) can overcome the Shh TAD boundary is dependent on their intrinsic properties (e.g. TFBS composition) or whether it relates with their distance to the border. This would require more complex genetic rearrangements (for example bringing floor plate enhancers in proximity of the border, and in combination with the TAD boundary deletion) and would significantly increase the scientific relevance of the work, yet at the expense of significant amount of work that could not be addressed in a reviewing process. In summary, the research of Williamsons l et al constitute an overall well performed piece of work that integrates well within other pieces of evidence of the field of gene regulation and chromatin organization. Thus, without constituting a major conceptual breakthrough in the field, it constitutes a valuable contribution to our understanding of basic principles of genome organization and gene transcription.
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Referee #1
Evidence, reproducibility and clarity
Summary and significance in the context of the field:
In this work, the authors conduct a detailed investigation of the 'ectopic'/'bystander' activation of the gene Mnx1 by enhancers of Shh, located in the neighboring TAD. TAD borders have been shown in a number of works to contribute to the remarkable specificity of enhancer-promoter choice, and the current dogma in the field is to view them as perfect boundaries to enhancer-promoter interaction. Notably, this current dogma also highlights a conundrum in our understanding of gene regulation, as available 3D genome data from both sequencing and microscopy show that TAD borders are regions of abrupt decrease in 3D proximity, but far from perfect borders, with numerous cross-TAD interactions detected by Hi-C and its variants and by single-cell microscopy (albeit fewer than the local intra-TAD interactions).
The authors show convincing data that Mnx1 indeed responds transcriptionally to several Shh-enhancers located over 100 kb distal and on the wrong side of the TAD boundary. The data come from developing mouse embryos, span several tissues, and include key controls for specificity of the method. This provides convincing data with which to challenge the currently widely accepted view of as TADs a significant boundary, complimenting the few examples that indicate that such regulation is possible in special cases (see further discussion in 2b below). I believe this work represents an important and substantive contribution to the field and should ultimately be published, after a few notable issues have been addressed.
Major comments:
Does the CTCF degron substantially remove CTCF from the Mnx1/Shh TAD border?<br /> In prior AID-CTCF degron studies 1,2, a considerable fraction of cohesin dependent TAD borders are retained upon CTCF removal. Moreover, CTCF sites at these retained borders still have clear ChIP-seq peaks - even though the protein is >95% depleted and scarcely detectable by western. Thus, while I suspect that the authors are correct that the shorter distance of the 35 kb border deletion contributes substantially to the increased crosstalk between the Mnx1 and Shh-enhancers, I suspect part of the reason for a lack of a similar effect in the CTCF degron is due to the known challenges in removing CTCF from this border. To argue that the border but not the CTCF is important, I think it would be helpful to show the CTCF signal is sufficiently lost in the degron by ChIP-seq and/or show that this TAD border has been lost by Hi-C. Alternatively, the authors could tone down this claim to something more conservative, as I did not find it to be presented as a key conclusion of the paper as a whole.
Minor comments:
I believe the manuscript could be strengthened by some textual revisions of the introduction: 2a) In particular, in my opinion, the authors' description of existing data for the importance of TAD borders in enhancer promoter regulation is not described in a sufficiently balanced and complete manner, and overall impression given by the text is that CTCF marked borders have little serious evidence for a role in developmental enhancer specificity and are maybe a cancer thing. This is doubly unfortunate, as it undermines the impact of the authors work in expanding our view of what TAD borders are in a regulatory sense, as well as presents an unbalanced view of work in the field. This is of course easily corrected. In particular I recommend the following revisions:
It is " depletion of CTCF has only a small effect on transcription in cell culture (Nora et al., 2017; Hsieh et al., 2022)." It should be clarified that there is only a small acute * effect on transcription (in the first 6-12 hours), which may tell us more about the timescale at which promoters sample, integrate and respond to changes in their enhancer environment than about the roles of CTCF particularly. Notably, this degradation is lethal*, it results in massive changes in transcription after 4 days, and I suspect the authors agree that this lethal affect arises from CTCF's role in transcription regulation (if you remove some key cytoskeletal protein or metabolic enzyme the primary cause of cell death is not transcriptional, but almost all the evidence for CTCF's vital role in the cell is linked in one way or another to transcription). The discussion of TAD border deletions is more one-sided than ideal. I appreciate the discussion is usually even more unbalanced when presenting the opposite view in the literature - many works only cite the examples where border deletion does lead to ectopic expression and phenotypes. The current text presented a subset of these border deletion data in such a way as to give me the impression the authors are deeply skeptical that CTCF plays a role as an insulator of E-P interactions in a developmental context (rather than just as a weird cancer thing). For example:
Pennacchio's lab has analyzed a series of TAD border deletions with more examples of both lethal effects and effects with no apparent phenotype 3
Deletion of TAD borders upstream of the FGF3/4/15 locus in mouse is embryonic lethal (particularly the border Kim et al label TB1 and didn't delete in their cancer model). https://www.biorxiv.org/content/10.1101/2024.08.03.606480v1
I appreciate that Bickmore and colleagues found quite phenotypically normal mice upon deletion of CTCF sites from Shh, but it might be balanced to still reference the work from Uishiki et al that indicate in humans the CTCF site does play a role in Shh - ZRS communication: 4
As the authors are doubtless aware, Andrey and colleagues show a CTCF dependent enhancement of a sensitized ZRS enhancer. 5
Zuin et al. in an elegant experiment in which an enhancer is mobilized to different distances away from its promoter using transposon induction, reported a complete lack of detection of enhancers mobilizing outside the TAD to activate gene expression 6.
A balanced presentation of the data on CTCF role might include some discussion of the above. In light of these earlier works, the findings the authors report about border bypass are all the more surprising.
2b) By contrast, direct evidence for cross TAD interactions at endogenous loci has not to my knowledge been shown as clearly as described in the current manuscript.
Recent work from Rocha and colleagues 7 showed evidence that some enhancers upstream of Sox2 can pass ectopically induced boundaries. While recent work has described examples of 'TAD border bypass' at endogenous loci (e.g. for Pitx1 8, Hoxa regulation 9), these reports really just expand the view of regulatory boundaries rather than provide evidence against it. They invoke a 3D stacking of boundaries that allows boundary proximal enhancers and promoters to stack with (and so bypass) an intervening TAD boundary. Notably, in this view enhancers and promoters that lie away from the border of their respective TADs are still separate, and indeed intervening genes between distal enhancers for Pitx1 and Hoxa appear to follow these rules.2 Mnx1 and the Shh enhancers by contrast do not appear to be an example of border stacking. Given that Sox2 at least is also a TAD border, and the position of the bypassing enhancers is not precisely known in the work from Rocha, it is possible that that case is also an example of boundary stacking, which appears less likely in the case of Mnx1 (which does not appear to be at CTCF marked border, at least in mESCs).
Statistics
Some of the bar graphs quantifying the %-expressing cells do not obviously have associated n-values, as are some of the violin plots of the distances. I think all these bar graphs could also benefit from adding errorbars (e.g. by bootstrapping from the sampled population). This will help the reader more easily appreciate how sampling error and sample size affect the variation seen in the plots.
Recommendations for improving the figures
Figure 2
I would have preferred the authors zoom in more on the FISH spots to help the reader appreciate the proximity. I do appreciate also seeing a field of more than 1 cell (to give some sense of the variability), but these images mostly have only 1 spot pair per panel, which is exceedingly small as they contain parts of more than 1 nucleus. There is also unnecessary white space in this figure that could have been used to show zoom in panels.
Figure 3 -image panels
The same applies to the image panels in this figure as for figure 2 - there is considerable unused whitespace, the image panels capture mostly a single nucleus and its pattern of DAPI dense heterochromatin (which isn't particularly relevant to the narrative) while the fluorescent spots that are the focus of the narrative are quite small. It is nice to have an example of the cell to see that this isn't just random background (that there is just one spot per cell) - in that sense though it's equally helpful to show its not just 1 cell in the field that has the signal-to-noise (SNR) shown.<br /> For this figure and the panels in figure 2, I'd recommend showing a zoom out showing ~3 nuclei with transcription foci (at least in the regions where the % transcribing is >60% it should be fine to have adjacent nuclei transcribing, for those where it is 10%, 1 of 3 nuclei transcribing in the image selected would also help get the sense of the data). These zoom out images would also give a sense of the SNR in the image, and then a zoom in where the FISH spots are sizable would make it easier to see the neighboring transcripts. Extended Data Fig 3 does a better job showing the context of the limb and then zooming in to an image where the RNA spots are appreciable. It looks like the resolution of the zoom in is lower, such that zooming in further on the spots in this data may not enhance the image.
Figure 3 - DNA FISH
It would be helpful to include a diagram indicated where the DNA FISH probes are located on the genome and their size in kb as an inset in the figure.
References cited above
- Nora, E. P., Goloborodko, A., Valton, A.-L., Gibcus, J. H., Uebersohn, A., Abdennur, N., Dekker, J., Mirny, L. A. & Bruneau, B. G. Targeted Degradation of CTCF Decouples Local Insulation of Chromosome Domains from Genomic Compartmentalization. Cell 169, 930-944.e22 (2017).
- Kubo, N., Ishii, H., Gorkin, D., Meitinger, F., Xiong, X., Fang, R., Liu, T., Ye, Z., Li, B., Dixon, J., Desai, A., Zhao, H. & Ren, B. Preservation of Chromatin Organization after Acute Loss of CTCF in Mouse Embryonic Stem Cells. bioRxiv 118737 (2017).
- Rajderkar, S., Barozzi, I., Zhu, Y., Hu, R., Zhang, Y., Li, B., Alcaina Caro, A., Fukuda-Yuzawa, Y., Kelman, G., Akeza, A., Blow, M. J., Pham, Q., Harrington, A. N., Godoy, J., Meky, E. M., von Maydell, K., Hunter, R. D., Akiyama, J. A., Novak, C. S., Plajzer-Frick, I., Afzal, V., Tran, S., Lopez-Rios, J., Talkowski, M. E., Lloyd, K. C. K., Ren, B., Dickel, D. E., Visel, A. & Pennacchio, L. A. Topologically associating domain boundaries are required for normal genome function. Commun. Biol. 6, 435 (2023).
- Ushiki, A., Zhang, Y., Xiong, C., Zhao, J., Georgakopoulos-Soares, I., Kane, L., Jamieson, K., Bamshad, M. J., Nickerson, D. A., University of Washington Center for Mendelian Genomics, Shen, Y., Lettice, L. A., Silveira-Lucas, E. L., Petit, F. & Ahituv, N. Deletion of CTCF sites in the SHH locus alters enhancer-promoter interactions and leads to acheiropodia. Nat. Commun. 12, 2282 (2021).
- Paliou, C., Guckelberger, P., Schöpflin, R., Heinrich, V., Esposito, A., Chiariello, A. M., Bianco, S., Annunziatella, C., Helmuth, J., Haas, S., Jerković, I., Brieske, N., Wittler, L., Timmermann, B., Nicodemi, M., Vingron, M., Mundlos, S. & Andrey, G. Preformed chromatin topology assists transcriptional robustness of Shh during limb development. Proc. Natl. Acad. Sci. U. S. A. 116, 12390-12399 (2019).
- Zuin, J., Roth, G., Zhan, Y., Cramard, J., Redolfi, J., Piskadlo, E., Mach, P., Kryzhanovska, M., Tihanyi, G., Kohler, H., Eder, M., Leemans, C., van Steensel, B., Meister, P., Smallwood, S. & Giorgetti, L. Nonlinear control of transcription through enhancer-promoter interactions. Nature 604, 571-577 (2022).
- Chakraborty, S., Kopitchinski, N., Zuo, Z., Eraso, A., Awasthi, P., Chari, R., Mitra, A., Tobias, I. C., Moorthy, S. D., Dale, R. K., Mitchell, J. A., Petros, T. J. & Rocha, P. P. Enhancer-promoter interactions can bypass CTCF-mediated boundaries and contribute to phenotypic robustness. Nat. Genet. 55, 280-290 (2023).
- Hung, T.-C., Kingsley, D. M. & Boettiger, A. N. Boundary stacking interactions enable cross-TAD enhancer-promoter communication during limb development. Nat. Genet. 56, 306-314 (2024).
- Hafner, A., Park, M., Berger, S. E., Murphy, S. E., Nora, E. P. & Boettiger, A. N. Loop stacking organizes genome folding from TADs to chromosomes. Mol. Cell 83, 1377-1392.e6 (2023).
Significance
The authors show convincing data that Mnx1 indeed responds transcriptionally to several Shh-enhancers located over 100 kb distal and on the wrong side of the TAD boundary. The data come from developing mouse embryos, span several tissues, and include key controls for specificity of the method. This provides convincing data with which to challenge the currently widely accepted view of as TADs a significant boundary, complimenting the few examples that indicate that such regulation is possible in special cases (see further discussion in 2b below). I believe this work represents an important and substantive contribution to the field and should ultimately be published, after a few notable issues have been addressed.
Audience: I believe this work will be of general interest to the eukaryotic transcription community, the 4D genome community, and the developmental biology community.
My expertise: developmental biology, 4D genome biology, microscopy
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Reply to the reviewers
point-by-point response is included in the submitted figure; named: Joshi et al response to Reviewer
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Referee #2
Evidence, reproducibility and clarity
Joshi et al. discovered that human pDC isolated from blood of healthy donors incubated in coculture with hepatitis E virus (HEV) infected hepatoma cell lines were triggered in a cell-cell contact-dependent manner to mount interferon-alpha responses. This conclusion was supported by experiments in a Transwell setting in which pDC were separated from infected cells by a permeable membrane and under which conditions pDC were not triggered to mount interferon-alpha responses. Treatment of the coculture system with an TLR7 inhibitor or with antibodies against the cell adhesion receptors ICAM-I and alphaLbeta2-integrin either entirely inhibited or reduced the induction of interferon-alpha responses, respectively. HepG2/C3A cells infected with different ORF2 mutants induced overall similar interferon-alpha responses in pDC. In contrast, PLC3 cells infected with an ORF2 variant that is not properly translocated into the nucleus did not induce interferon-alpha responses in pDC, whereas the other variants induced normal responses. This observation indicated that in the context of PLC3 cells the subcellular localization of ORF2 in the nucleus is critical to induce interferon-alpha responses in pDC. Finally, quantification of contact points between pDC and infected cells supported the conclusion that enhanced cell-cell contact was necessary to efficiently induce interferon-alpha responses in pDC.
Overall, the study was carefully carried out and shows interesting results. It is appreciated that responses of human pDC isolated from blood of healthy donors were analyzed. Furthermore, the stimulation of pDC with infected hepatoma cell lines is interesting. Earlier studies showed that infected cells might be better pDC stimulators than free virus. Furthermore, in line 140 of the manuscript the authors correctly state that pDCs are resistant to virtually all viruses. Thus, they should have included experiments in which they stimulate pDC with free HEV. Comparative analysis of the data presumably would further highlight the relevance of pDC triggering by infected cells.
Despite interesting observations are presented regarding differences in the induction of pDC responses with PLC3 and HepG2/C3A cells infected with an ORF2 variant that is not properly translocated into the nucleus, no experiments were offered to explain this phenomenon. It is highly recommended to include additional experiments that support concepts either in one or the other direction as presented in the discussion.
The paper is written in an unnecessarily complicated way. The authors should try to arrange the manuscript in a manner that the readability is enhanced. In the end, the observations that have to be communicated are not very complicated. Showing only controls in Fig. 1 is a cumbersome start in the manuscript. The authors should consider moving such results in a supplementary Figure. The authors do not thoroughly describe the experiment in Fig. 2I in the results section, although it seems to be rather interesting. The authors should give more explanations in the results section how they quantified cell-to-cell HEV infection in presence or absence of pDC.
Minor comments
In the legend of Fig. 1D and E it should be clarified what "Cont cells" and "HEV cells" means. I assume it is the mock control and HEV infected cells. It should be mentioned in the figure caption with which multiplicity of infections the cells were treated.
For reasons of consistency, in Fig. 2 controls should be identified also by "Cont cells" and not by "cont cell" (in A and B) and "cont cells" (in C and D).
In Fig. 2H, above "Fold-change in ORF2+ cells" there is indicated PLC3 in small letters. Should this be better moved above the panel, as done in F and G?
The schematic depiction of different cell types in Fig. 2H and J is not very helpful, if not explained in the figure caption.
In Fig. 3B the labels shown on the left side of A should be repeated.
Why above the second row in A and B it is indicated "ORF2-AF488", but everywhere else in the manuscript it is referred to ORF2. If the AF488 addition is necessary (presumably it refers to an Alexa Fluor 488 conjugate of the antibody used to detect ORF2), this should be introduced either in the Materials and Methods section or in the figure caption.
Significance
The study is of high relevance. The presented phenomena are clearly described. Mechanistic aspects are not fully covered, yet. This study will be interesting for a relatively broad audience of virologists and immunologists. Currently, the manuscript is written in a manner that it is relatively difficult to read. This should be improved to reach the relatively divergent audience.
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Referee #1
Evidence, reproducibility and clarity
Plasmacytoid dendritic cells (pDCs) are the major producers of type I interferon after viral infections and play key role in antiviral immune response. This article by Joshi et al. investigates the role of pDCs in regulating the Hepatitis E virus (HEV) infection. In Fig. 1, the authors investigated the immunocompetence of different cell lines and HepG2/C3A and PLC3 were chosen for further studies. By utilizing a combination of flow cytometry, RT-qPCR and other techniques, the authors showed in Fig. 2 that the cell-cell contacts between pDCs and HEV infected cells induce the pDCs to secrete interferon (IFN). This interaction is mediated by cell adhesion molecules and is dependent on TLR7 signaling. The authors then went on to show that the IFN produced by pDCs controlled the viral spread. Further, using several mutant forms of ORF2 protein and utilizing imaging, RT-qPCR and other techniques, in Fig. 3 and 4 the authors elucidated the importance of the glycosylation pattern, localization of different forms of HEV ORF2 protein, cell-cell contact in triggering the immune response. Overall, this study provided insights in the pDC mediated IFN response against HEV.
Major comments:
- The authors report that in the PLC3 cells, STOP mutation significantly reduced IFN⍺ production (Fig. 3f), significantly reduced pDC contact with infected cells (Fig. 4c) and thus concluded that the ORF2g/c is involved in pDC-infected cell interaction and IFN⍺ production. However, in the HepG2/C3A cells, the STOP mutation does not decrease the IFN⍺ production (Fig. 3e). In the manuscript, one of the key conclusions is that the glycosylated form of ORF2 leads to better recognition of the infected cells by pDC. So, it is critical that the difference in the IFN⍺ production between these two cell lines with STOP mutation is addressed with further details.
- The authors show that the IFN⍺ response was reduced in 5R/5A mutant HepG2/C3A cells (Fig. 3e), whereas the IFN⍺ response was completely absent in 5R/5A mutant PLC3 cells (Fig. 3f). The authors suggested that the difference in IFN⍺ response may be due to lack of ORF2i in PLC3 and other cell specific regulation in HepG2/C3A. Further evidence for this differential regulation would strengthen the claim.
- In the PLC3-pDC co-culture experiment (Fig. 2b), there is already an induction of IFN-1 (Interferon Lambda 1) in the uninfected PLC3-pDC co-culture (right panel, Fig. 2b). An explanation for the IFN-1 (Interferon Lambda 1) expression in the uninfected state would be helpful.
Additional comments:
- Authors checked the expression of two ISGs- MXA, ISG15 in Fig. 1a-c, 2a-b. Were the expressions of other ISGs, such as members of OAS family (OAS1, OAS2 etc.), IFITM family or any other ISGs checked? This may be helpful, since in the Fig. 2c there is IFN⍺ production in pDC-infected PLC3 co-culture, but the ISGs (MXA, ISG15) are not upregulated significantly in Fig. 2b.
- In the HepG2/C3A-pDC co-culture experiment (Fig. 2a), there is not much difference in IFN-1 (Interferon Lambda 1) level in the infected HepG2/C3A-pDC co-culture (right panel, Fig. 2a) in comparison to infected HepG2/C3A alone (left panel, Fig. 2b), and also this outcome is different from that in the PLC3 experiment (Fig. 2b). Further clarification would help to support the conclusion regarding the IFN-1 (Interferon Lambda 1) upregulation in HEV infected cells-pDC co-culture.
- The authors show that in the pDC-PLC3 co-culture system, IFN⍺ was induced at 18h (Fig. 2c-2e), but the viral replication was not decreased in PLC3 cells (Fig. 2g). But, the HepG2/C3A-pDC co-culture has reduced viral replication at 18h (Fig. 2f). An explanation for the difference in the observation in two different cell lines at the same timepoints would strengthen the antiviral role of pDCs on HEV infected cells.
- The authors quantified the fold change in HEV infected PLC3+ cells in Fig. 2h. Was it performed by flow cytometry? It would be helpful to mention it in the figure legend. Also, if the said quantitation was done by flow cytometry, performing similar assay with HEPG2/C3A cells at 48h would provide the readers a better idea about the antiviral response across the cell lines at<br /> comparable timepoints.
Minor comments:
- Was it expected to observe the increased induction of IL6 (Fig. 1b) in HepG2/C3A cells (but not in other cell lines) after IFN- (Interferon Lambda) treatment?
- In Fig. 3e, for the WT cells, 4 datapoints are visible while in the legend it is mentioned n=5.
- Typo: IRS661 in line 263, 699, Figure 2e.
- Typo: 200l in line 579.
- Catalogue number for ELISA kit is missing (Line 584).
- It would be helpful if the color code for the imaging in Supplementary figure 2f is provided on the top of the images, as it is provided in other images.
Significance
This article by Joshi et al. provides insight about the role of pDCs in controlling the HEV infection. However, the importance of pDC-infected cell contact mediated IFN-I secretion in antiviral response has been previously shown by the authors' group (Assil et al., 2019, Cell Host & Microbe) and others as well (E.g., Yun et al., 2021, Sci. Immunol.). The involvement of integrin mediated cell adhesion and TLR signaling in mediating this response was also shown. Though this manuscript does not advance the field of pDC biology or virology significantly, it does provide better understanding of the pDC antiviral response in the landscape of HEV infection. Although, it is out of the scope of this manuscript, elucidation of the mechanistic regulation how ORF2g/c controls the pDC-infected cell contact would be of great interest and significance. Overall, this study could be of interest to a general audience, especially to the virologists and researchers working in pDC biology.
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The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
In their manuscript titled "Single-molecule tracking reveals the dynamics of Ipl1 recruitment to the kinetochores and spindles in S. cerevisiae", authors Podh, Mehta, and colleagues track single molecules of members of the chromosomal passenger complex (CPC) to determine the dynamics of the complex during chromosome biorientation. The authors tagged members of the CPC with a HaloTag to titrate the number of fluorophores to image single molecules and performed microscopy with high temporal resolution and measure residence lifetimes and diffusion rates of the complex. Furthermore, they used mutations that disrupt different localization pathways for the complex to determine which lifetimes are associated with these pathways. Next, they arrested the cells in metaphase and treated the cells with a microtubule inhibitor to determine the effect of disrupting kinetochore-microtubule connections on CPC dynamics. In this way, they determined that the more long-lived CPC molecules are at unstable connections. Finally, they compared the dynamics of the different complex members and saw that two of the members (Nbl1 and Bir1) have more long-lived associations near kinetochores than the other two members (Sli15 and Ipl1). Overall, the study is quite interesting, as the dynamics of the CPC near the kinetochore are highly relevant for the function of the complex. However, I have some concerns about the methodology and conclusions.
Major comments:
- My largest concerns relate to the categorization of the fluorescent molecules. As far as I can tell from the methods, foci are only counted if they are "bound", meaning that they remain within a certain radius for a minimal number of frames. This requirement appears to assume diffusive movement. However, frequent directed movement would be expected for CPC molecules localized to the inner centromere, kinetochore or spindle. Firstly, the spindle itself would be subject to dynein-directed movements to position it near the bud neck during metaphase. Second, the kinetochores themselves would be undergoing directed movements from microtubule dynamics. These movements are quite rapid and would certainly take place over the timescale of the microscopy experiments performed in this study (See PMC1366782 for example). Kinetochores undergoing these directed movements would likely be the most relevant to CPC function, as these would still be undergoing biorientation.
To establish the parameters used for assessing bound molecules, the authors used histone H3. However, this would not be an appropriate measure for dynamics at the spindle/kinetochore for the reasons stated above, especially if the measurements were taken from cells that were not in metaphase. As an additional control, the authors could use cell lines with GFP-labeled centromeres in metaphase cells and subject them to the same analyses. Since these are not single molecules, all of the foci would be expected to have lifetimes for the full 40s duration of image collection. Any foci that fall short of this duration would indicate that the lifetimes of some single molecules are not measured for their full duration. 2. The authors repeatedly state that the double exponential decay equations fit the survival probability distributions "well". However, there is no statistical measurements of the fits presented. How much better does double exponential decay fit as compared to single exponential decay? In figure 2A (metaphase), both single and double decay curves are shown in the figure, but they seem to overlap to the point of being nearly indistinguishable. The methods section mentions that F-tests were performed for the fits, but I cannot find the results of the tests.
On a related note, some of the curves don't seem to fit the data well at all. Figures 2C, 3B, and S3 have especially bad fits. Is there an explanation for this? Would a different method fit these data better? For figure 3B, the data show that over 10 percent of the tracks last longer that 10 seconds. This is much higher than for any other condition, yet the authors conclude that there is no specifically bound fraction since the curve doesn't match the data. This is a substantial issue for the interpretation of these results. 3. For the interpretation of the differences in lifetimes for Bir1/Nbl1 vs. Sli15/Ipl1, the authors conclude that the longer lifetime of the former indicates earlier recruitment to the "kinetochore". A simpler explanation may be that there are different subcomplexes that have different recruitment dynamics. For example, a complex with all four subunits may have a longer lifetime than one that is just Sli15/Ipl1 due to different recruitment methods (Sgo1-dependent vs Ctf19-dependent). The lifetimes of Sli15 or Ipl1 molecules would therefore be a combination of both recruitment methods.
Minor comments:
- In figure 3B (without tension), the ROI and tracks numbers are likely switched based off of the numbers for the other graphs.
- In the introduction (Page 4 line 15), the authors conclude about their result from depleting Glc7 that "fast exchange of Ipl1 is essential to keep the Glc7 away from its kinetochore substrates." I'm not sure what this statement means, as it is unlcear what "its" is referring to Ipl1 or Glc7. Either way, I don't think the authors can conclude anything about keeping Glc7 away without looking at the localization of Glc7 itself.
- On page 7 line 2 the authors claim to track Ipl1 on "kinetochores" in metaphase. Later on the same page they clarify that they cannot differentiate between inner centromere, inner kinetochore, and outer kinetochore. I would think that they also can't distinguish these with microtubule binding. The authors again claim to be observing kinetochore localization on page 9 line 2. This is confusing, and a more accurate term for the localization should be adopted.
- The authors claim that Glc7 is "required for" fast turnover (page 11 line 5), yet they still see many instances of fast turnover following its depletion.
- The authors assume that the molecules have a longer lifetime are the ones "involved in phosphorylation" (page 12 line 21 and 24). This claim would need to be justified, as short periods of localization could be sufficient for phosphorylation of substrates.
- On page 12 line 23, the authors state numbers for how long it takes Ipl1 to "find its target sites". I cannot find where these numbers come from
- On page 13 line 15 the authors claim that tension evicts Ipl1 from kinetochores. However, tension was not specifically tested, as benomyl treatment will create unattached kinetochores.
Significance
General assessment: The strengths of the study are in quantifying the dynamics of the CPC at localizations near the kinetochore in a new and informative way. The limitations lye in not knowing where the CPC molecules are localized in relation to the kinetochore, which would have provided mechanistic insights into the recruitment pathways. Although this limits the ability to derive strong mechanistic insights from the results, the numbers themselves are valuable and interesting.
Advance: Previous studies on CPC localization come from conventional fluorescence microscopy that observes the average of many molecules. This manuscript uses a single molecule technique to observe the residence lifetime and diffusion rate of CPC complex members.
Audience: This study would be of general interest to researchers that study chromosome biorientation and segregation.
Reviewer's expertise: Chromosomal passenger complex, budding yeast, microscopy
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Referee #2
Evidence, reproducibility and clarity
This report investigates, using single-molecule approaches, the dynamics of Ipl1(Aurora B)and other Chromosomal Passenger Complex (CPC) components in budding yeast mitosis. The work is largely descriptive, and identifies a number of (confirmatory) results regarding the connections between Ipl1/CPC, its centromere/kinetochore receptors and GLC7/PP1. In addition, experimental rigor is lacking in certain instances, casting doubt on the conclusions made.
Significance
Generally, this paper provides some interesting suggestions but falls short in expanding our knowledge significantly. As such the impact of this study will be minor. The used methodology is hard to critically gauge due to strong statements which seem unsubstantiated. In addition, the manuscript is written in a way that appears to generate 'controversy/contrast' with earlier work, which seems unwarranted and unnecessary. Some technical questions remain, which will need to be addressed before publication.
Specific points:
- page 1, line 16, change 'the master regulator' to ' a master regulator'.
- line 18-20, this statement is unnecessary
- page 2, line 15-17, why does their overlapping ..... make studying Aur-B challenging?
- page 4, line 11, remove 'the' before Ipl1
- line 13-16, this is an unclear statement
Results:
- in general, why are experiments done in heterozygously tagged diploids? Is there a risk that the untagged, wt allele obscures behavior? Also, viability assays in this situation are not informative about functionality of the used alleles. These experiments will need to be done in 1)haploids or 2) homozygous diploids page 6, line 14-25: this paragraph makes strong statements about the behavior and function of certain Ipl1 pools. These statements seems unsubstantiated by solid data. The whole paper relies on these extrapolations so care should be taken. What evidence are the conclusions based on? Also, how can the authors actually distinguish KI, centromere and spindle pools with confidence here? By looking at tubulin-CloverGFP this seems unrealistic: in fact the imaging that is shown fails to recapitulate classical spindle MT patterns (rather, the whole cell lights up - what's up here?). What about co-localization with other factors? Additional/alternative experiments are needed here. If not, it remains unclear what the analysis downstream of these images signifies. page 12, line 1-6: how can the authors arrive on these conclusions (i.e. Bir1/Nbl1 arrives first -whatever that may even imply vis-a-vis molecular behavior?). These conclusions are not substantiated.
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Referee #1
Evidence, reproducibility and clarity
The CPC is involved in important functions during mitosis, including kinetochore assembly, chromosome biorientation, spindle assembly checkpoint and cytokinesis. In this report, the authors studied the dynamics of the CPC at the kinetochore and mitotic spindle in yeast using single-molecular tracking. They conclude that Ipl1 (the catalytic subunit of the CPC) shows different residence times A) between the kinetochore and the spindle, B) between Ctf19- and Bub1-mediated recruitment to the kinetochore, C) with and without tension during the metaphase arrest, D) in the presence and absence of Glc7 phosphatase, and E) between Bir1-Nbl1 and Sli15-Ipl1.
These are interesting findings, which provide people working in this research field with useful information. However, some clarifications and additional evidence are required regarding the following points:
- The statement about the pink and blue fractions (Page 6, Line 18-20): What is the evidence supporting this statement? More explanations and/or reference citations are required.
- Different dynamics between the kinetochore and the spindle (Page 7. Line 5-6): Is this difference due to the different locations in cells or the difference in the cell cycle (metaphase vs anaphase)? Is the dephosphorylation of Sli15, which promotes the relocalization of CPC from the kinetochore to the spindle at the anaphase onset (e.g. Pereira et al Science 2003), involved in this difference? What happens to the CPC dynamics if non-phosphorylatable Sli15 localizes to the spindle during metaphase?
- The statement about the Ipl1 dynamics at different kinetochore sites (Page 7, Line 24-25): It is not clear how solid this conclusion is. If they use other kinetics models, do they reach different conclusions? I do not think the functional redundancy of CPCs at three locations would necessarily support the conclusion because they may still redundantly support biorientation and cell viability even if they show different dynamics at the three locations.
- The conclusion about Ipl1 dynamics under tension (Page 10, Line 7-8): The kinetochores should also be under tension in metaphase in cycling cells. However, Fig 3C (left) shows the pink fraction (specific bound fraction) is still present in this condition. How do they explain the discrepancy between metaphase arrest (Fig 3B, left) and metaphase in cycling cells (Fig 3C, left)?
- Figure 4 should be cited in the last section of Results (Page 11-12).
Significance
Given the importance of CPC in the regulation of mitosis, it is useful knowledge for experts that the CPC shows different dynamics depending on different localization sites and under different conditions. However, this work does not tell us much about whether or how the different dynamics of the CPC are important to its function. It is also not clear what molecular mechanisms (e.g. phosphorylation or other post-translational modifications, different affinity between proteins) directly cause the different dynamics of the CPC. Therefore, in my view, the scientific significance is rather limited.
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Reply to the reviewers
Reviewer 1
Comment 1: A gallery of different cell cycle stages should be included to define KDM4A centrosomal localization at G1, S and G2 phases and whether it is localized to duplicating centrosomes.
Response: We thank the reviewer for this excellent suggestion. We have now included Fig S1H demonstrating the persistence/retention of KDM4A at the centrosome through the cell cycle. The text in the Results section has been updated to reflect this addition.
Comment 2: The immunoprecipitations in Fig. 1 and Supp. Fig. 1 must include appropriate controls. There is no positive control in Fig. 1E and the negative controls for the tagged pulldowns are not appropriate in that there is no other HA-tagged protein in cells. Antibody controls and the reciprocal immunoprecipitations should also be included in the same figure (with controls).
Response: To address the first point, we have included Histone H3 as a positive control for the KDM4A antibody in Figures 1E and 1F. As for the second point raised by the reviewer, the empty vector is an HA-tagged empty vector and so the antibody controls are already included in the Figure as the ‘empty vector’. We have now included detailed information in the Figure legend to clarify the same. In addition, as suggested by the reviewer we have moved the reverse IPs to the main Figure 1 (Figures 1G and 1I).
Comment 3: Fig. 1H: The use of overexpressed GFP-centrin for immunoprecipitations is questionable; centrin overexpression can cause centrosome amplification, so the level of centrin relative to the endogenous level should be demonstrated.
Response: This is with regards to the renumbered Fig. 1J. We have generated hTERT RPE-1 GFP-Centrin expressing stable cell lines that were used for our studies. This is a commonly used cell line in the field and although transient over-expression of GFP-centrin does cause centrosome defects, stable cells are less likely to have elevated centrosome defects. Importantly, the concern of overamplification of centrosomes in these cells is less of a concern given that we have only used these cells to validate the localization of KDM4A to centrosomes using centrin as a centrosome marker. Nonetheless, to ensure that we do not have an aberrant increase in centrosome defects in these cells we have included IF images of our cells (green channel in low-mag and high-mag images below) and are happy to report that we did not observe significantly elevated incidence of centrosome amplification in these stable cell lines.
Comment 4: The precise localization of KDM4A should be determined more clearly with respect to known centrosomal structures/ regions. One would speculate a PCM localization from the data presented here, but the use of centrobin as a marker does not allow the mother centriole's location to be determined with great clarity. It is unclear why the authors chose centrobin as a marker; further explanation of this might be helpful to the reader. Centrobin is usually cited as a daughter centriole marker (PMID: 16275750, but see 29440264). Supp. Fig. 3J appears to shows 2centrioles labelled with centrobin but the paper does not specify whether centrobin is chosen as a daughter marker or otherwise.
Response: We thank the reviewer for this astute observation. Our initial rationale for choosing centrobin was simply to use a centrosome marker that worked robustly and reliably with minimal background staining, essential for the single-molecule super-resolution imaging. The question we wanted to address was generating a geographic region in the cell showing nano-scale localization of KDM4A. The 2D images shown in Fig. 2 can be understandably static and hard to visualize the 3D distribution of KDM4A which is not exclusive to centrioles (centrobin although more daughter centriole, does show weaker signal at the mother centriole as well). We have now extensively re-worked Figure 2, including the inclusion of a video in Supplemental Information. We have now included new nano-scale imaging of KDM4A with g-tubulin (a more traditional centrosome marker), which shows a similar distribution of KDM4A across the centrosome and have also included distribution measurements along the x- and y-axis showing both KDM4A and centrobin/g-tubulin. We have modified the text to refer to centrobin as a centrosome marker (centrobin as the reviewer rightly noted can localize to both centrioles although predominantly at the daughter centriole).
Comment 5: Related to this localization issue, Fig. 2D is unclear to this reviewer. What is this normalized to- a marker or just a set of coordinates? This is an unusual means of representing a localization that does not help the reader understand the (sub-) centrosomal location of KDM4A. The analysis in Supp. Fig. 4 is of somewhat limited value and might be omitted.
__Response: __We apologize for the confusion with the Figure and have simplified the graphs to indicate the single-molecule distribution plotted along the x- and y-axis showing both KDM4A and the centrosome markers i.e. centrobin and g-tubulin.
Comment 6: Fig. 3 shows the amplification of gamma-tubulin signals, but there is no control for cell cycle stage. The Kdm4a knockout cells appear to be twice the size of the controls, suggesting a G2 phase arrest, which can potentiate centrosome overduplication, or cytokinetic failure in a previous cell cycle (this may also be the case in Figs. 6C and 7B). Therefore, these cells should be phenotyped more robustly with respect to their proliferative characteristics and cell cycle phase distribution. Cell cycle phenotypes should also be checked in the rescue experiments.
Response: We thank the reviewer for the comments above. The cells shown in Fig. 3 are interphase cells evaluated for centrosome numbers in Kdm4a-deficient cells, independent of mitosis. We apologize for the lack of clarity and the confusion generated by our erroneous statement at the beginning of the paragraph “we next investigated a functional role for KDM4A at mitotic centrosomes”. In fact, we started by first evaluating interphase cells to interrogate consequences of losing Kdm4a, followed by evaluations of the mitotic phenotypes once we observed increased centrosome numbers. This error has now been corrected in the Results.
As for the reviewer’s comment on phenotyping the cells further, we have now performed these evaluations and have included them in Figure 3 (as new panels Figures 3D, 3E, 3F). Our MTT proliferation assays showed the Kdm4a-null cells proliferated slower than control non-targeted MEFs, although this did not result in any significant issues with cell cycle progression with both cell lines progressing without any arrests and importantly without accumulating increased DNA content/aneuploidy. The rescue cell lines were also phenotyped (new Figures 7C, 7D and 7E) and similarly did not show any altered cell cycle progression.
Comment 7: Related to the previous point, in the DAPI staining in Figure 5A, 'pseudo-bipolar' cells #1 and #3 (from the top) seem to have greatly increased levels of DNA, suggesting failed cytokinesis as a mechanism of centrosome abnormality. This is a very different process to a centrosome overduplication within a single cell cycle; given that these are knockouts, it is not clear what conclusions should be drawn from the current analysis.
Response: The reviewer makes an excellent point, about the increased centrosome numbers arising from failure to complete cytokinesis. We have performed further phenotyping of the Kdm4a-null cells, included as new Figures 3D, 3E and 3F. Although the Kdm4a-deficient cells grew slower than their Kdm4a-proficient counterparts, there were no significant issues with cell cycle progression and importantly no evidence of increased aneuploidy. We have also now performed further analysis using centrin as a centriole marker to quantify centrosome numbers (new Figures 4C, 4D and 4E) and have found that there is a significant increase in disjointed centrioles (Figure 4E) suggesting that in addition to any potential amplification there also appears to be an increased loss of cohesion in cells deficient for KDM4A. We have also further confirmed presence of single/disjointed centrioles using TEM analysis (new Figure 4F)
Comment 8: The JIB-04 result may suggest that KDM4A inhibition causes fragmentation of spindle poles, given that it is a relatively short treatment that would probably not be long enough for centrosome overduplication. Whether this arises during M phase, distinct from the over duplication phenotype seen where there are >4 centrioles, should be posed as a separate question- these may be distinct outcomes from KMD4A inhibition at different cell cycle times.
Response: We completely agree with the reviewer that the JIB-04 treatment is relatively short and does in fact suggest that this is independent of any over duplication phenotype observed in the Kdm4a-CRISPR knockouts. We thank the reviewer for the suggestion of posing two separate questions to address this point and have made the changes in the manuscript (see Results). In addition, our new data discussed in Comment 7 above, corroborates this hypothesis.
Comment 9: It is unclear why the authors call the cell shown in Fig. 4B 'pseudo-bipolar'- there are clearly four poles here (as in the multipolar example shown in Fig 5A). This makes the data in Fig. 5 difficult to interpret. The authors should review their classification.
Response: We thank the reviewer for catching this error. We apologize for the misrepresentation of the representative image and have now included the correct image that shows pseudo-bipolar spindles (new Figure 5D) replacing the multipolar spindle. In addition, we have reviewed our data and the quantitation remains unchanged.
Comment 10: Expression of the vector control in the Kdm4a nulls in Fig. 7A appears to show a decline in the H3K36me3 levels, confusing the outcome of this experiment. Quantitation should be provided for these blots.
Response: We have now included the requested quantitation (new Figure 7B) for Figure 7A.
Comment 11: A rescue experiment should be included for the siRNA knockdown of KDM4A.
Response: A rescue experiment with the siRNA experiments is challenging as we use a pooled siRNA (4 siRNAs) targeting KDM4A. Rescue with a KDM4A construct would result in the knockdown of the exogenously expressed KDM4A as well. The rescue experiments have been therefore performed with the CRISPR knockout cell lines.
Comment 12: Size markers should be shown in all immunoblots.
Response: We have now included size markers as requested by the reviewer for all Figures showing immunoblots (Figures 1, 5, 7 and Supplementary Figures 1, 5).
Comment 13: p.6, 11 'the resulting payment' and 'caustic chromosome environment' are strange usages and should be rephrased.
Response: The text has been rephrased.
Comment 14: Are all panels shown at the same magnification in Fig. 1B? (The telophase DAPI appears different to the anaphase)
Response: We have confirmed that the magnification is the consistent across the entire panel of images in Figure 1.
Comment 15: Blow-up panels should be shown so that the centrosomes can be visualised more clearly (Fig. 1 and Supp. Fig. 1).
Response: We have now included blow-up panels for all centrosome images in Figure 1 and Supplemental Figure 1.
Comment 16: The MT labelling in Fig. 1D is not of good quality; this imaging should be improved.
Response: We believe that microtubule densities are impacted by modulating KDM4A in cells likely arising from alternate mechanisms that we are currently investigating. However, to the reviewer’s point we have placed the transient overexpression images in Supplementary information (Supplemental Figure 1I) and have replaced with new Figure 1D, using our stable clones expressing RFP-vector or RFP-KDM4A.
Reviewer 2
Comment 1: Coimmunoprecipitation and GFP-trap analyses demonstrated interactions between KDM4A and centrobin, CP110, and centrin-2 (Fig. 1). While the authors suggest a functional a functional association with the centrosome, it is noteworthy that no known centriole protein has been identified to interact simultaneously with centrobin, CP110, and centrin-2, located in distinct sub-centriolar regions. Additionally, 3D super-resolution microscopy indicates that KDM4A is not restrained to a particular region of the centrosome, surely not at the centriole (Fig. 4D). These results hint that centrobin, CP110 and centrin-2 may be potential substrates of KDM4A. Therefore, it is worth to conduct immunostaining and coimmunoprecipitation analyses with the JIB-04-treated cells.
Response: The reviewer makes an excellent point. The co-immunoprecipitation studies were not conducted to show a direct interaction between the centrosome proteins and KDM4A, but more as a proof-of principle that KDM4A is interacting with centrosome proteins (we do not know if this is direct or indirect, although the data would likely suggest an indirect mechanism). Given that we had used centrobin, centrin and CP110 in our immunofluorescence analysis we also used them for our co-IP studies to provide further evidence of a centrosome localization for KDM4A. It is intriguing that any one of these proteins could in fact be substrates for KDM4A, although an in-depth study would be required to prove this since the super-resolution localization would suggest that KDM4A is not at the centrioles per se and is in fact more of a pericentriolar protein. We have clarified this point in the Discussion. Although the experiments suggested with the JIB treatment would be intriguing, identifying a bone fide centrosome substrate for KDM4A’s demethylase activity is not trivial and would require identification of methylation on a substrate followed by then determining if KDM4A can demethylate the target. Methylation on non-chromatin substrates such as centrosome proteins is not currently well characterized.
Comment 2: The generation supernumerary centrioles in Kdm4a KO MEFs is intriguing yet warrants careful description (Fig. 3). First, supernumerary centrioles should be coimmunostained with multiple centriole markers, such as centrin-2, CP110 and centrobin antibodies at synchronized populations such as G1, S and M phases. Second, the number of centrioles per cells may be counted and statistically analyzed.
Response: We thank the reviewer for making this suggestion. We have now included new Figures 4C, 4D, 4E and 4F where we show immunofluorescence with Centrin 2 in Kdm4a-deficient cells. Having found an increased incidence of unpaired centrioles in cells deficient for Kdm4a we have further performed TEM to show the presence of these unpaired/disjoint centrioles.
Comment 3: The high proportion of pseudo-bipolar cells in the NT group requires attention (Fig. 5).
Response: We thank the reviewer for this astute observation. To obtain enough mitotic cells for analysis we synchronized the MEFs, which appeared to increase the baseline of pseudo-bipolar spindles reflected in the quants. Despite this increase the differential between the controls and Kdm4a-null cells is significant, as indicated, and we have now made this evident in the text for clarity.
Comment 4: The KO-rescue cells should be valuable tools to confirm specific roles of KDM4A at the centrosome (Fig. 7). The authors may generate stable cell lines in which wild type and H188A mutant KDM4A are expressed in the KO cells, and use them for centrosome localization of the ectopic proteins, spindle formation and supernumerary centriole generation.
Response: The reviewer makes an excellent point and in fact we generated the stables (Figure 7) with this idea in mind. Unfortunately (but not completely surprising as this is frequently observed in comparable settings) we observed decreased mitotic abnormalities and genomic instability in the Kdm4a-null cells over time in culture. This is likely arising from a compensatory mechanism/redundancy that perhaps kicks in to enable survival of these cells. The process of generating the stables was therefore tricky with us only being able to reliably analyze genomic stability as a downstream readout of mitotic abnormalities that might have occurred in these cells (early passages analyzed for genomic stability).
Reviewer 3
Comment 1: Figure 1D: the RFP vector alone localizes to the centrosome. How was the signal across the cells? Can the authors provide a fluorescence intensity measurement comparing the negative control RFP and RFP-KDM4A to demonstrate the localization at centrosomes of the enzyme? While I found the endogenous staining convincing, the fusion protein is less.
Response: The MEFs were transiently transfected with the RFP-vector/KDM4A for the images shown. In our experience it is not uncommon for the RFP/mCherry/GFP tags to be prominent at the spindle and often tagged vector controls are omitted from many prominent publications. However, in our case there is a significant increase in RFP-KDM4A signal observed at the spindle poles and we have now included the quantification of signal from the two poles in Supplemental Figure 1J where the signal is 3 times higher in the RFP-KDM4A expressing cells compared to vector. We have also included new Figure 1D demonstrating the RFP-KDM4A localization to spindle poles in our stable cell lines where the signal for the control RFP-vector is negligible. The transient transfection data has been moved to Supplemental Figure 1 (1I).
Comment 2: Figure 1E-F: How specific do the authors think the interactions with CP110 and centrobin are? Do they IP the entire centrosome proteome or do they think that they reveal some specific interactions within the centrosome? Can the authors comment on this? What is the significance of these interactions? Do the authors think that KDM4A is a centriolar component? Or a PCM component? This is only briefly mentioned in the discussion, it should be extended. Did they try to IP PCM components as well?
Response: The reviewer brings up an excellent point. The purpose of the immunoprecipitation was to demonstrate the ability of KDM4A to pull down centrosome associated proteins and vice versa. We are unable to comment on the interactions being direct or indirect, although we suspect that most of the interactions are likely indirect, given that KDM4A is not specifically localized to the centrioles. As per the reviewer’s suggestion, we have now expanded the Discussion to speculate on the potential significance of these interactions and how they might enable identification of novel KDM4A interactors and potential substrates.
Comment 3: Fig.S3: the signal of KDM4A seems broader than that of centrobin, with an average diameter of 749 nm. What is the diameter of centrobin for comparison using this method? The interpretation of the authors concerning this localization is not clear to me: "The quantification data of the diameter of the KDM4A distribution, independently in the different axes (x, y, z), revealed a relatively uniform/circular distribution (Fig. 2D) suggesting that KDM4A was not restrained to a particular region of the centrosome". Is KDM4A at centriole or at centrosomes? PCM or centriole component? From the interpretation stated above, it seems that KDM4A is everywhere from the proximal to the distal axis of the centriole, is it correct? But isn't more PCM?
Response: We would like to apologize for the lack of clarity with respect to the centrobin measurements compared to those of KDM4A. We have attempted to clarify the distributions measurements by showing the distributions for both the centrobin and KDM4A signals. In addition, we have anow included new data with g-tubulin to show co-localization of KDM4A signal with g-tubulin and to also demonstrate that the signal for KDM4A is not centriole specific but is essentially more uniformly distributed throughout the centrosome. We have also included a video (Video 1) as Supplemental data to clarify this point.
Comment 4: Fig.4B: The authors established that there is an increase in centrosome number upon short inactivation of KDM4A by JIB-04, which affects its enzymatic activity and not the scaffolding function. In addition, the loss of KDM4A phenocopies the effect of the drug: this means that the enzymatic activity is required to control the centrosome number. This is also re-enforced by the rescue with WT enzyme and not the enzymatically dead mutant of KDM4A (looking at micronuclei formation-Fig.7). Could the author speculate on this? The fast action of the inhibitor would exclude a block in S phase as stated in the discussion. The authors mention centrosome fragmentation but there is no evidence that this is happening here. The authors mentioned several possible mechanisms in the discussion without really exploring them. The authors also mention here that the chronic loss of KDM4A could arise through a distinct mechanism than that of the inhibitor, this statement was surprising. Could the authors check if they have a cell cycle delay or block in their KO cells? While it seems that the authors would like to address these points in the future, I think that the mechanistic aspect is lacking in this study or at least some hints of it.
Response: We agree with all the points brought up by the reviewer. We have elaborated the discussion as recommended, however the challenge with a demethylase is identifying a potential methyltransferase that can lay a methyl mark on a potential substrate followed by then establishing KDM4A as an eraser for the same substrate. To address, the comment about a cell cycle delay as also brought up by Reviewer 1 (Comment 6), we have performed additional phenotyping of the cells and these data are now included in Figure 3 (as new panels Figures 3D, 3E, 3F) and new Figures 7C, 7D and 7E (for the rescue cell lines) which did not show any altered cell cycle progression.
Comment 5: In general, the figures are organized in an unconventional manner with the panels from one figure distributed on several pages. Could the authors group the panels of each figure in one page to ease the understanding and the reading?
Response: Although we do understand how having multiple panels on several pages makes its difficult to read, the immunofluorescence images would be extremely difficult to observe clearly. Also, this comment will be resolved once the manuscript is accepted for publication as we will re-format per journal guidelines.
Comment 6: Figure S1F-G: the authors provide a large field of view showing a dozen of nuclei. While I acknowledge that this is to show the overall staining, itis difficult to really see the foci of KDM4A or g-Tubulin or centrin. The quality of the images looks really pixelated; this might be due to the PDF compression, but I cannot see any red signal on the panels. Could the authors enhance the B/C of the images so that one can see the signal corresponding to the centrosomes? Is it also possible to have a zoom on the centrosome itself with split channels to illustrate the co-localization? As it is, it is not clearly shown. In the panel G, there are many foci of KDM4A in the nucleus and 2 associated with a centrin staining, which correspond to the centrosomes. However, the signals do not seem to fully colocalize. What do the authors think about this?
Response: We have provided larger zoomed in view of the cells in Figure S1 as requested.
Comment 7: Figure 1A: same comment as above concerning the quality of the image. I am also concerned by the g-Tubulin staining as it looks not on focus and I do not see any foci that would correspond to the centrosome position, while the merge image clearly shows yellow signal, proof of co-localisation. Could the author correct this? In the inset, can the authors zoom on the centrosomes and display the split channels so that one can appreciate the co-localization of the 2 signals?The quality and display of Fig.1B is much better. Could we have the same rendering for the interphase cells of 1A?
Response: The picture in Figure 1A is a raw image. This image has not undergone the same post-image deconvolution applied to the other images in the manuscript. The deconvolved images reduce the KDM4A signal in the nucleus and only demonstrate the highly intense signal at the centrosomes especially in mitotic cells. If we show the deconvolved image here it would lead to the erroneous perception that there is no KDM4A signal in the nucleus and the rest of the cell. To clarify this point we have modified the figure legends to state that this is a raw image. In addition, we have also provided blow-ups of the centrosomes specifically.
Comment 8: Fig.3D: the nucleus of the cell is really affected with many blobs or micronuclei. Is this cell dying? The authors count the number of g-tubulin foci in interphase (Fig. 3C). Could they do it in mitosis and use centrin? In mitosis, there should be 4
Response: The cell in question is not dying and is micronucleated. The question of genomic instability is addressed later in the manuscript and hence the point was not made in this figure. We thank the reviewer for suggesting use of centrin. We have now included these data as new Figures 4C, 4D and 4E.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Centrosomes are microtubules-based structures surrounded by a pericentriolar material, serving as Microtubule Organizing Center (MTOC) and are thus important during cell division. Ensuring proper segregation of the genetic material is crucial and defects occurring during this step can lead to drastic consequences like aneuploidy and chromosome instability. It is well established that centrosome defects (number, function, structure) can give rise to defective mitosis. In the present study, Chowdhury et al. demonstrate that the lysine demethylase 4A (KDM4A), known as a chromatin methyl marks eraser, localizes to centrosomes both in interphase and mitosis and is important for centrosome homeostasis. Intriguingly, the authors propose that the novel role of KDM4A in regulating centrosome integrity is unrelated to its function in regulating gene expression but linked to its enzymatic activity without providing a mechanistic advance.
Major Comments
This manuscript reports convincingly the localization of KDM4A at centrosomes both in interphase and in mitosis as well as the phenotype linked to the loss of KDM4A. While these are interesting observations, there are some important aspects for improvements that are listed below:
- Figure 1D: the RFP vector alone localizes to the centrosome. How was the signal across the cells? Can the authors provide a fluorescence intensity measurement comparing the negative control RFP and RFP-KDM4A to demonstrate the localization at centrosomes of the enzyme? While I found the endogenous staining convincing, the fusion protein is less.
- Figure 1E-F: How specific do the authors think the interactions with CP110 and centrobin are? Do they IP the entire centrosome proteome or do they think that they reveal some specific interactions within the centrosome? Can the authors comment on this? What is the significance of these interactions? Do the authors think that KDM4A is a centriolar component? Or a PCM component? This is only briefly mentioned in the discussion, it should be extended. Did they try to IP PCM components as well?
- Fig.S3: the signal of KDM4A seems broader than that of centrobin, with an average diameter of 749 nm. What is the diameter of centrobin for comparison using this method? The interpretation of the authors concerning this localization is not clear to me: "The quantification data of the diameter of the KDM4A distribution, independently in the different axes (x, y, z), revealed a relatively uniform/circular distribution (Fig. 2D) suggesting that KDM4A was not restrained to a particular region of the centrosome". Is KDM4A at centriole or at centrosomes? PCM or centriole component? From the interpretation stated above, it seems that KDM4A is everywhere from the proximal to the distal axis of the centriole, is it correct? But isn't more PCM?
- Fig.4B: The authors established that there is an increase in centrosome number upon short inactivation of KDM4A by JIB-04, which affects its enzymatic activity and not the scaffolding function. In addition, the loss of KDM4A phenocopies the effect of the drug: this means that the enzymatic activity is required to control the centrosome number. This is also re-enforced by the rescue with WT enzyme and not the enzymatically dead mutant of KDM4A (looking at micronuclei formation-Fig.7). Could the author speculate on this? The fast action of the inhibitor would exclude a block in S phase as stated in the discussion. The authors mention centrosome fragmentation but there is no evidence that this is happening here. The authors mentioned several possible mechanisms in the discussion without really exploring them. The authors also mention here that the chronic loss of KDM4A could arise through a distinct mechanism than that of the inhibitor, this statement was surprising. Could the authors check if they have a cell cycle delay or block in their KO cells? While it seems that the authors would like to address these points in the future, I think that the mechanistic aspect is lacking in this study or at least some hints of it.
Minor comments
In general, the figures are organized in an unconventional manner with the panels from one figure distributed on several pages. Could the authors group the panels of each figure in one page to ease the understanding and the reading?
- Figure S1F-G: the authors provide a large field of view showing a dozen of nuclei. While I acknowledge that this is to show the overall staining, it is difficult to really see the foci of KDM4A or g-Tubulin or centrin. The quality of the images looks really pixelated; this might be due to the PDF compression, but I cannot see any red signal on the panels. Could the authors enhance the B/C of the images so that one can see the signal corresponding to the centrosomes? Is it also possible to have a zoom on the centrosome itself with split channels to illustrate the co-localization? As it is, it is not clearly shown. In the panel G, there are many foci of KDM4A in the nucleus and 2 associated with a centrin staining, which correspond to the centrosomes. However, the signals do not seem to fully colocalize. What do the authors think about this?
- Figure 1A: same comment as above concerning the quality of the image. I am also concerned by the g-Tubulin staining as it looks not on focus and I do not see any foci that would correspond to the centrosome position, while the merge image clearly shows yellow signal, proof of co-localisation. Could the author correct this? In the inset, can the authors zoom on the centrosomes and display the split channels so that one can appreciate the co-localization of the 2 signals? The quality and display of Fig.1B is much better. Could we have the same rendering for the interphase cells of 1A?
- Fig.3D: the nucleus of the cell is really affected with many blobs or micronuclei. Is this cell dying? The authors count the number of g-tubulin foci in interphase (Fig. 3C). Could they do it in mitosis and use centrin? In mitosis, there should be 4 centrin dots (2/spindle pole), it would nicely complement the phenotype of increased number of centrioles. How do the authors interpret these supernumerary centrioles? Is it due to overduplication? Centriole fragmentation? De novo centriole formation? Or failure of cytokinesis?
Significance
Controlling centrosome number is key to ensure faithful chromosome segregation. Increased number of centrosomes through diverse mechanisms can lead to abnormal mitosis despite the well-known centrosome clustering mechanism that permits the formation of a bipolar spindle. In this manuscript, the authors describe the presence of a chromatin eraser KDM4A at centrosomes across the cell cycle without specific localization within the centriole. While the role of KDM4A on chromatin has been described, the authors uncovered a novel function for KDM4A through its enzymatic activity in regulating centrosome numbers that would be independent on its impact on gene expression. The findings described in this manuscript are interesting despite lacking a mechanistical understanding ("Further studies will be necessary to understand the mechanistic underpinnings and molecular targets of KDM4A enzymatic activity at the centrosome"). Conceptually, it is interesting for scientists from centrosome and mitosis fields to consider uncanonical proteins, as exemplified by the enzyme KDM4A, in regulating centrosome function.
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Referee #2
Evidence, reproducibility and clarity
In this study, the authors explored the involvement of lysine demethylase 4A (KDM4A), a chromatin regulatory factor, in centrosome dynamics. They observed that KDM4A localizes to the centrosomes and identified physical interactions between KDM4A and key centriole proteins, such as centrobin, CP110, and centrin-2 through coimmunoprecipitation. Loss of KDM4A function resulted in various mitotic abnormalities including spindle defects, supernumerary centriole formation, chromatin bridges, and micronuclei formation. Additionally, treatment with JIB-04, a KDM4A inhibitor, exacerbated spindle defects, implicating enzymatic activity of KDM4A in spindle pole function during mitosis. From these findings, the authors inferred a novel role for KDM4A in maintaining centrosome integrity, ensuring mitotic fidelity, and preserving genomic stability.
A comprehensive characterization of centrosome phenotypes in KDM4A-null cells would be invaluable. While the quality of microscopic images immunoblots is commendable, the preliminary nature of some results prompts further inquiry. The follows are major questions to be answered.
- Coimmunoprecipitation and GFP-trap analyses demonstrated interactions between KDM4A and centrobin, CP110, and centrin-2 (Fig. 1). While the authors suggest a functional a functional association with the centrosome, it is noteworthy that no known centriole protein has been identified to interact simultaneously with centrobin, CP110, and centrin-2, located in distinct sub-centriolar regions. Additionally, 3D super-resolution microscopy indicates that KDM4A is not restrained to a particular region of the centrosome, surely not at the centriole (Fig. 4D). These results hint that centrobin, CP110 and centrin-2 may be potential substrates of KDM4A. Therefore, it is worth to conduct immunostaining and coimmunoprecipitation analyses with the JIB-04-treated cells.
- The generation supernumerary centrioles in Kdm4a KO MEFs is intriguing, yet warrants careful description (Fig. 3). First, supernumerary centrioles should be coimmunostained with multiple centriole markers, such as centrin-2, CP110 and centrobin antibodies at synchronized populations such as G1, S and M phases. Second, the number of centrioles per cells may be counted and statistically analyzed.
- The high proportion of pseudo-bipolar cells in the NT group requires attention (Fig. 5).
- The KO-rescue cells should be valuable tools to confirm specific roles of KDM4A at the centrosome (Fig. 7). The authors may generate stable cell lines in which wild type and H188A mutant KDM4A are expressed in the KO cells, and use them for centrosome localization of the ectopic proteins, spindle formation and supernumerary centriole generation.
Significance
This manuscript presents a novel function of KDM4A at the centrosome. Their immunostaining results clearly showed centrosome localization at the centrosome. It is surprising that a chromatin regulator plays a role at the centrosome. It is likely that KDM4A is essential for maintenance of centriole integrity, although the specific mechanisms remain unexplored. Nevertheless, the descriptive nature of the study leaves questions regarding precise contribution of KDM4A to centrosome function unanswered.
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Referee #1
Evidence, reproducibility and clarity
Chowdhury, Dere and colleagues here explore potential centrosomal roles for the lysine demethylase, KDM4A, better known for its functions in chromatin regulation. They show that it localises to centrosomes during mitosis and that its loss causes centrosome numerical aberrations and spindle multipolarity, along with micronuclei.
The major issue is the limited insight provided into the potential mechanism(s) by which KDM4A loss impacts centrosome/ spindle pole integrity. There are also several technical issues that should be improved upon.
Major points
- A gallery of different cell cycle stages should be included to define KDM4A centrosomal localisation at G1, S and G2 phases and whether it is localised to duplicating centrosomes.
- The immunoprecipitations in Fig. 1 and Supp. Fig. 1 must include appropriate controls. There is no positive control in Fig. 1E and the negative controls for the tagged pulldowns are not appropriate in that there is no other HA-tagged protein in cells. Antibody controls and the reciprocal immunoprecipitations should also be included in the same figure (with controls).
- Fig. 1H: The use of overexpressed GFP-centrin for immunoprecipitations is questionable; centrin overexpression can cause centrosome amplification, so the level of centrin relative to the endogenous level should be demonstrated.
- The precise localisation of KDM4A should be determined more clearly with respect to known centrosomal structures/ regions. One would speculate a PCM localisation from the data presented here, but the use of centrobin as a marker does not allow the mother centriole's location to be determined with great clarity. It is unclear why the authors chose centrobin as a marker; further explanation of this might be helpful to the reader. Centrobin is usually cited as a daughter centriole marker (PMID: 16275750, but see 29440264). Supp. Fig. 3J appears to shows 2 centrioles labelled with centrobin but the paper does not specify whether centrobin is chosen as a daughter marker or otherwise.
- Related to this localisation issue, Fig. 2D is unclear to this reviewer. What is this normalized to- a marker or just a set of coordinates? This is an unusual means of representing a localization that does not help the reader understand the (sub-)centrosomal location of KDM4A. The analysis in Supp. Fig. 4 is of somewhat limited value and might be omitted.
- Fig. 3 shows the amplification of gamma-tubulin signals, but there is no control for cell cycle stage. The Kdm4a knockout cells appear to be twice the size of the controls, suggesting a G2 phase arrest, which can potentiate centrosome overduplication, or cytokinetic failure in a previous cell cycle (this may also be the case in Figs. 6C and 7B). Therefore, these cells should be phenotyped more robustly with respect to their proliferative characteristics and cell cycle phase distribution. Cell cycle phenotypes should also be checked in the rescue experiments.
- Related to the previous point, in the DAPI staining in Figure 5A, 'pseudo-bipolar' cells #1 and #3 (from the top) seem to have greatly-increased levels of DNA, suggesting failed cytokinesis as a mechanism of centrosome abnormality. This is a very different process to a centrosome overduplication within a single cell cycle; given that these are knockouts, it is not clear what conclusions should be drawn from the current analysis.
- The JIB-04 result may suggest that KDM4A inhibition causes fragmentation of spindle poles, given that it is a relatively short treatment that would probably not be long enough for centrosome overduplication. Whether this arises during M phase, distinct from the overduplication phenotype seen where there are >4 centrioles, should be posed as a separate question- these may be distinct outcomes from KMD4A inhibition at different cell cycle times.
- It is unclear why the authors call the cell shown in Fig. 4B 'pseudo-bipolar'- there are clearly four poles here (as in the multipolar example shown in Fig 5A). This makes the data in Fig. 5 difficult to interpret. The authors should review their classification.
- Expression of the vector control in the Kdm4a nulls in Fig. 7A appears to show a decline in the H3K36me3 levels, confusing the outcome of this experiment. Quantitation should be provided for these blots.
- A rescue experiment should be included for the siRNA knockdown of KDM4A.
- Size markers should be shown in all immunoblots.
Minor points
- p.6, 11 'the resulting payment' and 'caustic chromosome environment' are strange usages and should be rephrased.
- Are all panels shown at the same magnification in Fig. 1B? (The telophase DAPI appears different to the anaphase)
- Blow-up panels should be shown so that the centrosomes can be visualised more clearly (Fig. 1 and Supp. Fig. 1).
- The MT labelling in Fig. 1D is not of good quality; this imaging should be improved.
Significance
- General assessment: Strengths:
Potential novel localisation and functions for KMD4A.
Weaknesses:
Limited mechanistic detail.
Technical concerns.
Key biological points not addressed. - Advance: While these observations are of potential interest, there are many questions that are not resolved clearly, resulting in a mainly descriptive advance. - Audience: Basic research, primarily. The study is likely to be of interest to the centrosome/ cell cycle field, with potential ramifications in genome stability and cancer biology.
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Reply to the reviewers
We thank the reviewers for their general comment and for the critical evaluation of our analyses and results interpretation. Their comments greatly helped us to improve the manuscript.
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Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: An analysis of an Arabidopsis VSP13 presumed lipid transport is provided. The analysis pretty much follows similar studies done on yeast and human homologs. Key findings are the identification of multiple products from the locus due to differential splicing, analysis of lipid binding and transport properties, subcellular location, tissue specific promoter activity, mutant analysis suggesting a role in lipid remodeling following phosphate deprivation, but no physiological or growth defects of the mutants. Major points: The paper is generally written and documented, the experiments are well conducted and follow established protocols. The following major points should be considered:
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There are complementary lipid binding assays that should be considered such as liposome binding assays, or lipid/western dot blots. All of these might give slightly different results and may inform a consensus. Of course, non-membrane lipids such as TAG cannot be tested in a liposome assay.
Concerning lipid transfer proteins (LTPs), it is important to differentiate the lipid binding capacity related to the transport specificity (which lipids are transported by a LTP?) from the lipid binding capacity linked to the targeting of a LTP to a specific membrane (a LTP can bind a specific lipid via a domain distinct from the lipid transfer domain to be targeted in cells, but will not transport this lipid). Both aspects are of high interest to be determined. Our goal here was to focus on the identification of the lipids bound to AtVPS13M1 and to be likely transported, which is why we used a truncation (1-335) corresponding to the N-term part of the hydrophobic tunnel. Liposome binding assays and lipid dot blots are necessary to answer the question of the membrane binding capacity of the protein. We think that this aspect is out of the scope of the current article as it will require to express and purify other AtVPS13M1 domains that are known to bind lipids such as the two PH domains and the C2. This will be the scope of future investigations in our lab.
Similarly, lipid transfer based only on fluorophore-labeled lipids may be misleading because the fluorophore could affect binding. It is mentioned that the protein in this assay is tethered by 3xHis to the liposomes. Un less I ma missing something, I do not understand how that should work. This needs to be better explained.
We truly agree with Reviewer 1 that the presence of a fluorophore could affect lipid binding to the protein. In this assay, lipids are labeled on their polar head and it is therefore difficult to conclude about the specificity of our protein in term of transport. This assay is used as a qualitative assay to show that AtVPS13M1(1-335) is able to transfer lipids in vitro, and in the manuscript, we did not make any conclusion about its transport specificity based on this assay, but rather used the binding assay to assess the binding, and likely transport, specificity of AtVPS13M1. FRET-based assay is a well-accepted assay in the lipid transfer community to easily probe lipid transport in vitro and has been used in the past to assess transfer capacity of different proteins, including for VPS13 proteins (for examples, see (Kumar et al., 2018; Hanna et al., 2022; Valverde et al., 2019)).
To be able to transfer lipids from one liposome to another, both liposomes have to be in close proximity. Therefore, we attached our protein on donor acceptors, to favor the transport of the fluorescent lipids from the donor to the acceptor liposomes. Then, we progressively increased acceptor liposomes concentration to favor liposome proximity and the chance to have lipid transfer. We added a scheme on Figure 3B of the revised version of the manuscript to clarify the principle of the assay. In addition, we provided further control experiments suggested by Reviewers 2 and 3 showing that the fluorescence signal intensity depend on AtVPS13M1(1-335) protein concentration and that no fluorescence increase is measured with a control protein (Tom20.3) (see Figure 3C-D of the revised manuscript).
The in vivo lipid binding assay could be obscured by the fact that the protein was produced in insect cells and lipid binding occurs during the producing. What is the evidence that added plants calli lipids can replace lipids already present during isolation.
Actually we don’t really know whether the insect cells lipids initially bound to AtVPS13M1(1-335) are replaced by calli lipids or whether they bound to still available lipid binding sites on the protein. But we have two main lines of evidence showing that our purified protein can bind plant lipids even in the presence of insect cells lipids: 1) our protein can bind SQDG and MGDG, two plants specific lipids, and 2) as explained p.8 (lines 243-254), lipids coming from both organisms have a specific acyl-chain composition, with insect cells fatty acids mainly composed of C16 and C18 with 0 or 1 unsaturation whereas plant lipids can have up to 3 unsaturations. By analyzing and presenting on the histograms lipid species from insect cells, calli and those bound to AtVPS13M1(1-335), we were able to conclude that for all the lipid classes besides PS, a wide range of lipid species deriving from both organisms was bound to our protein. The data about the lipid species bound to AtVPS13M1(1-335) are presented in Figure 2E and S2.
The effects on lipid composition of the mutants are not very drastic from what I can tell. Furthermore, how does this fit with the lipid composition of mitochondria where the protein appears to be mostly located?
It is true that lipid composition variations in the mutants are not drastic but still statistically significant. As a general point in the field of lipid transfer, it is not very common to have major changes in total lipidome on single mutants of lipid transfer proteins because of a high redundancy of lipid transport pathway in cells. This is particularly true for VPS13 proteins, as exemplified by multiple studies. Major lipid phenotypes can be revealed in specific conditions, such as phosphate starvation in our case, or when looking at specific organelles or specific tissues and/or developmental stages. This is explained and illustrated by examples in the discussion part p. 16 (line 526-532). In addition, as suggested by Reviewer 3, we performed further lipid analysis on calli and also on rosettes under Pi starvation and found a similar trend (Figure 4 and S4 of the revised version of the manuscript). Thus, we believe that, even if not drastic, these variations during Pi starvation are a real phenotype of our mutants.
As we found that our protein is located at the mitochondrial surface, we agree that Reviewer 1’s suggestion to perform lipidomic analyses on isolated mitochondria will be of high interest but this will be the scope of future studies that we will performed in our lab. First, we would like to identify all the organelles at which AtVPS13M1 is localized before performing subfractionations of these different organelles from the same pool of cell cultures grown in presence or absence of phosphate.
For the localization of the fusion protein, has it been tested whether the furoin is functional? This should be tested (e.g. by reversion of lipid composition).
As we did not observe major developmental phenotypes in our mutants, complementation should be indeed tested by performing lipidomic analyses in calli or plants grown in presence or absence of Pi, which is a time-consuming and expensive experiment. Because we used the fusions mainly for tissue expression study and subcellular localization and not for functional analyses, we believe that this is not an essential control to be performed for this work.
It is speculated that different splice forms are located to different compartments. Can that be tested and used to explain the observed subcellular location patterns?
Indeed some splice forms can modify the sequence of domains putatively involved in protein localization. This could be tested by producing synthetic constructs with one specific exon organization, which is challenging according to the size of AtVPS13M1 cDNA (around 12kb). In addition, our long-read sequencing experiment and PCR analyses revealed the existence of six transcripts, a major one representing around 92% and the five others representing less than 2.5% (Figure 1D). Among the five less abundant transcripts, four produce proteins with a premature stop codon and are likely to arise from splicing defects as explained in the discussion part p. 15 (lines 488-496). One produces a full-length protein with an additional loop in the VAB domain but because of the low abundance of this alternative transcript (1.4%), we believe it does not contribute significantly to the major localization we observed in plants and did not attend to analyze its localization.
GUS fusion data only probe promoter activity but not all levels of gene expression. That caveat should be discussed.
We are aware of this drawback and that is the reason why we fused the GUS enzyme directly to our protein expressed under its native locus (i.e. with endogenous promoter and exons/introns) as depicted in Figure 5A. Therefore, our construction allows to assess directly AtVPS13M1 protein level in plant tissues.
Minor points: 1. Extraplastidic DGDG and export from chloroplasts following phosphate derivation was first reported in PMID: 10973486.
We added this reference in the text.
Check throughout the correct usage of gene expression as genes are expressed and proteins produced.
Many thanks for this remark, we modified the text accordingly
In general, the paper is too long. Redundancies between introduction, results and discussion should be removed to streamline.
We reduced the text to avoid redundancy.
I suggest to redraw the excel graphs to increase line thickness and enlarge font size to increase presentation and readability.
We tried as much as we can to enlarge graphs and font size increasing readability.
Reviewer #1 (Significance (Required)):
Significance: Interorganellar lipid trafficking is an important topic and especially under studied in plants. Identifying components involved represents significant progress in the field. Similarly, lipid remodeling following phosphate derivation is an important phenomenon and the current advances our understanding.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary: The manuscript "AtVPS13M1 is involved in lipid remodelling in low phosphate and is located at the mitochondria surface in plants" by Leterme et al. identifies the protein VPS13M1 as a lipid transporter in Arabidopsis thaliana with important functions during phosphate starvation. The researchers were able to localise this protein to mitochondria via GFP-targeting in Arabidopsis. Although VPS13 proteins are well described in yeast and mammals, highlighting their importance in many vital cellular processes, there is very little information on them in plants. This manuscript provides new insights into plant VPS13 proteins and contributes to a better understanding of these proteins and their role in abiotic stress responses, such as phosphate starvation.
Major points: - Please describe and define the domains of the VPS13M1 protein in detail, providing also a figure for that. Figure 1 is mainly describing possible splice variants, whereas the characteristics of the protein are missing.
We have added information on AtVPS13M1 domain organization in the introduction (p.4, lines 103-109) and referred to Figure 1A that described protein domain organization. We did not added too much details as plant VPS13 protein domains organization was extensively described in two previous studies cited several times in the manuscript (Leterme et al., 2023; Levine, 2022).
- Please compare the expression level of VPS13M1 in the presence and in the absence of phosphate.
Many thanks for this suggestion. We performed qRT-PCR analyses of AtVPS13M1 from mRNA extracted from calli grown six days in presence and absence of phosphate. The results obtained did not reveal variations in mRNA level. The results were added in Figure S1A of the revised version of the manuscript and discussed in p.5 (lines 154-156).
- Page 9, second paragraph: Here, the lipid transport capability of AtVPS13M1 is described. Varying concentrations of this recombinant protein should be used in this test. Further, it is not highlighted, that a truncated version of VSP13M1 is able to transport lipids. This is surprising, since this truncated version is less than 10% of the total protein (only aa 1-335).
We agree with reviewer 2 that increasing protein concentration is an important control to perform. We included an experiment with an increasing quantity of protein (2X and 4X) in the revised version of the manuscript and showed that the signal intensity increased faster when protein concentration is higher (Figure 3D of the revised manuscript). As requested by Reviewer 3, we also included a negative control with Tom20.3 to show that the signal increase after the addition of AtVPS13M1(1-335) is specific to this protein (Figure 3C of the revised manuscript).
The transport ability of the N-terminal part of VPS13 was demonstrated in yeast and mammals VPS13D (Kumar et al., 2018; Wang et al., 2021). We highlighted this p. 7 (lines 213-218) of the revised version of the manuscript. This is explained by the inherent structure of VPS13 proteins that are composed of several repeats of the same domain type called RBG (for repeating β-groove), each forming a β-sheet with a hydrophobic surface. The higher the number of RBG repeats, the longer the hydrophobic tunnel is. The (1-335) N-terminal region corresponds to two RBG unit repeats forming a “small” tunnel able to bind and transfer lipids. The number of RBG repeats has influence on the quantity of lipids bound per protein in vitro, the longest the protein is, the highest the number of lipid molecules bound is (Kumar et al., 2018), but the effect on protein length on in vitro lipid transfer capacity has not been investigated yet to the best of our knowledge.
- Also, for phenotype analysis, T-DNA insertion mutants are used that still contain VPS13M1 transcripts. Although protein fragments where not detected by proteomic analysis, this might be due to low sensitivity of the proteomic assay. Further the lipid transport domain of VPS13M1 (aa 1-335) might not be affected by the T-DNA insertions at all. Here more detailed analysis needs to be done to prove that indeed loss-of protein function occurs in the mutants.
We do not have other methods than proteomic to test whether our mutants are KO or not. We tried unsuccessfully to produce antibodies. Mass spectrometry is the most sensitive method but the absence of detection indeed does not mean the absence of the protein. From proteomic data, we can conclude that at least, our mutants present a decrease in AtVPS13M1 protein level, thus we called them “knock down” in the revised version of the manuscript and added the following sentence p. 9 (lines 297-300): “As the absence of detection of a protein by mass spectrometry-based proteomics does not allow us to strictly claim the absence of this protein in the sample, we concluded that AtVPS13M1 expression in both atvps13m1-1 and atvps13m1-4 was below the detection limit and consider them as knock down (KD) for AtVPS13M1.”
- Localisation in mitochondria: As the Yepet signal is very weak, a control image of not transfected plant tissue needs to be included. Otherwise, it might be hard to distinguish the Yepet signal from background signal. The localisation data presented in Figure 5 does not allow the conclusion that VPS13M1 is localized at the surface of mitochondria as stated in the title. It only indicates (provided respective controls see above) that VPS13M1 is in mitochondria. Please provide more detailed analysis such as targeting to tobacco protoplasts, immunoblots or in vitro protein import assays. Also test +Pi vs. -Pi to see if VPS13M1 localisation is altered in dependence of Pi.
Indeed our Yepet signal is not very strong but on the experiments we performed on Col0 non-transformed plants, we did not very often see fluorescence background in the leaves’ vascular tissue, that is why we focused our study on this tissue. We sometimes observed some background signals in some cells that are clearly different from AtVPS13M1-3xYepet signals and never co-localized with mitochondria. Examples of these aspecific signals are presented in Figure S6E of the revised version of the manuscript.
We agree with reviewer 2 that our confocal images suggested, but not demonstrated, a localization at the surface of mitochondria. To confirm the localization, we generated calli cell cultures from AtVPS13M1-3xYepet lines and performed subcellular fractionations and western blot analyses confirming that AtVPS13M1 was indeed enriched in mitochondria and also in microsomal fractions (Figure 6G of the revised version). Then we performed mild proteolytic digestion of the isolated mitochondria with thermolysin and show that AtVPS13M1 was degraded, as the outer membrane protein Tom20.3, but not the inner membrane protein AtMic60, showing that AtVPS13M1 is indeed at the surface of mitochondria (Figure 5H of the revised manuscript). We believe that this experiment, in addition to the confocal images showing a signal around mitochondria, convincingly demonstrates that AtVPS13M1 is located at the surface of mitochondria.
The localization of AtVPS13M1 under Pi starvation is a very important question that we tried to investigate without success. Indeed, we intended to perform confocal imaging on seedlings grown in liquid media to easily perform Pi starvation as described for the analysis of AtVPS13M1 tissue expression with β-glucuronidase constructs. However, the level of fluorescence background was very high in seedlings and no clear differences between non-transformed and AtVPS13M1-3xYepet lines were observed, even in root tips where the protein is supposed to be the most highly expressed according to β-glucuronidase assays. Example of images obtained are presented in Figure R1. We concluded that the level of expression of our construct was too low in seedlings. The constructions of lines with a higher AtVPS13M1 expression level, by changing the promotor, to better analyze AtVPS13M1 in different tissues or in response to Pi starvation will be the scope of future work in our laboratory in order to investigate AtVPS13M1 localization under low Pi.
Phenotype analysis needs to be done under Pi stress and not under cold stress! Further, root architecture and root growth should also be done under Pi depletion. Here the title is also misleading, it is not at all clear why the authors switch from phosphate starvation to cold stress.
In the revised version of the manuscript, we analyzed the seedlings root growth of two mutants (atvps13m1-3 and m1-4) under low Pi and did not notice significant differences (Figure 7E, S7D of the revised version). We analyzed growth under cold stress because this stress also promotes remodeling of lipids, but we agree that it goes beyond the scope of this article that is focused on Pi starvation and we removed this part from the revised manuscript.
Minor points: Page 3, line 1: what does the abbreviation VPS stand for?
The definition of VPS (Vacuolar Protein Sorting) was added.
Page 3, line 1: change "amino acids residues" to "amino acid residues"
This was done.
Page 3, line 8 - 12: please rewrite this sentence. You write, that because of their distribution VPS13 proteins do exhibit many important physiological roles. The opposite is true: They are widely distributed in the cell because of their involvement in many physiological processes.
We changed the sentence to “ VPS13 proteins localize to a wide variety of membranes and membrane contact sites (MCSs) in yeast and human (Dziurdzik and Conibear, 2021). This broad distribution on different organelles and MCSs is important to sustain their important roles in numerous cellular and organellar processes such as meiosis and sporulation, maintenance of actin skeleton and cell morphology, mitochondrial function, regulation of cellular phosphatidylinositol phosphates level and biogenesis of autophagosome and acrosome (Dziurdzik and Conibear, 2021; Hanna et al., 2023; Leonzino et al., 2021).”
Page 6, line6: change "cDNA obtained from A. thaliana" to "cDNA generated from A. thaliana.
This was done.
Page 6, line 10: change" 7.6kb" to "7.6 kb"
This was done.
Page 7: address this question: can the isoforms form functional VPS13 proteins? This might help to postulate whether these isoforms are a result of defective splicing events.
We addressed this aspect in the discussion p.15 at lines 486-502.
Figure 2 B: Change "AtVPS13M1"to "AtVPS13M1(1-335)"
This was done.
Figure 2, legend: -put a blank before µM in each case.
This was done.
-Change 0,125µM to 0.125 µM
This was done.
-what does "in absence (A-0µM)" mean?
This means that the Acceptor liposomes are at 0 µM. To clarify, we changed it to “Acceptor 0 µM” in the revised version of the manuscript (Figure 3C).
-Which statistical analysis was employed?
We performed a non-parametric Mann-Whitney test in the revised version of the manuscript. This was indicated in the legend.
-Further, rewrite the sentence "Mass spectrometry (MS) analysis of lipids bound to AtVPS13M1(1-335) or Tom20 (negative control) after incubation with calli total lipids. Results are expresses in nmol of lipids per nmol of proteins (C) or in mol% (D)". -"C" and "D" are not directly comparable, as in "C" no Tom20 was used and in "C" no insect cells were used.
-Further, in "D" the experimental setup is not clear. AtVPS13(1-335) is supposed to be purified protein after incubation with calli lipids (figure 2, A). Further, in the same figure, lipid composition of "insect cells" and "calli-Pi" are compared àwhy? Please clarify this.
C and D are two different representations of the same results providing different types of information. In C., the results are expressed in nmol of lipids / nmol of proteins to assess 1) that the level of lipids found in AtVPS13M1(1-335) purifications is significantly higher than what we can expect from the background (assessed using Tom20) and 2) what are the classes of lipids that associate or not to AtVPS13M1(1-335). In D. the lipid distribution in mol% is presented for AtVPS13M1(1-335) as well as for total extracts from calli and insect cells to be able to compare if one lipid class is particularly enriched or not in AtVPS13M1(1-335) purifications compared to the initial extracts with which the protein was incubated. As an example, it allows to deduce that the absence of DGDG detected in the AtVPS13M1(1-335) purifications is not linked to a low level of DGDG in the calli extract, because it represented around 15 mol%, but likely to a weak affinity of the protein for this lipid. We did not represent the Tom20 lipid distribution on this graph because it represents background of lipid binding to the purification column and might suggest that Tom20 binds lipids. We changed the legend in this way and hope that it is clearer now: “C-D. Mass spectrometry (MS) analysis of lipids bound to AtVPS13M1(1-335) or Tom20 (negative control) after incubation with calli total lipids and repurification. Results are expresses in nmol of lipids per nmol of proteins in order to analyze the absolute quantity of the different lipid classes bound to AtVPS13M1(1-335) compared to Tom20 negative control (C), and in mol% to assess the global distribution of lipid classes in AtVPS13M1(1-335) purifications compared to the total lipid extract of insect cells and calli (D).”
Figure 3: -t-test requires a normal distribution of the data. This is not possible for an n=3. Please use an adequate analysis.
We performed more replicates and used non-parametric Mann-Whitney analyses in the revised version of the manuscript.
-Please clarify the meaning of the letters on the top of the bars in the legend.
This corresponded to the significance of t-tests performed in the first version of the manuscript that were reported in Table S3. As in the new version we performed Mann-Whitney tests, we highlighted the significance by stars and in the figure legends.
Please, make it clear that two figures belong to C.
This was clarified in the legend.
-Reorganise the order of figure 3 (AàBàCàD)
Because of the configuration of the different histograms presented in the figure, we were not able to change the order but we believed that the graphs can be easily red this way.
Page 10, 3. Paragraph: since the finding, that no peptides were found in the VSP13M1 ko lines, although transcription was not altered, is surprising, please include the proteomic data in the supplement
Proteomic data were deposited on PRIDE with the identifier PXD052019. They will remain not publicly accessible until the acceptance of the manuscript.
Page 11, line 17: The in vitro experiments showed a low affinity of VSP13M1 towards galactolipids. It is further claimed that this is consistent with the finding of the AtVSP13M1 Ko line in vivo, that in absence of PI, no change in DGDG content could be observed. However, the "absence" of VSP13M1 in vivo might still result in a bigger VSP13M1 protein, than the truncated form (1-335) used for the in vitro experiments
It is true that our in vitro experiments were performed only with a portion of AtVPS13M1 and that the length of the protein could influence protein binding specificity. We removed this assessment from the manuscript.
Page 13, lane 8: you should reconsider the use of a triple Yepet tag: If two or more identical fluorescent molecules are in close proximity, their fluorescence emission is quenched, which results in a weak signal (as the one that you obtained). See: Zhuang et al. 2000 (PNAS) Fluorescence quenching: A tool for single-molecule protein-folding study
Many thanks to point this paper. We use a triple Yepet because AtVPS13M1 has a very low level of expression and because this strategy was used successfully to visualize proteins for which the signal was below the detection level with a single GFP (Zhou et al., 2011). The quenching of the 3xYepet might also depend on the conformation they adopt on the targeting protein.
Page 13, line 14: change 1µm to 1 µm
This was done.
Page 13, line 29: please reduce the sentence to the first part: if A does not colocalize with B, it is not necessary to mention that B does not colocalise with A.
The sentence was modified accordingly.
Page 14, 2. Paragraph: it is not conclusive that phenotype analysis is suddenly conducted with plants under cold stress, since everything was about Pi-starvation and the role of VSP13M1. Lipid remodelling under Pi stress completely differs from the lipid remodelling under cold stress.
We eliminated this part in the revised version of the manuscript.
Page 14, line 20: change figure to Figure
This was done.
Page 07, line 17: change artifact to artefact
This was done.
Reviewer #2 (Significance (Required)):
General assessment: The paper is well written and technically sound. However, some points could be identified, that definitely need a revision. Overall, we got the impression that so far, the data gathered are still quite preliminary and need some more detailed investigations prior to publication (see major points).
Advance: The study definitely fills a gap of knowledge since not much is known on the function of plant VPS13 proteins so far.
Audience: The study is of very high interest to the plant lipid community but as well of general interest for Plant Molecular Biology and intracellular transport.
Our expertise: Plant membrane transport and lipid homeostasis.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript by Leterme et al. (2024) describes the characterization of VPS13M1 from Arabidopsis. VPS13 proteins have been analyzed in yeast and animals, where they establish lipid transfer connections between organelles, but not much is known about VPS13 proteins in plants. First, different splicing forms were characterized, and the form A was identified as the most relevant one with 92% of the transcripts. The protein (just N-terminal 335 amino acids out of ca. 3000 amino acids) was expressed in insect cells and purified. Next, the protein was used for lipid binding assays with NBD-labeled lipids followed by analysis in polyacrylamide gel electrophoresis. VPS13M1 bound to PC, PE, PS and PA. Then, the protein from insect cells was incubated with Arabidopsis callus lipids, and lipids bound to VPS13M1 analyzed by LC-MS/MS. Lipid transfer between liposomes was measured by the change in fluorescence in donor liposomes derived from two labeled lipids after addition of the protein caused by lipid transfer and dilution to acceptor liposomes. T-DNA insertion mutants were isolated and the lipids measured in callus derived from these mutants. Protein localization in different plant organs was recorded with a GUS fusion construct transferred into transgenic plants. The protein was localized to mitochondria using a VPS13M1-Yepet fusion construct transferred into mutant plants. The mutant plants show no visible difference to wild type, even when the plants were grown under stress conditions like low temperature. The main message of the title is that VPS13M1 localizes to the mitochondria which is well documented, and it is involved in lipid remodeling under low phosphate conditions.
The lipid transfer assay shown in Figure 2F lacks a negative control. This would be the experiment with donor and acceptor liposomes in the presence of another protein like Tom20.
Many thanks for this suggestion. In the revised version of the manuscript, we performed a fluorescent lipid transport assay with Tom20.3 in the presence of 25 µM of donor liposomes and 1.5 mM of acceptor liposomes, the condition for which we observed a maximum of transport for AtVPS13M1(1-335). As expected, no fluorescence increase was observed. The results are presented in the Figure 3C of the revised manuscript.
The lipid data (Fig. 3 and Fig. S4) do not sufficiently support the second claim, i.e. that the protein is involved in lipid remodeling under low P. Data in Fig. 3C are derived from only 3 replicates and in Fig. S4 from only 2 replicas with considerable error bars. Having only 2 replicates is definitely not sufficient. Fig. 3C shows a suppression in the decrease in PE and PC at 4 d of P deprivation (significant for two mutants for PE, for only one for PC). Fig. S4A shows suppression of the decrease in PC at 6 d after P deprivation (significant for both mutants), but no significant effect on PE. Fig. 4SB shows no significant change in PE or PC at -P after 8 d of P deprivation. The data are not consistent. There are also problems with the statistics in Fig. 3 and Fig. S4. The authors used T-test, but place letters a, b, c on top of the bars. Usually, asterisks should be used to indicate significant differences. Data indicate medians and ranges, not mean and SD. In Fig. S4, how can you indicate median and range if you have only 2 replicates? Why did the authors use callus for lipid measurements? Why not use leaves and root tissues? What does adjusted nmol mean? What does the dashed line at 1.05 on the y axis mean? Taken together, I suggest to repeat lipid measurements with leaves and roots from plantets grown under +P and -P conditions in tissue culture with 5 replcates. Significant differences can be analyzed on the level of absolute (nmol per mg FW/DW) or relative (%) amounts.
Here are our answers to concerns about the design of our lipidomics experiments:
We used calli for lipid measurement because it is very easy to control growth conditions and to performed phosphate starvation from this cell cultures. The second reason is that it is a non-photosynthetic tissue with a high level of phospholipids and a low level of galactoglycerolipids and it is easier to monitor the modification of the balance phospholipids/galactoglycerolipids in this system. The lipid analysis on calli at 4 days of growth in presence or absence of Pi were performed on 3 biological replicates but on two different mutants (atvps13m-1 and m1-3) and we drew our conclusions based on variations that were significant for both mutants. In the revised version of the manuscript, we performed further lipidomic analyses on calli from Col0 and another mutant (atvps13m1-2) after 6 days of growth in presence or absence of Pi (Figure 4E, S4A-C, n=4-5) and added new data on a photosynthetic tissue (rosettes) from Col0 and atvps13m1-3 mutant. For rosettes analysis, seeds were germinated 4 days in plates with 1 mM Pi and then transferred on plates with 1 mM or 5 µM of Pi. Rosettes were harvested and lipids analyzed after 6 days (Figure 4F-G, S4D, n=4-5). All the data were represented with medians and ranges because we believe that median is less sensitive to extreme values than mean and might better represent what is occurring. Ranges highlight the minimal and maximal value of the data analyzed and we believe it is a representative view of the variability we obtained between biological samples.
Lipid measurement are done by mass spectrometry. As it was already reported, mass spectrometry quantification is not trivial as the intensity of the response depends on the nature of the molecule (for a review, see (Jouhet et al., 2024)). To counteract this ionisation problem, we developed a method with an external standard that we called Quantified Control (QC) corresponding to an A. thaliana callus lipid extract for which the precised lipid composition was determined by TLC and GC-FID. All our MS signals were “adjusted” to the signal of this QC as described in (Jouhet et al., 2017). Therefore our lipid measurement are in adjusted nmol. In material and method we modified the sentence accordingly p22 lines 720-723: “Lipid amounts (pmol) were adjusted for response differences between internal standards and endogenous lipids and by comparison with a quality control (QC).” This allows to represent all the lipid classes on a same graph and to have an estimation of the lipid classes distribution. To assess the significance of our results, we used in the revised version of the manuscript non-parametric Mann-Whitney tests and added stars representing the p-value on charts. This was indicated in the figure legends.
Here are our answers to concerns about the interpretation of our lipidomics experiments:
To summarize, in the revised version of the manuscript, lipid analyses were performed in calli from 3 different mutants (two at day 4, one at day 6) and in the rosettes from one of these mutants. All the results are presented in Figure 4 and S4. In all the experiments, we found that in +Pi, there is no major modifications in the lipid content or composition. In –Pi, we found that the total glycerolipid content is always higher in the mutant compared to the Col0, whatever the tissue or mutant considered (Figure 4A and S4A, D). In calli, this higher increase in lipid content is mainly due to an accumulation of phospholipids and in rosettes, of galactolipids. Because of high variability between our biological replicates, we did not always found significant differences in the absolute amount of lipids in –Pi. However, the analysis of the fold change in lipid content in –Pi vs +Pi always pointed toward a reduced extent of phospholipid degradation. We also added in these graphs the fold change for the total phospholipids and total galactolipids contents in the revised version of the manuscript. We believe that the new analyses we performed strengthen our conclusion about the role of AtVPS13M1 in phospholipid degradation and not on the recycling of precursors backbone to feed galactoglycerolipids synthesis at the chloroplast envelope.
Page 9, line 15: Please use the standard form of abbreviations of lipid molecular species with colon, e.g. PC32:0, not PC32-0
The lipid species nomenclature has been changed accordingly.
Page 11, line 4, (atvps13m1.1 and m1.3: please indicate the existence of mutant alleles with dashes, i.e. (atvps13m1-1 and atvps13m1-3
Names of the mutants have been changed accordingly.
Page 14, line 21: which line is indicated by atvps13m1.2-4? What does -4 indicate here?
This indicates that mutants m1-2 to m1-4 were analyzed.
Page 16, line 25: many abbreviations used here are very specific and not well known to the general audience e.g. ONT, IR, PTC, NMD etc. I think it is OK to mention them here, but still use the full terms, given that they are not used very frequently in the manuscript.
We kept ONT abbreviation because it was cited many times in both the results and discussion part. IR, PTC and NMD were cited only in the discussion and were eliminated.
Page 19, line 11. The authors cite Hsueh et al and Yang et al for LPTD1 playing a role in lipid homeostasis during P deficiency. But Yang et al. described the function of a SEC14 protein in Arabidopsis and rice during P deficiency. Is SEC14 related to LPTD1?
Many thanks for noticing this mistake. We removed the citation Yang et al. in the revised version of the manuscript.
Reference Tangpranomkorn et al. 2022: In the text, it says that this is a preprint, but in the Reference list, this is indicated with "Plant Biology" as Journal. In the internet, I could only find this manuscript in bioRxiv.
This manuscript was accepted in “New Phytologist” in December 2024 and is now cited accordingly in the new version of the manuscript.
Reviewer #3 (Significance (Required)):
The manuscript by Leterme et al describes the characterization of the lipid binding and transport protein VTPS13M1 from Arabidopsis. I think that the liposome assay needs to be done with a negative control. Furthermore, I have major concerns with the lipid data in Fig. 3C and Fig. S4. These lipid data of the current manuscript need to be redone. I do not agree that the lipid data allow the conclusion that "AtVPS13M1 is involved in lipid remodeling in low phosphate" as stated in the title.
References cited in this document:
Dziurdzik, S.K., and E. Conibear. 2021. The Vps13 Family of Lipid Transporters and Its Role at Membrane Contact Sites. Int J Mol Sci. 22:2905. doi:10.3390/ijms22062905.
Hanna, M., A. Guillén-Samander, and P. De Camilli. 2023. RBG Motif Bridge-Like Lipid Transport Proteins: Structure, Functions, and Open Questions. Annu Rev Cell Dev Biol. 39:409–434. doi:10.1146/annurev-cellbio-120420-014634.
Hanna, M.G., P.H. Suen, Y. Wu, K.M. Reinisch, and P. De Camilli. 2022. SHIP164 is a chorein motif lipid transfer protein that controls endosome–Golgi membrane traffic. Journal of Cell Biology. 221:e202111018. doi:10.1083/jcb.202111018.
Jouhet, J., E. Alves, Y. Boutté, S. Darnet, F. Domergue, T. Durand, P. Fischer, L. Fouillen, M. Grube, J. Joubès, U. Kalnenieks, J.M. Kargul, I. Khozin-Goldberg, C. Leblanc, S. Letsiou, J. Lupette, G.V. Markov, I. Medina, T. Melo, P. Mojzeš, S. Momchilova, S. Mongrand, A.S.P. Moreira, B.B. Neves, C. Oger, F. Rey, S. Santaeufemia, H. Schaller, G. Schleyer, Z. Tietel, G. Zammit, C. Ziv, and R. Domingues. 2024. Plant and algal lipidomes: Analysis, composition, and their societal significance. Progress in Lipid Research. 96:101290. doi:10.1016/j.plipres.2024.101290.
Jouhet, J., J. Lupette, O. Clerc, L. Magneschi, M. Bedhomme, S. Collin, S. Roy, E. Maréchal, and F. Rébeillé. 2017. LC-MS/MS versus TLC plus GC methods: Consistency of glycerolipid and fatty acid profiles in microalgae and higher plant cells and effect of a nitrogen starvation. PLoS ONE. 12:e0182423. doi:10.1371/journal.pone.0182423.
Kumar, N., M. Leonzino, W. Hancock-Cerutti, F.A. Horenkamp, P. Li, J.A. Lees, H. Wheeler, K.M. Reinisch, and P. De Camilli. 2018. VPS13A and VPS13C are lipid transport proteins differentially localized at ER contact sites. J Cell Biol. 217:3625–3639. doi:10.1083/jcb.201807019.
Leonzino, M., K.M. Reinisch, and P. De Camilli. 2021. Insights into VPS13 properties and function reveal a new mechanism of eukaryotic lipid transport. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1866:159003. doi:10.1016/j.bbalip.2021.159003.
Leterme, S., O. Bastien, R.A. Cigliano, A. Amato, and M. Michaud. 2023. Phylogenetic and Structural Analyses of VPS13 Proteins in Archaeplastida Reveal Their Complex Evolutionary History in Viridiplantae. Contact (Thousand Oaks). 6:1–23. doi:10.1177/25152564231211976.
Levine, T.P. 2022. Sequence Analysis and Structural Predictions of Lipid Transfer Bridges in the Repeating Beta Groove (RBG) Superfamily Reveal Past and Present Domain Variations Affecting Form, Function and Interactions of VPS13, ATG2, SHIP164, Hobbit and Tweek. Contact. 5:251525642211343. doi:10.1177/25152564221134328.
Valverde, D.P., S. Yu, V. Boggavarapu, N. Kumar, J.A. Lees, T. Walz, K.M. Reinisch, and T.J. Melia. 2019. ATG2 transports lipids to promote autophagosome biogenesis. J Cell Biol. 218:1787–1798. doi:10.1083/jcb.201811139.
Wang, J., N. Fang, J. Xiong, Y. Du, Y. Cao, and W.-K. Ji. 2021. An ESCRT-dependent step in fatty acid transfer from lipid droplets to mitochondria through VPS13D−TSG101 interactions. Nat Commun. 12:1252. doi:10.1038/s41467-021-21525-5.
Zhou, R., L.M. Benavente, A.N. Stepanova, and J.M. Alonso. 2011. A recombineering-based gene tagging system for Arabidopsis. Plant J. 66:712–723. doi:10.1111/j.1365-313X.2011.04524.x.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Leterme et al. (2024) describes the characterization of VPS13M1 from Arabidopsis. VPS13 proteins have been analyzed in yeast and animals, where they establish lipid transfer connections between organelles, but not much is known about VPS13 proteins in plants. First, different splicing forms were characterized, and the form A was identified as the most relevant one with 92% of the transcripts. The protein (just N-terminal 335 amino acids out of ca. 3000 amino acids) was expressed in insect cells and purified. Next, the protein was used for lipid binding assays with NBD-labeled lipids followed by analysis in polyacrylamide gel electrophoresis. VPS13M1 bound to PC, PE, PS and PA. Then, the protein from insect cells was incubated with Arabidopsis callus lipids, and lipids bound to VPS13M1 analyzed by LC-MS/MS. Lipid transfer between liposomes was measured by the change in fluorescence in donor liposomes derived from two labeled lipids after addition of the protein caused by lipid transfer and dilution to acceptor liposomes. T-DNA insertion mutants were isolated and the lipids measured in callus derived from these mutants. Protein localization in different plant organs was recorded with a GUS fusion construct transferred into transgenic plants. The protein was localized to mitochondria using a VPS13M1-Yepet fusion construct transferred into mutant plants. The mutant plants show no visible difference to wild type, even when the plants were grown under stress conditions like low temperature. The main message of the title is that VPS13M1 localizes to the mitochondria which is well documented, and it is involved in lipid remodeling under low phosphate conditions. The lipid transfer assay shown in Figure 2F lacks a negative control. This would be the experiment with donor and acceptor liposomes in the presence of another protein like Tom20. The lipid data (Fig. 3 and Fig. S4) do not sufficiently support the second claim, i.e. that the protein is involved in lipid remodeling under low P. Data in Fig. 3C are derived from only 3 replicates and in Fig. S4 from only 2 replicas with considerable error bars. Having only 2 replicates is definitely not sufficient. Fig. 3C shows a suppression in the decrease in PE and PC at 4 d of P deprivation (significant for two mutants for PE, for only one for PC). Fig. S4A shows suppression of the decrease in PC at 6 d after P deprivation (significant for both mutants), but no significant effect on PE. Fig. 4SB shows no significant change in PE or PC at -P after 8 d of P deprivation. The data are not consistent. There are also problems with the statistics in Fig. 3 and Fig. S4. The authors used T-test, but place letters a, b, c on top of the bars. Usually, asterisks should be used to indicate significant differences. Data indicate medians and ranges, not mean and SD. In Fig. S4, how can you indicate median and range if you have only 2 replicates? Why did the authors use callus for lipid measurements? Why not use leaves and root tissues? What does adjusted nmol mean? What does the dashed line at 1.05 on the y axis mean? Taken together, I suggest to repeat lipid measurements with leaves and roots from plantets grown under +P and -P conditions in tissue culture with 5 replcates. Significant differences can be analyzed on the level of absolute (nmol per mg FW/DW) or relative (%) amounts. Page 9, line 15: Please use the standard form of abbreviations of lipid molecular species with colon, e.g. PC32:0, not PC32-0 Page 11, line 4, (atvps13m1.1 and m1.3: please indicate the existence of mutant alleles with dashes, i.e. (atvps13m1-1 and atvps13m1-3
Page 14, line 21: which line is indicated by atvps13m1.2-4? What does -4 indicate here? Page 16, line 25: many abbreviations used here are very specific and not well known to the general audience e.g. ONT, IR, PTC, NMD etc. I think it is OK to mention them here, but still use the full terms, given that they are not used very frequently in the manuscript. Page 19, line 11. The authors cite Hsueh et al and Yang et al for LPTD1 playing a role in lipid homeostasis during P deficiency. But Yang et al. described the function of a SEC14 protein in Arabidopsis and rice during P deficiency. Is SEC14 related to LPTD1? Reference Tangpranomkorn et al. 2022: In the text, it says that this is a preprint, but in the Reference list, this is indicated with "Plant Biology" as Journal. In the internet, I could only find this manuscript in bioRxiv.
Significance
The manuscript by Leterme et al describes the characterization of the lipid binding and transport protein VTPS13M1 from Arabidopsis. I think that the liposome assay needs to be done with a negative control. Furthermore, I have major concerns with the lipid data in Fig. 3C and Fig. S4. These lipid data of the current manuscript need to be redone. I do not agree that the lipid data allow the conclusion that "AtVPS13M1 is involved in lipid remodeling in low phosphate" as stated in the title.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The manuscript "AtVPS13M1 is involved in lipid remodelling in low phosphate and is located at the mitochondria surface in plants" by Leterme et al. identifies the protein VPS13M1 as a lipid transporter in Arabidopsis thaliana with important functions during phosphate starvation. The researchers were able to localise this protein to mitochondria via GFP-targeting in Arabidopsis. Although VPS13 proteins are well described in yeast and mammals, highlighting their importance in many vital cellular processes, there is very little information on them in plants. This manuscript provides new insights into plant VPS13 proteins and contributes to a better understanding of these proteins and their role in abiotic stress responses, such as phosphate starvation.
Major points:
- Please describe and define the domains of the VPS13M1 protein in detail, providing also a figure for that. Figure 1 is mainly describing possible splice variants, whereas the characteristics of the protein are missing.
- Please compare the expression level of VPS13M1 in the presence and in the absence of phosphate.
- Page 9, second paragraph: Here, the lipid transport capability of AtVPS13M1 is described. Varying concentrations of this recombinant protein should be used in this test. Further, it is not highlighted, that a truncated version of VSP13M1 is able to transport lipids. This is surprising, since this truncated version is less than 10% of the total protein (only aa 1-335).
- Also, for phenotype analysis, T-DNA insertion mutants are used that still contain VPS13M1 transcripts. Although protein fragments where not detected by proteomic analysis, this might be due to low sensitivity of the proteomic assay. Further the lipid transport domain of VPS13M1 (aa 1-335) might not be affected by the T-DNA insertions at all. Here more detailed analysis needs to be done to prove that indeed loss-of protein function occurs in the mutants.
- Localisation in mitochondria: As the Yepet signal is very weak, a control image of not transfected plant tissue needs to be included. Otherwise, it might be hard to distinguish the Yepet signal from background signal. The localisation data presented in Figure 5 does not allow the conclusion that VPS13M1 is localized at the surface of mitochondria as stated in the title. It only indicates (provided respective controls see above) that VPS13M1 is in mitochondria. Please provide more detailed analysis such as targeting to tobacco protoplasts, immunoblots or in vitro protein import assays. Also test +Pi vs. -Pi to see if VPS13M1 localisation is altered in dependence of Pi.
- Phenotype analysis needs to be done under Pi stress and not under cold stress! Further, root architecture and root growth should also be done under Pi depletion. Here the title is also misleading, it is not at all clear why the authors switch from phosphate starvation to cold stress.
Minor points:
Page 3, line 1: what does the abbreviation VPS stand for?
Page 3, line 1: change "amino acids residues" to "amino acid residues"
Page 3, line 8 - 12: please rewrite this sentence. You write, that because of their distribution VPS13 proteins do exhibit many important physiological roles. The opposite is true: They are widely distributed in the cell because of their involvement in many physiological processes.
Page 6, line6: change "cDNA obtained from A. thaliana" to "cDNA generated from A. thaliana.
Page 6, line 10: change" 7.6kb" to "7.6 kb"
Page 7: address this question: can the isoforms form functional VPS13 proteins? This might help to postulate whether these isoforms are a result of defective splicing events.
Figure 2 B: Change "AtVPS13M1"to "AtVPS13M1(1-335)"
Figure 2, legend:
- put a blank before µM in each case.
- Change 0,125µM to 0.125 µM
- what does "in absence (A-0µM)" mean?
- Which statistical analysis was employed?
- Further, rewrite the sentence "Mass spectrometry (MS) analysis of lipids bound to AtVPS13M1(1-335) or Tom20 (negative control) after incubation with calli total lipids. Results are expresses in nmol of lipids per nmol of proteins (C) or in mol% (D)".
- "C" and "D" are not directly comparable, as in "C" no Tom20 was used and in "C" no insect cells were used.
- Further, in "D" the experimental setup is not clear. AtVPS13(1-335) is supposed to be purified protein after incubation with calli lipids (figure 2, A). Further, in the same figure, lipid composition of "insect cells" and "calli-Pi" are compared why? Please clarify this. Figure 3:
- t-test requires a normal distribution of the data. This is not possible for an n=3. Please use an adequate analysis.
- Please clarify the meaning of the letters on the top of the bars in the legend. Please, make it clear that two figures belong to C.
- Reorganise the order of figure 3 (ABCD)
Page 10, 3. Paragraph: since the finding, that no peptides were found in the VSP13M1 ko lines, although transcription was not altered, is surprising, please include the proteomic data in the supplement
Page 11, line 17: The in vitro experiments showed a low affinity of VSP13M1 towards galactolipids. It is further claimed that this is consistent with the finding of the AtVSP13M1 Ko line in vivo, that in absence of PI, no change in DGDG content could be observed. However, the "absence" of VSP13M1 in vivo might still result in a bigger VSP13M1 protein, than the truncated form (1-335) used for the in vitro experiments
Page 13, lane 8: you should reconsider the use of a triple Yepet tag: If two or more identical fluorescent molecules are in close proximity, their fluorescence emission is quenched, which results in a weak signal (as the one that you obtained). See: Zhuang et al. 2000 (PNAS) Fluorescence quenching: A tool for single-molecule protein-folding study
Page 13, line 14: change 1µm to 1 µm
Page 13, line 29: please reduce the sentence to the first part: if A does not colocalize with B, it is not necessary to mention that B does not colocalise with A.
Page 14, 2. Paragraph: it is not conclusive that phenotype analysis is suddenly conducted with plants under cold stress, since everything was about Pi-starvation and the role of VSP13M1. Lipid remodelling under Pi stress completely differs from the lipid remodelling under cold stress.
Page 14, line 20: change figure to Figure
Page 07, line 17: change artifact to artefact
Significance
General assessment:
The paper is well written and technically sound. However, some points could be identified, that definitely need a revision. Overall, we got the impression that so far, the data gathered are still quite preliminary and need some more detailed investigations prior to publication (see major points).
Advance: The study definitely fills a gap of knowledge since not much is known on the function of plant VPS13 proteins so far.
Audience: The study is of very high interest to the plant lipid community but as well of general interest for Plant Molecular Biology and intracellular transport.
Our expertise: Plant membrane transport and lipid homeostasis.
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Referee #1
Evidence, reproducibility and clarity
Summary: An analysis of an Arabidopsis VSP13 presumed lipid transport is provided. The analysis pretty much follows similar studies done on yeast and human homologs. Key findings are the identification of multiple products from the locus due to differential splicing, analysis of lipid binding and transport properties, subcellular location, tissue specific promoter activity, mutant analysis suggesting a role in lipid remodeling following phosphate deprivation, but no physiological or growth defects of the mutants.
Major points: The paper is generally written and documented, the experiments are well conducted and follow established protocols. The following major points should be considered:
- There are complementary lipid binding assays that should be considered such as liposome binding assays, or lipid/western dot blots. All of these might give slightly different results and may inform a consensus. Of course, non-membrane lipids such as TAG cannot be tested in a liposome assay.
- Similarly, lipid transfer based only on fluorophore-labeled lipids may be misleading because the fluorophore could affect binding. It is mentioned that the protein in this assay is tethered by 3xHiis to the liposomes. Un less I ma missing something, I do not understand how that should work. This needs to be better explained.
- The in vivo lipid binding assay could be obscured by the fact that the protein was produced in insect cells and lipid binding occurs during the producing. What is the evidence that added plants calli lipids can replace lipids already present during isolation.
- The effects on lipid composition of the mutants are not very drastic from what I can tell. Furthermore, how does this fit with the lipid composition of mitochondria where the protein appears to be mostly located?
- For the localization of the fusion protein, has it been tested whether the furoin is functional? This should be tested (e.g. by reversion of lipid composition).
- It is speculated that different splice forms are located to different compartments. Can that be tested and used to explain the observed subcellular location patterns?
- GUS fusion data only probe promoter activity but not all levels of gene expression. That caveat should be discussed.
Minor points:
- Extraplastidic DGDG and export from chloroplasts following phosphate derivation was first reported in PMID: 10973486.
- Check throughout the correct usage of gene expression as genes are expressed and proteins produced.
- In general, the paper is too long. Redundancies between introduction, results and discussion should be removed to streamline.
- I suggest to redraw the excel graphs to increase line thickness and enlarge font size to increase presentation and readability.
Significance
Interorganellar lipid trafficking is an important topic and especially under studied in plants. Identifying components involved represents significant progress in the field. Similarly, lipid remodeling following phosphate derivation is an important phenomenon and the current advances our understanding.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
Reviewer #1
Evidence, reproducibility and clarity
The authors study early events initiated in T cells upon chemokine activation leading to cell polarization preceding cell migration. The show that actin dynamics at the centrosome are regulated in a cAMP/PKA dependent manner using different reporter systems and imaging approaches
Significance
This work will be of interest to immunologists with a strong cell biology background. The strength of the study is the detailed cell biological / near biophysical analysis of early changes in acting dynamics in relation to centrosome positioning. THe data is well controlled and convincing. Conclusions adequate based on available data.
The limitation I see is the use of a single cell line system of cancerous origin and the fact that only changes in cellular morphology are quantified, but not cellular behavior itself - e.g migration, T cell intrinsic signalling. If some key observations can be validated in primary T cells this would be perfect, or at least in a second model system. If signalling related to changed morphology is affect by regional inhibition of PKA/actin remodelling, remains uncertain, too - maybe there is a way to monitor additional parameters, other than roundness/PKA activity.
- In order to complete our results, key experiments have been performed in primary human blood T lymphocytes (PBT). 4 experiments have been realized with 4 different donors. In this model, as in CEM T cell line, we have observed that:
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chemokine-induced PBT deformation is inhibited by the PKA inhibitor H89. Unstimulated PBT are poorly deformed so that H89 by itself only slightly (but not significantly) increases resting roundness.
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chemokine stimulation induces a decrease of centrosomal actin which is partly prevented by H89 treatment. In these conditions, centrosomal actin remains at a level superior to the resting one. H89 by itself is sufficient to promote an increase in centrosomal actin.
Altogether, these results demonstrate that in PBT as in CEM, in resting conditions as well as after chemokine stimulation, the level of centrosomal actin is controlled by PKA activity and is associated with cell deformation.
These new results are presented in Fig Supp 4 and described in the manuscript (lines 158-164 and 199-202).
- In order to investigate the consequences of the PKA inhibition on chemokine-induced cell behavior, Transwell migratory assays have been performed. As presented in Fig Supp 3b, the chemokine-triggered T cell migration through 5µm pores is reduced by H89 This suggests that the reduction of T cell deformation after PKA inhibition has a direct consequence on the physiological behavior of T lymphocyte. This result is described in the manuscript (lines 165-169).
__Reviewer #2 __
Evidence, reproducibility and clarity
The ability of T cells to migrate along chemotactic cues is critical for the initiation and regulation of adaptive immune responses. To migrate directionally, cells require a polarised shape and intracellular organisation, allowing intracellular force generation towards the intended direction of migration.
The manuscript by Simao et al investigates the role of the centrosome and its co-localised pool of the actin cytoskeleton in defining the direction of an initial polarisation while being surrounded by homogenous chemokine concentration. The authors (i) describe a correlation of the intracellular position of the centrosome with the site of polarisation, (ii) identify that a reduced amount of actin at the centrosome is beneficial for cell polarisation and that (iii) the protein kinase PKA regulates this actin pool at the centrosome.
Overall, the data presented appear convincing but would benefit from a more detailed presentation of representative image examples and additional experimental data.
Major points:
(i) Many experimental microscopy datasets are quantified but lack representative images. These representative images are important to be able to judge the underlying data and should be included for the datasets shown in Figure 2, Fig. 3c, Fig. 4d (Lifeact examples), and Fig. 6 (Centrin VCA). In addition, the differences in the signal intensity of the actin cytoskeleton are sometimes hardly visible in the provided representative images (e.g., Fig. 4b, control vs CXCL12). Could the authors come up with solutions to show this in a better way (e.g., zooms and/or fire-colour coding)?
As suggested by the reviewer, some representative images have been added for all experiments (Figures 3, Supp 3a, Supp 4 a & c, Supp 7a, Supp 8). Furthermore, a zoom of actin network around the centrosome in different conditions is now presented in Fig 3b (previous Fig 4b)).
(ii) The authors use an experimental setup in which the cells 'see' a homogenous chemokine concentration around them. Further, they discuss different models in the introduction of how the local cellular polarisation in such a uniform chemokine sounding is defined, including a model of polarised localisation of chemokine receptors on the plasma membrane. However, in a tissue, chemokines are typically not homogeneously distributed but are present in the form of a gradient, e.g. due to local chemokine sources or the self-generation of chemotactic gradients by neighbouring migrating cells. Therefore, it would be interesting to know whether the described repositioning of the centrosome and the changes in the actin pool are also important if cells are in a chemokine gradient. Experimentally, this could be addressed by providing a local chemokine source (e.g. from a micropipette). If this goes beyond the scope of the manuscript, then at least this aspect of chemokine gradients should be clearly mentioned and discussed in the introduction and the discussion sections.
We thank the reviewer for this remark. We had not actually addressed this point.
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We have tried to create chemokine gradients. But, we have never been able to observe all cells polarizing in the same direction (along this gradient). This negative result might however be due to our experimental setup so that this result is not mentioned in the manuscript. However, to our knowledge, no real chemotaxis has been described in T lymphocytes.
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We have performed some Transwell assays that mimic trans-endothelial migration and have shown the involvement of PKA (Figure Supp 3b, lines 165-169). But the involvement of centrosomal actin could not be investigated in this experiment.
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This question is now addressed in introduction (lines 44-47) and in discussion (lines 400-405).
(iii) Given that the centrosome acts as a microtubule-organising center (MTOC), it would be interesting to see the microtubules during polarisation. Did the author try the visualisation of microtubules in live or by immunofluorescence stainings? Would it be a plausible model that reduced actin polarisation allows microtubule polarisation towards the cell periphery and thereby induces a protrusion by delivering signalling and cytoskeleton components towards the newly forming protrusion? May this be only targeted towards one side of the centrosome, as the nucleus may sterically hinder the efficient growth of microtubules to the other cellular side? What would happen in cells without a nucleus? And what would happen in cells without a centrosome (e.g., by PLK4 inhibition via centrinone) - are cells still able to form protrusions efficiently? These experimental suggestion are optional but could significantly improve the study.
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As suggested by the reviewer, we performed new experiments to quantify microtubules together with actin in the centrosomal area. As previously mentioned in discussion, we were expecting that microtubule evolution mirrors that of actin. This is indeed the case after chemokine stimulation, where the quantity of microtubules present in the centrosomal area increases as actin decreases. However, after H89 treatment, an enrichment of both cytoskeleton networks is observed. It suggests that PKA could affect directly microtubule polymerization/depolymerization equilibrium independently of actin level possibly through Microtubules Associated proteins phosphorylation. Nevertheless, the effect of centrosomal actin level on cell polarization we emphasize, might partly be due to its consequence on microtubule growth. These results are now presented in Figure 4c and representative images shown in Figure Supp 9. Furthermore, this observation is mentioned in the results part (line 256-263) and in discussion (line 428-442).
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The sterical role of the nucleus on the polarization is an interesting hypothesis that we have been however unable to test it. T lymphocytes are small cells (around 15µm diameter for CEM cell line) that seems to be difficult to enucleate.
- We have tried to obtain cells without centrosomes in order to test their ability to polarize. To this purpose, we have treated cells with the PLK4 inhibitor, centrinone (500nM) for 24 to 48h hours. Although we could observe after 33 hours, that the large majority of cells present only one centriole, we never got cells without centrioles. After this one-centriole stage, the cells started dying.
(iv) Bringing actin artificially to the centrosome via the centrin-VCA construct is a very nice approach. However, the dataset would strongly benefit from samples, in which the chemokine CXCL12 is included.
The effect of chemokine on centrin-VCA expressing cells has been investigated. This result is displayed in Fig 4e. In cells expressing the centrin-VCA construct, the level of actin at the centrosome is higher than in control cells. However, upon chemokine stimulation, the signaling pathways leading to actin reduction are still active so that the level of centrosomal actin decreases (insignificantly different to the control) and the cells do not deform (insignificantly different to the control). These results are now discussed in the manuscript (lines 283-287).
Minor points:
(i) The study is based on a lymphoblastic cell line called CEM T cells. This should be clearly stated at the beginning of the results section and in all figure legends, as it remains unknown whether primary T lymphocytes would show the same behavior. Additionally, the methods section should contain more details about this cell line, e.g. whether it is from mouse or human origin.
The cells used for the experiments are now clearly indicated in the figure legends and specified in the manuscript (results and methods).
Furthermore, as mentioned in the response to reviewer #1, new experiments have been performed with primary human blood T cells. Similar effect of PKA on cell deformation and centrosomal actin regulation have been observed. These new experiments are presented in Fig Supp 4 and described in the manuscript (lines 158-164 and 199-202).
(ii) The authors mention 'suboptimal' conditions of stimulation. However, it remains unclear in the results sections what this means. Some of the experimental modulations (e.g. the cAMP analog Rp-8-CT-cAMPS) seem to only show an effect in these suboptimal conditions but not in the optimal conditions. This should be clearly stated and discussed.
In resting cells, Rp-8-CT-cAMPS has a similar effect as H89: it induces an increase in cell roundness as well as in centrosomal actin. However, after chemokine simulation, the inhibition of cell deformation and of centrosomal actin reduction is only observed with low intensity stimulation (Figures 2g & 3d vs Supp 5a). Our interpretation is that this chemical is not potent enough to counteract strong activation of PKA. Indeed, in resting conditions or after mild PKA activation, it blocks the effect of chemokine. This might be due to the fact that the inhibitor concentration reached within the cells is not enough. The text concerning this part has been modified for more clarity (lines 171-178 and 203-206) and the figure legends mention explicitly the chemokine concentrations used for stimulation.
(iii) 23 out of the 38 references are older than 5 years, and most of them are older than 10 years. While it is surely very important to refer to these older findings, the authors may include more knowledge from recent years. This may include references about centrosome positioning in immune cells and motile cells (e.g., PMID32379884, PMID29934494, PMID30944468, PMID37987147, PMID36398880, PMID38627564, PMID33634136) and the actin cytoskeleton at the centrosome (PMID33609453), PMID33184056, PMID36111670.
The references have been updated and more recent publications added.
(iv) The first paragraph of the discussion (lines 253 - 262) needs references.
Some references have been added to this paragraph.
(v) Some Figures maybe combined as the findings are closely related (e.g., Figures 2 and 3; and Figures 5 and 6).
The figures have been modified according to this suggestion.
(vi) Line 94, Supply. Fig. 1: the authors that the chemokine receptor CXCR4 has a uniform distribution in non-stimulated cells. This is not directly evident in the images as there are areas of more and less signal. It would be important to clearly describe this in the text. Further, the labelling of the figure would benefit from labelings such as 'cell 1', 'cell 2', etc to directly make clear that these are images from 3 representative cells.
We thank the reviewer for this remark. The CXCR4 distribution is indeed not strictly uniform. We now use the fire-colour coding which makes this point more obvious in images (Fig Supp 1).
However, although some high spots of CXCR4 accumulation can be observed, they are not associated to the location of centrosome and, can thus not explain the preferential position of the polarization axis we observe. This point is discussed in the new version of the manuscript (lines 113-120).
Furthermore, in the new figure concerning CXCR4 distribution (Fig Supp 1), we labelled each image with " cell x" in order to explicit that this figure displays different representative cells.
(vii) Different centrin isoforms exist (centrin 1, 2, 3). It should be mentioned in the results and methods section, which isoform was used for their genetic constructs (e.g., centrin-GFP, centrin-VCA).
We have corrected this omission. The fact that centrin1 isoform was used is now mentioned in the text, the legends and in the methods part.
Significance
This manuscript employs a lymphoblastic cell line called CEM T cells as a model for T lymphocytes. Using imaging of these cells on 2D substrates with and without chemokine, the authors identify a PKA-controlled actin pool at the centrosome that appears to regulate the local site of protrusion formation during cell polarisation. This is an interesting finding that adds to the knowledge of (i) the functions of centrosome positioning and (ii) the functions of the actin cytoskeleton at the centrosome. Thus, the study will be interesting to readers in the centrosome and migration fields. To broaden the scope of the manuscript, the findings could be tested in primary T lymphocytes and mechanistically address the role of microtubules within the described process.
Reviewer #3
__SUMMARY __Using a variety of live- and fixed-cell imaging techniques, the authors make correlative and causative connections between chemokine-stimulated increases in cAMP and localized PKA activity, positioning and F-actin content of the centrosome, and cell polarity in T-cells.
MAJOR COMMENTS
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The authors state that "uniform CXCR4 labelling is observed in unstimulated cells" (ln94-95). While the panels in Supp Fig1a show a pattern in unstimulated cells that is obviously less dramatically asymmetrical than seen for stimulated (and, importantly, already polarized) cells in Supp Fig1b, the labelling in unstimulated cells is still far from uniform, as there is considerable heterogeneity of signal intensity along the perimeter. This is important, given that they are looking at a membrane receptor, even small fluctuations in which may be greatly amplified and thus have considerable effects on symmetry downstream. The authors should either quantify the intensity (e.g. signal as a function of polar coordinate value) or soften their language to more accurately reflect the data.
We thank the reviewer for this remark. The distribution of CXCR4 receptors is indeed not strictly uniform even in unstimulated cells. In order to make this point more obvious, we have used the fire-colour coding for CXCR4 distribution in Fig Supp1. As mentioned in the response to the reviewer #2, high spots of CXCR4 accumulation exist but they are not associated to the location of centrosome and thus, cannot explain the preferential position of the polarization axis we observe. This point is discussed in the new version of the manuscript (lines 113-120).
Regarding the analysis of polarization events in Fig. 1c and 1d, it is not clear how, exactly, the time point for the cortex opening is determined. For example, in the sample images in 1c, would the +110s or the +340s time point be used? The reason this is important is that the angle seems to change with time (at least in the example given) and there are also heterogeneities (specifically, decreases) in SiR-actin intensity along the cortex that precede cortex opening. Thus, it is not clear whether the cortex begins to open in closer proximity to the centrosome or whether the centrosome is further aligned after the cortex opens.
First of all, we would like to apologize that there is an error in the time labeling in Fig 1c. The third image corresponds to + 210s and not +110s. This has been corrected in the new Fig 1c. In this example, +210 s and not +340 s has been considered as the opening of the actin cortex.
More generally, the time for angle determination was performed by watching movies. Images were analyzed only relatively to previous and the next ones. Indeed, we first determined the period when 1/ SiRActin labeling stably decreases at one pole of the cell and 2/ the cell simultaneously deforms (transmitted light pictures). Playing the sequence backward, allowed us to determine the beginning of cortex opening. This time was then the reference for measuring the polarization angles. Thus, transient SiRActin heterogeneities we could indeed observe sometimes, were not considered. These precisions have been added to the Methods section (lines 559-564)
Also regarding Figure 1d, it is not clear how many cells and experimental replicates are represented in the data - the Results text reports 60 cells from 11 experiments (ln106) but the legend reports 58 cells (ln564) without mention of experimental replicate number.
We thank the reviewer for pointing out this error which we have corrected. The correct number of cells is 60. These cells are from 11 different experiments. We pooled all individual values of the 60 cells to establish angle distribution.
Also, while the rose plot is useful, it is important to have statistical analysis on the skewness of the response and/or to report something other than the average angle - for example, the percentage of cells with a cortex opening in the same 90-degree quadrant as the centrosome.
For more clarity, the position of the median has been added to the rose plot. Furthermore, a pie plot reports now the distribution of the angles (Fig 1d).
Finally, it might be clearer to the reader to have the rose plot and the model cell oriented in the same direction.
We agree to this suggestion and the cell (Fig 1c), the schematic drawing and the rose plot (Fig 1d) are all three oriented in the same direction.
For their PKA inhibition experiments, the authors introduce H89 as "a competitive inhibitor of ATP on the PKA catalytic subunit" (ln138-139). H89 is a very non-specific inhibitor, as demonstrated by Davies et al (PMID 10998351) and reviewed by Lochner & Moolman (PMID 17214602) and should be introduced more accurately.
H89, as a competitive inhibitor of ATP, is indeed not specific to PKA. At the low concentration we used, few other kinases can be inhibited. We now introduce more precisely the inhibitor and add references (lines 152-155 and 172-173).
To their credit, the authors use an orthogonal approach of PKA inhibition with a cAMP analog and see comparable effects. Those data, currently in Supp Fig. 3, should be included as primary data, given that the H89 data can only be correctly interpreted in the context of the Rp-cAMPS data (this applies to both Fig3 and Fig4).
As suggested, the data concerning Rp-8CPT-cAMPS are now included in the main figures (Fig 2g & Fig 3d) and the text has also been modified (lines 172-178 and 203-206).
As part of Fig.5, the authors state "AKAP450 is a type II AKAP i.e. it is able to bind RII subunits of PKA. In order to determine whether this AKAP allows a [sic] compartmentalization of the PKA activity responsible for centrosomal F-actin regulation, we used the specific peptide (Ht31)". Ht31 broadly inhibits PKA anchoring; its effect is not specific for any individual AKAP, including AKAP9/AKAP450.
We completely agree with this point and did not want to mean that Ht31 was specific to AKAP450. Therefore, the sentence has been modified to be more explicit (lines 234-236).
Moreover, the authors neither show/confirm PKA localization to the centrosome nor its displacement with the indicated concentration of Ht31, and they do not include data that PKA is displaced from the centrosome with any greater specificity or sensitivity than its displacement from any other subcellular location. Therefore, this statement (as well as the claim in the Abstract that "a specific pool of protein kinase A) (ln15) by the authors is not accurate. The authors should, at the very least, re-word the statement and, for the sake of rigor and support of their hypotheses, confirm that PKA (subunits and/or activity) is displaced from the centrosome.
We thank the reviewer for raising this fair point. We have performed experiments to determine the distribution of PKA (immunofluorescence, using an antibody against PKA Ca). Although the labeling is not very good, we can evidence a slight accumulation of the protein around the centrosome. This enrichment is statistically reduced in the presence of the inhibitory peptide Ht31. This suggest that 1) a pool of PKA accumulates around the centrosome and 2) it is displaced by Ht31.
These results as well as the corresponding images are presented in Figure Supp 7 and described in the new version of the manuscript (lines 236-245).
The experiments & results using VCA-centrin-GFP are very intriguing. However, it is crucial that primary data (i.e. photomicrographs/panels of fluorescent images of centrin/VCA-centrin localization, centrosomal F-actin, and roundness) be included for the readers' inspection. Also, it is not clear whether the graphically summarized data on centrosomal F-actin and roundness (Fig. 6b) represent analysis of cells before and after CXCL12 stimulation, or only before or only after. If either of the latter, analysis of these parameters both before and after stimulation should be included.
Representative images of cells expressing centrin/centrin-VCA are now provided and shown in Fig Supp 8. They show that centrin-VCA expressing cells are less deformed (a, b) but also display a higher quantity of F-actin around the centrosome (a).
The effect of stimulation in centrin-VCA expressing cells is now shown in Figure 4e. As mentioned in response to the reviewer #2 (iv), in cells expressing the centrin-VCA construct, the level of actin at the centrosome is higher than in control cells. However, upon chemokine stimulation, the signaling pathways leading to actin reduction are still active so that the level of centrosomal actin decreases (insignificantly different to the control) and the cells do not deform (insignificantly different to the control). These results are now discussed in the manuscript (lines 283-287).
MINOR COMMENTS
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The writing is generally clear and accurate, but often somewhat 'choppy'. As one of many examples: "As depicted in Figure 1b, in this configuration, we observed that cell deformation starts rapidly after stimulation. The mean delay between cell stimulation and the time when cells start to deform is 112 {plus minus} 10 s (n=48 cells)" could be re-written as "As depicted in Figure 1b, in this configuration, we observed that cell deformation starts rapidly after stimulation, with a mean delay between cell stimulation and initiation of cell deformation of 112 {plus minus} 10 s (n=48 cells)." This is completely stylistic, of course, and would simply (albeit slightly) improve the readability of the work.
We have tried to improve the writing.
The authors comment that the contribution of calcium, PI3K, and cAMP signaling "in the early processes allowing the establishment of the asymmetric distribution of cellular components and of the polarity regulators is still elusive" (ln33-35) seems a bit overstated, as there have been numerous, impactful contributions investigating each of those pathways.
We agree that these actors have been already clearly shown to be involved in polarization and/or migration as reported in the review by V. Niggli. However, the precise role they play in the initiation of T cell polarization is not clear. The sentence concerning this point has been modified in the introduction (lines 36-40).
The work seems to starts off as being focused on symmetry breaking rather than polarization, but this can be mitigated through rewriting the Introduction.
Symmetry breaking is for us the initial step (prerequisite) for cell polarization. Two sentences have been modified to clarify this point (lines 46-47 and 74-75).
The phrase "the major one" (ln47), presumably referring to one of "several local signaling poles" (earlier in ln47) is ambiguous and should be reworded (e.g. "the pole with the highest density of receptors").
The sentence has been modified (lines 53-54).
The phrase "variations of cAMP after CXCL12 addition upon dynamic cell imaging" (ln112) is not clear.
The sentence has been modified (lines 124-125).
The authors may want to reconsider the use of an ellipsis (ln25), which stands out as somewhat informal for a scientific manuscript.
The sentence has been reworded (lines 27-29).
There are several typographical errors throughout that should be addressed (e.g. "AMPc" rather than "cAMP" in the header of Fig7b.; "we were able establish" (ln 130); "while PKA are rapidly activated" (ln133)).
We have corrected typographical errors.
The figure legends most often read more like miniature, repeated results sections than detailed descriptions of experimental details and data processing, analysis and depiction.
Figure legends have been rewritten.
__SIGNIFICANCE __Directional cell migration is a fundamentally important aspect of cell biology. Understanding the molecular mechanisms that govern cellular symmetry breaking, polarization, and migration are - in turn - important for a fuller understanding of how cells efficiently move from location to location. T cells, which are highly dependent on efficient and dynamically, cytokine-directed migration for their physiologic function, are an excellent model system in which to unravel such molecular mechanisms. The authors efforts to connect localized cytokine-initiated signaling events with changes in centrosomal actin decoration and thence into cell polarity are, therefore, of considerable potential significance.
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Referee #3
Evidence, reproducibility and clarity
Summary
Using a variety of live- and fixed-cell imaging techniques, the authors make correlative and causative connections between chemokine-stimulated increases in cAMP and localized PKA activity, positioning and F-actin content of the centrosome, and cell polarity in T-cells.
Major Comments
- The authors state that "uniform CXCR4 labelling is observed in unstimulated cells" (ln94-95). While the panels in Supp Fig1a show a pattern in unstimulated cells that is obviously less dramatically asymmetrical than seen for stimulated (and, importantly, already polarized) cells in Supp Fig1b, the labelling in unstimulated cells is still far from uniform, as there is considerable heterogeneity of signal intensity along the perimeter. This is important, given that they are looking at a membrane receptor, even small fluctuations in which may be greatly amplified and thus have considerable effects on symmetry downstream. The authors should either quantify the intensity (e.g. signal as a function of polar coordinate value) or soften their language to more accurately reflect the data.
- Regarding the analysis of polarization events in Fig. 1c and 1d, it is not clear how, exactly, the time point for the cortex opening is determined. For example, in the sample images in 1c, would the +110s or the +340s time point be used? The reason this is important is that the angle seems to change with time (at least in the example given) and there are also heterogeneities (specifically, decreases) in SiR-actin intensity along the cortex that precede cortex opening. Thus, it is not clear whether the cortex begins to open in closer proximity to the centrosome or whether the centrosome is further aligned after the cortex opens.
- Also regarding Figure 1d, it is not clear how many cells and experimental replicates are represented in the data - the Results text reports 60 cells from 11 experiments (ln106) but the legend reports 58 cells (ln564) without mention of experimental replicate number. Also, while the rose plot is useful, it is important to have statistical analysis on the skewness of the response and/or to report something other than the average angle - for example, the percentage of cells with a cortex opening in the same 90-degree quadrant as the centrosome. Finally, it might be clearer to the reader to have the rose plot and the model cell oriented in the same direction.
- For their PKA inhibition experiments, the authors introduce H89 as "a competitive inhibitor of ATP on the PKA catalytic subunit" (ln138-139). H89 is a very non-specific inhibitor, as demonstrated by Davies et al (PMID 10998351) and reviewed by Lochner & Moolman (PMID 17214602) and should be introduced more accurately. To their credit, the authors use an orthogonal approach of PKA inhibition with a cAMP analog and see comparable effects. Those data, currently in Supp Fig. 3, should be included as primary data, given that the H89 data can only be correctly interpreted in the context of the Rp-cAMPS data (this applies to both Fig3 and Fig4).
- As part of Fig.5, the authors state "AKAP450 is a type II AKAP i.e. it is able to bind RII subunits of PKA. In order to determine whether this AKAP allows a [sic] compartmentalization of the PKA activity responsible for centrosomal F-actin regulation, we used the specific peptide (Ht31)". Ht31 broadly inhibits PKA anchoring; its effect is not specific for any individual AKAP, including AKAP9/AKAP450. Moreover, the authors neither show/confirm PKA localization to the centrosome nor its displacement with the indicated concentration of Ht31, and they do not include data that PKA is displaced from the centrosome with any greater specificity or sensitivity than its displacement from any other subcellular location. Therefore, this statement (as well as the claim in the Abstract that "a specific pool of protein kinase A) (ln15) by the authors is not accurate. The authors should, at the very least, re-word the statement and, for the sake of rigor and support of their hypotheses, confirm that PKA (subunits and/or activity) is displaced from the centrosome.
- The experiments & results using VCA-centrin-GFP are very intriguing. However, it is crucial that primary data (i.e. photomicrographs/panels of fluorescent images of centrin/VCA-centrin localization, centrosomal F-actin, and roundness) be included for the readers' inspection. Also, it is not clear whether the graphically summarized data on centrosomal F-actin and roundness (Fig. 6b) represent analysis of cells before and after CXCL12 stimulation, or only before or only after. If either of the latter, analysis of these parameters both before and after stimulation should be included.
Minor Comments
- The writing is generally clear and accurate, but often somewhat 'choppy'. As one of many examples: "As depicted in Figure 1b, in this configuration, we observed that cell deformation starts rapidly after stimulation. The mean delay between cell stimulation and the time when cells start to deform is 112 {plus minus} 10 s (n=48 cells)" could be re-written as "As depicted in Figure 1b, in this configuration, we observed that cell deformation starts rapidly after stimulation, with a mean delay between cell stimulation and initiation of cell deformation of 112 {plus minus} 10 s (n=48 cells)." This is completely stylistic, of course, and would simply (albeit slightly) improve the readability of the work.
- The authors comment that the contribution of calcium, PI3K, and cAMP signaling "in the early processes allowing the establishment of the asymmetric distribution of cellular components and of the polarity regulators is still elusive" (ln33-35) seems a bit overstated, as there have been numerous, impactful contributions investigating each of those pathways.
- The work seems to starts off as being focused on symmetry breaking rather than polarization, but this can be mitigated through rewriting the Introduction.
- The phrase "the major one" (ln47), presumably referring to one of "several local signaling poles" (earlier in ln47) is ambiguous and should be reworded (e.g. "the pole with the highest density of receptors").
- The phrase "variations of cAMP after CXCL12 addition upon dynamic cell imaging" (ln112) is not clear.
- The authors may want to reconsider the use of an ellipsis (ln25), which stands out as somewhat informal for a scientific manuscript.
- There are several typographical errors throughout that should be addressed (e.g. "AMPc" rather than "cAMP" in the header of Fig7b.; "we were able establish" (ln 130); "while PKA are rapidly activated" (ln133)).
- The figure legends most often read more like miniature, repeated results sections than detailed descriptions of experimental details and data processing, analysis and depiction.
Significance
Directional cell migration is a fundamentally important aspect of cell biology. Understanding the molecular mechanisms that govern cellular symmetry breaking, polarization, and migration are - in turn - important for a fuller understanding of how cells efficiently move from location to location. T cells, which are highly dependent on efficient and dynamically, cytokine-directed migration for their physiologic function, are an excellent model system in which to unravel such molecular mechanisms. The authors efforts to connect localized cytokine-initiated signaling events with changes in centrosomal actin decoration and thence into cell polarity are, therefore, of considerable potential significance.
-
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Referee #2
Evidence, reproducibility and clarity
The ability of T cells to migrate along chemotactic cues is critical for the initiation and regulation of adaptive immune responses. To migrate directionally, cells require a polarised shape and intracellular organisation, allowing intracellular force generation towards the intended direction of migration.
The manuscript by Simao et al investigates the role of the centrosome and its co-localised pool of the actin cytoskeleton in defining the direction of an initial polarisation while being surrounded by homogenous chemokine concentration. The authors (i) describe a correlation of the intracellular position of the centrosome with the site of polarisation, (ii) identify that a reduced amount of actin at the centrosome is beneficial for cell polarisation and that (iii) the protein kinase PKA regulates this actin pool at the centrosome.
Overall, the data presented appear convincing but would benefit from a more detailed presentation of representative image examples and additional experimental data.
Major points:
(i) Many experimental microscopy datasets are quantified but lack representative images. These representative images are important to be able to judge the underlying data and should be included for the datasets shown in Figure 2, Fig. 3c, Fig. 4d (Lifeact examples), and Fig. 6 (Centrin VCA). In addition, the differences in the signal intensity of the actin cytoskeleton are sometimes hardly visible in the provided representative images (e.g., Fig. 4b, control vs CXCL12). Could the authors come up with solutions to show this in a better way (e.g., zooms and/or fire-colour coding)?
(ii) The authors use an experimental setup in which the cells 'see' a homogenous chemokine concentration around them. Further, they discuss different models in the introduction of how the local cellular polarisation in such a uniform chemokine sounding is defined, including a model of polarised localisation of chemokine receptors on the plasma membrane. However, in a tissue, chemokines are typically not homogeneously distributed but are present in the form of a gradient, e.g. due to local chemokine sources or the self-generation of chemotactic gradients by neighbouring migrating cells. Therefore, it would be interesting to know whether the described repositioning of the centrosome and the changes in the actin pool are also important if cells are in a chemokine gradient. Experimentally, this could be addressed by providing a local chemokine source (e.g. from a micropipette). If this goes beyond the scope of the manuscript, then at least this aspect of chemokine gradients should be clearly mentioned and discussed in the introduction and the discussion sections.
(iii) Given that the centrosome acts as a microtubule-organising center (MTOC), it would be interesting to see the microtubules during polarisation. Did the author try the visualisation of microtubules in live or by immunofluorescence stainings? Would it be a plausible model that reduced actin polarisation allows microtubule polarisation towards the cell periphery and thereby induces a protrusion by delivering signalling and cytoskeleton components towards the newly forming protrusion? May this be only targeted towards one side of the centrosome, as the nucleus may sterically hinder the efficient growth of microtubules to the other cellular side? What would happen in cells without a nucleus? And what would happen in cells without a centrosome (e.g., by PLK4 inhibition via centrinone) - are cells still able to form protrusions efficiently? These experimental suggestion are optional but could significantly improve the study.
(iv) Bringing actin artificially to the centrosome via the centrin-VCA construct is a very nice approach. However, the dataset would strongly benefit from samples, in which the chemokine CXCL12 is included.
Minor points:
(i) The study is based on a lymphoblastic cell line called CEM T cells. This should be clearly stated at the beginning of the results section and in all figure legends, as it remains unknown whether primary T lymphocytes would show the same behaviour. Additionally, the methods section should contain more details about this cell line, e.g. whether it is from mouse or human origin.
(ii) The authors mention 'suboptimal' conditions of stimulation. However, it remains unclear in the results sections what this means. Some of the experimental modulations (e.g. the cAMP analog Rp-8-CT-cAMPS) seem to only show an effect in these suboptimal conditions but not in the optimal conditions. This should be clearly stated and discussed.
(iii) 23 out of the 38 references are older than 5 years, and most of them are older than 10 years. While it is surely very important to refer to these older findings, the authors may include more knowledge from recent years. This may include references about centrosome positioning in immune cells and motile cells (e.g., PMID32379884, PMID29934494, PMID30944468, PMID37987147, PMID36398880, PMID38627564, PMID33634136) and the actin cytoskeleton at the centrosome (PMID33609453, PMID33184056, PMID36111670). (iv) The first paragraph of the discussion (lines 253 - 262) needs references.
(v) Some Figures maybe combined as the findings are closely related (e.g., Figures 2 and 3; and Figures 5 and 6).
(vi) Line 94, Supply. Fig. 1: the authors that the chemokine receptor CXCR4 has a uniform distribution in non-stimulated cells. This is not directly evident in the images as there are areas of more and less signal. It would be important to clearly describe this in the text. Further, the labelling of the figure would benefit from labelings such as 'cell 1', 'cell 2', etc to directly make clear that these are images from 3 representative cells.
(vii) Different centrin isoforms exist (centrin 1, 2, 3). It should be mentioned in the results and methods section, which isoform was used for their genetic constructs (e.g., centrin-GFP, centrin-VCA).
Significance
This manuscript employs a lymphoblastic cell line called CEM T cells as a model for T lymphocytes. Using imaging of these cells on 2D substrates with and without chemokine, the authors identify a PKA-controlled actin pool at the centrosome that appears to regulate the local site of protrusion formation during cell polarisation. This is an interesting finding that adds to the knowledge of (i) the functions of centrosome positioning and (ii) the functions of the actin cytoskeleton at the centrosome. Thus, the study will be interesting to readers in the centrosome and migration fields. To broaden the scope of the manuscript, the findings could be tested in primary T lymphocytes and mechanistically address the role of microtubules within the described process.
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Referee #1
Evidence, reproducibility and clarity
The authors study early events initiated in T cells upon chemokine activation leading to cell polarization preceding cell migration. The show that actin dynamics at the centrosome are regulated in a cAMP/PKA dependent manner using different reporter systems and imaging approaches
Significance
This work will be of interest to immunologists with a strong cell biology background. The strength of the study is the detailed cell biological / near biophysical analysis of early changes in acting dynamics in relation to centrosome positioning. THe data is well controlled and convincing. Conclusions adequate based on available data. The limitation I see is the use of a single cell line system of cancerous origin and the fact that only changes in cellular morphology are quantified, but not cellular behavior itself - e.g migration, T cell intrinsic signalling. If some key observations can be validated in primary T cells this would be perfect, or at least in a second model system. If signalling related to changed morphology is affect by regional inhibition of PKA/actin remodelling, remains uncertain, too - maybe there is a way to monitor additional parameters, other than roundness/PKA activity
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Reply to the reviewers
Response to Reviewers
We thank the reviewers for their fair and thorough review. With regards to the reviewers’ comments, both largely focused on something that is a misunderstanding. For unclear reasons, both reviewers thought that most of the data shown was in pregnant or previously pregnant mice and both requested a significant amount of preliminary data regarding virgin mice (R1 comment #3, #4, R2 comment #1). This may be due to a (now-corrected) typo in the results section despite the methods section being correct, or the very few instances of pregnant mice being used for analyses that led to confusion. As mentioned below, the entire manuscript evaluates virgin mice, with a few specific exceptions, so the preliminary revisions have emphasized the parity status of the mice used in every experiment. We regret this misunderstanding happened and we are concerned this may have led to reviews that were biased towards a negative viewpoint. We hope the completed preliminary revisions (indicated in red text in the manuscript) and the planned revisions will, combined, satisfy the reviewer’s concerns and clarify points of confusion, while leading to a greatly improved manuscript.
Reviewer 1:
Major points
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Major Comment 1: “Several of the conclusions are made based on a limited number of replicates (often n=3) which is not a robust sample size to make a rigorous conclusion.”
We have consulted with a biostatistician (Adam Lane, now included in acknowledgements) and plan to add at least 3 more mice per group to bring the total sample size to 6-7. Given our results are already statistically significant with an n=3-4, we do not anticipate any changes in the overall results of our data. We have already collected at least 3 more age-matched and parity-matched mice per group for the molecular analyses and are working on performing the immunohistochemical stains, western blots, etc.
Major Comment 2: The main text for Figure 1C mentions repression of luciferase expression by doxycycline chow, however the figure does not show any discernable repression in the Dek-OE conditions.
We believe the reviewer may have mis-interpreted the figure. The mouse on the far left (“control”) with no luciferase signal is the dox chow-repressed condition. We have revised the figure label to specify that “Control” is the “+dox condition” and throughout the manuscript have specified “+dox controls” instead of just “controls.”
Major Comments 3: To evaluate the impact of prolonged Dek overexpression on mammary epithelium in Figure 1G and 1H, the authors used multiparous females. One confounding factor with this experimental set up is the impact of previous pregnancies on the development of the mammary epithelium and in lowering tumorigenesis. Therefore, the impact of Dek on tumorigenesis cannot be determined in multiparous animals alone. To get a full picture, nulliparous animals should also be examined.
__We have revised the text on page 6 to explain that we have monitored tumor growth in both aged virgins and in multiparous mice (our female breeders) and neither group develops tumors. __
Major Comment 4: “To elucidate the molecular underpinning of Dek-OE phenotypes, the authors performed bulk RNA sequencing in Figure 2. Similar to point 2 however, only multiparous animals were used. As it has been previously shown that pregnancy significantly impacts the transcriptome of mammary glands, the effects of Dek overexpression can't be generalized to mammary glands as a whole. To make it generalizable, nulliparous Dek-OE animals must also be characterized.”
As mentioned in the introduction to the review, the reviewer has misunderstood the experimental design, perhaps through a single typo in the Results section when the Methods were correct, or through poor writing on our behalf. Regardless, the RNA-Seq, whole mounts, and all subsequent molecular validations were conducted on virgin mice. The only exceptions are in Figure 4, where we do explore the expression of endogenous Dek during pregnancy and the impact of pregnancy in the transgenic model. We have revised the typographical error, confirmed the parity status of all mice in the study to date, and have specifically added the parity status to each experiment in Results section and/or Figure Legend.
Major Comment 5: To validate findings from their transcriptomics work, the authors used IHC and western blots of candidate proteins that were found to be down regulated. In Figure 3A and 3C, the decrease in p21 protein levels through western blot seem much more modest than what the decrease seen in 3A would suggest.
We thank the reviewer for pointing this out. With increased sample sizes, as requested, we hope this will resolve. We plan to increase sample size and quantify the p21 western blot to potentially resolve the concern. In addition, we would like to note that the p21 IHC is specific for mammary epithelium signal while the western blot is whole mammary gland lysate that includes quiescent stromal cells, which may explain the slight discrepancy between the two methods.
Major Comment 6: In Figure 3G-3I, the authors test the CDK4/6 inhibitor palbociclib to establish a direct link between the phenotypes seem in Dek-OE and cell cycle progression in organoid culture. Have the authors verified these findings with treatment of Dek-OE mice with palbociclib? In addition, have the authors checked to see if palbociclib corrected any of the transcriptional features associated with the Dek-OE model found in their transcriptomics data? In addition, the authors claim that the effect is specific to Dek-OE organoids as the effects of palbociclib on growth are not seen in control organoids. However, the data on unperturbed growth of control cells are not seen. To determine the specificity of the effects of palbociclib on Dek-OE derived organoids, the authors must show a time course tracking the growth of organoids with and without palbociclib. Rather than conclude the effects of palbociclib being specific to Dek-OE organoids, the authors most likely wanted to conclude that the increased growth of Dek-OE organoids compared to control organoids is dependent on the increase in cell cycle factors. (The validity of this is also weird though because even if division and growth were triggered through other transcriptional changes they found, like increased metabolism, growth in that scenario would be stopped by palbo as well)
- Because the hyperplasia phenotype accumulates over the lifetime of the animal, the amount of treatment time required to abrogate the hyperplasia phenotype could be from days to weeks to months. For this reason, we believe it is outside the scope of this revision to test the effects of palbociclib in vivo.
- We plan to re-do this experiment with palbociclib treatment to test organoid growth over time as suggested and, time permitting, perform immunofluorescence for some of the transcription targets such as cyclins, CDKs, Ki67, and p27/p21
- We have revised the text on page 8 to say “____We observed that the increased growth of Dek over-expressing organoids was dependent on the Dek-induced increase in CDK4/6, since palbociclib treatment resulted in smaller Dek over-expressing organoids that were comparable to organoids from +dox controls.” We also agree that CDK inhibitor treatment may impact multiple downstream signaling pathways. However, the authors do not see this as a negative because cell proliferation, induced by cyclin/CDK complexes, requires metabolic regulation to support physical growth of the cell. The two processes are intricately integrated and have a bidirectional relationship. Thus, it is possible that DEK induces both processes, or it may only promote one process (i.e.: cell cycle) and the other one (i.e.: metabolism) is induced as a secondary result of cell cycle demands. This is one reason why we indicate that metabolic dysregulation should be further studied in the Discussion section. Indeed, a colleague in the DEK field (Susanne Wells) is already working on the relationship between DEK over-expression and metabolic dysfunction, thus this particular aspect of the request is outside the scope of this manuscript.
Major Comment 7: In the main text of Figure 4, the authors conclude that markers for luminal hormone sensing cells were unchanged in Dek-OE mammary glands, however the data to show this is not shown. This is problematic because the authors are directly drawing the conclusion that Dek-OE specifically upregulates luminal alveolar markers using this data.
We have revised the manuscript to include a new supplementary figure (now Fig S4) to include a western blot for HER2 and ERa and a summary of RNA expression data from the bulk RNA-Seq experiment. We will also perform additional western blots to increase the sample size to demonstrate this negative data as part of our planned
Major comment 8: In figure 7, the authors look at a conditional knockout of Dek and conclude that pup death in the knockout was due to insufficient milk production by dams. While the authors establish that H3K27me3 and Ezh2 expression are abrogated, morphological analysis of the ducts is missing and would present convincing data. For instance, in the Dek conditional knockout, are luminal alveolar cells unable to differentiate fully, or are there far fewer? Decreased levels of histone modifications does not tell you much about whether repressive chromatin has changed its landscape in Dek KO mice, which is actually what influences transcription.
__We plan to add histological and whole mount imaging of Dek knockout mammary glands in the revision. We have preliminary data that supports this from 2 mice and will be collecting more samples for the revision. However, as noted in Fig 7C-D, heterozygous females also have small litter sizes and this will pose a breeding challenge for generating knockout females for this experiment in a timely manner. __
Minor Points:
All figures need some sort of reformatting. Several of the conclusions are made based on a limited number of replicates (often n=3) which is not a robust sample size to make a rigorous conclusion. Many figures have text that is stretched. Histology and whole mount images are missing scale bar. IHC quantifications are obscure - what is an optical density? how many animals were analyzed and how many fields of vision were captured? Figure 2F is absolutely impossible to understand. Neither figures nor legends disclose the number of animals or samples analyzed. The statistical test utilized across all figures is not appropriated. Fig5B GSEA plots are missing statistical significance, and without this information one cannot properly access the relevance of the findings. Fig5C - how were co-expressed genes defined? is this just random genes that are expressed in cells that have higher levels of DEK? The term co-expressed suggests a specific type of analysis that would investigate linkage of expression between genes, which i dont think is the case here.
__As the reviewer already mentioned in major comment #1, there was a concern with sample size, which we addressed above in the planned revisions. We believe this concern about sample size was the rationale for the minor comment about “The statistical test utilized across all figures is not appropriate.” We have consulted with a biostatistician, Adam Lane PhD, who has confirmed that our statistical approaches were correct but were limited by our sample size. Thus, we do not agree with the reviewer’s view of statistical analyses. We have revised the text to include sample size information in figure legends and statistical significance information for GSEA plots in Fig 5, With regards to figure text being stretched, it does not look like that in our version of the document and reviewer 2 did not comment on this, so we would like the reviewer to identify a specific instance of this. We plan to capture images with size bars for IHC while we are performing the additional sample size collection. The reviewer asked about the number of fields of view for IHC quantification and we would like to note that our methods section already had that information in the first submission, “at least 3 fields of view from at least 3 different mice per group.” Our methods section also already had information regarding the identification of co-expressed genes in scRNA-Seq data and quantifying IHC with Image J. However, we have revised the text to add some clarifying sentences that we hope helps the reviewer better understand our methods. Finally, we are not sure what is “absolutely impossible to understand” about Fig 2F, which is a network visualization of functional enrichment analyses for differentially expressed genes in our RNA-Seq data. Is the text too small, or does the reviewer not understand the network? We would appreciate it if the reviewer could clarify this concern in their next review. __
Minor Point 1: Throughout, it would be better to indicate the genotype of the "Control" animals on each figure so as the rigor the experiment can be evaluated fully.
It appears that the reviewer was not aware that all controls were the same genotype and were the bitransgenic mice on dox chow. We have revised the manuscript to better clarify that “controls” = “+dox chow” bitransgenics and have added text on page 5 to directly state this. We have also revised Fig 1C to specify that the mouse with no luciferase signal is the “+dox” control.
Minor Point 2: Standard nomenclature for gene names and protein names should be corrected throughout the text.
__We have revised the text to confirm gene and protein names are correct. We have followed convention in using italics for gene names, non-italics for protein names, all capital letters for human genes/proteins (i.e.: DEK) and only first letter capitalization for non-human gene names (i.e.: Dek). __
Minor Point 3: Similar to the point above, the use of Dek-OE to either refer to the mouse model or function as an acronym for "Dek overexpression" is inconsistent throughout the text.
We thank the reviewer for pointing out this inconsistency and we have revised the text so that the “-OE” notation is only used when discussing the mice and have changed to writing out “over-expression” for function.
Minor Point 4: In the main text for Figure 4I-J, the authors state that DEK was previously published as an Erα target gene, however there is no citation to support this.
We have revised the text to include this citation, which is:
16. Privette Vinnedge, L.M., et al., The DEK Oncogene Is a Target of Steroid Hormone Receptor Signaling in Breast Cancer. PLoS One, 2012. 7(10): p. e46985.
Minor Point 5: It is unclear what the conclusion drawn from the experiments shown in Figure 4G-H and Figure 4I-J mean with respect to the goal of Figure 4, which was to show that Dek-OE mice have an expanded luminal alveolar compartment.
We have revised the text to better explain that we were investigating the impact of ovarian hormones and pregnancy on endogenous Dek expression in wild-type mice, since this information has not been previously reported and adds context to our study.
Minor Point 6: Optical density was used to quantify IHC experiments, which was performed using color deconvolution in ImageJ. Something that is unclear is whether the authors are measuring density in the entire field of view, or if the authors are measuring optical density per cell. This has implications whether there are more cell expressing the protein of interest, or if the existing cells are expressing a higher level of the protein of interest.
We have revised the text to include more information in the methods. The Methods now states: “____Image J color deconvolution was utilized to measure the staining intensity only within mammary epithelial cells from at least 3 fields of view from at least 3 different mice per group. Specifically, cross-sections of similarly sized ducts were outlined such that only the collective epithelial cells within that cross section were measured, removing background signal from the stromal cells. Only single cross-sections of ducts were analyzed to minimize the impact of epithelial hyperplasia in experimental mice compared to controls fed dox chow.”
Minor Point 7: In the main text for Figure 6D, the system being used to overexpress DEK protein is not described. It is not the same genetic system as is used in the Dek-OE mice, as doxycycline is inducing Dek expression.
We have revised the figure 6 legend to specify “____DEK over-expression was accomplished with a dox-inducible pTRIPZ vector while DEK knockdown was accomplished with a pLKO.1 shRNA vector” and we kindly point the reviewer to the Methods section (“human cell lines” subsection) as written in the first submission which included detailed information for the subcloning of DEK cDNA into the pTRIPZ vector.
Reviewer 2
_All comments_
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Comment 1: This study would be improved by sharing important data including virgin mammary gland development in the DEK-OE and DEK-KO models (ductal growth and branching) and the expression of markers including ESR1, PGR, and ERBB2 (data not shown, page 8). Although there may be no differences, this is important data to share regarding the goal of this study. For example, in the DEK-OE model, data are only evaluated in the aged/multiparous stage and in the DEK-KO model, data are only evaluated during lactation. Furthermore, the DEK-KO model resembles germline DEK loss (under control of the CMV promoter), and there is limited validation of a MEC-intrinsic function.
We have revised the manuscript to include data on Esr1/ERa and Erbb2/Her2 by western blot in new Fig S4 as well as the bulk RNA-Seq mRNA levels (by FPKM) for select basal and hormone sensing cell populations. The concern regarding parity was also mentioned by Reviewer 1 (major comments 3&4 above). Briefly we have clarified that ____nearly all data in the manuscript is from nulliparous (virgin) females and have revised the text throughout to more clearly state this fact. We have also revised the text to address the limitation of the CMV promoter. The Discussion section now states “____However, it is noted that one weakness of this CMV-Cre knockout model, is that there is a constitutive loss of Dek, which limits the interpretation for mammary epithelial cell-specific Dek functions.”
Comment 2: Another major concern with this manuscript is the use of immunohistochemistry (IHC) and bulk mammary gland lysate western blots. IHC is non-quantitative, and the images are low resolution. For example, using IHC DEK expression is observed in all MECs (control and DEK-OE mice, Figure 1F), however, in the scRNAseq data DEK expression is confined to basal cells and a subset of stem/progenitor cells (Figure 5A). Furthermore, the hyperplasia in the DEK-OE model will bias bulk analysis (such as western blot and RNAseq) towards increased expression of MEC markers.
- __We have revised the text to point out that IHC images for Dek in control tissues show some cells have higher expression than others, which is what would be predicted by scRNA-Seq. The text now states on page 16 “____The scRNA-Seq data suggests that Dek is more highly expressed in specific subpopulations of cells, and the variable intensity of immunohistochemical staining for Dek in epithelial cells within control mouse tissue supports this (see Fig 3I, 4I, 4K, and 7H).” Furthermore, on page 10 in the Result section we have revised the text to state “The mammary gland undergoes substantial hormone-induced remodeling across the murine lifespan. We show that Dek is highest during pregnancy and minimally expressed during lactation and involution (Fig 4K), and that Dek protein expression is not uniform across all epithelial cells in wild-type glands (Fig 3I, 4I-K). This suggests that certain epithelial subpopulations express more Dek than others.” __
- __We acknowledge that IHC and western blots are only semi-quantitative, which is why we attempt to perform both as orthogonal approaches or find additional ways to support our findings throughout the manuscript (i.e.: co-expression at the RNA level from other sources, small molecule inhibitor treatment, etc). We also note that these methods are used to validate the quantitative method of RNA-Seq, and (often) validation of differentially expressed genes can be limited by antibody availability and the applications those antibodies are suitable for. __
- We also have revised the text to acknowledge that we knew the bulk RNA-Seq would be biased towards the hyperplastic cells. We wanted to take advantage of that bias to identify a gene signature that could be used to determine which cell type was leading to the hyperplasia phenotype. We used the differentially expressed genes to identify biomarkers for specific cell populations. On pages 6-7 the text now reads “____We performed bulk RNA sequencing on whole mammary tissue from two +dox control and two Dek-OE adult virgin females at 15 months of age to discover molecular targets regulated by Dek over-expression and to reveal a gene signature that could help identify the expanded cell population(s) in hyperplastic glands.” And “DEGs were plotted as a heatmap and ontologies for biomarkers of cell populations were defined to help identify the expanded cell population driving Dek-induced hyperplasia.”
Comment 3: A third major concern is the mechanistic link between DEK and H3K27me3. Most of the data are correlative and rely on bulk analysis or IHC. For example, in the DEK-OE organoid model, is there an increase in H3K27me3. Additionally, in the DEK-OE organoids, can loss of EZH2 block the increased cell proliferation?
__We plan to revise the manuscript to include an experiment in which we treat primary mammary epithelial cell organoids from Dek-OE mice with EZH2 inhibitor, GSK-126, +/- doxycycline for a mechanistic or functional link between DEK and H3K27me3 levels. We will then determine organoid size and attempt molecular characterization with IF. This will support the biochemical studies in Fig 6 showing DEK interacts with the PRC2 complex. __
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Referee #2
Evidence, reproducibility and clarity
In the manuscript "The chromatin remodeler DEK promotes proliferation of mammary epithelium and is associated with H3K27me3 epigenetic modifications", Johnstone et al. investigate the proto-oncogene, DEK, in control of normal mammary gland development. The authors utilize transgenic mouse models, including conditional DEK-overexpression (OE) and DEK-knockout (KO) model, highlighting the role for DEK in control of mammary epithelial cell (MEC) proliferation and differentiation during pregnancy and lactation. Furthermore, the authors demonstrate DEK expression correlates with EZH2 and H3K27me3, which have previously been reported to control mammary gland lactation [Pal et al., Cell Reports, 2013].
Overall, this manuscript is interesting and well prepared. This group have previously established a role for DEK in breast cancer, however, the function of DEK in normal mammary gland development is unknown. Towards this goal, two novel mouse models were developed, conditional DEK-OE and DEK-KO. This manuscript would be substantially improved by formal investigation of virgin mammary gland development in these models, high-resolution analysis of the MEC subpopulations at different stages, and strengthening the mechanistic link between DEK and EZH2. The following are detailed major concerns.
- This study would be improved by sharing important data including virgin mammary gland development in the DEK-OE and DEK-KO models (ductal growth and branching) and the expression of markers including ESR1, PGR, and ERBB2 (data not shown, page 8). Although there may be no differences, this is important data to share regarding the goal of this study. For example, in the DEK-OE model, data are only evaluated in the aged/multiparous stage and in the DEK-KO model, data are only evaluated during lactation. Furthermore, the DEK-KO model resembles germline DEK loss (under control of the CMV promoter), and there is limited validation of a MEC-intrinsic function.
- Another major concern with this manuscript is the use of immunohistochemistry (IHC) and bulk mammary gland lysate western blots. IHC is non-quantitative, and the images are low resolution. For example, using IHC DEK expression is observed in all MECs (control and DEK-OE mice, Figure 1F), however, in the scRNAseq data DEK expression is confined to basal cells and a subset of stem/progenitor cells (Figure 5A). Furthermore, the hyperplasia in the DEK-OE model will bias bulk analysis (such as western blot and RNAseq) towards increased expression of MEC markers.
- A third major concern is the mechanistic link between DEK and H3K27me3. Most of the data are correlative and rely on bulk analysis or IHC. For example, in the DEK-OE organoid model, is there an increase in H3K27me3. Additionally, in the DEK-OE organoids, can loss of EZH2 block the increased cell proliferation?
Significance
Using genetic approaches in mice, this paper explores the role of the chromatin remodeler and oncogene DEK in the development of the normal mammary gland. This work will be of interest to researchers in the mammary development and breast cancer fields.
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Referee #1
Evidence, reproducibility and clarity
Johnstone & Leck et al. report their findings on the DEK chromatin remodeler and its newly discovered role in the development of the mammary gland through the use of a mammary epithelium-specific Dek overexpression model (Dek-OE). Using immunohistochemistry (IHC) and whole mounts of mammary glands, they show that the Dek-OE model is characterized by epithelial hyperplasia in multiparous, 15-month-old females. Through performing and analyzing bulk RNA sequencing of whole mammary tissue, they find that overexpression of Dek is correlated with cell cycle entry and progression, and the expression of luminal alveolar and mammary progenitor genes. The deregulation of cell cycle inhibitors was confirmed through IHC and western blot. To further support the connection between Dek and the cell cycle, it was also shown that palbociclib treatment of mammary epithelial organoids derived from Dek-OE mice was able to rescue the hyperplastic phenotype. To validate their transcriptomic findings of increased expression of luminal progenitor genes, IHC and western blots for alveolar markers and milk proteins were performed. By performing ovariectomy and looking at DEK expression throughout the development of the mammary gland, it was also found that Dek expression was promoted by ovarian hormones. Analysis of single-cell data from a previously published single cell gene atlas of the mammary gland, the authors found that Dek expression heavily overlapped with mammary stem cells and luminal progenitor populations, and was heavily correlated with expression of PRC2 components. Using western blots and a GFP-trap assay, it was found that Dek overexpression leads to increased H3K27me3, and PRC2 components directly interact with DEK. Using a conditional knockout of Dek, the authors found that Dek loss leads to decreased expression of PRC2 components in mammary epithelial cells by IHC and a failure for dams to lactate efficiently. While the authors findings are novel, there are major points that need to be strengthened and elaborated for clarity.
Major points:
- Several of the conclusions are made based on a limited number of replicates (often n=3) which is not a robust sample size to make a rigorous conclusion.
- The main text for Figure 1C mentions repression of luciferase expression by doxycycline chow, however the figure does not show any discernable repression in the Dek-OE conditions.
- To evaluate the impact of prolonged Dek overexpression on mammary epithelium in Figure 1G and 1H, the authors used multiparous females. One confounding factor with this experimental set up is the impact of previous pregnancies on the development of the mammary epithelium and in lowering tumorigenesis. Therefore, the impact of Dek on tumorigenesis cannot be determined in multiparous animals alone. To get a full picture, nulliparous animals should also be examined.
- To elucidate the molecular underpinning of Dek-OE phenotypes, the authors performed bulk RNA sequencing in Figure 2. Similar to point 2 however, only multiparous animals were used. As it has been previously shown that pregnancy significantly impacts the transcriptome of mammary glands, the effects of Dek overexpression can't be generalized to mammary glands as a whole. To make it generalizable, nulliparous Dek-OE animals must also be characterized.
- To validate findings from their transcriptomics work, the authors used IHC and western blots of candidate proteins that were found to be down regulated. In Figure 3A and 3C, the decrease in p21 protein levels through western blot seem much more modest than what the decrease seen in 3A would suggest.
- In Figure 3G-3I, the authors test the CDK4/6 inhibitor palbociclib to establish a direct link between the phenotypes seem in Dek-OE and cell cycle progression in organoid culture. Have the authors verified these findings with treatment of Dek-OE mice with palbociclib? In addition, have the authors checked to see if palbociclib corrected any of the transcriptional features associated with the Dek-OE model found in their transcriptomics data? In addition, the authors claim that the effect is specific to Dek-OE organoids as the effects of palbociclib on growth are not seen in control organoids. However, the data on unperturbed growth of control cells are not seen. To determine the specificity of the effects of palbociclib on Dek-OE derived organoids, the authors must show a time course tracking the growth of organoids with and without palbociclib. Rather than conclude the effects of palbociclib being specific to Dek-OE organoids, the authors most likely wanted to conclude that the increased growth of Dek-OE organoids compared to control organoids is dependent on the increase in cell cycle factors. (The validity of this is also weird though because even if division and growth were triggered through other transcriptional changes they found, like increased metabolism, growth in that scenario would be stopped by palbo as well)
- In the main text of Figure 4, the authors conclude that markers for luminal hormone sensing cells were unchanged in Dek-OE mammary glands, however the data to show this is not shown. This is problematic because the authors are directly drawing the conclusion that Dek-OE specifically upregulates luminal alveolar markers using this data.
- In figure 7, the authors look at a conditional knockout of Dek and conclude that pup death in the knockout was due to insufficient milk production by dams. While the authors establish that H3K27me3 and Ezh2 expression are abrogated, morphological analysis of the ducts is missing and would present convincing data. For instance, in the Dek conditional knockout, are luminal alveolar cells unable to differentiate fully, or are there far fewer? Decreased levels of histone modifications does not tell you much about whether repressive chromatin has changed its landscape in Dek KO mice, which is actually what influences transcription.
Minor points:
All figures need some sort of reformatting. Several of the conclusions are made based on a limited number of replicates (often n=3) which is not a robust sample size to make a rigorous conclusion. Many figures have text that is stretched. Histology and whole mount images are missing scale bar. IHC quantifications are obscure - what is an optical density? how many animals were analyzed and how many fields of vision were captured? Figure 2F is absolutely impossible to understand. Neither figures nor legends disclose the number of animals or samples analyzed. The statistical test utilized across all figures is not appropriated. Fig5B GSEA plots are missing statistical significance, and without this information one cannot properly access the relevance of the findings. Fig5C - how were co-expressed genes defined? is this just random genes that are expressed in cells that have higher levels of DEK? The term co-expressed suggests a specific type of analysis that would investigate linkage of expression between genes, which i dont think is the case here.
- Throughout, it would be better to indicate the genotype of the "Control" animals on each figure so as the rigor the experiment can be evaluated fully.
- Standard nomenclature for gene names and protein names should be corrected throughout the text.
- Similar to the point above, the use of Dek-OE to either refer to the mouse model or function as an acronym for "Dek overexpression" is inconsistent throughout the text.
- In the main text for Figure 4I-J, the authors state that DEK was previously published as an Erα target gene, however there is no citation to support this.
- It is unclear what the conclusion drawn from the experiments shown in Figure 4G-H and Figure 4I-J mean with respect to the goal of Figure 4, which was to show that Dek-OE mice have an expanded luminal alveolar compartment.
- Optical density was used to quantify IHC experiments, which was performed using color deconvolution in ImageJ. Something that is unclear is whether the authors are measuring density in the entire field of view, or if the authors are measuring optical density per cell. This has implications whether there are more cell expressing the protein of interest, or if the existing cells are expressing a higher level of the protein of interest.
- In the main text for Figure 6D, the system being used to overexpress DEK protein is not described. It is not the same genetic system as is used in the Dek-OE mice, as doxycycline is inducing Dek expression.
Significance
The role of Dek in tumorigenesis and in maintaining stem-like qualities in breast cancer cell lines have been previously reported. However, Dek has never been studied in the context of the normal mammary gland. The authors work revealing the role of Dek in normal development of the mammary gland is significant as understanding it has the potential of revealing additional roles Dek may have as an oncogene in breast cancers.
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Reply to the reviewers
1. General Statements [optional]
We thank the reviewers for their insightful comments regarding our study and for appreciating the range of experiments used, the depth of our study and the significance of our work. We also thank reviewers with expertise in evolutionary biology for highlighting the need for precise wrong of some parts of the manuscript and the need for balancing the various viewpoints on the current understanding of early metazoan evolution. A point-by-point response to each reviewer comment is given below. We believe that we can effectively address most reviewer comments in a revised version. The revised improved manuscript will be the first insightful study of intracellular signalling pathways in the context of early animal evolution. We thank the reviewer for noting that this study is highly impactful and can have a broader influence on the scientific community.
2. Description of the planned revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
__ Summary: The researchers identified PIP4K (phosphatidylinositol 5 phosphate 4-kinase) as a lipid kinase that is specific to metazoans. In order to determine its conserved function across metazoans, they compared PIP4K activity in both early-branching metazoans and bilaterian animals. Biochemical assays demonstrated a conserved catalytic activity between the sponge Amphimedon queenslandica (AqPIP4K) and human PIP4K. In in-vivo experiments, AqPIP4K was found to rescue the reduced cell size, growth, and development phenotype in larvae of null mutant in Drosophila PIP4K. Based on these findings, the authors suggest that the function of PIP4K was established in early metazoans to facilitate intercellular communication. The experiments were well designed, and a range of biochemical, in vitro, and in vivo experiments were conducted.__
__ That being said, there are some questions that require further discussion before we can fully accept the author's conclusion of an evolutionarily conserved function of PIP4K across metazoans.__
Major comments:
- The authors mentioned that PIP4K is metazoan-specific and involved in intercellular communication. How can we explain the presence of PIP4K in choanoflagellate genomes? Despite its high similarity with conserved domains and functionally important residues, experimental results with the PIP4K from Choanoflagellate (Monosiga brevicollis, MbPIP4K) such as Mass spectrometry-based kinase assay and mutant Drosophila PIP4K didn't show similar activity to sponge AqPIP4K. The authors suggested that "In the context of other ancient PIP4K it is possible that since choanoflagellates exist as both single-cell and a transient multicellular state and do not have the characteristics of metazoans, PIP4K does not play any important functional role in these." However, this explanation is not well justified; they need to provide a more detailed discussion on this. Response: PIP4K is found in the genome of the choanoflagellate, M.brevicollis. MbPIP4K has the requisite kinase domain and the critical residue in the activation loop (A381) required for PIP4K activity is also conserved with the Amphimedon enzyme. Despite this, MbPIP4K was unable to rescue the growth and cell size phenotype of dPIP4K mutants (dPIP4K29) unlike AqPIP4K.
We have previously published a comparison of the in vitro activity versus in vivo function for the three PIP4K enzymes in the human genome (Mathre et.al PMID: __30718367). While all three human PIP4K isoforms can functionally rescue the Drosophila dPIP4K mutant, there is a nearly 104-fold difference for in vitro activity between them with PIP4K2C showing almost no in vitro activity. __The difference in in vitro enzyme activity between MbPIP4K and AqPIP4K is similarly notable. We would however highlight that this is more likely a reflection of the limitations of the in vitro PIP4K activity.
However, while AqPIP4K can rescue function in vivo (rescue of fly mutant phenotypes) MbPIP4K could not when expressed in fly cells. This must imply that there are differences in the polypeptide sequences of AqPIP4K and MbPIP4K that allow the former but not the latter to couple to the Insulin PI3K signalling pathway in fly cells. Given that Amphimedon and Choanoflagellates are separated by 100-150 Mya in evolution, this is possible. Our data on expression of AqPIP4K and MbPIP4K in fly S2 cells shows that they do not have equivalent localization (Fig 2C). What are the differences in the two polypeptides that lead to this? We will perform a multiple sequence alignment using PIP4K sequences from multiple choanoflagellates and sponges to identify these differences.
We will include the results of this analysis and an appropriate discussion in the revised manuscript.
• Likewise, the PIP4K gene has been identified in cnidarians, which are a sister group to bilaterian animals. However, the Cnidaria HvPIP4K showed no activity in biochemical or functional assays. In comparison to sponges, cnidarians are relatively complex organisms, and I believe that PIP4K is highly important for intercellular communication, as it is in bilaterians. The authors attempted to explain this by suggesting that "Based on theories of parallel evolution between cnidarians and sponges during early metazoan evolution, it is possible that the PIP4K gene was retained functional in one lineage and not in other." However, I am not convinced by this statement.
Response: This is a really interesting and challenging question from the reviewer. We are aware that both sponges (Porifera) and Cnidaria are examples of primitive metazoans separated by 80-90 Mya of evolution, yet while AqPIP4K shows activity and can functionally rescue dPIP4K mutants, HvPIP4K cannot. What does this mean?
A key difference between sponges and cnidarians is that while cnidarians have a simple “nerve-net” like nervous system, sponges do not have such a mode of communication. Therefore, it is possible that PIP4K, which we propose works in the context of hormone-based communication, is functionally important in sponges.
We are of course aware and acknowledge that in a like for like experimental system (Drosophila cells) our data shows that the two proteins behave differently, be it in terms of in vitro activity or in vivo function. This must imply inherent differences in the two polypeptides.
What we propose to do is to compare available PIP4K sequences from multiple Porifera and Cnidaria genomes and try and understand differences in the protein sequence that might explain differences in function. These results and their implications will be included in the revised manuscript.
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Please provide details of the databases (Uniprot-KB, NCBI sequence database, Pfam) versions. After identifying the specific PIP4K protein in each species (e.g. AqPIP4K and HvPIP4K), have you considered performing a reciprocal blast against the human genome to see if you have a top hit to PIP4K? Hence, the main focus of the project is on PIP4K as a metazoan-specific protein. We need to include a wider representation of non-bilaterian animals, including multiple species from sponges, ctenophores, placozoans, and cnidarians. Additionally, please check if homologues of PIP4K are present in other unicellular holozoans besides choanoflagellates. Response: We will add the NCBI IDs for all the sequences. We have carried out reciprocal blast to human proteome and then classified the selected sequences as PIP4K, we will add the results in the supplementary for the same. We will add more species of sponges, ctenophores, placozoans, and cnidarians in our analysis of PIP4K sequences. We will also include an analysis of other unicellular holozoans where genome sequence is available.
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Authors suggested the identification of other components of the PI signaling pathway along with PIP4k in the sponge. What is the status of these PI signaling pathway genes in other non-bilaterians and choanoflagellates? Response: We will add the details of the same in the revised manuscript and agree that this will help enhance the interpretation of our results.
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Phylogenetic tree of all PIP4K sequences (Figure 1C): How authors can be certain that the identified PIP4K sequences (e.g. AqPIP4K, HvPIP4K, and MbPIP4K) are indeed PIP4K, especially when there are several closely related proteins? It is important to conduct phylogenetic analysis alongside other PIP sequences (such as PI3K, PI4K, PIP5K, and PIP4K). If this analysis is carried out, the identified AqPIP4K, HvPIP4K, and MbPIP4K should be grouped together with human PIP4K in the same cluster. Response: As described in the methods, we have searched all the individual genomes analyzed for all PIK and PIPK enzyme sequences. We have marked the domains (using Pfam and Interpro) on these sequences and eliminated other PIK and PIPK sequences (such as PI3K, PI4K, PIP5K) and selected only PIP4K. To additionally confirm the distinction between PIP5K and PIP4K, we have manually inspected each sequence to establish the identity of the A381 amino acid residue in the activation loop. The identity of the amino acid at this position in the activation loop has been experimentally demonstrated to be an essential feature of PIP4K (Kunze et.al PMID: 11733501) and we have also confirmed this independently in a recent study (Ghosh et.al PMID: 37316298).
We will perform the phylogenetic analysis of the phosphoinositide kinases in the format suggested by the reviewer and add it in the revision as a supporting evidence.
Minor comments:
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Line 157: Phylogenetic conservation of PIP4Ks: Please provide details about bootstrap analysis. Response: Will be added
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Line 230: symbol correction 30{degree sign}C Response: Will be done
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Line 429-430: "from early metazoans like Sponges, Cnidaria and Nematodes." Nematodes are not considered early metazoans. Response: Apologies for the typo. This will be corrected. We agree that nematodes are not early metazoans.
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Line 477-478: "However, interestingly, MbPIP4K::GFP localizes only at the plasma membrane in S2 cells (Figure 2C)." This part was not further discussed. Can you please elaborate on why MbPIP4K::GFP localizes only at the plasma membrane in S2 cells? Response: We have discussed this point specifically in response to major comment by the reviewer and it will be addressed as described.
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Line 598: "the earliest examples of metazoa, namely the coral A. queenslandica" A. queenslandica is a sponge, not coral. Response: Apologies for the error. We will correct it.
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Line 602: "Amphimedon and human enzyme, although separated by 50Mya years of evolution" I think it's 500 million years ago, not 50 million years ago. Response: This typo will be corrected.
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Line 612: "coordinated communication between the cells is the most likely function" the cell. Response: Will change the sentence accordingly
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Line 614: "intracellular phosphoinositide signalling the identity of the hormone" missing full stop punctuation. Response: Will change the sentence accordingly
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Line 802 - 804: "other by way of difference in colour. The sub clusters have been numbered (1- early metazoans, 2- Nematodes, 3- Arthropods, 4- Molluscs, 5- Vertebrates (isoform PIP4K2C), 6- Vertebrates (isoform PIP4K2A), 7- Vertebrates (isoform PIP4K2B)." In the Figure, I can't find numbers on the subclusters. Response: Will add the numbers in the figure.
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Line 805- 807: "Phylogenetic analysis of selected PIP4K sequences from model organisms of interest. PIP4K from A. queenslandica has been marked in rectangular box." The rectangular box is missing in the figure. Response: Will change the figure accordingly
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Figure 1C: full forms of species names are missing. Response: Will change the figure accordingly
Reviewer #1 (Significance (Required):
The data is presented well, and the authors used a wide range of assays to support their conclusion. The study is highly impactful and can have a broader influence on the scientific community, particularly in evolutionary molecular biology, development, and biochemistry.
The study provides interesting findings; however, the reasons for PIP4K not being functional in cnidarians as in sponges and why PIP4K is present in unicellular holozoans but not functional are unclear.
We thank the reviewer for appreciating the significance and impact of our study. The very helpful questions raised by the reviewer will help enhance the quality of our study even further. We will make every effort to address these queries.
Reviewer #2 (Evidence, reproducibility and clarity (Required):
The manuscript by Krishnan et al. uses molecular phylogenetics, in vitro kinase assays, heterologous expression assays in Drosophila S2 cells and mutant complementation assays in yeast to study the evolution and function of putative PIP4 kinase genes from a sponge, a cnidarian and a choanoflagellate. Based on these experiments, the authors conclude that PIP4K is metazoan-specific and that the sponge PIP4K has conserved functions in selectively phosphorylating PI5P.
The study is in principle of interest and it could all be valid data, but the large number of flaws in the data presentation and/or analysis just makes it hard to assess the quality and thus validity of the data and conclusions.
We thank the reviewer for appreciating the potential interest in our findings of PIP4K function in early metazoans. We thank them for noting the need for correcting data presentation and these will be done in the revision.
__ Major comments:__
Overall, the manuscript lacks scientific rigor in the analysis and representation of the results, and the validity of many of the conclusions is therefore difficult to assess.
Major problems are:
(i) The authors base their study on the evolution of PIP4K genes on a deeply flawed concept of animal evolution. On multiple occasions, including the title, the authors refer to extant species (e.g. Amphimedon) as 'early metazoan', 'regarded as the earliest evolved metazoan' (l. 46-7) or 'the earliest examples of metazoans' just to name a few. This reflects a 'ladder-like' view on evolution that suggests that extant sponges are identical to early 'steps' of animal evolution.
We thank the reviewer who is clearly vastly more experienced in the field of evolutionary biology for the possible imprecise/incorrect usage of the word “ancient metazoan”. As new entrants to this area of evolutionary biology, we have of course referred to the existing literature such as PMID: 20686567 to guide us. This paper describes the sequencing of the A. queenslandica genome. It is clear that there is perceived value in studying this sponge in the context of early animal evolution although we are aware of there are a multitude of sponges and not all of them may be of value in the study of early animal evolution. We will peruse the literature more carefully and revise the manuscript to provide a more balanced view of this very interesting but unresolved area.
Also, the author's interpretation that one cluster of genes 'contained the sequences from early metazoans like sponges, cnidaria and nematodes' is referring to an outdated idea of animal phylogeny where nematodes were thought to be ancestrally simple organisms grouped as 'Acoelomata'. This idea of animal phylogeny was however disproven by molecular phylogenetics since the 1990ies.
Response: We are aware that the field of animal classification is undergoing continuous evolution. While earlier classifications may have been based of the presence or otherwise of a coelom and/or other anatomical features, we are aware of the use of molecular phylogenetics.
The phylogeny presented in Fig 1C is based on the sequence relationships between the PIP4K sequences from various animal genomes. Any errors in the labelling of groups such as that highlighted by the reviewer will be revised or corrected after a careful consideration of extant views in the field, which are somewhat varied.
(ii) The description of taxa in the phylogenetic tree in Fig. 1B lacks any understanding of phylogenetic relationships between animals and other eukaryotic groups. What kind of taxa are 'invertebrates' or 'parasites'? And why would 'invertebrates' exclude cnidarians and sponges? Also, why is the outgroup of opisthokonts named 'Eukaryota'?? Are not all organisms represented on the tree eukaryotes?
Response: We apologize for this imprecision in labelling taxa. This will be corrected.
(iii) The methods part lacks any information about the type of analysis (ML, Bayesian, Parsimony?) used to perform the phylogenetic analysis shown in Fig. 1C. Also, the authors mention three distinct clusters (l.428) that are not labelled in the figure.
Response: We will update the methods to include the additional details requested by the reviewer. Fig 1C will be re-labelled.
(iv) The validity of the Western Blot is difficult to assess as the authors have cut away the MW markers. Without, it is for example difficult to assess the size differences visible between Hydra and Monosiga PIP4K-GFP proteins on Fig. 2B. Also, it has become standard practice to show the whole Western blot as supplementary data in order to assess the correct size of the bands and the specificity of the antibody. This is also missing from this manuscript.
Response: Cropped Western blots have been shown to facilitate figure preparation in the main manuscript. The complete uncropped Western blots, in all cases, will be shared as Source data as is the standard practice for multiple journals in the review Commons portfolio.
(v) The authors claim that AqPIP4K was able to convert PI3P into PI with very low efficiency (Figure 2E), but without further label in the figure or explanation, it remains unclear how the authors come to this conclusion.
Response: We regret the typo in line 500 of the manuscript we have stated that “Further,……… was able to convert PI3P into PI with very low efficiency (Figure 2E).” What we intended to write was “Further,……… was able to convert PI3P into PI (3,4) P2 with very low efficiency (Figure 2E).” The efficiency with which this reaction takes place is very low and has been reported by us (Ghosh et.al PMID: 31652444) and others (Zhang et.al PMID: 9211928). At the exposure of the TLC shown in Fig 2E the PI(3,4)P2 spot cannot been seen. Much longer exposures of the TLC plate will be needed to see the PI(3,4)P2 spot. This will be corrected in a revised version of the manuscript.
(vi) The box plots in Fig. 3C and D lack error bars and thus seem to be consisting of only single data points without replicates. Also, Fig. 3C is a quantification of Fig. 3B but it remains unclear what has been quantified and how. It is also unclear how %PIP2 was determined.
Response: For Fig 3C, the colony count has been done from three replicates and the average has been considered to calculate the % growth for each genotype. We will include error bars and clarify this in the revised figure legend. For Fig 3D, the PIP2/PIP ratio has been calculated from biological replicates and average has been represented in the graphs. The individual values can be provided as supplementary data.
(vii) Throughout Fig. 4, I do not understand the genotypes indicated on the x-axis of the plots and below the images. I read the figure legends and manuscript describing these results at least 3 times, but cannot figure out what it all means. On Fig. 4C, what is the wild-type situation?
Response: We apologize for the lack of precision in labelling the figures versus the figure legends. This will be corrected in the revision:
The genotypes are as follows
- w1118 (control) * Act-GAL4. This has been referred to as wild type in the figure legend and called Act-Gal4 in Fig4 panels A-E
- dPIP4K29 – This refers to the protein null strain of dPIP4K. This strain is the background in which all reconstitutions of PIP4K genes have been done.
- PIP4K transgene from A. queenslandica.
- AqPIP4KKD Kinase dead PIP4K transgene from * queenslandica. In panels A, B, D and E, Act-GAL4: dPIP4K29* indicates the genetic background in which either AqPIP4K or AqPIP4KKD has been reconstituted.
Reviewer #2 (Significance (Required)):
If validated and put in the right phylogenetic context, the study is potentially contributing to expanding our knowledge on the evolution of metazoan-specific features, especially the evolution of proteins involved in cell-cell signalling and growth control. My field of expertise is broadly in evo-devo, molecular phylogentics, developmental genetics and cell biology. The in vitro lipid analysis seems interesting and potentially valid but I do not have sufficient expertise to evaluate its validity.
We thank the reviewer for appreciating the novelty of our contribution and its potential to contribute to understanding the evolution of metazoan specific signalling systems, once appropriate corrections have been made. We also appreciate their positive comment on our in vitro experimental analysis. This paper is a big effort to not only perform phylogenetic analysis but address the emerging interpretations experimentally as much as possible.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary In this manuscript, the authors investigate the evolutionary origins of metazoan Phosphatidylinositol phosphates (PIPs) signaling by elucidating the sequence and function of the PIP4K enzyme, which is crucial for converting PI5P to PI(4,5)P2 through phosphorylation. The authors have described PIP4K-like sequences distributed throughout metazoans and choanoflagellates through an extensive sequence screening. With in vitro and in vivo functional assays, the authors have shown that the sponge A. queenslandica PIP4K (AqPIP4K) is functionally similar to its human counterpart and highlight the major discovery of this study - that PIP4K protein function dates back to as early as sponges.
We thank the reviewer for noting the major finding of our study and our efforts to experimentally validate, using multiple approaches, the findings of our detailed bioinformatics analysis of PIP4K gene distribution across the tree of life.
Major comments
There are two key limitations to this paper. Like the sponges, ctenophores are one of the earliest branching metazoans. They are not well addressed in the paper. Secondly, despite finding PIP4 homologs in choanoflagellates, the authors claim that PIP4 is metazoan-specific.
We thank the reviewer for highlighting these two points; we recognize that both of these are important to address, to the extent that it is possible to do so. These will be addressed using the approaches detailed in the response to reviewer 1 comments.
- Line 46: A. queenslandica is the earliest branching metazoan. The phylogeny of sponges and ctenophores is not conclusively defined and hence, the statement must be rephrased. Despite the brief description of the evolution of metazoan lineage in the discussion section, ctenophores are missing from the phylogenetic tree. At least a sequence-level information PIP4K in ctenophores would strongly back the claims of the manuscript. Here is the link to the Mnemiopsis database. Response: We thank the reviewer for highlighting this point and pointing us to the Mnemiopsis database. We will most certainly analyse ctenophore genome sequences and add the ctenophore PIP4K sequence to the phylogeny, post analysis and the discussion will be modified to reflect the findings.
Mentioning that choanoflagellates contain homologs of PIP4K contradicts the statement that PIP4K is metazoan-specific. As per Fig 1E., the domain organization of PIP4K is conserved among choanoflagellates and metazoans. What is the percent sequence similarity to the query? This could answer why it doesn't show activity in Drosophila rescues - the system might simply not be compatible with the choanoflagellate homolog. The same may apply to the cnidarian homolog HvPIP4K. Further evidence is needed before concluding that MbPIP4K doesn't phosphorylate PIP5. It is additionally fascinating that MbPIP4K localizes at the plasma membrane unlike other homologs - this function might be choano-specific. Overall, PIP4K's possible origin in the choanoflagellate-metazoan common ancestor backs the current research that choanoflagellates indeed hold clues to understanding metazoan evolution. Further research is necessary before concluding (as in line 648) in the discussions section, where it is mentioned that "PIP4K does not play any important functional role in choanos".
Response: We thank the reviewer for highlighting the very interesting but incompletely understood facets of our study vis-à-vis choanoflagellates versus metazoans. The proposal for additional analysis is indeed interesting and we will carry out these analysis and revise the text accordingly.
__ Minor Comments__
- A detailed comparison of the sequence of the hydra PIP4K might help understand why it may not have worked like the sponge PIP4K. The discussion on the cnidarian PIP4K evolution is not convincing. It may not have worked because of it being expressed in a non-natural system. Structure prediction and comparison of proteins from different early branching animals should be used. Response: Thank you for these suggestions to understand why the cnidarian PIP4K may not have been functional. We will perform the suggested analysis and incorporate the data into the revision.
78 - Multicellularity evolved many times. Maybe say 'first evolved metazoans'
Response: Thank you for the suggestion.
Line 598 A. queenslandica is not a coral, it's a sponge.
Response: Text will be changed accordingly
Line 612 'thcells' à 'the cells'
Response: Text will be changed.
Line 623 - full stop missing after metazoans.
Response: Text will be changed
Figure 1B - Classification should be consistent - C. elegans is a species name, whereas ctenophores and vertebrates belong to a different classification. Invertebrates is not a scientific group. The edges of the lines of the phylogenetic tree don't join and they need to be arranged correctly.
Response: The names in the phylogeny will be changed to maintain uniformity. The representation of the phylogeny will be changed as mentioned.
Figure 2B The full blot could be shown in the supplement.
Response: Full blot will be provided as source data on resubmission or included as supplementary based on the destination journal’s specification.
Optional
- Heterologous overexpression does not always provide the full picture of the gene functionality. To make claims on the evolution of function, testing gene functions homologous systems can give a better picture. For example, performing in vitro kinase activity assays of MbPIP4K after overexpressing PIP4K in Monosiga brevicollis. would be a great. Data is missing also about the presence and function of ctenophore PIP4K. Overexpression of ctenophore-PIP4K in Drosophila for functional analyses could help in understanding the distribution/diversity of function of PIP4K in early animals. Response: We agree with the reviewer that heterologous expression may sometimes not replicate the biochemical environment of cells in the organism from which the gene being expressed was originally derived. Yet, heterologous expression experiments do sometimes provide an insight into properties solely dependent on the polypeptide with limited or no contribution from the cellular environment. In principle expressing PIP4K in M.brevicollis cells and then performing kinase assays would be a very good idea. However, we would like to highlight that till date there has been only one study where septins have been transfected in Choanoflagellates and their localization being observed. We are not set up to culture M. brevicollis and will be unable to do this for a revision of the current manuscript. However, we appreciate the importance of this experiment and will do this in collaboration with a choanoflagellate lab in a follow up study to this one.
Ctenophores like cnidarians have two main layers of cells that sandwich a middle layer of jelly-like material, while, more complex animals have three main cell layers and no intermediate jelly-like layer. Hence ctenophores and cnidarians have traditionally been labelled diploblastic. Studies have shown that ctenophores and unicellular eukaryotes share ancestral metazoan patterns of chromosomal arrangements, whereas sponges, bilaterians, and cnidarians share derived chromosomal rearrangements. Conserved syntenic characters unite sponges with bilaterians, cnidarians, and placozoans in a monophyletic clade while ctenophores are excluded from this clustering, placing ctenophores as the sister group to all other animals. Ctenophore PIP4K sequence can be identified and compared as discussed before to other PIP4K sequences used in this study.
Reviewer #3 (Significance (Required)):
Significance: This is the first study that addresses PIP signaling pathway in early metazoans. The findings of this manuscript contribute to the understanding of second-messenger signaling and its link with the origin and evolution of metazoan multicellularity. PIP signaling is crucial in different metazoan aspects such as cytoskeletal dynamics, neurotransmission, and vesicle trafficking, and hence, plays a critical role in metazoan multicellularity. Through this study, it was interesting to see that some components of the PIP signaling pathway are conserved in yeast, but some, such as the PIP4K protein evolved at the brink of metazoan evolution, highlighting the need for complexity in metazoans and their close relatives - the facultatively multicellular choanoflagellates. Since this is a crucial pathway in human biology and has medical significance due to its role in tumorigenesis and cancer cell migration, this study serves the audience in basic research such as evolutionary biology, and applied research such as human medicine. My field of expertise is molecular biology, cell biology and microbiology, with specific expertise on choanoflagellates. Therefore, it is exciting to see the homologs of PIP4K present in choanoflagellates.
__ Evidence, Reproducibility, and clarity:__
The authors have made a clear case of why PIP4K needs to be studied. They have thoroughly mapped PIP4K throughout the tree of life. The results are clear and reproducible. With the findings of this study, they have linked the PIP signalling cascade and metazoan evolution. Using the heterologous expression of sponge A. queensladica PIP4K, they have made compelling evidence that AqPIP4K functions in PIP5 phosphorylation, as seen in humans and Drosophila. However, it was not convincing why the hydra PIP4K was not functional. It was also not convincing why the PIP4K is metazoan-only when there is a conserved sequence (with conserved domain structure) present in choanoflagellates.
We thank the reviewer for appreciating the novelty and importance of our findings in multiple areas of basic biology related to early metazoans and basic biomedical sciences. We also note their comments on the clear and reproducible results presented. Points raised related to the lack of functionality of PIP4K from Hydra and choanoflagellates are noted and will be addressed as indicated in response to other reviewer comments.
Experiments/Analysis to be done
- We will perform a multiple sequence alignment using PIP4K sequences from multiple choanoflagellates and sponges to identify these differences.
- What we propose to do is to compare available PIP4K sequences from multiple Porifera and Cnidaria genomes and try and understand differences in the protein sequence that might explain differences in function.
- We will add more species of sponges, ctenophores, placozoans, and cnidarians in our analysis of PIP4K sequences. We will also include an analysis of other unicellular holozoans where genome sequence is available.
- We will perform the phylogenetic analysis of the phosphoinositide kinases in the format suggested by the reviewer and add it in the revision as a supporting evidence.
- Structure prediction and comparison of proteins from different early branching animals should be used.
- Uniformity of terminology and alignment with conventions in the field of animal taxonomy
- NCBI ID of sequences to be added and include more non-bilaterian animals sequences in phylogeny- redo the phylogeny.
- Check for PI signalling genes in choanoflagellates
- More detailed description of phylogenetic analysis.
- Add complete Western blot as source data.
- *
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.
- *
4. Description of analyses that authors prefer not to carry out
- Expression of PIP4K in choanoflagellates and in vitro kinase assays with lysates. It is beyond our technical ability to perform these experiments at this stage.
-
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Referee #3
Evidence, reproducibility and clarity
Summary
In this manuscript, the authors investigate the evolutionary origins of metazoan Phosphatidylinositol phosphates (PIPs) signaling by elucidating the sequence and function of the PIP4K enzyme, which is crucial for converting PI5P to PI(4,5)P2 through phosphorylation. The authors have described PIP4K-like sequences distributed throughout metazoans and choanoflagellates through an extensive sequence screening. With in vitro and in vivo functional assays, the authors have shown that the sponge A. queenslandica PIP4K (AqPIP4K) is functionally similar to its human counterpart and highlight the major discovery of this study - that PIP4K protein function dates back to as early as sponges.
Major comments
There are two key limitations to this paper. Like the sponges, ctenophores are one of the earliest branching metazoans. They are not well addressed in the paper. Secondly, despite finding PIP4 homologs in choanoflagellates, the authors claim that PIP4 is metazoan-specific.
- Line 46: A. queenslandica is the earliest branching metazoan. The phylogeny of sponges and ctenophores is not conclusively defined and hence, the statement must be rephrased. Despite the brief description of the evolution of metazoan lineage in the discussion section, ctenophores are missing from the phylogenetic tree. At least a sequence-level information PIP4K in ctenophores would strongly back the claims of the manuscript. Here is the link to the Mnemiopsis database.
- Mentioning that choanoflagellates contain homologs of PIP4K contradicts the statement that PIP4K is metazoan-specific. As per Fig 1E., the domain organization of PIP4K is conserved among choanoflagellates and metazoans. What is the percent sequence similarity to the query? This could answer why it doesn't show activity in Drosophila rescues - the system might simply not be compatible with the choanoflagellate homolog. The same may apply to the cnidarian homolog HvPIP4K. Further evidence is needed before concluding that MbPIP4K doesn't phosphorylate PIP5. It is additionally fascinating that MbPIP4K localizes at the plasma membrane unlike other homologs - this function might be choano-specific. Overall, PIP4K's possible origin in the choanoflagellate-metazoan common ancestor backs the current research that choanoflagellates indeed hold clues to understanding metazoan evolution. Further research is necessary before concluding (as in line 648) in the discussions section, where it is mentioned that "PIP4K does not play any important functional role in choanos".
Minor Comments
- A detailed comparison of the sequence of the hydra PIP4K might help understand why it may not have worked like the sponge PIP4K. The discussion on the cnidarian PIP4K evolution is not convincing. It may not have worked because of it being expressed in a non-natural system. Structure prediction and comparison of proteins from different early branching animals should be used.
- 78 - Multicellularity evolved many times. Maybe say 'first evolved metazoans'
- Line 598 A. queenslandica is not a coral, it's a sponge.
- Line 612 'thcells' 'the cells'
- Line 623 - full stop missing after metazoans.
- Figure 1B - Classification should be consistent - C. elegans is a species name, whereas ctenophores and vertebrates belong to a different classification. Invertebrates is not a scientific group. The edges of the lines of the phylogenetic tree don't join and they need to be arranged correctly.
- Figure 2B The full blot could be shown in the supplement.
Optional
- Heterologous overexpression does not always provide the full picture of the gene functionality. To make claims on the evolution of function, testing gene functions homologously systems can give a better picture. For example, performing in vitro kinase activity assays of MbPIP4K after overexpressing PIP4K in Monosiga brevicollis. would be a great. Data is missing also about the presence and function of ctenophore PIP4K. Overexpression of ctenophore-PIP4K in Drosophila for functional analyses could help in understanding the distribution/diversity of function of PIP4K in early animals.
Significance
This is the first study that addresses PIP signaling pathway in early metazoans. The findings of this manuscript contribute to the understanding of second-messenger signaling and its link with the origin and evolution of metazoan multicellularity. PIP signaling is crucial in different metazoan aspects such as cytoskeletal dynamics, neurotransmission, and vesicle trafficking, and hence, plays a critical role in metazoan multicellularity. Through this study, it was interesting to see that some components of the PIP signaling pathway are conserved in yeast, but some, such as the PIP4K protein evolved at the brink of metazoan evolution, highlighting the need for complexity in metazoans and their close relatives - the facultatively multicellular choanoflagellates. Since this is a crucial pathway in human biology and has medical significance due to its role in tumorigenesis and cancer cell migration, this study serves the audience in basic research such as evolutionary biology, and applied research such as human medicine. My field of expertise is molecular biology, cell biology and microbiology, with specific expertise on choanoflagellates. Therefore, it is exciting to see the homologs of PIP4K present in choanoflagellates.
Evidence, Reproducibility, and clarity:
The authors have made a clear case of why PIP4K needs to be studied. They have thoroughly mapped PIP4K throughout the tree of life. The results are clear and reproducible. With the findings of this study, they have linked the PIP signalling cascade and metazoan evolution. Using the heterologous expression of sponge A. queensladica PIP4K, they have made compelling evidence that AqPIP4K functions in PIP5 phosphorylation, as seen in humans and Drosophila. However, it was not convincing why the hydra PIP4K was not functional. It was also not convincing why the PIP4K is metazoan-only when there is a conserved sequence (with conserved domain structure) present in choanoflagellates.
-
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Krishnan et al. uses molecular phylogenetics, in vitro kinase assays, heterologous expression assays in Drosophila S2 cells and mutant complementation assays in yeast to study the evolution and function of putative PIP4 kinase genes from a sponge, a cnidarian and a choanoflagellate. Based on these experiments, the authors conclude that PIP4K is metazoan-specific and that the sponge PIP4K has conserved functions in selectively phosphorylating PI5P.
The study is in principle of interest and it could all be valid data, but the large number of flaws in the data presentation and/or analysis just makes it hard to assess the quality and thus validity of the data and conclusions.
Major comments:
Overall, the manuscript lacks scientific rigour in the analysis and representation of the results, and the validity of many of the conclusions is therefore difficult to assess.
Major problems are:
(i) The author base their study on the evolution of PIP4K genes on a deeply flawed concept of animal evolution. On multiple occassions, including the title, the authors refer to extant species (e.g. Amphimedon) as 'early metazoan', 'regarded as the earliest evolved metazoan' (l. 46-7) or 'the earliest examples of metazoans' just to name a few. This reflects a 'ladder-like' view on evolution that suggests that extant sponges are identical to early 'steps' of animal evolution. Also, the author's interpretation that one cluster of genes 'contained the sequences from early metazoans like sponges, cnidaria and nematodes' is referring to an outdated idea of animal phylogeny where nematodes were thought to be ancestrally simple organisms grouped as 'Acoelomata'. This idea of animal phylogeny was however disproven by molecular phylogenetics since the 1990ies.
(ii) The description of taxa in the phylogenetic tree in Fig. 1B lacks any understanding of phylogenetic relationships between animals and other eukaryotic groups. What kind of taxa are 'invertebrates' or 'parasites'? And why would 'invertebrates' exclude cnidarians and sponges? Also, why is the outgroup of opisthokonts named 'Eukaryota'?? Are not all organisms represented on the tree eukaryotes?
(iii) The methods part lacks any information about the type of analysis (ML, Bayesian, Parsimony?) used to perform the phylogenetic analysis shown in Fig. 1C. Also, the authors mention three distinct clusters (l.428) that are not labelled in the figure.
(iv) The validity of the Western Blot is difficult to assess as the authors have cut away the MW markers. Without, it is for example difficult to assess the size differences visible between Hydra and Monosiga PIP4K-GFP proteins on Fig. 2B. Also, it has become standard practice to show the whole Western blot as supplementary data in order to assess the correct size of the bands and the specificity of the antibody. This is also missing from this manuscript.
(v) The authors claim that AqPIP4K was able to convert PI3P into PI with very low efficiency (Figure 2E), but without further label in the figure or explanation, it remains unclear how the authors come to this conclusion.
(vi) The box plots in Fig. 3C and D lack error bars and thus seem to be consisting of only single data points without replicates. Also, Fig. 3C is a quantification of Fig. 3B but it remains unclear what has been quantified and how. It is also unclear how %PIP2 was determined.
(vii) Throughout Fig. 4, I do not understand the genotypes indicated on the x-axis of the plots and below the images. I read the figure legends and manuscript describing these results at least 3 times, but cannot figure out what it all means. On Fig. 4C, what is the wild-type situation?
Significance
If validated and put in the right phylogenetic context, the study is potentially contributing to expanding our knowledge on the evolution of metazoan-specific features, especially the evolution of proteins involved in cell-cell signalling and growth control.
My field of expertise is broadly in evo-devo, molecular phylogentics, developmental genetics and cell biology. The in vitro lipid analysis seems interesting and potentially valid but I do not have sufficient expertise to evaluate its validity.
-
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Referee #1
Evidence, reproducibility and clarity
Summary:
The researchers identified PIP4K (phosphatidylinositol 5 phosphate 4-kinase) as a lipid kinase that is specific to metazoans. In order to determine its conserved function across metazoans, they compared PIP4K activity in both early-branching metazoans and bilaterian animals. Biochemical assays demonstrated a conserved catalytic activity between the sponge Amphimedon queenslandica (AqPIP4K) and human PIP4K. In in-vivo experiments, AqPIP4K was found to rescue the reduced cell size, growth, and development phenotype in larvae of null mutant in Drosophila PIP4K. Based on these findings, the authors suggest that the function of PIP4K was established in early metazoans to facilitate intercellular communication. The experiments were well designed, and a range of biochemical, in vitro, and in vivo experiments were conducted. That being said, there are some questions that require further discussion before we can fully accept the author's conclusion of an evolutionarily conserved function of PIP4K across metazoans.
Major comments:
- The authors mentioned that PIP4K is metazoan-specific and involved in intercellular communication. How can we explain the presence of PIP4K in choanoflagellate genomes? Despite its high similarity with conserved domains and functionally important residues, experimental results with the PIP4K from Choanoflagellate (Monosiga brevicollis, MbPIP4K) such as Mass spectrometry-based kinase assay and mutant Drosophila PIP4K didn't show similar activity to sponge AqPIP4K. The authors suggested that "In the context of other ancient PIP4K it is possible that since choanoflagellates exist as both single-cell and a transient multicellular state and do not have the characteristics of metazoans, PIP4K does not play any important functional role in these." However, this explanation is not well justified; they need to provide a more detailed discussion on this.
- Likewise, the PIP4K gene has been identified in cnidarians, which are a sister group to bilaterian animals. However, the Cnidaria HvPIP4K showed no activity in biochemical or functional assays. In comparison to sponges, cnidarians are relatively complex organisms, and I believe that PIP4K is highly important for intercellular communication, as it is in bilaterians. The authors attempted to explain this by suggesting that "Based on theories of parallel evolution between cnidarians and sponges during early metazoan evolution, it is possible that the PIP4K gene was retained functional in one lineage and not in other." However, I am not convinced by this statement.
- Please provide details of the databases (Uniprot-KB, NCBI sequence database, Pfam) versions. After identifying the specific PIP4K protein in each species (e.g AqPIP4K and HvPIP4K), have you considered performing a reciprocal blast against the human genome to see if you have a top hit to PIP4K? Hence, the main focus of the project is on PIP4K as a metazoan-specific protein. We need to include a wider representation of non-bilaterian animals, including multiple species from sponges, ctenophores, placozoans, and cnidarians. Additionally, please check if homologues of PIP4K are present in other unicellular holozoans besides choanoflagellates.
- Authors suggested the identification of other components of the PI signaling pathway along with PIP4k in the sponge. What is the status of these PI signaling pathway genes in other non-bilaterians and choanoflagellates?
- phylogenetic tree of all PIP4K sequences (Figure 1C): How authors can be certain that the identified PIP4K sequences (e.g AqPIP4K, HvPIP4K, and MbPIP4K) are indeed PIP4K, especially when there are several closely related proteins? It is important to conduct phylogenetic analysis alongside other PIP sequences (such as PI3K, PI4K, PIP5K, and PIP4K). If this analysis is carried out, the identified AqPIP4K, HvPIP4K, and MbPIP4K should be grouped together with human PIP4K in the same cluster.
Minor comments:
- Line 157: Phylogenetic conservation of PIP4Ks: Please provide details about bootstrap analysis.
- Line 230: symbol correction 30{degree sign}C
- Line 429-430: "from early metazoans like Sponges, Cnidaria and Nematodes." Nematodes are not considered early metazoans.
- Line 477-478: "However, interestingly, MbPIP4K::GFP localizes only at the plasma membrane in S2 cells (Figure 2C)." This part was not further discussed. Can you please elaborate on why MbPIP4K::GFP localizes only at the plasma membrane in S2 cells?
- Line 598: "the earliest examples of metazoa, namely the coral A.queenslandica" A.queenslandica is a sponge, not coral.
- Line 602: "Amphimedon and human enzyme, although separated by 50Mya years of evolution" I think it's 500 million years ago, not 50 million years ago.
- Line 612: "co-ordinated communication between thcells is the most likely function" the cell.
- Line 614: "intracellular phosphoinositide signalling The identity of the hormone" missing full stop punctuation.
- Line 802 - 804: "other by way of difference in colour. The sub clusters have been numbered (1- early metazoans, 2- Nematodes, 3- Arthropods, 4- Molluscs, 5- Vertebrates (isoform PIP4K2C), 6- Vertebrates (isoform PIP4K2A), 7- Vertebrates (isoform PIP4K2B)." In the Figure, I can't find numbers on the subclusters.
- Line 805- 807: "Phylogenetic analysis of selected PIP4K sequences from model organisms of interest. PIP4K from A.queenslandica has been marked in rectangular box." The rectangular box is missing in the figure.
- Figure 1C: full forms of species names are missing.
Significance
The data is presented well, and the authors used a wide range of assays to support their conclusion. The study is highly impactful and can have a broader influence on the scientific community, particularly in evolutionary molecular biology, development, and biochemistry.
The study provides interesting findings; however, the reasons for PIP4K not being functional in cnidarians as in sponges and why PIP4K is present in unicellular holozoans but not functional are unclear.
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Reply to the reviewers
Overall comments:
Reviewer #1:
Evidence, reproducibility and clarity
The study by Parker et al describes the innovative use of single-cell RNA sequencing to detect markers and traits of pollen. Using pollen allows detection of recombinant events allowing the ability to do quantitative trait mapping using gene expression as a trait. This led to the discovery of expected cis QTL, but there is an intriguing trans QTL as well. The trans eQTL was mapped to a candidate causal locus DUO1. This is exciting given its role in pollen development and will obviously be followed up on in future studies.
We thank the reviewer for these kind comments.
Overall, this is an exciting technological advance that will rapidly advance our ability to map pollen traits and arguably more importantly create high density recombination maps across organisms. I realize this is a proof of principle study and the questions below are intended to improve this already strong manuscript.
We agree with the reviewer that the main conceptual advance in our manuscript is the novel methodology for meiotic recombination breakpoint identification and eQTL mapping using snRNA-seq.
Significance
The overall strength is the development of a method to map recombination using single-cell genomics of pollen. The weakness is the limitation to studying pollen traits at least for now. The other weakness is helping the reader apply this to their own research questions. This is easily addressable through updating the writing in a way that is more accessible.
We thank the reviewer again for their praise of our work. Although we chose to focus on eQTL mapping in pollen at this stage, we believe that the methods we have developed could also be applied to other organisms and tissues, so long as there as genetic diversity between individual cells in the population. This was previously demonstrated using F4 segregant populations of C. elegans (Ben-David et al. 2021). We therefore state in the Discussion that “eQTL mapping with snRNA-seq is also possible in diploid cells from segregating populations, provided sufficiently high numbers of individuals are used as input”.
The methods described in the study could be of great use to other researchers who wish to peform eQTL analyses. We therefore agree with both reviewers that the method should be made as transparent as possible in the paper. We have therefore updated the Results, Methods and Discussion sections to address the specific points of both reviewers about clarity.
Reviewer #2:
Evidence, reproducibility and clarity
In the manuscript by Parker et al, the authors developed a methodology to scale up eQTL studies using single-nucleus RNA-Seq of meiotic products using Arabidopsis thaliana pollen. Each pollen grain, collected from an F1 hybrid between two inbred lines, is haploid and contains a unique combination of the two parental genomes, effectively replacing the F2 population typically used in QTL studies. Single-nucleus RNA-Seq served as both phenotype and genotype, as it was used also to infer recombination profiles. In their experiments, the authors used a mixture of pollen from five different crosses - each between the reference Arabidopsis accession and another accession - for the first experiment, followed by a repeat of one of the original crosses in a second experiment to increase coverage and show reproducibility. They describe their approach to genotyping, performing the eQTL analysis, and describe statistics of the cis- and trans-eQTLs. In particular, they identify and discuss in detail a strong trans-eQTL on chromosome 1 that significantly and reproducibly associate with the expression of hundreds of genes. The likely candidate gene underlying this trans-eQTL is DUO3, a transcription factor known to play a role in pollen development, although the authors acknowledge that further experiments are needed to show that this is the causal variant.
As this is a novel methodology with the potential for widespread adoption by the community, most of my comments focus on expanding the details provided about the methodology and discussing its limitations. Additional clarity of information will help to ensure its reproducibility and wider applicability.
We agree with the reviewer that the main advance demonstrated in our study is the novel method for eQTL mapping. As noted in our response to reviewer 1, we agree that the method should be as made as clear as possible for readers, so that they can apply it to their own research questions. We have therefore updated the Results, Methods and Discussion sections to address the specific points of both reviewers about clarity.
Significance:
The methodology developed in this study significantly advances the feasibility of eQTL studies. The ability to easily scale up the number of individuals analyzed provides unprecedented resolution for identifying underlying alleles. In addition, because the pollen from each cross can be collected and processed together, environmental and technical variables between individuals (in this case, pollen nuclei) are tightly controlled. This advantage is underlined by the discovery of a strong trans-eQTL, which is likely to play an important role in pollen biology, and whose different alleles are spread across natural populations. I am therefore very excited about the publication of this manuscript.
We thank the reviewer for these very positive remarks.
Specific Comments:
Reviewer #1:
- What is the average maker resolution in bp and cM? What numbers of nuclei would be needed to profile to gain single gene resolution in Arabidopsis?
For haplotyping and crossover analysis, we grouped informative reads into 25kb bins to reduce both the number of predictions required for haplotype inference, and the number of statistical tests required for eQTL mapping. We can therefore estimate the median distance between non-empty bins for each nucleus as a proxy for marker resolution resolution. We find that the median distances between non-empty bins across all nuclei are 350 kb and 500 kb for the first and second dataset respectively. To address this question, we have updated the Results section to include some analysis of marker distributions, and added histograms of distributions as Figure 2 – figure supplement 1A.
The question of how many nuclei would be needed to gain single gene resolution of eQTL mapping is extremely difficult to answer, as the detection and resolution of eQTLs depends on several different factors:
- First and foremost, as pointed out by the reviewer, the sample size i.e. number of nuclei profiled, makes a difference to the ability to detect eQTLs.
- Secondly, as in all RNA-seq experiments the effect size of the measured change in gene expression has a large impact on the ability to detect eQTLs. eQTLs causing small effect size changes will therefore require more nuclei to detect than those with large effect size changes. This likely explains why we were able to detect a larger proportion of trans-eQTLs in the second dataset with more nuclei compared to the first dataset, since trans-eQTLs tend to have smaller effect size changes.
- Finally, the rate of meiotic recombination around the eQTL locus has a large impact on the resolution of the mapped eQTL. Loci in low-recombining regions, such as close to or within the centromere, will likely never be mappable to single-gene resolution, even with extremely high numbers of nuclei, due to genetic-linkage over large regions of the genome. This is exemplified by the CPV1 locus that we mapped in the Col-0 x Db-1 cross, that maps to the centromere of Chromosome 1. Despite correlating with a relatively large effect size change in gene expression in PLL1 (Figure 6 – figure supplement 3B), we could only map this trans-eQTL within a 1.5 LOD drop interval of >10 Mb. One way to address the issue of resolution caused by crossover rate could be to use mutants such as recq4 or figl1 which increase the rate of recombination, as was demonstrated recently (Capilla-Pérez et al. 2024). In summary, the number of nuclei required to map an eQTL to single-gene resolution is not fixed, and depends on both the effect size of the change in expression, and the genomic location of the causal variant. We have added a new paragraph to the Discussion to address this question and provide some potential future solutions.
- It would be great if the authors could add a discussion of how the resolution of mapping could be improved and what it would take to get there?
We agree this is an important question for future work. As mentioned in response to point 1 of Reviewer #1, we have added a new paragraph to the Discussion to address this question and provide some potential future solutions.
- Could this approach be feasible for species where there is not a reference genome?
While this would require different methods to the ones used in our study, it is possible to identify and genetically map markers using single-cell sequencing of recombinant pollen without a reference sequence. For example, we have used single cell sequencing of hybrid pollen to assemble the genome of the pollen mother plant by providing linkage information (Sun et al. 2022; Campoy et al. 2020). We mention this in the Discussion, where we state: “it has been demonstrated that genotypes created using gamete single cell sequencing can be used to disentangle complex genome assemblies and resolve the haplotypes of polyploid species such as potato.”
- It is stated 67% are located with in 2Mb of a gene and technically that is is cis-, but there aren't really long range interactions in Arabidopsis (>30kb)...so are these really cis or trans? It might be worth considering how cis vs trans are defined. Basically, what is truly cis vs trans?
We agree with the reviewer that eQTLs that are genuinely located more than 30 kb from a gene are likely to be trans-eQTLs in Arabidopsis. Our reasoning for using a conservative approach to classifying eQTLs as cis is due to the resolution of the mapping procedure, and the expectation that the majority of identifiable eQTLs willl act in cis, not in trans. By using a conservative threshold we hope to prevent the misclassification of cis eQTLs as trans. To better inform the reader, we provide histograms showing the distribution of distances from mapped eQTLs classified as cis to the gene which was used as a phenotype as Figure 3B and figure 6 – figure supplement 3A, and report the median distances from cis-eQTLs to the affected gene in the Results.
- What percent of the time was the actual crossover captured, if at all? Is this possible with snRNA-seq?
Assessing the accuracy of crossover mapping without ground truth information would be complex. In future we hope to use simulations and/or samples with known recombination patterns to benchmark the quality of crossover calling. This is outside the scope of the current paper. However, without knowing the true locations of crossovers we can use the probabilities produced by the rHMM to estimate 95% confidence intervals on the locations of individual crossovers. We find that the 95% confidence intervals of called crossovers follow a bi-modal distribution depending on their proximity to the centromere. For crossovers that were predicted to occur in the chromosome arms, the median 95% confidence interval size was 1.1 Mb. For crossovers that are proximal to the centromere, or where lack of markers means that the haplotype of the centromere is ambiguous, then the median 95% confidence interval size was 8.2 Mb. We have now added information from these confidence intervals and corresponding plots showing the resolution of individual crossover calls to the Results and Figure 2 – figure supplement 1B.
- How many eQTL were there per locus?
We detected an average of 0.14 and 0.63 eQTLs per expressed gene in the first and second datasets, respectively. We have added this information to the Results.
- How deep were the libraries sequenced and were they sequenced to saturation? In general, I did not find any sequencing summary statistics. How many reads were sequenced per library, per genotype etc, how many aligned, how many UMIs per cell, how many transcripts detected per cell. This will help give the reader a baseline for knowing what kind of quality is needed to successfully implement this strategy in their own lab.
We thank the reviewer for this important comment. We have now added a table of sequencing statistics to the supplemental data as Supplementary file 1.
__ Reviewer #2:__
(1) Given the sparseness of snRNA-Seq data per nucleus/cell (inherent to single cell technologies), what happens to low expressed genes? Is any filtering done? For example, in the extreme case of a gene that is either undetected or has only a single read in some nuclei, would a sufficiently large number of nuclei theoretically allow the detection of an eQTL signal for that gene? Alternatively, does this method remain inherently blind to genes expressed below a certain threshold? Please discuss these limitations and prospects.
We indeed applied a expression filtering threshold to remove lowly expressed genes from the analysis, before performing eQTL mapping. Specifically, we removed genes which were detectably expressed (i.e. with at least one mapping read) in fewer than 5% of the cells. This is stated in the Methods section entitled “eQTL mapping analysis”, and we have added clarifications to make the filtering method clearer.
(2) The methods section states that FACS sorting was used to isolate 40,000 nuclei; however, the final datasets contained only 1,394 or 7,458 nuclei. Could you provide a detailed breakdown of the losses at each step of the filtering process? As this is a key factor in the overall efficiency of the method, it would be valuable to discuss the potential for increasing throughput in future implementations. Also, how the total yield increased fivefold between repeats - could you elaborate on the factors that contributed to this improvement?
We apologise for the lack of precision in this section of the Methods in the first draft of the manuscript. We have now updated the relevant Methods sections to include more of the concrete statistics about the preparation of nuclei samples and single-nucleus library construction. The statistic 40,000 referred to the approximate average number of “events” that we generally aim to sort using the FACS machine. We have now removed this statistic from the Methods as it does not accurately reflect the numbers of events that were sorted in the case of these two libaries, which was in fact slightly higher__.__
For the sample used to generate the first dataset, we sorted approximately 53,000 events. Some of these may in fact represent debris, and so the true number of sorted nuclei will be less than this amount. At the time that this sample was prepared, we did not have access to a cell counter to orthogonally validate the output of the FACS machine. After sorting, the nuclei were concentrated by centrifugation before loading onto the 10x Chromium controller. Although the aim of this centrifugation step was to concentrate the nuclei, it almost certainly causes some nuclei to burst and or clump together, resulting in losses. This may explain the low recovery rate of the first dataset.
For the sample used to generate the second dataset, we sorted approximately 55,500 events and then measured the number of nuclei after sorting using a Luna FX cell counter. According to these estimates, the total number of nuclei in the sample was around 38,250 in 135µl volume. From this, 43µl containing approximately 13,150 nuclei was loaded onto the 10x Chromium controller, without centrifugation. According to the 10x Chromium next GEM single cell 3’ v3.1 user guide, the reported recovery rate of singlet barcodes for this number of input cells/nuclei is around 53%, or 7000 nuclei, which is in line with the 7458 that we recovered.
The difference in yield between the first and second datasets may stem from a higher quality input sample with less debris, or from the removal of the centrifugation step from our protocol. Alternatively, it may result from advances in the 10x platform, kits and technology - the first dataset was collected in 2022, whilst the second was collected in 2024. There are many technical variables that differ between the two datasets - including the individuals who performed the experiment. This means that any prediction about the cause of the difference in yield can only really be speculative.
(3) It is unclear what happened to the vegetative nuclei during this process. While the authors attribute the differences between the first and second experiments to handling, previous studies (Schoft et al. 2015) suggest that vegetative and sperm nuclei can be distinguished in FACS analysis after DNA staining. This suggests that, for future applications, it may be possible to refine this method to specifically focus on either vegetative or sperm nuclei by adjusting the gating parameters in FACS. The inclusion of the FACS sorting graphs with the gating used as a supplementary figure would also be helpful in understanding and replicating this aspect of the methodology.
We agree with the reviewer that some of the loss of vegetative nuclei may result from the gating stragety applied during FACS. We have added a statement clarifying this to the Results. We are now working with fluorescent reporter lines that distinguish the sperm and vegetative nuclei, to determine more appropriate gating strategies for vegetative nuclei, and hope to improve the recovery of these nuclei in the future. As suggested by the reviewer, we have added the gating strategies of the two FACS experiments as supplementary Figure 1 – figure supplement 1, and Figure 6 - figure supplement 1.
(4) For the benefit of future users, it would be helpful to discuss the following points: (1) Would you recommend using a combination of several parents, as in the first experiment, or limiting the analysis to two parents? (e.g. it seems some of the recombination patterns could not be called due to the mix of five crosses) What are the advantages and disadvantages of each approach? (2) How should one estimate the optimal number of nuclei/cells to use? Can you downsample the nuclei and see the effects of lower numbers on identified eQTLs? (3) Given that different single-cell RNA-Seq protocols involve trade-offs between the number of cells and the number of UMIs per cell in relation to cost, what strategy would you recommend to users to optimize their experiments?
The design of experiments depends on the research goals and budget of the project. However, we do have some recommendations for mixing of different genotypes – namely, that care must be taken to select genotypes to pool which are sufficiently genetically distinct, since genetically similar genotypes will be harder to distiguish and demultiplex correctly. Where possible, pooling is invaluable as a way to control for technical variation. When pooling is not possible due to genetically indistiguishable genotypes, then experimental design and randomisation must be considered carefully to prevent confounding. We have added some recommendations in this regard to the Discussion.
We addressed the question of number of nuclei on the detection and resolution of eQTLs in response to Reviewer #1 point 1. __We have added a new section to the Discussion to address this question. __In general it is hard to perform power analyses prior to conducting transcriptomic experiments, because the effect size of gene expression changes of interest are generally not known in advance. More general investigations of the trade off between number of cells and number of UMIs in single-nucleus sequencing experiments have been thoroughly investigated by other groups (Svensson et al. 2017; Mandric et al. 2020).
(5) It is not entirely clear which exact linear model was used for the association study. Specifically: (1) How were the five parents included in the model? (2) Why was it necessary to correct for population structure in this case, given the controlled crosses? (3) How was technical variation accounted for, and how were principal components derived and incorporated into the analysis? (4) What is meant by "cell type control" and were the few VN included in the analysis? To ensure clarity and reproducibility, it would be helpful to provide more detailed explanations and to explicitly state the linear equation used for fitting.
We apologise for the confusion as to the linear model used. __We have now updated the Methods section to make this clearer and explictly included the formula of the linear model. __For an experiment containing N pooled hybrids, for each barcode the predicted genotype of Parent 2 (as the first parent was always Col-0) was included in the model using (N - 1) dummy variables, and the predicted haplotype as a set of N continuous variables (with zero representing either the Col-0 allele, and one representing the Parent 2 allele). Despite the controlled crosses, the genotype of Parent 2 has to be controlled in the equation because the mix of five hybrids is similar to population structure, i.e. haplotypes that are only found in Kar-0 are reasonably correlated in the dataset, even when these haplotypes are completely unlinked, because only nuclei from the Col-0 x Kar-0 cross can share them. Controlling for Parental genotype prevents the detection of these as spurious correlations.
In the initial models, cell type was not explicitly controlled but was captured primarily by principal component 1 of the PCA, which was used as a covariate. The vegetative nuclei were not excluded from the analysis, but likely did not contribute strongly due to their limited number. In the models where haplotype x cell type was modelled, cell type cluster was also explicitly used as a covariate. We have updated the Methods to make these points clearer.
(6) To my understanding, utilizing the 10X platform with the ATAC-Seq option could provide a much more accurate recombination map. This approach would allow the inclusion of SNP information from non-transcribed regions and lowly expressed genes, which are often missed with current methods. Perhaps this is a useful consideration to add to the discussion as a potential improvement for future studies?
We indeed already mention the possibility of using single nucleus ATAC-seq for crossover analysis in the Discussion section. In addition to the theoretical improvements to the recombination mapping mentioned by the reviewer, snATAC-seq could also be used for molecular QTL mapping, to identify so-called “chromatin-accessibility” or caQTLs. A limitation however, would be reduced power for QTL mapping, due to the noisier nature of the molecular phenotype: on a single cell level, chromatin accessibility is an inherently binary phenotype, i.e. either a read is identified in a peak or it is no, but the absence of a read in a peak is not necessarily evidence that the region is closed in that cell, due to sparsity and high levels of dropouts.
(7) Could you clarify how the five parents from the larger panel referred to in the paper were selected? What criteria were used and how might this selection affect the results or applicability of the methodology?
The selection of the five parents in the larger panel was determined by practical considerations. We performed a large number of crosses to Col-0 using available accessions for which there were available genome assemblies, that also had similar flowering times and vernalisation requirements. Only a proportion of these crosses were successful. From the remaining hybrids we then selected five which represented geographical diversity, and that we also felt should be genetically distinct enough to demultiplex using variants. We do not see how the selection could have affected the applicability or generalisability of the methodology.
(8) In most of the analyses using the mixture of pollen from five crosses, the data are predominantly from sperm cells. It is not clear whether VN data were explicitly removed, or whether VN data were included at any stage of the analysis of the first experiment. Given that almost all the data are from sperm nuclei, it might be more accurate to consistently refer to results from this first dataset as "sperm" rather than using terms such as "pollen nuclei" or "pollen gene expression".
To be clear, vegetative nuclei were not specifically removed from any analyses, however due to their low number in the first dataset we did not attempt to map vegetative nucleus specific eQTLs. We have updated the Methods to make this point clearer.
(9) As many of the cis-eQTLs result from structural variations, in some cases related to the presence or absence of the gene in question itself, and given that expression quantification is performed relative to the reference genome, could you provide statistics on the following? Specifically, how often is gene expression higher when the haplotype is from the reference parent compared to the other parents? It would be helpful to break this down into different categories of identified eQTLs (cis vs. trans, and within cis, structural variants vs. other variations). This analysis would provide an estimate of the reference bias inherent in this quantification approach.
The reviewer is correct that there will be some reference bias when it comes eQTLs caused specifically by gene presence-absence variation, because only genes which are present in the Araport11 annotation are tested for eQTLs. This means that genes which are present in Col-0 but absent in other accessions can be identified, whereas genes that are absent in Col-0 but present in other accessions cannot. __We have added a caveating statement to the Discussion to make this clear. __In aggregate, however, we do not see a strong bias gene expression change direction amongst genes with cis-eQTLs. In the Col-0 x Db-1 cross, for example, we see that 51.3% of genes with cis-eQTLs have higher expression in Db-1 than in Col-0 (𝛘2 p = 0.53).
(10) The authors discuss some examples of PSV1 genes in the context of the cell cycle. Could you provide additional statistical measures to support these findings, such as GO enrichment analyses for these genes?
We performed hypergeometric test analysis to test the enrichment of cell cycle factors reported in Supplementary File IX of Van Leene et al., 2010, amongst the genes with a PSV1 trans-eQTL (Van Leene et al. 2010). Of the 501 unique genes in this list, 112 were expressed in our snRNA-seq dataset to a sufficient level to be tested for eQTLs. Of these, 33 had a mappable trans-eQTL at the PSV1 locus. This represents a statistically significant enrichment of genes annotated as involved in the cell cycle (p = 0.032). We have added this analysis to the Results section.
(11) The crosses were grown under slightly different conditions. While I do not suggest repeating the experiment, especially as the main PSV1 result was reproducible, it would be useful to determine whether pollen collected from these two conditions show similar gene expression patterns, even in bulk. This analysis could shed light on whether pollen gene expression is relatively insensitive to these environmental variations, and whether the second trans-eQTL CPV1 is specific to plants grown in 18C.
We agree that the combination of pollen from two temperature conditions in the second experiment is not an ideally designed experiment. We had expected that differences caused by temperature variation would be identifiable as prinicipal components in the single nucleus sequencing data, however this was not obviously the case. Unfortunately, we do not have remaining pollen material from these samples to perform bulk RNA sequencing or qPCR analysis. Although we cannot rule out a temperature effect explaining CPV1, we believe that the most likely explanation for why this was not identified in the first dataset is that it is specific to vegetative nuclei, of which there were very few in the first dataset, and also perhaps to the Col-0 x Db-1 comparison (unlike PSV1 which appears to be shared in at least Col-0 x Db-1 and Col-0 x Rubezhnoe-1 sperm nuclei), meaning that there was much less power to detect it.
(12) "Approximately 87.4% of cis-eQTLs were specific to sperm nuclei, likely reflecting the greater statistical power for sperm. " - This claim can be checked by downsampling the number of nuclei used in the sperm analysis to the same number as in the VN.
In principal downsampling analysis could be used to test this hypothesis, however we feel it would unnecessarily add a confusing element to the manuscript. In future we will consider performing a thorough benchmarking and power analysis of the eQTL method, however we feel this is currently out of the scope of this “proof of principle” manuscript. __We have instead opted to remove this speculative statement from the manuscript. __
(13) "This suggests either that CPV1 affects different sets of genes in sperm and vegetative nuclei, or possibly that there are two independent variants underlying CPV1 which affect sperm and vegetative gene expression respectively" - As done with the genes affected by PSV1, can you use the data from Ichino et al. to show in which cell types the genes affected by CPV1 alleles are expressed?
As requested, we have added a UMAP-plot for the example CPV1 trans-eQTL affected gene PLL1 showing its expression in mature vegetative nuclei, as Figure 6 – figure supplement 4C.
(14) "DUO3 is expressed at a low level however, and is only detectable in 14.1% of Col-0 × Db-1 nuclei," - Can you pseudo-bulk the nuclei according to the haplotype of PSV1 to get a better estimate of DUO3 expression levels in each allele? Or would this be equivalent to the linear model currently used?
As the reviewer themself states, this approach would be somewhat equivalent to the linear model approach currently used, with the downside of not being able to control for other factors such as cell type or principal components.
(15) "studies suggesting that Arabidopsis pollen is 2C (Friedman, 1999", - To my understanding Friedman 1999 reports that sperm are stopped in the middle of S phase and thus not with 2C genomic content.
Thank you for this correction. We have now updated this statement to read: “studies suggesting that Arabidopsis pollen has a DNA content greater than 1C”.
(16) Could you include a heatmap showing the called recombination profiles, similar to the background colors in Fig. 2C, for all nuclei arranged by their snRNA-Seq coverage? This would provide a clearer visualization of the data distribution and the relationship between coverage and accuracy of recombination patterns calling.
We have added a heatmap as requested as Figure 2 – figure supplement 2.
(17) Some figures ( & captions) are missing important details or could benefit from clarification: (1) In Fig. 2B, it is not defined what the orange represents. (2) In Figs. 3A and 6A, the size of the dots is not defined. (3) Aesthetic note: In Fig. 3B, the same colors as in Fig. 3A are used, although they represent different categories. I suggest modifying the color scheme for better clarity. (4) In Fig. 4A, it is unclear what "All" refers to. (5) In the caption for Fig. 4, panels (E) and (F) are mistakenly labeled as (B) and (C).
We thank the reviewer for these important suggestions. We address them here point-by-point:
- In the lower panels of figure 2B, the blue and orange lines represent the marker read distributions supporting the Col-0 and parent 2 haplotypes, respectively. We have updated the figure legend to clarify this.
- The sizes of the points in figures 3A and 6A are proportional to the LOD score of the eQTLs. We have updated the figure legends to make this clear.
- We have altered the colours of Figure 3B (now also relabeled as Figure 3C).
- In Figure 4A, as well as Figure 4D, Figure 4 – figure supplement 1A and Figure 5 – figure supplement 1A, the black line “All” represents the negative log10 FDR from the log ratio test of all haplotypes, i.e. whether there is an overall association between a locus and the expression of the target gene, in any or all genotypes compared to Col-0. To make this clearer, we have updated the figure legends.
- We thank the reviewer for spotting this oversight. We have now corrected the labelling of the figure legends. Ben-David, Eyal, James Boocock, Longhua Guo, Stefan Zdraljevic, Joshua S. Bloom, and Leonid Kruglyak. 2021. “Whole-Organism EQTL Mapping at Cellular Resolution with Single-Cell Sequencing.” ELife 10 (March). https://doi.org/10.7554/eLife.65857.
Campoy, José A., Hequan Sun, Manish Goel, Wen-Biao Jiao, Kat Folz-Donahue, Nan Wang, Manuel Rubio, et al. 2020. “Gamete Binning: Chromosome-Level and Haplotype-Resolved Genome Assembly Enabled by High-Throughput Single-Cell Sequencing of Gamete Genomes.” Genome Biology 21 (1): 306.
Capilla-Pérez, Laia, Victor Solier, Elodie Gilbault, Qichao Lian, Manish Goel, Bruno Huettel, Joost J. B. Keurentjes, Olivier Loudet, and Raphael Mercier. 2024. “Enhanced Recombination Empowers the Detection and Mapping of Quantitative Trait Loci.” Communications Biology 7 (1): 829.
Mandric, Igor, Tommer Schwarz, Arunabha Majumdar, Kangcheng Hou, Leah Briscoe, Richard Perez, Meena Subramaniam, et al. 2020. “Optimized Design of Single-Cell RNA Sequencing Experiments for Cell-Type-Specific EQTL Analysis.” Nature Communications 11 (1): 5504.
Schoft, Vera K., Nina Chumak, János Bindics, Lucyna Slusarz, David Twell, Claudia Köhler, and Hisashi Tamaru. 2015. “SYBR Green-Activated Sorting of Arabidopsis Pollen Nuclei Based on Different DNA/RNA Content.” Plant Reproduction 28 (1): 61–72.
Sun, Hequan, Wen-Biao Jiao, Kristin Krause, José A. Campoy, Manish Goel, Kat Folz-Donahue, Christian Kukat, Bruno Huettel, and Korbinian Schneeberger. 2022. “Chromosome-Scale and Haplotype-Resolved Genome Assembly of a Tetraploid Potato Cultivar.” Nature Genetics 54 (3): 342–48.
Svensson, Valentine, Kedar Nath Natarajan, Lam-Ha Ly, Ricardo J. Miragaia, Charlotte Labalette, Iain C. Macaulay, Ana Cvejic, and Sarah A. Teichmann. 2017. “Power Analysis of Single-Cell RNA-Sequencing Experiments.” Nature Methods 14 (4): 381–87.
Van Leene, Jelle, Jens Hollunder, Dominique Eeckhout, Geert Persiau, Eveline Van De Slijke, Hilde Stals, Gert Van Isterdael, et al. 2010. “Targeted Interactomics Reveals a Complex Core Cell Cycle Machinery in Arabidopsis Thaliana.” Molecular Systems Biology 6 (1): 397.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript by Parker et al, the authors developed a methodology to scale up eQTL studies using single-nucleus RNA-Seq of meiotic products using Arabidopsis thaliana pollen. Each pollen grain, collected from an F1 hybrid between two inbred lines, is haploid and contains a unique combination of the two parental genomes, effectively replacing the F2 population typically used in QTL studies. Single-nucleus RNA-Seq served as both phenotype and genotype, as it was used also to infer recombination profiles. In their experiments, the authors used a mixture of pollen from five different crosses - each between the reference Arabidopsis accession and another accession - for the first experiment, followed by a repeat of one of the original crosses in a second experiment to increase coverage and show reproducibility. They describe their approach to genotyping, performing the eQTL analysis, and describe statistics of the cis- and trans-eQTLs. In particular, they identify and discuss in detail a strong trans-eQTL on chromosome 1 that significantly and reproducibly associate with the expression of hundreds of genes. The likely candidate gene underlying this trans-eQTL is DUO3, a transcription factor known to play a role in pollen development, although the authors acknowledge that further experiments are needed to show that this is the causal variant.
As this is a novel methodology with the potential for widespread adoption by the community, most of my comments focus on expanding the details provided about the methodology and discussing its limitations. Additional clarity of information will help to ensure its reproducibility and wider applicability.
- Given the sparseness of snRNA-Seq data per nucleus/cell (inherent to single cell technologies), what happens to low expressed genes? Is any filtering done? For example, in the extreme case of a gene that is either undetected or has only a single read in some nuclei, would a sufficiently large number of nuclei theoretically allow the detection of an eQTL signal for that gene? Alternatively, does this method remain inherently blind to genes expressed below a certain threshold? Please discuss these limitations and prospects.
- The methods section states that FACS sorting was used to isolate 40,000 nuclei; however, the final datasets contained only 1,394 or 7,458 nuclei. Could you provide a detailed breakdown of the losses at each step of the filtering process? As this is a key factor in the overall efficiency of the method, it would be valuable to discuss the potential for increasing throughput in future implementations. Also, how the total yield increased fivefold between repeats - could you elaborate on the factors that contributed to this improvement?
- It is unclear what happened to the vegetative nuclei during this process. While the authors attribute the differences between the first and second experiments to handling, previous studies (e.g., Schoft et al. 2015) suggest that vegetative and sperm nuclei can be distinguished in FACS analysis after DNA staining. This suggests that, for future applications, it may be possible to refine this method to specifically focus on either vegetative or sperm nuclei by adjusting the gating parameters in FACS. The inclusion of the FACS sorting graphs with the gating used as a supplementary figure would also be helpful in understanding and replicating this aspect of the methodology.
- For the benefit of future users, it would be helpful to discuss the following points: (1) Would you recommend using a combination of several parents, as in the first experiment, or limiting the analysis to two parents? (e.g. it seems some of the recombination patterns could not be called due to the mix of five crosses) What are the advantages and disadvantages of each approach? (2) How should one estimate the optimal number of nuclei/cells to use? Can you downsample the nuclei and see the effects of lower numbers on identified eQTLs? (3) Given that different single-cell RNA-Seq protocols involve trade-offs between the number of cells and the number of UMIs per cell in relation to cost, what strategy would you recommend to users to optimize their experiments?
- It is not entirely clear which exact linear model was used for the association study. Specifically: (1) How were the five parents included in the model? (2) Why was it necessary to correct for population structure in this case, given the controlled crosses? (3) How was technical variation accounted for, and how were principal components derived and incorporated into the analysis? (4) What is meant by "cell type control" and were the few VN included in the analysis? To ensure clarity and reproducibility, it would be helpful to provide more detailed explanations and to explicitly state the linear equation used for fitting.
- To my understanding, utilizing the 10X platform with the ATAC-Seq option could provide a much more accurate recombination map. This approach would allow the inclusion of SNP information from non-transcribed regions and lowly expressed genes, which are often missed with current methods. Perhaps this is a useful consideration to add to the discussion as a potential improvement for future studies?
Other comments (I quoted lines from the manuscript as there were no line numbers): 7. Could you clarify how the five parents from the larger panel referred to in the paper were selected? What criteria were used and how might this selection affect the results or applicability of the methodology? 8. In most of the analyses using the mixture of pollen from five crosses, the data are predominantly from sperm cells. It is not clear whether VN data were explicitly removed, or whether VN data were included at any stage of the analysis of the first experiment. Given that almost all the data are from sperm nuclei, it might be more accurate to consistently refer to results from this first dataset as "sperm" rather than using terms such as "pollen nuclei" or "pollen gene expression". 9. As many of the cis-eQTLs result from structural variations, in some cases related to the presence or absence of the gene in question itself, and given that expression quantification is performed relative to the reference genome, could you provide statistics on the following? Specifically, how often is gene expression higher when the haplotype is from the reference parent compared to the other parents? It would be helpful to break this down into different categories of identified eQTLs (cis vs. trans, and within cis, structural variants vs. other variations). This analysis would provide an estimate of the reference bias inherent in this quantification approach. 10. The authors discuss some examples of PSV1 genes in the context of the cell cycle. Could you provide additional statistical measures to support these findings, such as GO enrichment analyses for these genes? 11. The crosses were grown under slightly different conditions. While I do not suggest repeating the experiment, especially as the main PSV1 result was reproducible, it would be useful to determine whether pollen collected from these two conditions show similar gene expression patterns, even in bulk. This analysis could shed light on whether pollen gene expression is relatively insensitive to these environmental variations, and whether the second trans-eQTL CPV1 is specific to plants grown in 18C. 12. "Approximately 87.4% of cis-eQTLs were specific to sperm nuclei, likely reflecting the greater statistical power for sperm. " - This claim can be checked by downsampling the number of nuclei used in the sperm analysis to the same number as in the VN. 13. "This suggests either that CPV1 affects different sets of genes in sperm and vegetative nuclei, or possibly that there are two independent variants underlying CPV1 which affect sperm and vegetative gene expression respectively" - As done with the genes affected by PSV1, can you use the data from Ichino et al. to show in which cell types the genes affected by CPV1 alleles are expressed? 14. "DUO3 is expressed at a low level however, and is only detectable in 14.1% of Col-0 × Db-1 nuclei," - Can you pseudo-bulk the nuclei according to the haplotype of PSV1 to get a better estimate of DUO3 expression levels in each allele? Or would this be equivalent to the linear model currently used? 15. "studies suggesting that Arabidopsis pollen is 2C (Friedman, 1999", - To my understanding Friedman 1999 reports that sperm are stopped in the middle of S phase and thus not with 2C genomic content. 16. Could you include a heatmap showing the called recombination profiles, similar to the background colors in Fig. 2C, for all nuclei arranged by their snRNA-Seq coverage? This would provide a clearer visualization of the data distribution and the relationship between coverage and accuracy of recombination patterns calling. 17. Some figures ( & captions) are missing important details or could benefit from clarification: (1) In Fig. 2B, it is not defined what the orange represents. (2) In Figs. 3A and 6A, the size of the dots is not defined. (3) Aesthetic note: In Fig. 3B, the same colors as in Fig. 3A are used, although they represent different categories. I suggest modifying the color scheme for better clarity. (4) In Fig. 4A, it is unclear what "All" refers to. (5) In the caption for Fig. 4, panels (E) and (F) are mistakenly labeled as (B) and (C).
Schoft, Vera K., Nina Chumak, János Bindics, Lucyna Slusarz, David Twell, Claudia Köhler, and Hisashi Tamaru. 2015. "SYBR Green-Activated Sorting of Arabidopsis Pollen Nuclei Based on Different DNA/RNA Content." Plant Reproduction 28 (1): 61-72.
Significance
The methodology developed in this study significantly advances the feasibility of eQTL studies. The ability to easily scale up the number of individuals analyzed provides unprecedented resolution for identifying underlying alleles. In addition, because the pollen from each cross can be collected and processed together, environmental and technical variables between individuals (in this case, pollen nuclei) are tightly controlled. This advantage is underlined by the discovery of a strong trans-eQTL, which is likely to play an important role in pollen biology, and whose different alleles are spread across natural populations. I am therefore very excited about the publication of this manuscript.
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Referee #1
Evidence, reproducibility and clarity
The study by Parker et al describes the innovative use of single-cell RNA sequencing to detect markers and traits of pollen. Using pollen allows detection of recombinant events allowing the ability to do quantitative trait mapping using gene expression as a trait. This led to the discovery of expected cis QTL, but there is an intriguing trans QTL as well. The trans eQTL was mapped to a candidate causal locus DUO1. This is exciting given its role in pollen development and will obviously be followed up on in future studies.
Overall, this is an exciting technological advance that will rapidly advance our ability to map pollen traits and arguably more importantly create high density recombination maps across organisms. I realize this is a proof of principle study and the questions below are intended to improve this already strong manuscript.
- What is the average maker resolution in bp and cM? What numbers of nuclei would be needed to profile to gain single gene resolution in Arabidopsis?
- It would be great if the authors could add a discussion of how the resolution of mapping could be improved and what it would take to get there?
- Could this approach be feasible for species where there is not a reference genome?
- It is stated 67% are located with in 2Mb of a gene and technically that is is cis-, but there aren't really long range interactions in Arabidopsis (>30kb)...so are these really cis or trans? It might be worth considering how cis vs trans are defined. Basically, what is truly cis vs trans?
- What percent of the time was the actual crossover captured, if at all? Is this possible with snRNA-seq?
- How many eQTL were there per locus?
- How deep were the libraries sequenced and were they sequenced to saturation? In general, I did not find any sequencing summary statistics. How many reads were sequenced per library, per genotype etc, how many aligned, how many UMIs per cell, how many transcripts detected per cell. This will help give the reader a baseline for knowing what kind of quality is needed to successfully implement this strategy in their own lab.
Referees cross-commenting
Seems our reviews are fairly consistent and positive. Both of us would like greater transparency with the method and how it can be used by other labs.
Significance
The overall strength is the development of a method to map recombination using single-cell genomics of pollen. The weakness is the limitation to studying pollen traits at least for now. The other weakness is helping the reader apply this to their own research questions. This is easily addressable through updating the writing in a way that is more accessible.
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- Dec 2024
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Reply to the reviewers
Reviewer #1
General Comment. *Using ubiquitous and targeted heterologous expression of the honeybee venom peptide Apamin in Drosophila, the authors find that apamin has antimicrobial activity that is enhanced by membrane-tethering and dependent on the Drosophila pattern-recognition receptors PGRP-LA and PGRP-SC2. Expression of apamin in the Drosophila gut or ingestion of Apamin by honeybees has positive effects on gut health as shown by a number of metrics. *
__ Answer: __We thank the reviewer for their insightful comments. We agree that the findings of this study are significant and have broad implications for understanding the antimicrobial properties of apamin. As suggested, we have further delved into the molecular mechanisms underlying apamin's antimicrobial activity, providing additional details on its interactions with target bacteria. We have also expanded our discussion on the role of membrane-tethering in enhancing apamin's activity and its potential impact on its localization. We believe that these additional illustrations strengthen our conclusions and provide a more comprehensive understanding of apamin's biological functions.
Major comments:
Comment 1. *The key conclusions are convincing and largely supported by the data as shown. The data is presented clearly, save for some areas in the results where the authors should be more explicit about the methods that were used as they affect the reader's interpretation of the results (see minor comments). *
__ Answer: __We would like to express our gratitude to the reviewer for their constructive feedback and positive remarks regarding our manuscript. We are pleased to note that the reviewer found our key conclusions convincing and largely supported by the data presented. This affirmation encourages us as we strive to contribute meaningful insights to the field. We acknowledge the reviewer's suggestion to enhance clarity in certain areas of the Results section, particularly concerning the methods employed. We appreciate this guidance and have taken it into account. In our revised manuscript, we have made explicit revisions to ensure that the methodology is clearly articulated, thereby improving the reader's interpretation of our results. Thank you once again for your valuable feedback, which has undoubtedly strengthened our work. We look forward to your continued guidance as we finalize our manuscript.
Comment 2. *If the authors wish to conclude that PGRP-LE and PGRP-LC are not required for the demonstrated functions of Apamin, the authors should do a double knock-down of PGRP-LC and LE together, as these pattern recognition receptors function partially redundantly in activation of the Imd pathway (e.g. doi: 10.1038/ni1356). *
__ Answer:__ We appreciate reviewer's interesting suggestion to know PGRP-LE and LC's functions are redundant to activate Imd pathway or Apamin is totally independent of Imd pathway. As reviewer suggested, we have conducted double knockdown of PGRP-LE and PGRP-LC and showed that apamin still suppress bacterial infection regardless of these double knockdowns of these genes. This data suggests that apamin's antimicrobial function is totally not dependent on PGRP-LE or LC and open new questions about apamin's unique function as AMP. We added new data in Fig. 5d and described in main text as below:
"Knockdown of PGRP-LC or LE, as well as their combined knockdown, did not affect the antimicrobial efficacy of apamin (Fig. 5b-d), suggesting that the antimicrobial properties of apamin are independent of PGRP-LC and LE functions (Fig. 5a)."
Comment 3. *The Introduction and Discussion would benefit from providing more context that helps the reader understand the significance of the research. Where is apamin expressed in the honeybee? Is it likely to be ingested and have effects on gut health in natural conditions? Do honeybees have homologs of PGRP-LA and PGRP-SC2? Do these findings translate to the honeybee system in any way or are they restricted to heterologous expression in Drosophila? *
__Answer: __We thank the reviewer for valuable suggestions. We agree that providing additional context on the natural role of apamin in honeybees and the relevance of our findings to the honeybee system is crucial.
Natural expression and function of apamin: While apamin is primarily known for its neurotoxic effects, studies have suggested that it may also play a role in antimicrobial defense. While its specific expression pattern in honeybees is not fully understood, it is conceivable that it is mainly expressed in venom sacs according to research on biochemistry and pharmacology of apamin (Habermann, 1972; Schumacher et al, 1994; INOUE et al, 1987) . We have outlined this information in the Introduction section as follows:
"Apamin, an 18 amino acid peptide neurotoxin, is one of the bioactive components of bee venom, making up 2%-3% of its total dry weight, naturally expressed in bee venom sacs (RIETSCHOTEN et al, 1975; E.H., 1976; Son et al, 2007; Zhou et al, 2010; Habermann, 1972)."
Potential for ingestion and gut effects: Although direct evidence for apamin ingestion and its impact on gut health in natural conditions is limited, it is plausible that honeybees could be exposed to apamin through various means, including foraging and social interactions. However, artificial interference is the potential application method that we are more focusing on. We have included additional details regarding the function of apamin in the Introduction section as follows:
"It is the smallest known neurotoxic polypeptide and exhibits elevated basicity and sulfur content, demonstrating prolonged action relative to other pharmacological agents influencing the central or peripheral nervous systems(Habermann, 1972)."
Honeybee homologs of PGRPs: Concerning the honeybee PGRPs and their homologs in Drosophila, we have provided an explanation as follows:
"While honeybees possess homologs of PGRP family, including PGRP-LC and PGRP-S2, their specific roles in response to apamin and other antimicrobial peptides remain to be elucidated(Larsen et al, 2019a)."
Relevance to the honeybee system: While our study primarily utilized Drosophila as a model system, the conserved nature of innate immune pathways suggests that the findings may have broader implications for honeybee health. Future studies aimed at directly investigating the effects of apamin in honeybees will be essential to fully understand its role in their physiology and behavior. We have incorporated these points into the Discussion sections to provide a more comprehensive and informative overview of our research as below:
"In conclusion, it is important to note that much of our understanding of the honeybee immune system is derived from studies conducted on the Drosophila model, owing to the evolutionary proximity of these two species (Larsen et al, 2019b). This close relationship allows for valuable insights into immune mechanisms that are conserved across species (Evans et al, 2006; Morfin et al, 2021). Research has demonstrated that the fruit fly Drosophila melanogaster serves as an effective model for studying the effects of insecticides on honeybees, particularly in understanding the sub-lethal impacts of neonicotinoids, which are known to affect pollinators significantly (Tasman et al, 2021).
By investigating the function of honeybee AMPs within the Drosophila platform, we can further enhance our knowledge of immune responses and their implications. Just as research on Drosophila has significantly advanced our understanding of human genetic diseases (Bellen et al, 2010; Casci & Pandey, 2015; Bier, 2005; Perrimon et al, 2016; Rieder & Larschan, 2014; Bilder et al, 2021), studying honeybee AMPs in this context holds the potential to uncover novel therapeutic avenues and deepen our comprehension of immune function across taxa."
Comment 4. *It is surprising that there is no speculation or hypothesis provided about why PGRP-LA and -SC2 may enhance apamin activity whereas other components are nonessential. It was a significant part of the paper but receives almost no discussion. *
Answer: We thank the reviewer for highlighting this important point. The specific mechanism by which PGRP-LA and PGRP-SC2 enhance apamin's activity is an intriguing question that warrants further investigation. Our findings indicate that both PGRP-LA and PGRP-SC2 are crucial for the antimicrobial action of apamin, as their knockdown abolishes this effect, suggesting a specific functional relationship between these peptidoglycan recognition proteins and apamin's mechanism of action in the gut environment.
PGRP-LA is known to play a significant regulatory role as positive regulator of immune responses, while PGRP-SC2 has been shown to promote gut immune homeostasis and prevent dysbiosis, which is essential for maintaining a balanced microbiome in Drosophila (Guo et al, 2014). The enhancement of apamin activity by these proteins could be attributed to their ability to modulate the immune response and facilitate a more effective antimicrobial environment, thereby allowing apamin to exert its effects more efficiently.
Furthermore, our study aligns with previous research indicating that PGRP-SC2 can limit commensal dysbiosis and promote tissue homeostasis, which may enhance the overall efficacy of antimicrobial peptides like apamin in combating pathogenic bacteria (Guo et al, 2014). By leveraging the evolutionary insights gained from Drosophila, we can better understand how these mechanisms operate in honeybees, ultimately contributing to our knowledge of immune function across species. We have provided a detailed explanation of the potential roles of PGRP-LA and PGRP-SC2 in the action of apamin, as outlined below:
"The PGRP-LA gene is located in a cluster with PGRP-LC and PGRP-LF, which encode a receptor and a negative regulator of the Imd pathway, respectively; structural predictions suggest that PGRP-LA may not directly bind to peptidoglycan, indicating a potential regulatory role for this PGRP in modulating immune responses (Gendrin et al, 2013). PGRP-SC2 possesses amidase activity, which means it can cleave the peptidoglycan layer of bacterial cell walls, rendering them susceptible to further degradation and ultimately leading to bacterial cell death. This amidase activity contributes to the insect's innate immune response by directly targeting and neutralizing bacterial threats (Takehana et al, 2002; Park et al, 2007; Paredes et al, 2011)."
Comment 5. *Line 264: The fact that Rel knockdown did not impair antimicrobial activity of Apamin is a bit odd since upregulation of PGRP-SC2 upon infection is at least partially dependent on Rel (de Gregorio 2002, EMBO J), and the authors find that PGRP-SC2 is required for apamin activity. This is somewhat incongruous. *
__Answer: __We thank the reviewer for highlighting this important point. The observation that Rel knockdown did not impair apamin's antimicrobial activity, despite its role in upregulating PGRP-SC2, is indeed intriguing.
Several factors may contribute to this discrepancy:
Redundancy in PGRP-SC2 regulation: It is possible that other transcription factors, in addition to Rel, may regulate PGRP-SC2 expression. Therefore, even in the absence of Rel, sufficient levels of PGRP-SC2 may be maintained to support apamin's activity(Bischoff et al, 2006) . Direct effects of apamin: Apamin may directly interact with bacterial cells or host immune cells and contribute to its antimicrobial activity, even in the absence of optimal PGRP-SC2 levels.
We cited (de Gregorio 2002, EMBO J) paper and added explanation for this result as below:
"It is known that the upregulation of PGRP-SC during infection is partially reliant on the Rel pathway (Gregorio et al, 2002). Our findings indicate that apamin can exert its antimicrobial activity independently of Rel's transcriptional activation function. This observation can be attributed to two key factors. First, there may be redundancy in the regulation of PGRP-SC2 expression, as other transcription factors could compensate for the absence of Rel, allowing sufficient levels of PGRP-SC2 to be maintained to support apamin's activity. Second, apamin may have direct interactions with bacterial cells or host immune cells, contributing to its antimicrobial effects even when optimal levels of PGRP-SC2 are not present. These mechanisms suggest that apamin can function effectively in the immune response, highlighting its potential as a versatile antimicrobial agent."
Comment 6. *I cannot comment on the adequacy of the statistical analyses. Some recommendations to improve the methods: *
*- Be specific about the kind of medium used to rear flies (provide or cite recipe). Different cornmeal-yeast media have very different compositions and can affect fly physiology and microbiome characteristics. *
*- Specify flipping schedule (every 2-3 days?) - this also affects microbiome. *
__Answer: __We thank the reviewer for their valuable comments. We agree that precise experimental details are crucial for reproducibility and accurate interpretation of results.
To address the reviewer's specific concerns:
Culture medium: We used a standard cornmeal-molasses-agar medium. The specific recipe for this medium is as follows: water add up to 5 L,agar 47g, inactive yeast 65.5g, corn flour 232.5g, soy flour 30g, molasses 350 ml, tegosept sol. 35g, propionic acid 12.5ml, phosphoric acid 2.5ml. Flipping schedule: Flies were flipped every 2-3 days to prevent overcrowding and maintain optimal culture conditions.
We have included these details in the Methods section to enhance the clarity and reproducibility of our experiments.
Minor comments:
*- Line 90: Be specific about how the constructs differ from endogenous Melittin and Apamin. Do the endogenous versions have signal peptides? *
Answer: The endogenous versions do not have signal peptides we have used, we have specified this in the manuscript for readers to have a better understanding as below:
"To assess the functionality of genetically encoded honeybee VPs in the Drosophila model, we developed UAS-Melittin, and UAS-Apamin constructs that incorporate a previously characterized signal peptide at their N-termini (Choi et al, 2009), which original AMP and VP sequences do not have (Fig. 1a)."
- Line 92: What is 'broad expression'? Ubiquitous? Specify driver or extent of expression.
Answer: We have added "by tub-GAL4 driver"
*- Line 93: Was this oral or septic P. aeruginosa infection? *
Answer: We have added "oral"
*- Lines 97-98: Melittin expressed genetically did not show activity against the one pathogen that was tested; making a broad statement without qualification about activity seems excessive. *
Answer: We have added "against P. aeruginosa"
*- Line 105: Various Gal4 drivers that express in different tissues or a similar subset of tissues? *
Answer: We utilized tub-GAL4 and da-GAL4 in this part of screening, they both drive expression in ubiquitous tissues. Daughterless (da) involves in the transcriptional regulation of various processes, including oogenesis, neurogenesis, myogenesis, and cell proliferation. While tub-GAL4 is ubiquitous expression throughout most tissues and cell types in the Drosophila body. We have added "various ubiquitously expressing"
*- Line 134: Present as a commensal? Pathobiont? Pathogen? *
Answer: Apibacter raozihei is generally considered a commensal bacterium in the honeybee gut. We have added to manuscript "which is present as a commensal bacterium in the guts of".
*- Line 149: Are Cyanobacteria naturally present in gut microbiota? What are photosynthetic bacteria doing as part of a gut microbiome? *
Answer: While cyanobacteria are not typically found in the gut, cyanobacterial 16S rRNA-like sequences have been previously detected in human gut samples, bovine rumen, termite gut, and other animal intestines, suggesting the presence of a non-photosynthetic cyanobacterial lineage in these aphotic environments(Hu & Rzymski, 2022; Hongoh et al, 2003).
*- Line 171: Where is apamin endogenously expressed in the honeybee? Only in the venom gland? Or in gut cells as done here in Drosophila? *
Answer: Natural expression and function of apamin: While apamin is primarily known for its neurotoxic effects, studies have suggested that it may also play a role in antimicrobial defense. While its specific expression pattern in honeybees is not fully understood, it is conceivable that it is mainly expressed in venom sacs according to research on biochemistry and pharmacology of apamin (Habermann, 1972; Schumacher et al, 1994; INOUE et al, 1987) .
*- Line 252: -LC and -LE work in a complementary/semi-redundant fashion, so single knockdown is not an effective method of indicating that they are not required for antimicrobial function. *
Answer: We appreciate reviewer's interesting suggestion to know PGRP-LE and LC's functions are redundant to activate Imd pathway or Apamin is totally independent of Imd pathway. As reviewer suggested, we have conducted double knockdown of PGRP-LE and PGRP-LC and showed that apamin still suppress bacterial infection regardless of these double knockdowns of these genes. This data suggests that apamin's antimicrobial function is totally not dependent on PGRP-LE or LC and open new questions about apamin's unique function as AMP. We added new data in Fig. 5d and described in main text as below:
"Knockdown of PGRP-LC or LE, as well as their combined knockdown, did not affect the antimicrobial efficacy of apamin (Fig. 5b-d), suggesting that the antimicrobial properties of apamin are independent of PGRP-LC and LE functions (Fig. 5a)."
*- Lines 279-283: The bacterial infections that expression of these AMPs were tested against should be mentioned in the text, as all bacteria are not equivalent. *
Answer: Added with "P. aeruginosa"
*- Line 296: Challenged with which bacteria? *
Answer: Added with "P. aeruginosa"
*- Line 328: Provide brief explanation of what Ttk depletion is for reader context. *
Answer: Added with short explanation as below:
"which refers to the reduction or elimination of a protein called TTK (Monopolar Spindle 1 Kinase) that plays a crucial role in cell division, specifically in ensuring accurate chromosome segregation during mitosis (Mason et al, 2017)."
*- Line 719: This should say, '5 days after eclosion'. *
Answer: Corrected
*- General comment on figures: The little icons used to denote what the figure is depicting (gut health, climbing, aging, etc.) are very effective. *
Answer: We thank the reviewer for their appreciation on figures.
*- General comment on figure titles: Use of the term 'infectious dose' throughout does not make sense. I think what the authors mean is 'pathogen load' as they are testing using CFUs. 'Infectious dose' should only be used to refer to the amount/OD of pathogen that was initially administered to establish an infection. Also, 'oral feeding' should be used throughout instead of 'orally feeding'. *
Answer: We thank the reviewer for their insightful comment. We agree that the use of the term 'infectious dose' was inaccurate in certain contexts. We have revised the manuscript to use 'pathogen load' to refer to the number of CFUs administered or recovered, as this more accurately reflects the bacterial burden.
We have also replaced 'orally feeding' with 'oral feeding' throughout the manuscript to improve clarity and consistency.
We appreciate the reviewer's attention to detail and believe that these changes have significantly enhanced the clarity and accuracy of the manuscript.
*- Figure 1O: Abrupt die-offs at 1000hrs and 2800hrs in the UAS-Melittin line suggest that lifespan experiment was only performed once and that die-offs may have been exacerbated due to infrequent flipping. This is perhaps not an issue as the lifespans appear to be quite different between the active line and control regardless. *
Answer: We thank the reviewer for their careful observation. The abrupt die-offs in the UAS-Melittin line at 1000 hours and 2800 hours were unexpected. While we cannot definitively rule out the possibility that infrequent flipping might have contributed to these events, we believe that the overall lifespan difference between the experimental and control groups is substantial and likely reflects a genuine biological effect of Melittin overexpression.
*- Figure 2F would be improved by putting the legend in the same descending order that the genotypes are displayed on the graph (tApamin infected, GFP infected, tApamin, GFP) *
Answer: We have corrected error.
*- Figure 3I: Unclear what small image inserted in the graph depicts. *
Answer: This is an image of fly stem cells that is available for free licensing.
- Figures 3N and 3O are verry low resolution and difficult to identify the differences that the authors *intend to show. *
Answer: We have utilized a higher resolution image and revised the figure accordingly.
- Figure 4 title is confusing. Do the authors mean, "Locomotion of flies expressing neuronal Apamin, sleep in flies with ubiquitous expression of Apamin, and Smurf results induced by different types of stress."?
Answer: Corrected as below:
"Locomotion of flies expressing neuronal tApaminDC, sleep in flies with ubiquitous expression of tApaminDC, and Smurf results induced by different types of stress."
*- Figure 5: Some of these graphs are very cluttered and difficult to parse (particularly 5H). Suggest putting peptide sequences in figure title rather than underneath graphs to simplify and increase visual effectiveness. *
Answer: We have improved by removing the sequences to figure legend part.
*-Throughout: Methods section in particular could use a solid edit for grammar. Homogenize capitalization of "Gram-negative/-positive" and "gram-negative/-positive" *
Answer: We have corrected error.
*- Line 98: "an AMPs" should be "an AMP" *
Answer: We have corrected error.
*- Line 119: Incorrect grammar. Suggest, "which did not affect the lifespan of female flies and had only a slight effect on male flies" *
Answer: We have corrected error.
Reviewer #1 (Significance (Required)):
*The paper reveals that apamin has antimicrobial properties. The intended significance seems to be an exploration of apamin for therapeutic potential in gut health, but this is not explicitly stated by the authors. The contribution mainly appears to be conceptual in nature. *
*The findings appear to be in line with other recent in vitro results suggesting that apamin has antimicrobial properties (DOI: 10.9775/kvfd.2024.32125). *
*Researchers interested in developing therapeutic applications for bee venom constituents or promoting gut health and microbiome balance will likely find this research of interest. *
*My expertise is primarily in Drosophila molecular genetics and immunity. I have a broad understanding of Drosophila immune pathways, epithelial immunity, and infection dynamics. I do not feel qualified to comment on the statistics or data analysis aspects of this paper. *
Answer: We sincerely appreciate the reviewer's positive feedback regarding our findings on the antimicrobial properties of apamin. We are grateful for the acknowledgment that our results align with recent in vitro studies, such as the one referenced (DOI: 10.9775/kvfd.2024.32125), which further supports the significance of our work. We have cited this paper in the Discussion section as below.
"Our findings are consistent with recent in vitro studies demonstrating the antimicrobial and antibiofilm effects of apamin (AYDIN et al, 2024)."
We recognize the reviewer's observation that our intended significance-specifically, the exploration of apamin's therapeutic potential for gut health-was not explicitly stated in the original manuscript. To address this, we have revised the Introduction and Discussion sections to clearly articulate our aim of investigating apamin as a candidate for promoting gut health and microbiome balance. We believe this clarification will enhance the conceptual contribution of our study and its relevance to researchers interested in therapeutic applications of bee venom constituents.
"Apamin shows promising therapeutic potential for enhancing bee gut health by exhibiting antimicrobial properties that can help maintain a balanced microbiome. Its ability to modulate immune responses and promote gut integrity, particularly in the presence of harmful bacteria, positions apamin as a valuable candidate for developing strategies aimed at improving gut health in honeybees."
Additionally, we appreciate the reviewer's expertise in Drosophila molecular genetics and immunity, and we are grateful for their insights regarding the broader implications of our research. We will ensure that our manuscript reflects these considerations more explicitly.
Thank you once again for your valuable feedback, which has helped us improve the clarity and impact of our work.
Reviewer #2
*Reviewer #2 (Evidence, reproducibility and clarity (Required)): *
General Comment. *The reviewer would like to thank the authors for their contributions to the research of animal venoms and their therapeutic value. The manuscript is very well and clearly written. Additionally, the choice of using a model organism such as D. melanogaster in the context of venoms research strengthens the manuscript by providing evidence that is both robust and broadly applicable, thus enhancing the manuscript's scientific merit and relevance to the field. *
Answer: We would like to express our sincere gratitude to the reviewer for the positive feedback and thoughtful comments regarding our manuscript. We are pleased to hear that the reviewer appreciates our contributions to the research on animal venoms and their therapeutic potential. The reviewer's acknowledgment of the clarity and quality of our writing is particularly encouraging, as we strive to communicate our findings effectively. Additionally, we are glad that the choice of Drosophila melanogaster as a model organism was recognized for its ability to strengthen our research by providing robust and broadly applicable evidence. This endorsement enhances the scientific merit and relevance of our work within the field. Thank you once again for the constructive feedback, which has been invaluable in refining our manuscript.
Comment 1. *What is the significance of that the biological property of apamin is independent of its disulfide bonds? Does it suggest that the core functional parts of apamin might not entirely depend on its stabilized structure? Could it mean that modifications to the molecule that disrupt disulfide bonds wouldn't necessarily eliminate all of its activity, which could be important in designing analogs or derivatives of apamin for research or therapeutic purposes etc.? This sentence is written in the abstract which means that it should be a key finding, and it should be clear and a given to the reader. However, it is not the case, and it should be stated more clearly. *
Answer: We greatly appreciate the reviewer for the perceptive notation. The fact that the biological functioning of apamin needs no disulfide bonds should bring forth the attention of the scientists because it has further implications. This hints that apamin's major functional units are most likely to compose from its polypeptide instead of being rooted in the disulfide-stabilized tertiary structure(Habermann, 1972). The strategy can then lead to the optimization of apamin-based drugs with altered disulfide bridges granting them either higher activity or reduced toxicity. These changes can give apamin additional properties like stability, bioavailability, or selectivity, which make it suitable for research and applied use. We have included an explanation for this in both the Results and Discussion sections.
"This finding suggests that the core functional components of apamin may not be entirely reliant on its stabilized structure."
"We discovered that apamin lacking the C-terminus retains its function as an antimicrobial agent, despite missing one of its two disulfide bridges. This finding suggests that the core functional components of apamin may not be entirely dependent on its stabilized structure, indicating that modifications to the molecule that disrupt these disulfide bonds could still maintain some level of activity. These insights are vital for designing analogs or derivatives of apamin, as they pave the way for developing new compounds that could retain therapeutic potential even without the native disulfide bond configuration (Habermann, 1972)."
Comment 2. *The authors well explained the evolutionary proximity between apamin producing Honeybees and D. melanogaster in order to justify the choice of the model organism which we can all agree on for genetics and developmental biology studies. However, when addressing the behavior of the insects (sleeping, locomotion, social etc.) which are driven by their ecological roles, evolutionary strategies, and social complexity. How much can you really tell about the role of apamin in the behavior of Honeybees (highly social and form colonies) by studying it on an insect (D. melanogaster) which has a completely different and divergent behavior (solitary and exhibit only few basic forms of social interaction)? *
Answer: We appreciate the reviewer's insightful comment. While Drosophila melanogaster is an excellent model organism for investigating fundamental biological processes, we recognize the limitations of using it to fully comprehend the complex behavioral effects of apamin in honeybees. Nevertheless, our study establishes a foundational understanding of apamin's potential impact on behavior, including its effects on sleep and locomotion-core behavioral processes that are conserved across many organisms, including insects (Zimmerman et al, 2008).
By employing Drosophila as a model, we were able to identify potential mechanisms of action for apamin, particularly regarding its effects on intestinal systems. Although honeybees and fruit flies exhibit ecological differences, there is substantial consensus and experimental evidence that many molecular pathways involved in immune responses are conserved between these species. Thus, while the interpretation of behavioral changes induced by apamin may be limited by the ecological and evolutionary divergence between honeybees and fruit flies, the molecular pathways governing the immune response in honeybees can be effectively studied using the Drosophila platform. This approach has previously revealed functions of genes related to human genetic diseases. We have clearly articulated this limitation and the advantages of using the fly model to study the honeybee immune system in the Discussion section as follows:
"In conclusion, it is important to note that much of our understanding of the honeybee immune system is derived from studies conducted on the Drosophila model, owing to the evolutionary proximity of these two species (Larsen et al, 2019b). This close relationship allows for valuable insights into immune mechanisms that are conserved across species (Evans et al, 2006; Morfin et al, 2021). Research has demonstrated that the fruit fly Drosophila melanogaster serves as an effective model for studying the effects of insecticides on honeybees, particularly in understanding the sub-lethal impacts of neonicotinoids, which are known to affect pollinators significantly (Tasman et al, 2021).
By investigating the function of honeybee AMPs within the Drosophila platform, we can further enhance our knowledge of immune responses and their implications. Just as research on Drosophila has significantly advanced our understanding of human genetic diseases (Bellen et al, 2010; Casci & Pandey, 2015; Bier, 2005; Perrimon et al, 2016; Rieder & Larschan, 2014; Bilder et al, 2021), studying honeybee AMPs in this context holds the potential to uncover novel therapeutic avenues and deepen our comprehension of immune function across taxa."
Comment 3. *Please include the following references: *
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Wehbe R, Frangieh J, Rima M, El Obeid D, Sabatier JM, Fajloun Z. Bee Venom: Overview of Main Compounds and Bioactivities for Therapeutic Interests. Molecules. 2019 Aug 19;24(16):2997. *
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Nader RA, Mackieh R, Wehbe R, El Obeid D, Sabatier JM, Fajloun Z. Beehive Products as Antibacterial Agents: A Review. Antibiotics. 2021; 10(6):717. *
Answer: We have incorporated the references mentioned above in appropriate sections of the manuscript. We appreciate the reviewer's suggestions.
Reviewer #2 (Significance (Required)):
The manuscript is very well and clearly written. Additionally, the choice of using a model organism such as D. melanogaster in the context of venoms research strengthens the manuscript by providing evidence that is both robust and broadly applicable, thus enhancing the manuscript's scientific merit and relevance to the field.
Answer: We would like to express our sincere gratitude to the reviewer for their positive feedback regarding our manuscript. We are thrilled to hear that the clarity and quality of our writing were appreciated. Additionally, we are glad that the choice of Drosophila melanogaster as a model organism in our venoms research was recognized for its ability to provide robust and broadly applicable evidence. This endorsement underscores the scientific merit and relevance of our work within the field, and we appreciate the reviewer's acknowledgment of this important aspect. Thank you for your encouraging comments, which motivate us to continue exploring this vital area of research.
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Referee #2
Evidence, reproducibility and clarity
The reviewer would like to thank the authors for their contributions to the research of animal venoms and their therapeutic value. The manuscript is very well and clearly written. Additionally, the choice of using a model organism such as D. melanogaster in the context of venoms research strengthens the manuscript by providing evidence that is both robust and broadly applicable, thus enhancing the manuscript's scientific merit and relevance to the field.
Some comments to add:
- What is the significance of that the biological property of apamin is independent of its disulfide bonds? Does it suggest that the core functional parts of apamin might not entirely depend on its stabilized structure? Could it mean that modifications to the molecule that disrupt disulfide bonds wouldn't necessarily eliminate all of its activity, which could be important in designing analogs or derivatives of apamin for research or therapeutic purposes etc.? This sentence is written in the abstract which means that it should be a key finding, and it should be clear and a given to the reader. However, it is not the case, and it should be stated more clearly.
- The authors well explained the evolutionary proximity between apamin producing Honeybees and D. melanogaster in order to justify the choice of the model organism which we can all agree on for genetics and developmental biology studies. However, when addressing the behavior of the insects (sleeping, locomotion, social etc.) which are driven by their ecological roles, evolutionary strategies, and social complexity. How much can you really tell about the role of apamin in the behavior of Honeybees (highly social and form colonies) by studying it on an insect (D. melanogaster) which has a completely different and divergent behavior (solitary and exhibit only few basic forms of social interaction)?
Please include the following references:
- Wehbe R, Frangieh J, Rima M, El Obeid D, Sabatier JM, Fajloun Z. Bee Venom: Overview of Main Compounds and Bioactivities for Therapeutic Interests. Molecules. 2019 Aug 19;24(16):2997.
- Nader RA, Mackieh R, Wehbe R, El Obeid D, Sabatier JM, Fajloun Z. Beehive Products as Antibacterial Agents: A Review. Antibiotics. 2021; 10(6):717.
Significance
The manuscript is very well and clearly written. Additionally, the choice of using a model organism such as D. melanogaster in the context of venoms research strengthens the manuscript by providing evidence that is both robust and broadly applicable, thus enhancing the manuscript's scientific merit and relevance to the field.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Using ubiquitous and targeted heterologous expression of the honeybee venom peptide Apamin in Drosophila, the authors find that apamin has antimicrobial activity that is enhanced by membrane-tethering and dependent on the Drosophila pattern-recognition receptors PGRP-LA and PGRP-SC2. Expression of apamin in the Drosophila gut or ingestion of Apamin by honeybees has positive effects on gut health as shown by a number of metrics.
Major comments:
The key conclusions are convincing and largely supported by the data as shown. The data is presented clearly, save for some areas in the results where the authors should be more explicit about the methods that were used as they affect the reader's interpretation of the results (see minor comments). If the authors wish to conclude that PGRP-LE and PGRP-LC are not required for the demonstrated functions of Apamin, the authors should do a double knock-down of PGRP-LC and LE together, as these pattern recognition receptors function partially redundantly in activation of the Imd pathway (e.g. doi: 10.1038/ni1356 ). The Introduction and Discussion would benefit from providing more context that helps the reader understand the significance of the research. Where is apamin expressed in the honeybee? Is it likely to be ingested and have effects on gut health in natural conditions? Do honeybees have homologs of PGRP-LA and PGRP-SC2? Do these findings translate to the honeybee system in any way or are they restricted to heterologous expression in Drosophila? It is surprising that there is no speculation or hypothesis provided about why PGRP-LA and -SC2 may enhance apamin activity whereas other components are nonessential. It was a significant part of the paper but receives almost no discussion. Line 264: The fact that Rel knockdown did not impair antimicrobial activity of Apamin is a bit odd since upregulation of PGRP-SC2 upon infection is at least partially dependent on Rel (de Gregorio 2002, EMBO J), and the authors find that PGRP-SC2 is required for apamin activity. This is somewhat incongruous.
I cannot comment on the adequacy of the statistical analyses. Some recommendations to improve the methods:
- Be specific about the kind of medium used to rear flies (provide or cite recipe). Different cornmeal-yeast media have very different compositions and can affect fly physiology and microbiome characteristics.
- Specify flipping schedule (every 2-3 days?) - this also affects microbiome.
Minor comments:
Line 90: Be specific about how the constructs differ from endogenous Melittin and Apamin. Do the endogenous versions have signal peptides?
Line 92: What is 'broad expression'? Ubiquitous? Specify driver or extent of expression.
Line 93: Was this oral or septic P. aeruginosa infection?
Lines 97-98: Melittin expressed genetically did not show activity against the one pathogen that was tested; making a broad statement without qualification about activity seems excessive.
Line 105: Various Gal4 drivers that express in different tissues or a similar subset of tissues?
Line 134: Present as a commensal? Pathobiont? Pathogen?
Line 149: Are Cyanobacteria naturally present in gut microbiota? What are photosynthetic bacteria doing as part of a gut microbiome?
Line 171: Where is apamin endogenously expressed in the honeybee? Only in the venom gland? Or in gut cells as done here in Drosophila?
Line 252: -LC and -LE work in a complementary/semi-redundant fashion, so single knockdown is not an effective method of indicating that they are not required for antimicrobial function.
Lines 279-283: The bacterial infections that expression of these AMPs were tested against should be mentioned in the text, as all bacteria are not equivalent.
Line 296: Challenged with which bacteria?
Line 328: Provide brief explanation of what Ttk depletion is for reader context.
Line 719: This should say, '5 days after eclosion'.
General comment on figures: The little icons used to denote what the figure is depicting (gut health, climbing, aging, etc.) are very effective.
General comment on figure titles: Use of the term 'infectious dose' throughout does not make sense. I think what the authors mean is 'pathogen load' as they are testing using CFUs. 'Infectious dose' should only be used to refer to the amount/OD of pathogen that was initially administered to establish an infection. Also, 'oral feeding' should be used throughout instead of 'orally feeding'.
Figure 1O: Abrupt die-offs at 1000hrs and 2800 hrs in the UAS-Melittin line suggest that lifespan experiment was only performed once and that die-offs may have been exacerbated due to infrequent flipping. This is perhaps not an issue as the lifespans appear to be quite different between the active line and control regardless.
Figure 2F would be improved by putting the legend in the same descending order that the genotypes are displayed on the graph (tApamin infected, GFP infected, tApamin, GFP)
Figure 3I: Unclear what small image inserted in the graph depicts.
Figures 3N and 3O are verry low resolution and difficult to identify the differences that the authors intend to show.
Figure 4 title is confusing. Do the authors mean, "Locomotion of flies expressing neuronal Apamin, sleep in flies with ubiquitous expression of Apamin, and Smurf results induced by different types of stress."?
Figure 5: Some of these graphs are very cluttered and difficult to parse (particularly 5H). Suggest putting peptide sequences in figure title rather than underneath graphs to simplify and increase visual effectiveness.
Throughout: Methods section in particular could use a solid edit for grammar. Homogenize capitalization of "Gram-negative/-positive" and "gram-negative/-positive"
Line 98: "an AMPs" should be "an AMP"
Line 119: Incorrect grammar. Suggest, "which did not affect the lifespan of female flies and had only a slight effect on male flies"
Significance
The paper reveals that apamin has antimicrobial properties. The intended significance seems to be an exploration of apamin for therapeutic potential in gut health, but this is not explicitly stated by the authors. The contribution mainly appears to be conceptual in nature.
The findings appear to be in line with other recent in vitro results suggesting that apamin has antimicrobial properties (DOI: 10.9775/kvfd.2024.32125).
Researchers interested in developing therapeutic applications for bee venom constituents or promoting gut health and microbiome balance will likely find this research of interest.
My expertise is primarily in Drosophila molecular genetics and immunity. I have a broad understanding of Drosophila immune pathways, epithelial immunity, and infection dynamics. I do not feel qualified to comment on the statistics or data analysis aspects of this paper.
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1. General Statements
We thank the reviewers for their thorough and positive evaluation of the manuscript.
2. Point-by-point description of the revisions
We revised the manuscript following the suggestions of the reviewers to make the article more concise and comprehensible to a wider audience. Specifically, we rearranged Section 5, rewrote the difficult-to-understand sections 5 and 6, and removed unnecessary or overlapping text in Introduction and Discussion. We have also addressed the specific points raised by the reviewers. The responses to individual points are detailed below.
Reviewer 1:
The reviewer did not ask for any changes to the manuscript.
We thank the reviewer for the positive evaluation of the manuscript.
Reviewer 2:
1/ Title: Structure-based mechanism of RyR channel operation by calcium and magnesium ions
The authors may consider using an alternative term instead of "operation".
Thank you for the suggestion. We considered and discussed the term "RyR channel operation" very thoroughly with several colleagues, including native English speakers, and we found it to represent the complex RyR behavior in situ and in experiments most exactly. Alternative terms such as "control" suggest a one-way deterministic action from the ion binding to the protein state, which is not the case. The terms such as "modulation" implicate the presence of a higher RyR state-governing principle, such as phosphorylation, nitrosylation, binding of auxiliary proteins, etc.
2/ Abstract: Please spell out CFF and MWC theorem.
Thank you for the proposal. CFF was changed to caffeine; MWC was changed to Monod-Wyman-Changeaux
3/ Line 87-88: "In striated muscle cells, RyR channels cluster at discrete sites of sarcoplasmic reticulum attached to the sarcolemma where electrical excitation triggers transient calcium release by activation of RyRs."
There is no attachment between sarcoplasmic reticulum and sarcolemma, please rewrite.
We respectfully disagree, since there is strong evidence for the existence of discrete contact sites between the sarcolemma and sarcoplasmic reticulum both at triads of skeletal muscle (Rossi et al., 2019) and at dyads of cardiac muscle (Mackrill, 2022), at which both membranes are firmly attached.
However, to avoid potential misunderstanding, we changed the sentence to "In striated muscle cells, RyR channels cluster at the discrete sites of sarcoplasmic reticulum attached to the sarcolemma in triads or dyads, where electrical excitation triggers transient calcium release by activation of RyRs" (lines 86-87).
4/ Lines 104-107: "Recently, mathematical modeling of the cardiac calcium release site (Iaparov et al., 2022) confirmed that Mg2+ ions could at the same time act as the negative competitor at the calcium activation site and as an inhibitor at the inhibition site. Unfortunately, the structural counterpart of RyR inactivation, an inhibitory binding site for divalent ions, has not been located yet in RyR structures."
Note that the exact structural counterpart exists (Nayak et al., 2022, 2024), where Ca and Mg were found both at the activation and inhibition sites. The paragraph should be updated accordingly.
We respectfully disagree. In the cited works of Nayak et al. (2022; 2024) it was shown that Ca and Mg ions bind firmly at the activation site. Both atoms were also observed at the ACP molecule bound at the ATP binding site. However, they were not observed at the divalent ion-binding inhibition site, which is distinct from the ATP binding site and resides in the loops of the EF-hand region.
However, to clarify the meaning of the disputed sentence, we have changed it to: "Although binding of Ca2+ or Mg2+ to an inhibitory binding site has not been observed yet in RyR structures, a consensus is emerging that the EF-hand loops constitute this site (Gomez et al., 2016; Zheng and Wen, 2020; Nayak et al., 2024; Chirasani et al., 2024 )" (lines 107-109).
5/ Lines 108-110: The activation of RyR by agonists was shown to be accompanied by a conformational change around the Ca2+ binding site that leads to a decrease in the free energy and to a concomitant increase of the Ca2+ binding affinity and a population shift between the closed and open conformations (Dashti et al., 2020).
Please clarify to what state does the "decrease in free energy" refer, to the open or to the closed state?
Thank you for the proposal. The text was changed to: "The activation of RyR by agonists was shown to be accompanied by a conformational change around the Ca2+ binding site that leads to a decrease in the free energy of the open state and concomitantly to an increase of the Ca2+ binding affinity of the activation site. As a result, the occurrence probability of a RyR state/conformation shifts from the closed toward the open (Dashti et al., 2020)" (lines 110-113).
6/ Figure 2: please indicate if distances were measured between the C-alphas or side chains.
Thank you for the proposal. The figure legend was modified to "Distances D1 between the Cα atoms of E4075 and R4736 or equivalent. Right - Distances D2 between the Cα atoms of K4101 and D4730 or equivalent."
7/ Line 353-357: "These data suggest that interactions between the basic arginine residue R4736 and the acidic residues at the start of the initial helix E of the EF1-hand are specific for Ca2+-dependent inactivation in RyR1, whereas the interactions between the lysine K4101 that immediately follows the F helix of EF1 and the middle of the S23 loop (corresponding to D4730 and I4731 in RyR1) may play a part in the inactivation of both RyR1 and RyR2 isoforms.
Sentence is unclear; please rewrite. Overall, the entire section "Spatial interactions between the EF-hand and S23* regions" should be simplified and shortened.
Thank you for the proposal. The text was changed to: "These data suggest that interactions between the basic arginine residue R4736 and the acidic residues E4075 and D4079 are specific for Ca2+-dependent inactivation in RyR1, whereas the interactions between the lysine K4101 and the residues D4730 and I4731 (rRyR1 notation)* may play a part in the inactivation of both RyR1 and RyR2 isoforms." (lines 334-337).
We did not find a way how to make the whole section simpler and shorter at the same time without losing clarity.
8/ Lines 246-249 and Table 1. "all structures corresponding to rRyR1 residues 4063-4196 were<br /> subjected to energy minimization and submitted to the MIB2 server for evaluation of the ion binding score (IBS) of individual amino acid residues and the number of ion binding poses (NIBP) for Ca and Mg ions."
Please elaborate on the "ion binding score" and "number of ion binding poses" concepts and provide reference for the MIB2 server.
Thank you for the proposal. We added the reference for the server (Lu et al., 2022) (line 228) and added the information: "IBS values of individual residues are determined using sequence and structure conservation comparison with 409 and 209 respective templates from the PDB database for Ca2+ and Mg2+ (Lin et al., 2016) and assessing the similarity of the configuration of the residue to its configurations in known structures of its complexes with the given metal (Lu et al., 2012). Ion binding sites are determined by locally aligning the query protein with the metal ion-binding templates and calculating its score as the RMSD-weighted scoring function Z. The site is accepted if it has a scoring function Z>1, and based on the local 3D structure alignment between the query protein and the metal ion-binding template, the metal ion in the template is transformed into the query protein structure (Lin et al., 2016). The larger the IBS value, the higher the tendency of the residue to bind the ion. The larger the NIBP value, the larger the number of such complexes with acceptable structure" (lines 224-234).
9/ Lines 460-466: Nine structural models of RyR were selected, and then these are referred to in the text only with the pdb code. The reviewer understands that it would be difficult to recapitulate all conditions but either a table in the main manuscript file or a minimal description in the text following the pdb code would increase clarity and help readers to follow the content.
Thank you for the proposal. We added a new Table 2 "Model structures used for identifying the allosteric pathways" on line 452 that contains the required information, and inserted a reference to it in the text at line 446 "According to these criteria we selected five RyR1 model structures (Table 2)..."
10/ Line 467: "In the selected structures, we identified residues with high allosteric coupling intensities (ACI) for both the inhibition and activation network and compared them with residues important for ligand binding and gating of RyR (Table 2)."
Please define further the concept of "allosteric coupling intensities". The corresponding methods section appears to focus on the outputs of the OHM server without delving too much on the algorithm or principles followed. Is the allosteric coupling between neighboring residues, or reflect movement of the residues due to ligand binding? Is there a "reference" state or are the comparisons carried out within each allosteric state? This would help to introduce better the sections "The inhibition network" and "The activation network".
Thank you for this suggestion. We have lately realized, considering both the server output and the original work of Wang et al. (2020), that a better term for the variable depicting the role of the residue in the allosteric pathway would be the residue importance RI rather than the ACI. The allosteric pathway is determined on the basis of the network of contacts between pairs of residues in the given structure. The more contacts are present between two residues, the higher is the probability that a perturbation will be propagated from one to the other residue (Eq. 3 of Wang et al. (2020)). An allosteric pathway is then defined as the pathway that transmits the signal the whole way from the allosteric site to the active site.
Based on this we have changed in the manuscript the term "allosteric coupling intensity" to "residue importance" throughout the text and figures of the manuscript. It should be underlined, that this change has no effect whatsoever on presented data and conclusions. We inserted the following formulation in the Results section:
"The term residue importance defines the extent to which the given residue is involved in the propagation of a perturbation from the allosteric site to the active site, i.e., the fraction of simulated perturbations transmitted through this particular residue. The more contacts are present between two residues, the higher is the probability that a perturbation will be propagated from one to the other residue (Wang et al., 2020)." (lines 439-443).
We also inserted the following formulations into the Methods section: "The simulation of the perturbation propagation was performed 10 000 times per structure and pathway to estimate the values of residue importance." (lines 1093-1095), and we expanded the relevant sentence: "Allosteric pathways were traced using the server OHM (https://dokhlab.med.psu.edu/ohm/#/home, (Wang et al., 2020)), in which the allosteric pathway is determined on the basis of the network of contacts between pairs of residues in the given structure." (lines 1082-1084).
11/ Figure 8: The figure would be more meaningful if the pathways were drawn in the context of the 3D structure.
Thank you for the proposal. The pathways described in Fig. 8 are too complex for description in the RyR 3D structure, therefore they were not presented in the original manuscript. However, to follow the reviewer's proposal we have illustrated the pathways observed in the inactivated RyR1 channel (7tdg) and the open RyR2 channel (7u9) in Expanded View Figure EV1 and added the corresponding Expanded View Movie EV1 and EV2. These RyR structures were selected for displaying both the intra- and inter-monomeric inactivation pathways.
12/ Lines 610-612: "The structure of the inactivated RyR2 has not been determined yet; however, it is plausible to suppose that it exists at high concentrations of divalent ions and differs from the inactivated RyR1 structure by the extent of EF-hand - S23* coupling. "
The speculation would be more fit for the discussion section.
Thank you for the proposal; however, the sentence introduces a logical supposition, necessary there for reasoning on the construction of the model. We reformulated the sentence to: "In the absence of a structure of the inactivated RyR2, the model assumes that such a structure exists at high concentrations of divalent ions and differs from the inactivated RyR1 structure by the extent of EF-hand - S23* coupling." (lines 573-575).
13/ Lines 617-619: Closed and primed macrostates could be combined into a single closed macrostate of the model since both are closed and cannot be functionally distinguished at a constant ATP concentration.
The rationale for combining closed with primed does not seem a good idea, especially since the authors also mention that "the primed state is structurally very close to the open state" (lines 925-926). If the COI model is based on the structural findings, in principle it seems that primed should be treated separately.
Thank you for the proposal. The use of both the closed and primed states was crucial for solving the model. As a matter of fact, although the primed and closed states are in part structurally different, functionally they are identical, that is, closed. Consequently, to be distinguished in a functional model we would need to incorporate single-channel data obtained under conditions when the ratio of closed and primed channels was modulated under otherwise identical conditions. Unfortunately, such a set of data, for instance at a varying ATP concentration for a range of cytosolic Ca2+ concentrations, does not exist for either RyR1 or RyR2 channels. Moreover, while there are several RyR1 high-resolution structures in the primed state (such as the 7tzc that we used; 2.45 Å; Melville et al. (2022)), the resolution of the corresponding RyR2 structures (6jg3, 6jh6, 6jhn; 4.5 - 6.1 Å; Chi et al. (2019)) is not sufficient for determination of allosteric pathways. Fortunately, however, the two sets of conditions for RyR2 open probability data that were available in the literature turned out to represent activation of channels either selectively from the closed state (Fig. 10C), or almost selectively from the primed state (Fig. 10A, B). This allowed us to interpret the difference in the allosteric coefficients as a consequence of this fact.
To better clarify the idea, the corresponding text of the Discussion was modified as follows (lines 926-931): "RyR channels can be considered mostly in the primed state under these conditions since the binding of ATP analogs induces the primed structural macrostate in RyRs even in the absence of Ca2+ (Cholak et al., 2023). Fortunately, the two sets of conditions for RyR2 open probability data that were available in the literature turned out to represent activation of channels either selectively from the closed state (Fig. 10C), or selectively from the primed state (Fig. 10A, B).", and "construction of such a model is at present hampered by the lack of open probability data at a sufficiently wide range of experimental conditions and the absence of high-resolution structures of WT RyR2 in the primed state" (lines 934-937).
14/ Line 619. Please define the "COI" acronym. I assume it is closed, open and inactivated but this is not mentioned.
We thank the reviewer for noticing the insufficiency. We expanded the specific sentence as follows: Therefore, we constructed the model of RyR operation, termed the COI (closed-open-inactivated) model, in which we assigned a functional macrostate corresponding to each of the closed, open, and inactivated structural macrostates (Figure 9A)" (line 582).
15/ Figure 9: The diagrams are difficult to follow. Something that could improve it is to differentiate more between open and closed gates, but further elaboration would help the reader.
We thank the reviewer for paying attention to details. The open state was differentiated in Figure 9 (after line 603) by adding a pore opening to the gate.
To elaborate on the gating transitions and to keep the manuscript concise, we added a new Expanded View Figure EV2, which illustrates the relationship between the ion binding within macrostates and the transitions between macrostates.
Nevertheless, for the complexity of the model, which would need a multidimensional presentation, we had to limit the illustration to only the binding of the first ions at the binding sites. We hope that it will help the reader to grasp the principle of the model function more easily.
16/ One comment is that the manuscript is too long; the manuscript exceeds the typical length required by most journals. To enhance its suitability for publication, the content needs to be synthesized and streamlined. The manuscript is written for an audience specialized in the RyR field and may be challenging for outsiders or for readers unfamiliar with structure and/or biophysical models.
We thank the reviewer for opening this problem. The specific contribution to the understanding of RyR operation communicated by this manuscript was achieved by the synergy of approaches coming from different fields of RyR research - the structural, the functional, and the synthetic/systems ones. This needed deep immersion into complex studies performed over several decades to unwrap their complementary contributions. Only then we could synthesize the stepwise advances and integrate the mosaic of partial discoveries into the COI model. When conceptualizing the manuscript we were also considering a two-paper version, one on structural aspects and the other on modeling aspects. We realized that the two papers would need to have a very high overlap at the allosteric mechanism to be understandable in separation and would be difficult to publish in the same journal. We also anticipated a typical side effect that structuralists and modelers would read just their parts and would not appreciate enough the feedback from alternative views - how to design and interpret future structural, functional, and modeling studies.
Compacting the manuscript would be extremely difficult for us. In our view, the dense text would make it even more challenging for readers unfamiliar with some of the numerous approaches used here, as often happens to prominent multidisciplinary journals. Maybe it would be possible with the help of AI, but for now, we prefer to remain authentic.
Nevertheless, we made some effort. To shorten the manuscript, we have removed the paragraph describing the timeline of the search for the RyR inhibition site that was originally on lines 126-151 and replaced it with the paragraph on lines 129-134: "The regulatory domains involved in both, activation and inactivation of RyRs (Figure 1) are located in the C-terminal quarter of the RyR. The Central domain participates in the Ca2+ binding activation site; the C-terminal domain bears several residues of Ca-, ATP- and caffeine-binding activation sites; the U-motif participates at the ATP- and caffeine-binding sites; the EF-hand region contains the putative Ca-binding pair EF1 and EF2; and the S23 loop bears one residue of the caffeine-binding site and two residues interacting with the EF-hand region of a neighboring monomer (Samso, 2017; Hadiatullah et al., 2022)". We also removed the statements about the proposed kinetic mechanism of inactivation by Nayak et al. (2022), originally on lines 175-184. Finally, we removed the discussion of the work of Gomez et al. (2016) originally on lines 882-889, since it fully overlapped with the statements in Results on lines 358-367 (now lines 338-347). We also moved the text of the subsection "Relationship between the COI model and RyR allosteric pathways" (originally lines 670-685) into subsection "Construction of the model of RyR operation", lines 592-603 and 645-662 of the revised version.
17/ Another comment is the limited consideration of two relevant published works. One is by Chirasani et al. (2024), focused on allosteric pathways similar to the ones described here. The other work is by Nayak et al (2024), with cryo-EM structures of RyR1 focused on the interplay with Mg2+ and Ca2+. Overall, the manuscript would be strengthened by incorporating such related results in the literature.
We thank the reviewer for the concerns, but we cannot fully agree. The paper of Chirasani et al. (2024 ) was cited in the manuscript as its online-first version, Chirasani et al. (2023). The manuscript now refers to the printed version proposed by the reviewer. The Chirasani et al. work was discussed on lines 870-881. The paper concentrates on the interaction between the EF-hand region and the S23 segment and its effect on RyR inactivation, which we referenced in the manuscript, but not on the allosteric pathways as mentioned by the reviewer. To broaden the consideration of this important work, we have introduced a more detailed discussion of Chirasani et al. (2024 ) by adding the following text to the manuscript: Lines 881-888: "Based on their structural analysis of the open RyR1 structure 5tal, Chirasani et al. (2024 ) proposed that narrowing the gap between the EF-hand domain and S23 loop, resulting in H-bonding interactions between the EF-hand residue K4101 and the S23 loop residue D4730, and those between the EF-hand residues E4075, Q4076, D4079 and the S23 loop residue R4736, is a consequence of the binding of Ca2+ to the EF-hands. However, our PDBePISA analysis revealed a similar number of interactions between the EF-hand region and the S23 loop not only in open and inactivated but also in primed RyR1 structures (Figure 3). The presence of EF hand-S23 hydrogen bonds in the primed and open RyR1 structures suggests that the proximity of the EF-hand domain and S23 loop is a structural trait distinguishing RyR1 from RyR2, not a consequence of Ca2+ binding to the EF hand.*"
The data and ideas of the illuminating work of Nayak et al. (2024) were discussed and referred to in the manuscript in several places, originally lines 74, 77, 164 (Introduction), 311, 340 (Results), 892-893, and 971 (Discussion). To broaden consideration of this work, we have expanded the discussion of this paper by adding the text shown in bold into the Introduction: "Recent studies reporting RyR structure at a high divalent ion concentration provide only indirect support for the molecular mechanism of Ca2+/Mg2+-dependent inactivation. Wei et al. (2016) and Nayak et al. (2024) observed a change in the conformation of the RyR1 EF-hands in the presence of 100 µM Ca2+ and 10 mM Mg2+, respectively, compared to low-calcium or low-magnesium conditions." (lines 135-138) and in the Discussion (lines 889-891): "The recent RyR1 structure 7umz (Nayak et al., 2024) provided evidence of Mg2+ ion bound in the RyR activation site, thus confirming the functional studies that established competition between Ca2+ and Mg2+ at this activation site (Laver et al., 1997; Zahradnikova et al., 2003; Zahradnikova et al., 2010)."
Reviewer 3:
Minor comment: While I am not an expert in allosteric model construction and therefore cannot fully assess their methodological approach, I observed that the authors fixed a number of parameters to achieve model convergence. A more detailed explanation of the rationale behind these fixed parameters would enhance clarity. Currently, these parameters are not clearly specified in the text and are somewhat obscured by the broader description of all parameters included in the model.
We thank the reviewer very much for this comment, which made us realize that the relevant sections were written in a too technical manner, without sufficient explanation of the ideas behind the derivation and optimization of the model. To clarify the rationale of this process, we have rewritten the subsection "Derivation of the model open probability equation" and the section "Description of RyR operation by the COI model". In the subsection "Derivation of the model open probability equation", we have explained the simplification of the full set of equations (Eqs. 3A-C) into Eqs. 4A-C (lines 642 - 666). In the section "Description of RyR operation by the COI model", we have explained the extent of over-parametrization and the rationale of reducing it by three methods: combining the data into groups with common parameter values; eliminating parameter interdependence by fixation of one parameter at a preset value taken from the literature or postulated a priori; and sharing parameter values between data groups when no significant difference between these values was observed (lines 683-685, 702-710, 719-740).
We hope that these changes make the manuscript more comprehensible.
REFERENCES
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Chirasani, V.R., M. Elferdink, M. Kral, J.S. Carter, S. Heitmann, G. Meissner, and N. Yamaguchi. 2024 Structural and functional interactions between the EF hand domain and S2-S3 loop in the type-1 ryanodine receptor ion channel. The Journal of biological chemistry. 300:105606.
Cholak, S., J.W. Saville, X. Zhu, A.M. Berezuk, K.S. Tuttle, O. Haji-Ghassemi, F.J. Alvarado, F. Van Petegem, and S. Subramaniam. 2023. Allosteric modulation of ryanodine receptor RyR1 by nucleotide derivatives. Structure. 31:790-800 e794.
Dashti, A., G. Mashayekhi, M. Shekhar, D. Ben Hail, S. Salah, P. Schwander, A. des Georges, A. Singharoy, J. Frank, and A. Ourmazd. 2020. Retrieving functional pathways of biomolecules from single-particle snapshots. Nature communications. 11:4734.
Gomez, A.C., T.W. Holford, and N. Yamaguchi. 2016. Malignant hyperthermia-associated mutations in the S2-S3 cytoplasmic loop of type 1 ryanodine receptor calcium channel impair calcium-dependent inactivation. American journal of physiology. 311:C749-C757.
Hadiatullah, H., Z. He, and Z. Yuchi. 2022. Structural Insight Into Ryanodine Receptor Channelopathies. Frontiers in pharmacology. 13:897494.
Laver, D.R., T.M. Baynes, and A.F. Dulhunty. 1997. Magnesium inhibition of ryanodine-receptor calcium channels: Evidence for two independent mechanisms. J.Membrane.Biol. 156:213-229.
Lin, Y.F., C.W. Cheng, C.S. Shih, J.K. Hwang, C.S. Yu, and C.H. Lu. 2016. MIB: Metal Ion-Binding Site Prediction and Docking Server. Journal of chemical information and modeling. 56:2287-2291.
Lu, C.H., C.C. Chen, C.S. Yu, Y.Y. Liu, J.J. Liu, S.T. Wei, and Y.F. Lin. 2022. MIB2: metal ion-binding site prediction and modeling server. Bioinformatics. 38:4428-4429.
Lu, C.H., Y.F. Lin, J.J. Lin, and C.S. Yu. 2012. Prediction of metal ion-binding sites in proteins using the fragment transformation method. PLoS One. 7:e39252.
Mackrill, J.J. 2022. Evolution of the cardiac dyad. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 377:20210329.
Melville, Z., K. Kim, O.B. Clarke, and A.R. Marks. 2022. High-resolution structure of the membrane-embedded skeletal muscle ryanodine receptor. Structure. 30:172-180 e173.
Nayak, A.R., W. Rangubpit, A.H. Will, Y. Hu, P. Castro-Hartmann, J.J. Lobo, K. Dryden, G.D. Lamb, P. Sompornpisut, and M. Samso. 2024. Interplay between Mg(2+) and Ca(2+) at multiple sites of the ryanodine receptor. Nature communications. 15:4115.
Nayak, A.R., and M. Samso. 2022. Ca(2+) inactivation of the mammalian ryanodine receptor type 1 in a lipidic environment revealed by cryo-EM. eLife. 11.
Rossi, D., A.M. Scarcella, E. Liguori, S. Lorenzini, E. Pierantozzi, C. Kutchukian, V. Jacquemond, M. Messa, P. De Camilli, and V. Sorrentino. 2019. Molecular determinants of homo- and heteromeric interactions of Junctophilin-1 at triads in adult skeletal muscle fibers. Proceedings of the National Academy of Sciences of the United States of America. 116:15716-15724.
Samso, M. 2017. A guide to the 3D structure of the ryanodine receptor type 1 by cryoEM. Protein science : a publication of the Protein Society. 26:52-68.
Wang, J., A. Jain, L.R. McDonald, C. Gambogi, A.L. Lee, and N.V. Dokholyan. 2020. Mapping allosteric communications within individual proteins. Nature communications. 11:3862.
Wei, R., X. Wang, Y. Zhang, S. Mukherjee, L. Zhang, Q. Chen, X. Huang, S. Jing, C. Liu, S. Li, G. Wang, Y. Xu, S. Zhu, A.J. Williams, F. Sun, and C.C. Yin. 2016. Structural insights into Ca(2+)-activated long-range allosteric channel gating of RyR1. Cell research. 26:977-994.
Zahradnikova, A., M. Dura, I. Gyorke, A.L. Escobar, I. Zahradnik, and S. Gyorke. 2003. Regulation of dynamic behavior of cardiac ryanodine receptor by Mg2+ under simulated physiological conditions. American journal of physiology. 285:C1059-1070.
Zahradnikova, A., I. Valent, and I. Zahradnik. 2010. Frequency and release flux of calcium sparks in rat cardiac myocytes: a relation to RYR gating. The Journal of general physiology. 136:101-116.
Zheng, W., and H. Wen. 2020. Investigating dual Ca(2+) modulation of the ryanodine receptor 1 by molecular dynamics simulation. Proteins. 88:1528-1539.
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Referee #3
Evidence, reproducibility and clarity
The authors conducted a meta-analysis of a large set of deposited structures of ryanodine receptors (RyR) 1 and 2, aiming to elucidate the gating and inactivation mechanisms of these channels. This work is significant as the functional understanding of this giant ion channel remains limited, despite extensive research efforts. The challenges primarily stem from its large size and complexity, with numerous modulating and activating ligands as well as post-translational modifications.<br /> This is a comprehensive and well-executed study, providing valuable insights into the differences in ligand sensitivities between RyR1 and RyR2. Notably, the authors clarify the role of the EF-hand pairs in the inhibition mechanism and how these differences drive the distinct inhibition sensitivities between the two isoforms. Additionally, they constructed an allosteric model that effectively recapitulates single-channel measurements available in the literature.
Minor comment: While I am not an expert in allosteric model construction and therefore cannot fully assess their methodological approach, I observed that the authors fixed a number of parameters to achieve model convergence. A more detailed explanation of the rationale behind these fixed parameters would enhance clarity. Currently, these parameters are not clearly specified in the text and are somewhat obscured by the broader description of all parameters included in the model.
Significance
Overall, this study marks a significant advance in understanding the mechanism of this channel, which plays a critical role in excitation-contraction coupling. It also lays the groundwork for more robust and thorough methods to study allosteric mechanisms in large protein complexes.<br /> Insights from this study may be used for the rational design of allosteric drugs targetting this channel to treat heart and muscular diseases.
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Referee #2
Evidence, reproducibility and clarity
This is an interesting contribution by Zahradnikova et al. on the structure-based mechanism of RyR by calcium and magnesium. To this effect, they systematically and quantitatively compare multiple structures from the pdb database using bioinformatics. The comparisons between structures are rigorous and include RyR reconstructions in multiple conditions. They represent the more comprehensive structural comparison to date. The study proposes main long-allosteric pathways between the activation and inhibition ion binding sites and the ion gate and reasons the different inactivation properties of RyR1 and RyR2, which is an important question on the field.
Based on the allosteric model, they built a model of "RyR operation" using Monod-Wyman-Changeaux and Markov theorems. While the reviewer cannot comment on the mathematical model due lack of expertise, it appears to have high predictive power and strong agreement with single channel functional data.
Specific issues are noted below.
Title: Structure-based mechanism of RyR channel operation by calcium and magnesium ions
The authors may consider using an alternative term instead of "operation".
Abstract: Please spell out CFF and MWC theorem.
Line 87-88: "In striated muscle cells, RyR channels cluster at discrete sites of sarcoplasmic reticulum attached to the sarcolemma where electrical excitation triggers transient calcium release by activation of RyRs."
There is no attachment between sarcoplasmic reticulum and sarcolemma, please rewrite.
Lines 104-107: "Recently, mathematical modeling of the cardiac calcium release site (Iaparov et al., 2022) confirmed that Mg2+ ions could at the same time act as the negative competitor at the calcium activation site and as an inhibitor at the inhibition site. Unfortunately, the structural counterpart of RyR inactivation, an inhibitory binding site for divalent ions, has not been located yet in RyR structures."
Note that the exact structural counterpart exists (Nayak et al., 2022, 2024), where Ca and Mg were found both at the activation and inhibition sites. The paragraph should be updated accordingly.
Lines 108-110: The activation of RyR by agonists was shown to be accompanied by a conformational change around the Ca2+ binding site that leads to a decrease in the free energy and to a concomitant increase of the Ca2+ binding affinity and a population shift between the closed and open conformations (Dashti et al., 2020).
Please clarify to what state does the "decrease in free energy" refer, to the open or to the closed state?
Figure 2: please indicate if distances were measured between the C-alphas or side chains.
Line 353-357: "These data suggest that interactions between the basic arginine residue R4736 and the acidic residues at the start of the initial helix E of the EF1-hand are specific for Ca2+-dependent inactivation in RyR1, whereas the interactions between the lysine K4101 that immediately follows the F helix of EF1 and the middle of the S23 loop (corresponding to D4730 and I4731 in RyR1) may play a part in the inactivation of both RyR1 and RyR2 isoforms.
Sentence is unclear; please rewrite. Overall, the entire section "Spatial interactions between the EF-hand and S23* regions" should be simplified and shortened.
Lines 246-249 and Table 1. "all structures corresponding to rRyR1 residues 4063-4196 were<br /> subjected to energy minimization and submitted to the MIB2 server for evaluation of the ion binding score (IBS) of individual amino acid residues and the number of ion binding poses (NIBP) for Ca and Mg ions."
Please elaborate on the "ion binding score" and "number of ion binding poses" concepts and provide reference for the MIB2 server.
Lines 460-466: Nine structural models of RyR were selected, and then these are referred to in the text only with the pdb code. The reviewer understands that it would be difficult to recapitulate all conditions but either a table in the main manuscript file or a minimal description in the text following the pdb code would increase clarity and help readers to follow the content.
Line 467: "In the selected structures, we identified residues with high allosteric coupling intensities (ACI) for both the inhibition and activation network and compared them with residues important for ligand binding and gating of RyR (Table 2)."
Please define further the concept of "allosteric coupling intensities". The corresponding methods section appears to focus on the outputs of the OHM server without delving too much on the algorithm or principles followed. Is the allosteric coupling between neighboring residues, or reflect movement of the residues due to ligand binding? Is there a "reference" state or are the comparisons carried out within each allosteric state? This would help to introduce better the sections "The inhibition network" and "The activation network".
Figure 8: The figure would be more meaningful if the pathways were drawn in the context of the 3D structure.
Lines 610-612: "The structure of the inactivated RyR2 has not been determined yet; however, it is plausible to suppose that it exists at high concentrations of divalent ions and differs from the inactivated RyR1 structure by the extent of EF-hand - S23* coupling. "
The speculation would be more fit for the discussion section.
Lines 617-619: Closed and primed macrostates could be combined into a single closed macrostate of the model since both are closed and cannot be functionally distinguished at a constant ATP concentration.
The rationale for combining closed with primed does not seem a good idea, especially since the authors also mention that "the primed state is structurally very close to the open state" (lines<br /> 925-926). If the COI model is based on the structural findings, in principle it seems that primed should be treated separately.
Line 619. Please define the "COI" acronym. I assume it is closed, open and inactivated but this is not mentioned.
Figure 9: The diagrams are difficult to follow. Something that could improve it is to differentiate more between open and closed gates, but further elaboration would help the reader.
Significance
Overall, the work is a valuable conceptual contribution to the field that will help in the mechanistic understanding of RyR function.
One comment is that the manuscript is too long; the manuscript exceeds the typical length required by most journals. To enhance its suitability for publication, the content needs to be synthesized and streamlined. The manuscript is written for an audience specialized in the RyR field and may be challenging for outsiders or for readers unfamiliar with structure and/or biophysical models.
Another comment is the limited consideration of two relevant published works. One is by Chirasani et al. (2024), focused on allosteric pathways similar to the ones described here. The other work is by Nayak et al (2024), with cryo-EM structures of RyR1 focused on the interplay with Mg2+ and Ca2+. Overall, the manuscript would be strengthened by incorporating such related results in the literature.
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Referee #1
Evidence, reproducibility and clarity
Summary: The data provides evidence for a novel model of divalent cation activation and inhibition of RyR1 and RyR2. The model defines allosterically coupled closed, open, and inactivated RyR states common for both RyR isoforms. The model is consistent with published single channel open probability data. The model is justified and brings together relevant novel and published structural and functional data and will provide a useful framework for discussion of future functional and structural findings. The manuscript will have the greatest appeal to a specialized audience dedicated to understanding the structure and function of the ryanodine receptor and excitation-contraction coupling.
Major comments
The claims and conclusions are supported by the data. I do not suggest any additional experiments. The data and methods are presented in an adequate fashion for reproduction by specialised investigators. The statistical analysis is adequate
Minor comments
Prior studies are appropriately referenced and clearly described.
Significance
The study is highly significant because the divalent cations are the primary regulators of ryanodine receptor activity during excitation-contraction coupling. The structural basis of this regulation if of the utmost importance in terms of understanding the basic molecular mechanisms and in future drug design.
My expertise is in ryanodine receptor function and structure
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Reply to the reviewers
Reply to the Reviewers
We sincerely thank the reviewers for their comprehensive and constructive feedback.
Reviewer #1
Major comments:
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The data and key conclusions of the paper are convincing. However, the reliability of the findings in terms of the new interaction could be improved by not relying solely on proximity ligation approaches (BioID, PLA), but employing a complementary biochemical strategy. The authors state that an immunoprecipitation (IP) was not possible due to a lack of antibodies for IP. This does not seem convincing since in the paper Saito-Diaz et al which they cite commercial antibodies were used to immunoprecipitate APC. Alternatively, the cell line expressing tagged ROBO1 could be used together with endogenous or tagged APC for an biochemical interaction experiment.
Response
We thank the reviewer for this important suggestion. In our initial studies, we attempted co-immunoprecipitation (co-IP) experiments using several different antibodies directed to APC. The signal detected was very low, possibly reflecting relatively low endogenous expression of ROBO1 in COS-7 cells, technical challenges associated with co-IP of APC and ROBO1, which are both large proteins (>200 kDa), and/or transient interactions between the two proteins. As part of the revision plan we will carry out co-IP experiments using HEK293A cells stably expressing full length ROBO1 (5H9 cells).
Regarding the PLA experiment, I was very surprised by the very strong labeling for Clathrin+ROBO1 shown in the representative image. It is hard to believe that this image is representative when the average number of dots in the quantification is about 100. From the image it is also hard to see how it would be possible to quantify individual dots. For this, a zoom would be helpful.
Response
We thank the reviewer for this helpful comment. In the revised manuscript, we have added a magnified panel to Figure 4E.
Clathrin and ROBO1 are likely not even direct interactors but come together by their common interaction with AP2. Therefore, to back this surprisingly strong result up, I would recommend to include one more control such as another rabbit antibody recognizing a protein that does not associate with clathrin or use e.g. the ROBO1 wildtype vs the ROBO1 mutant, that does not bind AP2 and therefore should also not associate with clathrin, for the experiment. Even better, the authors could confirm the PLA results by the mentioned complementary biochemical experiments to bolster the findings by an independent approach.
Response
We thank the reviewer for this suggestion. As recommended, we will use the complementary biochemical approaches suggested, and will perform immunoprecipitation experiments to examine interactions with clathrin in cells that express wildtype ROBO1 vs. cells that express mutant ROBO1 that does not bind AP2. As recommended, we will further perform experiments using control antibody directed to a protein that does not associate with clathrin.
Minor comments:
In general, data and methods are presented in a manner that should make them reproducible by others. Some small things to improve are:
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In the paragraph on antibodies the used concentrations for the different applications should be provided.
Response
We thank the reviewer for this suggestion and apologize for the omission. In the revised manuscript, we have added a supplementary table to clarify the concentrations of antibodies used for different experimental applications. Please see Table s1.
It should be described how the poly-D-lysine coating was exactly performed.
Response
We thank the reviewer for this comment. In the revised manuscript, we have added the procedure for poly-D-lysine coating in the "Materials and Methods" section. Please see page 7 line 143-144.
The statistical analysis looks adequate. There are just some minor things that should be specified:- Just to make sure: Is it really always SD which is provided and not SEM? Sometimes the error bars look so small that I was wondering about this.
Response
We appreciate the opportunity to clarify that we used SD consistently in the manuscript.
- It should be specified for each experiment which post-hoc test is used or stated that one is always used for the One-Way ANOVA and the other for the Two-Way ANOVA resp. a rationale should be provided why two different post-hoc tests are used.
Response
We have added the post hoc tests used for each assay in the figure legend. The rationale for the different post hoc tests used has also been added in the "Materials and Methods" section as "Two-tailed paired Student's t-test was used for two-group comparisons. One-way ANOVA followed by Tukey's post hoc multiple comparison test was used for multiple-group comparisons with a single independent variable, and two-way ANOVA followed by Sidak's post hoc multiple comparison test was used for multiple-group comparisons with two independent variables". Please see page 12 line 275-279, page 20 line 519-520, page 21 line 524-525, 533-534, 537-538, 540-541, 543-544, page 22 line 551-552, 555-556, 561-562, 576-577, page 23 line 582, 584-585, 587, 589-590, 595, 599, page 24 line 624-625, 629, 635-636, page 25 638-639.
- When using the t-test, it should be stated whether it is paired or unpaired and one- or two-tailed.
Response
Two-tailed paired Student's t-test was used in Fig. 5C. We have added in the "Materials and Methods" section and figure legend in the revised manuscript. Please see page 12 line 275-276, page 23 line 587.
- It should be stated whether it was tested that the data fulfill the requirements for parametric tests (normal distribution).
Response
We have added "The data fulfilled the requirements for normal distribution using the Shapiro-Wilk test" in the "Materials and Methods" section in the revised manuscript. Please see page 12 line 274-275 in the revised manuscript.
Text and figures are mostly clear, apart from some small things:
- I was wondering about figure 1B. If I understand the methods description right, all cells were permeabilized prior to secondary antibody application. Why then is so little fluorescence for Flag visible in the first PBS row at 30 min? That would only make sense for me if the cell was not permeabilized and the protein internalized. So where did the majority of the protein end up after 30 min since you should see the entire population in a permeabilized cell? Could you please comment on this?
Response
We thank the reviewer for this comment. The cells were permeabilized prior to secondary antibody application. Since NSLIT2 binding to ROBO1 can facilitate ADAM10-mediated ROBO1 cleavage to release the extracellular domain of ROBO1 (Coleman et al., 2010), this may have caused little fluorescence for Flag to be visible in the first PBS row at 30 min. In the revised manuscript we have added a comment about the finding described. Please see page 13 line 293-296.
- Fig. 2A the upper left image (0 min PBS) should be very similar to the upper left image in Fig. 1B, shouldn´t it? But it looks quite different to me in terms of surface amount of ROBO1-Flag. Could you please comment on this?
Response
We apologize for the confusing images included in the original version of the manuscript. As noted, the upper left image (0 min PBS) in Fig. 2A should be very similar to the upper left image in Fig. 1B. We have now instead included an image for Fig. 2A that is more representative of the data from the experiments we performed.
- Please explain what the molecular difference between bio-active NSLIT2 and bio-inactive CSLIT2 is. Please provide a rationale why you sometimes use CSLIT2 as negative control and sometimes DD2SLIT2. In Fig. 3G you are using DD2SLIT2. Even though there is no significance reached with the analyzed n, it is very striking that the bars are consistently higher upon DD2SLIT2 application. Can you comment on this effect? Or am I misunderstanding the labeling of the figure?
Response
Bio-active NSLIT2 consists of the N-terminal fragment of SLIT2 and contains the second leucine-rich repeat (LRR) domain (D2), which binds to the first two Ig domains of the ROBO1 receptor (Ig1-2). Bio-inactive CSLIT2 consists of the C-terminal fragment of SLIT2, which does not bind ROBO1. DD2SLIT2 consists of the N-terminal fragment of SLIT2 but lacks D2 LRR domain that is essential for ROBO1 binding. Neither CSLIT2 nor DD2SLIT2 can bind the ROBO1 receptor (Bhosle et al., 2020; Mukovozov et al., 2015; Patel et al., 2012). In Fig. 3G, DD2SLIT2 was used as negative control and did not affect cell spreading, so the bars are consistently higher upon D2SLIT2 application. The use of CSLIT2 or DD2SLIT2 in different experiments was due to the availability of these reagents. In Fig. 3F and 3G, we have made modifications to the X axis to clarify.
- On page 3 it states "...endocytosis of ROBO1...requires...APC": I found this confusing since it is the dissociation of APC that is required for promoting endocytosis. Therefore, it would be good to rephrase this sentence.
Response
We apologize for the confusing language. In the revised manuscript, we have changed "endocytosis of ROBO1 from the cell surface requires the tumor suppressor protein, APC" to "endocytosis of ROBO1 from the cell surface requires the dissociation of the tumor suppressor protein, APC". Please see page 4 line 35-36.
- On page 8 is written "...cells surface ROBO1 [is] removed". Please be more accurate since the acid wash does not remove ROBO1, but only the antibody bound to the extracellular epitope.
Response
We apologize for the confusing language. In the revised manuscript, we have changed "cell surface ROBO1 removed" to "anti-Flag antibody binding ROBO1 removed from the cell surface". Please see page 8 line 153-154.
- On page 8 provide an explanation for the abbreviation HAC.
Response
To enhance clarity, in the revised manuscript we have used the full name "acetic acid" instead of using the abbreviation "HAC". Please see page 8 line 155.
- On page 15 you speak of "mutant AP2". Please be more accurate since there is no mutant AP2 involved, but you are refering to ROBO1 with mutations in its AP2 binding motifs.
- On page 14 you speak of "cells expressing the mutant alleles of AP2". As above, please be more accurate and replace with "cells expressing ROBO1 harboring mutations in both AP2 binding sites".
Response
We thank the reviewer for this suggestion and apologize for the confusion. For the sake of accuracy, we have made the changes as suggested by the reviewer. Please see page 15 line 351 and page 14 line 331-332.
- On page 19 you write: "Using proximity ligation assays, we observed that ROBO1, APC and clathrin interact with one another". I am maybe a bit picky here, but in my eyes with these assays you only show that they are very close together and might be in a complex, but you do not show (direct) interaction in a strict sense. Therefore, I would downtone this a bit.
Response
We thank the reviewer for this important comment. As suggested, in the revised manuscript, we replaced "Using proximity ligation assays, we observed that ROBO1, APC and clathrin interact with one another" with "Using proximity ligation assays, we observed that ROBO1, APC and clathrin are in close proximity to one another". Please see page 18 line 458. We have similarly amended the language throughout the manuscript. Please see page 3 line 11-12, page 4 line 37, page 16 line 391, 394, 396, 398, page 22 line 564.
- In Fig. 5B I would find it easier for the reader if siRNA and control were shown side by side for the different conditions.
Response
In the revised manuscript, we have made the changes suggested by the reviewer to enhance clarity.
- Between the internalization assays and the spreading assays, you switch from HEK293 cells to COS7 cells. Please provide a rationale for this for the reader.
Response
Because the endogenous expression of ROBO1 is relatively low in COS-7 cells, we generated a HEK293A cell line that stably expresses ROBO1, and used these cells to examine subcellular traffic of ROBO1 and explore interactors of ROBO1. We next sought to explore the functional consequences of internalization of ROBO1 and the functional role of APC. As we and others previously showed that SLIT2-ROBO1 signaling inhibits cell spreading (Bhosle et al., 2020; Patel et al., 2012; Tole et al., 2009), we elected to use this measure as a biologic read-out. Because HEK293A cells do not spread as much as COS-7 cells, we instead used COS-7 cells for the spreading assays.
- You provide a table with putative interactors within the paper and as supplementary table. Could you please explain better to the reader what your criteria were for including hits into the "short-list" presented in Table1.
Response
We chose proteins based on two criteria. The first was association with full-length ROBO1, but not with ROBO1 lacking the intracellular domain. The second was association with full-length ROBO1 under basal conditions, but loss of association with full-length ROBO1 after exposure of cells to NSLIT2. In the revised manuscript, we have added the criteria in the manuscript. Please see page 15 line 363-366.
Typos - p. 6: CO2 instead of CO2
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p21 last line: Immunoblotting should not be capitzalized.
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Figure s1 legend: full-lenth is missing a g
Response
We apologize for the oversight. In the revised manuscript, we have corrected these typos. Please see page 6 line 97, page 21 line 535 and page 24 line 611.
Significance
It was already known from Drosophila and for mammalian cells that SLIT2 induces the endocytosis of ROBO1 and that this is necessary for its repulsive function in axon guidance as the authors point out. The key advance of the study is the identification of APC as an interactor of ROBO1 which decreases its endocytosis until it dissociates upon SLIT2 binding to ROBO1. This is an interesting aspect which opens up parallels to the regulation of Wnt signaling by APC as the authors discuss. The significance of this finding would be even greater if it would have been shown that this mechanism actually operates in axon guidance. That not being the case, the authours might want to discuss in more detail if APC has previously been implicated to affect axon guidance.
Researchers working on endocytosis, adhesion, cellular signaling and the development of the nervous system will be interested in these findings.
Response
We thank the reviewer for the positive comments regarding the significance of our findings. As recommended, in the discussion section of the revised manuscript we will discuss in more detail what is known about the role of APC in axon guidance.
Reviewer #2
Major comments:
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As the authors emphasize the role of NSlit2 in Robo1 internalization throughout their manuscript, I suggest authors include "NSlit" in their title. Something like this "Adenomatous polyposis coli (APC) regulates the NSlit2-induced internalization and signaling of the chemo repellent receptor, hRoundabout (ROBO) 1" or maybe a better title.
Response
As suggested, we have changed the title of the revised manuscript to "Adenomatous polyposis coli (APC) regulates the NSLIT2-induced internalization and signaling of the chemorepellent receptor, Roundabout (ROBO) 1".
In addition to transferrin as the control for their internalization studies, have the authors tested the specificity of NSlit-2-induced internalization with other Robo receptors such as Robo2? Does the APC bind to Robo2 also?
Response
We thank the reviewer for this comment. Due to significant cost constraints, we focused our BioID experiments on identifying proteins that interact with ROBO1. In the revised manuscript, we will expand the discussion to consider the questions raised here by the reviewer.
The N-Slit group at 0' in Figure 1 b and Figure 2a, the Flag-Robo staining looks very different. Is it because the authors did not use ADAM protease inhibitor in Figure 2a that's why they are seeing more internalized Flag-Robo at 0'? It is not very clear either in the Results or the legend.
Response
We apologize for the confusing images. We used ADAM protease inhibitor for all endocytosis assays, as mentioned in the "Materials and Methods" section. The upper left image (0 min PBS) in Fig. 2A should be very similar to the upper left image in Fig. 1B. We have now replaced the image in 2A with one that is more representative of the overall results.
Have the authors tested the Surface Robo1 pool in siAPC cells induced with or without N-Slit2?
Response
We added NSLIT2 to cells as we started endocytosis assay. At the time point of 0 min, the surface ROBO1 pool was not affeacted by NSLIT2.
Does the Robo1 mutated with AP2 binding motifs interact with APC? Have authors performed a Proximity ligation assay with AP2-binding motifs mutated Robo1 and APC?
Response
We thank the reviewer for this suggestion. As recommended, we will perform proximity ligation assays to examine interactions between APC and ROBO1 which lacks AP2-binding motifs.
The resolution of PLA dots in the current version is very low. Authors should include higher magnification pictures for these interactions and also PLA dots channel should be separately represented in addition to the DAPI merged images for better clarity and interpretation.
Response
We thank the reviewer for these suggestions. In the revised manuscript, we have included figures with the recommended modifications to enhance clarity. Please see figure 4A, 4C and 4E.
Do the Slit2 treated cells affect APC mRNA expression? Or does Slit2 only inhibit the interaction between APC and Robo1? Have the authors tested the mRNA expression of APC in slit2-treated and untreated cells?
Response
We thank the reviewer for this question. We will perform the experiments suggested and include the results in the revised manuscript.
The authors have tested the effect of Slit2-induced inhibition of cell spreading under different experimental conditions however it is also important to test the cell migration/proliferation rates under control and siAPC conditions with or without Slit2 treatment.
Response
We thank the reviewer for this comment. In order to test the effect of APC on SLIT2-induced cell migration, a migratory cell type would be required. This would involve introducing a third cell type in addition to the HEK293 and COS-7 cells we have already used, and first validating our key experimental findings in the new cell type. Please see our response to the 10th sub-comment in Minor Comment 4) of Reviewer 1.
Do authors see the inhibition of Robo1 and Cyfip interactions also in the presence of Slit2 by PLA assay?
Response
We thank the reviewer for this interesting question. As this was beyond the scope of the current study, we did not examine whether SLIT2 inhibits interactions between ROBO1 and CYFIP. In the Discussion section of the revised manuscript, we will address this question as a potential line of future investigation.
Studying the endogenous Robo1 and APC interaction by PLA is good but I suggest authors do standard co-IP assays to visualize these interactions since authors have already generated a variety of general epitope- tagged constructs for both Robo1 and APC. These epitope-specific antibodies that are best suitable for IP are easily available with many antibody companies. This is the first study to suggest that the interaction between Robo1 and APC so the strong biochemistry would have a good impact on the findings.
Response
We appreciate this important suggestion. We will perform the recommended studies and include the results in the revised manuscript. Please also see our response to Reviewer 1, Major Comment 1).
Minor comments:
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I suggest the authors show the single-channel images of Flag-robo (green) in Figure 2B for a clear visualization of internalized Robo in a cell. With DAPI-merged images, it is hard to specifically visualize Robo in these cells.
Response
We assume the reviewer was referring Figure 2A instead of 2B. To enhance clarity, in the revised manuscript we have made the changes suggested by the reviewer.
In Figure 1C, the Y axis should have a clear indication. Instead of "% internalized" it should be mentioned as "% Internalized Robo1".
Response
We thank the reviewer for this suggestion and apologize for the oversight. In the revised manuscript, we have made the suggested change in Figure 1C, 2B, 2D, 2E, 5B and 5D.
I suggest authors to include the simple schematic of the mechanism they are proposing in the manuscript.
Response
We thank the reviewer for the suggestion. To enhance clarity, in the revised manuscript we will include a simple schematic of the mechanism our findings suggest.
The authors should mention the rationale or the function of using the acid wash method for their experimental conditions for a better understanding of the reader.
Response
We thank the reviewer for this suggestion and apologize for the oversight. We performed acid wash experiments to remove the anti-Flag antibody that binds ROBO1 from the cell surface for the endocytosis assay. To increase the clarity, in the "Materials and Methods" section of the revised manuscript we have included the rationale for using acid wash. Please see page 8 line 153-154.
siRNA-mediated knockdown of specific genes should be correctly denoted in the figure. For example, instead of "CLTC", it should be "siCLTC" for easy understanding. The same correction has to be done in all the figures with siRNA data.
Response
We thank the reviewer for this helpful comment and apologize for the oversight. As suggested, we have made the suggested changes throughout the revised manuscript and in Figure 2C, 2D, 5A, 5B, 6A, 6B, 6C, s2C and s2D.
Reference
Bhosle, V.K., Mukherjee, T., Huang, Y.W., Patel, S., Pang, B.W.F., Liu, G.Y., Glogauer, M., Wu, J.Y., Philpott, D.J., Grinstein, S., et al. (2020). SLIT2/ROBO1-signaling inhibits macropinocytosis by opposing cortical cytoskeletal remodeling. Nat Commun 11, 4112.
Coleman, H.A., Labrador, J.P., Chance, R.K., and Bashaw, G.J. (2010). The Adam family metalloprotease Kuzbanian regulates the cleavage of the roundabout receptor to control axon repulsion at the midline. Development (Cambridge, England) 137, 2417-2426.
Mukovozov, I., Huang, Y.W., Zhang, Q., Liu, G.Y., Siu, A., Sokolskyy, Y., Patel, S., Hyduk, S.J., Kutryk, M.J., Cybulsky, M.I., et al. (2015). The Neurorepellent Slit2 Inhibits Postadhesion Stabilization of Monocytes Tethered to Vascular Endothelial Cells. J Immunol 195, 3334-3344.
Patel, S., Huang, Y.W., Reheman, A., Pluthero, F.G., Chaturvedi, S., Mukovozov, I.M., Tole, S., Liu, G.Y., Li, L., Durocher, Y., et al. (2012). The cell motility modulator Slit2 is a potent inhibitor of platelet function. Circulation 126, 1385-1395.
Tole, S., Mukovozov, I.M., Huang, Y.W., Magalhaes, M.A., Yan, M., Crow, M.R., Liu, G.Y., Sun, C.X., Durocher, Y., Glogauer, M., et al. (2009). The axonal repellent, Slit2, inhibits directional migration of circulating neutrophils. Journal of leukocyte biology 86, 1403-1415.
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Referee #3
Evidence, reproducibility and clarity
Comments on Huang et al.
In their manuscript, Robinson and colleagues explore the role of the APC protein in the regulation of Slit-Robo signaling in mammalian cell culture. In particular, the authors investigate the importance of APC in controlling the endocytosis of Robo and its subsequent signaling. The authors begin by demonstrating that as previously reported in a Drosophila model that Slit induces the internalization of Robo through Clathrin dependent endocytosis and that this effect depends on two AP binding motifs in the Robo c-terminal tail. In a cellular readout for Slit- signaling the authors demonstrate that Slit inhibition of cell spreading in COS7 cells depends on the receptor endocytosis. A BioID screen for proteins that bind Robo in a slit-dependent manner identified a number of candidate proteins and the authors chose to focus on the APC protein whose association with Robo is down-regulated by Slit. Using a series of in vitro and cell based experiments the authors propose a model in which APC negatively regulates Robo internalization and subsequently impacts receptor signaling. Overall the findings are interesting and many of the experiments are well controlled; however, significant technical and conceptual concerns limit enthusiasm for the manuscript in its current form.
General- throughout the manuscript the magnification of the cells in the micrographs are too low. I would recommend including insets to more clearly show the observed effects (especially for the localization figure).
Specific comments
Figure 1: The effect of Slit on Robo internalization appears to be robust and the transferrin control is welcome. The authors need to do a better job of explaining how exactly they are quantifying the internalized pool, especially given the likely cell to cell variability in the amount of expression of the transfected constructs. Labeling the figure more clearly to indicate what is Scarlet tagged would help and the purpose of the acid-washing procedure should be more clearly explained.
Figure 2: There seems to be a mismatch in the representative images for the Dyn treatments- both concentrations appear to reduce internalization of Robo R ~ 2 fold, but the images show essentially 0 internalized receptor. (2A and B)
Figure 3: The analysis of the Robo c-terminal AP binding motifs is a bit confusing. The authors refer to these manipulations as Robo alleles; however, my understanding is that these are over-expressed constructs in stably transfected lines. They are not 'alleles.' It is puzzling how the effects of Slit appear to be restricted to the transfected cells, especially given that all of these cells should be expressing endogenous Robo. Some clarification would be welcome.
Figure 4: This figure presents the 'validation' of the Robo APC interaction. There are major problems here. First, PLA is not an adequate substitute for biochemical interaction and it does not allow for clear documentation of ligand-dependent effects. To suggest as the authors do that this analysis provides evidence for a multi-protein complex between Robo, APC and clathrin is not legitimate. Furthermore, the nature of the PLA assay and what they are counting is unlear and misleading. In the methods the author's state
'Interactions were quantified by counting the number of dots per nucleus as well as the intensity of the signal per dot. An increase in intensity is the consequence of a concentration of interactions in the same cellular dots (Gauthier et al., 2015)'
Surely, we are not to believe that nuclear PLA signals would exist between this transmembrane protein and endocytic machinery.
The authors should perform co-IP experiments +/- Slit to validate their findings from BioID.
Figures 5 and 6: These data represent the functional analysis of APC's role in Robo signaling. There are several observations that do not it with the model which states that APC associates with Robo to prevent endocytosis until Slit arrives. For example, this model would predict that loss of APC should lead to an increase in Robo internalization and an increase in Robo dependent inhibition of cell spreading- neither of these predictions match their data.
Significance
In their manuscript, Robinson and colleagues explore the role of the APC protein in the regulation of Slit-Robo signaling in mammalian cell culture. In particular, the authors investigate the importance of APC in controlling the endocytosis of Robo and its subsequent signaling. The authors begin by demonstrating that as previously reported in a Drosophila model that Slit induces the internalization of Robo through Clathrin dependent endocytosis and that this effect depends on two AP binding motifs in the Robo c-terminal tail. In a cellular readout for Slit- signaling the authors demonstrate that Slit inhibition of cell spreading in COS7 cells depends on the receptor endocytosis. A BioID screen for proteins that bind Robo in a slit-dependent manner identified a number of candidate proteins and the authors chose to focus on the APC protein whose association with Robo is down-regulated by Slit. Using a series of in vitro and cell based experiments the authors propose a model in which APC negatively regulates Robo internalization and subsequently impacts receptor signaling. Overall the findings are interesting and many of the experiments are well controlled; however, significant technical and conceptual concerns limit enthusiasm for the manuscript in its current form.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In this paper, the authors sought to identify the mechanism of Slit2-induced hRobo1 internalization and its signaling. They demonstrated that Slit2-induced hRobo1 internalization is regulated by one of the Robo1 C-terminal binding partners, adenomatous polyposis coli (APC). By using various in vitro experiments, the authors have concluded that APC constitutively interacts with hRobo1, and this interaction is disrupted upon the binding of Slit2 to the extracellular domain of hRobo1. They also showed that the dissociation of interaction between APC and hRobo1 is important for clathrin-mediated endocytosis of hRobo1 and subsequent cell morphology. In conclusion, while this study presents intriguing findings, there are notable experimental concerns. In several instances, the authors fail to sufficiently elucidate the experimental setup or provide specific conditions for certain experiments, which may pose challenges for readers in understanding the methodology thoroughly. Also, the labels for the figures can be more accurate and clearly stated.
Major comments:
- As the authors emphasize the role of NSlit2 in Robo1 internalization throughout their manuscript, I suggest authors include "NSlit" in their title. Something like this "Adenomatous polyposis coli (APC) regulates the NSlit2- induced internalization and signaling of the chemo repellent receptor, hRoundabout (ROBO) 1" or maybe a better title.
- In addition to transferrin as the control for their internalization studies, have the authors tested the specificity of NSlit-2-induced internalization with other Robo receptors such as Robo2? Does the APC bind to Robo2 also?
- The N-Slit group at 0' in Figure 1 b and Figure 2a, the Flag-Robo staining looks very different. Is it because the authors did not use ADAM protease inhibitor in Figure 2a that's why they are seeing more internalized Flag-Robo at 0'? It is not very clear either in the Results or the legend.
- Have the authors tested the Surface Robo1 pool in siAPC cells induced with or without N-Slit2?
- Does the Robo1 mutated with AP2 binding motifs interact with APC? Have authors performed a Proximity ligation assay with AP2-binding motifs mutated Robo1 and APC?
- The resolution of PLA dots in the current version is very low. Authors should include higher magnification pictures for these interactions and also PLA dots channel should be separately represented in addition to the DAPI merged images for better clarity and interpretation.
- Do the Slit2 treated cells affect APC mRNA expression? Or does Slit2 only inhibit the interaction between APC and Robo1? Have the authors tested the mRNA expression of APC in slit2-treated and untreated cells?
- The authors have tested the effect of Slit2-induced inhibition of cell spreading under different experimental conditions however it is also important to test the cell migration/proliferation rates under control and siAPC conditions with or without Slit2 treatment.
- Do authors see the inhibition of Robo1 and Cyfip interactions also in the presence of Slit2 by PLA assay?
- Studying the endogenous Robo1 and APC interaction by PLA is good but I suggest authors do standard co-IP assays to visualize these interactions since authors have already generated a variety of general epitope- tagged constructs for both Robo1 and APC. These epitope-specific antibodies that are best suitable for IP are easily available with many antibody companies. This is the first study to suggest that the interaction between Robo1 and APC so the strong biochemistry would have a good impact on the findings.
Minor comments:
- I suggest the authors show the single-channel images of Flag-robo (green) in Figure 2B for a clear visualization of internalized Robo in a cell. With DAPI-merged images, it is hard to specifically visualize Robo in these cells.
- In Figure 1C, the Y axis should have a clear indication. Instead of "% internalized" it should be mentioned as "% Internalized Robo1".
- I suggest authors to include the simple schematic of the mechanism they are proposing in the manuscript.
- The authors should mention the rationale or the function of using the acid wash method for their experimental conditions for a better understanding of the reader.
- siRNA-mediated knockdown of specific genes should be correctly denoted in the figure. For example, instead of "CLTC", it should be "siCLTC" for easy understanding. The same correction has to be done in all the figures with siRNA data.
Referees cross-commenting
Reviewer 1 comments and suggestions are valid and carry significant weight in improving the manuscript.
Significance
Strengths: The manuscript writing is good and the authors have generated a lot of constructs for a thorough understanding of Robo1 internalization events under different conditions. Studying the differential protein interactions with and without Slit2 with the Robo1-Bir*Flag method is convincing.
Limitations: The representation of figures and their labels, Figure resolution, poor quality, missing important controls and experiments.
Advance: Not very conceptual
Audience: Broad and basic research
My field of expertise: Endocytosis, receptor surface labeling studies, ligand mediated receptor signaling and its effect on axon guidance during embryonic development.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The authors investigate the endocytosis of the axon guidance receptor ROBO1 that is triggered by binding to its ligand SLIT2 and required for its ligand-induced inhibition of cell spreading. For this, they perform internalization experiments in a cell line expressing extra- and intracellularly tagged ROBO1. Using BioID they identify the protein APC as novel regulator of ROBO1 endocytosis. Their main finding is that SLIT2 induces the dissociation of APC thereby promoting endocytosis.
Major comments:
The data and key conclusions of the paper are convincing. However, the reliability of the findings in terms of the new interaction could be improved by not relying solely on proximity ligation approaches (BioID, PLA), but employing a complementary biochemical strategy.
The authors state that an immunoprecipitation (IP) was not possible due to a lack of antibodies for IP. This does not seem convincing since in the paper Saito-Diaz et al which they cite commercial antibodies were used to immunoprecipitate APC. Alternatively, the cell line expressing tagged ROBO1 could be used together with endogenous or tagged APC for an biochemical interaction experiment.
Regarding the PLA experiment, I was very surprised by the very strong labeling for Clathrin+ROBO1 shown in the representative image. It is hard to believe that this image is representative when the average number of dots in the quantification is about 100. From the image it is also hard to see how it would be possible to quantify individual dots. For this, a zoom would be helpful.
Clathrin and ROBO1 are likely not even direct interactors but come together by their common interaction with AP2. Therefore, to back this surprisingly strong result up, I would recommend to include one more control such as another rabbit antibody recognizing a protein that does not associate with clathrin or use e.g. the ROBO1 wildtype vs the ROBO1 mutant, that does not bind AP2 and therefore should also not associate with clathrin, for the experiment. Even better, the authors could confirm the PLA results by the mentioned complementary biochemical experiments to bolster the findings by an independent approach.
Minor comments:
In general, data and methods are presented in a manner that should make them reproducible by others. Some small things to improve are:
- In the paragraph on antibodies the used concentrations for the different applications should be provided.
- It should be described how the poly-D-lysine coating was exactly performed.
The statistical analysis looks adequate. There are just some minor things that should be specified:
- Just to make sure: Is it really always SD which is provided and not SEM? Sometimes the error bars look so small that I was wondering about this.
- It should be specified for each experiment which post-hoc test is used or stated that one is always used for the One-Way ANOVA and the other for the Two-Way ANOVA resp. a rationale should be provided why two different post-hoc tests are used.
- When using the t-test, it should be stated whether it is paired or unpaired and one- or two-tailed.
- It should be stated whether it was tested that the data fulfill the requirements for parametric tests (normal distribution).
Text and figures are mostly clear, apart from some small things:
- I was wondering about figure 1B. If I understand the methods description right, all cells were permeabilized prior to secondary antibody application. Why then is so little fluorescence for Flag visible in the first PBS row at 30 min? That would only make sense for me if the cell was not permeabilized and the protein internalized. So where did the majority of the protein end up after 30 min since you should see the entire population in a permeabilized cell? Could you please comment on this?
- Fig. 2A the upper left image (0 min PBS) should be very similar to the upper left image in Fig. 1B, shouldn´t it? But it looks quite different to me in terms of surface amount of ROBO1-Flag. Could you please comment on this?
- Please explain what the molecular difference between bio-active NSLIT2 and bio-inactive CSLIT2 is. Please provide a rationale why you sometimes use CSLIT2 as negative control and sometimes D2SLIT2. In Fig. 3G you are using D2SLIT2. Even though there is no significance reached with the analyzed n, it is very striking that the bars are consistently higher upon D2SLIT2 application. Can you comment on this effect? Or am I misunderstanding the labeling of the figure?
- On page 3 it states "...endocytosis of ROBO1...requires...APC": I found this confusing since it is the dissociation of APC that is required for promoting endocytosis. Therefore, it would be good to rephrase this sentence.
- On page 8 is written "...cells surface ROBO1 [is] removed". Please be more accurate since the acid wash does not remove ROBO1, but only the antibody bound to the extracellular epitope.
- On page 8 provide an explanation for the abbreviation HAC.
- On page 15 you speak of "mutant AP2". Please be more accurate since there is no mutant AP2 involved, but you are refering to ROBO1 with mutations in its AP2 binding motifs.
- On page 14 you speak of "cells expressing the mutant alleles of AP2". As above, please be more accurate and replace with "cells expressing ROBO1 harboring mutations in both AP2 binding sites".
- On page 19 you write: "Using proximity ligation assays, we observed that ROBO1, APC and clathrin interact with one another". I am maybe a bit picky here, but in my eyes with these assays you only show that they are very close together and might be in a complex, but you do not show (direct) interaction in a strict sense. Therefore, I would downtone this a bit.
- In Fig. 5B I would find it easier for the reader if siRNA and control were shown side by side for the different conditions.
- Between the internalization assays and the spreading assays, you switch from HEK293 cells to COS7 cells. Please provide a rationale for this for the reader.
- You provide a table with putative interactors within the paper and as supplementary table. Could you please explain better to the reader what your criteria were for including hits into the "short-list" presented in Table1.
Typos
- p. 6: CO2 instead of CO2
- p21 last line: Immunoblotting should not be capitzalized.
- Figure s1 legend: full-lenth is missing a g
Referees cross-commenting
I find the comments of Reviewer 2 productive and reasonable, and they overlap in part with mine (e.g. regarding biochemical approaches such as co-IP).
Significance
It was already known from Drosophila and for mammalian cells that SLIT2 induces the endocytosis of ROBO1 and that this is necessary for its repulsive function in axon guidance as the authors point out. The key advance of the study is the identification of APC as an interactor of ROBO1 which decreases its endocytosis until it dissociates upon SLIT2 binding to ROBO1. This is an interesting aspect which opens up parallels to the regulation of Wnt signaling by APC as the authors discuss. The significance of this finding would be even greater if it would have been shown that this mechanism actually operates in axon guidance. That not being the case, the authours might want to discuss in more detail if APC has previously been implicated to affect axon guidance. Researchers working on endocytosis, adhesion, cellular signaling and the development of the nervous system will be interested in these findings.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility____,____ and clarity)
This manuscript by Tsai et al. shows that phage resistance mutations (LPS truncation) confer a cost during interbacterial competition. The authors show that various phage resistant mutants of S. enterica are inhibited by E. cloacae in a contact-dependent manner (on a solid surface but not in liquid). Further experiments showed that this inhibition of S. enterica was mediated by T6SS in E. cloacae. The authors then dissect which parts of the LPS are required for resistance against T6SS attacks and show that a similar resistance is conferred against T6SS of B. thailandensis and C. rodentium. Moreover, the authors show that enzymatic degradation of LPS by a phage enzyme can also increase sensitivity to T6SS (including when such enzymes are on phage particles). Finally, the authors suggest that the change in the thickness of the LPS surface layer could be the reason for changes in T6SS susceptibility. Overall, the manuscript is very well-written. The experiments and controls are explained in sufficient detail and in a logical order. The figures are clear and easy to navigate. The findings are very interesting and important for the T6SS field but also for general understanding how different evolutionary pressures combine and influence each other. I believe that this manuscript will initiate further research in this direction.
- We thank the reviewer for their positive remarks on our manuscript and the valuable suggestions for its improvement. Major comments
The only major point that I would like to raise is that I am not generally convinced that the 2 nm difference in the thickness of LPS is the main reason for the observed differences in T6SS-mediated killing of S. enterica. Based on what we know about T6SS mode of action, we expect that it is potentially pushing effectors by up to several hundreds of nanometers. Therefore, the change in the LPS thickness by a few nanometers (as measured by AFM) seems insufficient to provide enough spacing between the attacker and the prey to significantly decrease T6SS effector delivery. While it is clear that understanding the exact reason for the LPS mediated resistance is beyond the scope of this manuscript, I would suggest that the authors consider the fact that T6SS is known to deliver proteins even to the cytoplasm of target gram-negative cells and discuss the mode of action of the machine in the context of their finding. If the T6SS was drawn to scale in the model figure, it would become apparent that 2 nm change in the distance between two cells has probably no major impact on killing by T6SS and the actual reason for the observed phenotype is likely more complicated than what is proposed.
We appreciate the reviewer's comments and acknowledge that our manuscript leaves open questions regarding the exact mechanisms underlying LPS-mediated resistance. We have now moderated the Discussion in our revised manuscript to reflect the complexity of this phenomenon (Lines 410-423). Although we agree that the nanometer difference in LPS thickness may not fully explain the observed protective phenotype, we believe it remains a plausible contributing factor that is worth considering.
To fully understand how LPS influences T6SS effector delivery, future studies will need to address key mechanistic questions regarding the T6SS injection process. For example, 1) how deeply does the T6SS apparatus penetrate the target Gram-negative cells during injection; 2) what is the magnitude of the injection force generated by the T6SS; and 3) does the structural integrity of the T6SS apparatus remain intact throughout and after contraction? While it is well documented that some T6SS effectors act in the cytosol of target cells, there is evidence to suggest that cytosolic effectors are initially delivered into the periplasm and subsequently translocated into the cytosol for intoxication1,2. Furthermore, although contraction of the T6SS apparatus occurs within milliseconds3,4, this rapid action does not preclude the possibility that the injection force could be influenced by the thickness of the LPS layer. In addition, the stability of T6SS structural or delivered proteins-such as PAAR, VgrG, and Hcp-within the delivery complex might be compromised upon encountering physical barriers such as the LPS layer and the outer membrane of target cells. These potential interactions could affect the efficiency of effector delivery, leading to reduced competitiveness during interbacterial antagonism, as shown in our study.
- We appreciate the reviewer's suggestions and acknowledge that the precise reasons for LPS-mediated resistance likely involve a combination of factors beyond those proposed here. We are actively pursuing these questions as part of an ongoing, long-term effort to better elucidate the mechanisms of T6SS action. Minor comments
Specify which T6SS of B. thailandensis was tested.
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We now cite studies by Schwarz, S., et al., 20105 and LeRoux, M., et al., 20156, from which we used the tssM (BTH_I2954) gene deletion strain abrogating the T6SS-1 of the B. thailandensis E264 (Line 234, Supplementary Table 1). Use a different naming of the two strains used in competition assays than "donor" and "recipient".
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Thank you for this suggestion. In the revised manuscript, we have replaced the terms "donor" and "recipient" with "attacker" and "prey" for clarity. This change has been applied to the text (Lines 441, and 649-667) and to revised Figures 2c-h, Figures 3b, d, g, i, j, Figures 4f, g, Figures 5b, e, g, h, Supplementary Figures 3d-f, and Supplementary Figures 4b-d. Indicate in the material and methods ODs of bacterial mixtures used in the "Bacterial competition assays".
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We apologize for this oversight. The ODs of bacterial mixtures used in the "Bacterial competition assays" have now been specified in the revised Methods section (Line 6____51). Reviewer #1 (Significance)
This manuscript is interesting for researchers who study T6SS, phage predation and other evolutionary pressures shaping bacterial interactions. The work provides new and interesting insights. My expertise in LPS biology is limited.
- We sincerely appreciate the reviewer's interest in and support of our study. Reviewer #____2____ (Evidence, reproducibility____,____ and clarity)
This work investigates the fitness trade-offs in Salmonella enterica resistant to phages. The authors performed co-culture experiments with S. enterica, E. coli, and E. cloacae and found that phage-resistant S. enterica strains displayed reduced fitness in the presence of E. cloacae. Further experiments demonstrated that phage-resistant S. enterica strains were more susceptible to the type VI secretion system (T6SS) of E. cloacae. The authors then examined the role of the O-antigen of lipopolysaccharide (LPS) in T6SS-mediated interbacterial antagonism. By constructing S. enterica mutants with varying O-antigen chain lengths, the authors demonstrated that the O-antigen protects S. enterica from T6SS attack. They then demonstrated that the O-antigen-deficient S. enterica, E. coli, and C. rodentium strains were more susceptible to T6SS attack by E. cloacae. Finally, the authors showed that phage tail spike proteins (TSPs) with endoglycosidase activity could cleave the bacterial O-antigen, thereby increasing susceptibility to T6SS attack.
The study is well-designed and the experiments are well-executed. The findings are significant and have implications for the understanding of microbial community dynamics.
- We thank the reviewer for their positive comments regarding our original submission. Major comments
While the study elegantly demonstrates the link between phage resistance, LPS structure, and T6SS susceptibility, we must remember that these LPS-defective strains are likely at a significant disadvantage in real-world environments without the influence of competing bacteria. Whether it's the gut or external environments, Salmonella needs its LPS for protection against a myriad of host and environmental factors. It seems a bit redundant for T6SS mediated antagonism to select for LPS structures when those structures are essential for bacterial survival outside of this very specific context. It would benefit some discussion about the likelihood of these phage-resistant, LPS-defective strains actually persisting and competing effectively in a more natural setting.
- We thank the reviewer for their insightful comments and appreciate the opportunity to clarify this point. We agree that LPS-defective bacterial strains face significant disadvantages in natural environments, where they must contend with various host and environmental stresses. Consequently, we did not intend to suggest that T6SS-mediated antagonism is the primary driving force in selecting specific LPS structures. Rather, our study highlights an additional role for LPS during interbacterial interactions, complementing its well-established functions. This notion aligns with the hypotheses proposed in prior studies7-9. The reviewer's comments raise an intriguing question about the essentiality of LPS in Gram-negative bacteria under natural conditions. During our revision process, we identified several examples in the literature demonstrating that LPS may not always be indispensable. For instance, LPS-depleted Neisseria meningitidis strains with an early block in lipid A biosynthesis have been shown to remain viable10,11. These strains may possess adaptive advantages under specific circumstances12. Similarly, some pathogenic bacteria produce truncated LPS structures lacking O-antigen or introduce modified LPS to evade host immune responses13. Additionally, evolutionary pressures, such as phage predation, often drive mutations in O-antigen biosynthesis pathways, resulting in alterations to or an absence of O-antigen14. Furthermore, recent studies have also indicated that trade-offs between abiotic and biotic stresses can influence LPS integrity. For instance, LPS-deficient strains may exhibit selective advantages in extreme environments15,16. These findings underscore the context-dependent nature of LPS functionality and its potential dispensability in certain ecological niches.We sincerely appreciate the reviewer's thought-provoking comments. Our current study aims to provide evidence for the role of interbacterial antagonism as an additional factor influencing LPS integrity. However, we did not mean to overstate the contribution of this mechanism. Instead, we only seek to contribute to a broader understanding of the multifaceted functions of LPS in bacterial survival and adaptation. We have modified the Discussion in our revised manuscript to clarify this idea (Lines 453-466). Minor comments
Figure 5 could be more effective is panels b and c are together
- We appreciate this suggestion. We have revised the manuscript accordingly, so panels b and c have been combined in revised Figure 5, __and the respective figure legends have been modified for improved clarity (__Lines 810-823).
69 Authors should define mucoid
- The term "mucoid" has now been defined in the revised manuscript (Lines 69-70).
155 Authors should explain that this result is expected since T6SS acts on solid surface while CDI works in liquid cultures
- Thank you for this comment. Prior studies have demonstrated that while CDI-mediated antibacterial activity is less efficient in liquid environments, it can still occur on both solid surfaces and in liquid cultures, provided the competitors possess the necessary CdiA binding unit, such as BamA17,18. This understanding supports our initial hypothesis that T6SS and/or CDI contribute to the observed protective phenotype in S. enterica phage-resistant variants (Figure 2).
clarify what it is meant by unicellular cultures. Should it be monocultures?
- We apologize for this error and have now replaced "unicellular cultures" with "monocultures" in the revised manuscript (Lines 137, 180, and 258).
618 add to the text how much dead phage was added per bacterial cell
- Apologies for this oversight. The multiplicity of infection (MOI) describing the amount of inactivated phages used to treat bacterial cells has now been included in the revised Methods section (Line 661).
364 references needed for "consistent with predictions for intact LPS structures "
- We thank the reviewer for pointing out this omission. The relevant reference has now been added to the revised manuscript19 (Line 368). Reviewer #____2____ (Significance)
This study offers a new perspective on the interplay between phage resistance and bacterial fitness in the context of microbial communities. While the concept of fitness trade-offs associated with antibiotic resistance is well-established, the authors extend this paradigm to phage resistance. They demonstrate that phage-resistant Salmonella enterica strains exhibit reduced fitness in the presence of Enterobacter cloacae due to increased susceptibility to the type VI secretion system (T6SS). This finding is significant as it highlights the potential for interbacterial antagonism to shape the evolution of phage resistance. The authors further show that the O-antigen of lipopolysaccharide (LPS) plays a crucial role in protecting S. enterica from T6SS attack. This observation provides mechanistic insights into the fitness trade-offs associated with phage resistance.
The study's strength lies in its elegant experimental design and the comprehensive analysis of the interplay between phage resistance, T6SS susceptibility, and O-antigen structure. The authors employ a combination of co-culture experiments, genetic manipulations, and structural analyses to dissect the underlying mechanisms. The findings are robust and have implications for understanding the evolution of bacterial communities in the presence of phages and competing bacterial species.
This research will be of interest to a broad audience, including researchers in microbiology, synthetic biology, and microbial ecology. The findings have implications for understanding the evolution of phage resistance, and the dynamics of microbial communities. The study's insights into the role of the O-antigen in T6SS susceptibility could also inform the design of novel antimicrobial strategies.
My expertise is microbial physiology
- We thank the reviewer for their positive remarks and careful reading of our manuscript. Reviewer #____3____ (Evidence, reproducibility____,____ and clarity)
Tsai et al. describe LPS biosynthesis mutants arising in selection for phage resistance that increase susceptibility to T6SS-mediated interbacterial antagonism. Phage-derived LPS degrading enzymes also contribute to T6SS susceptibility, which may be due to weakening of the physical barrier of LPS. The mechanisms of this fitness trade-off are elucidated with well-executed and presented experiments.
- We are grateful to the reviewer for their kind words and critical reading of the manuscript. Major comments
No major critiques.
Minor comments
Others have described two T6SS in Enterobacter cloacae ATCC 13047 (PMID 33072020). Please clarify which of the two are inactivated by the tssM deletion in this study and either provide compelling evidence that both are inactive or change the text throughout to indicate T6SS-1 or T6SS-2 being inactivated.
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We thank the reviewer for this comment. In our study, we refer to the work by Whitney, J., et al., 201420, from which we used the tssM (ECL_01536) gene deletion strain in which T6SS-1 of the E. cloacae ATCC 13047 is abrogated. Consistent with this detail, we have now clarified in the revised manuscript (Line 155, Supplementary Table 1) that T6SS-1 is inactivated. Moreover, the reference suggested by the reviewer provides additional evidence supporting that T6SS-1, but not T6SS-2, is involved in bacterial competition21, which we also now specify in the revised manuscript. It seems the authors used EHEC EDL933, which has T6SS, in co-culture experiments (Figure 1C). Why do the authors think the S. enterica LPS mutants don't have a competitive disadvantage against EHEC? It seems to run counter to the conclusion that LPS is broadly protective against T6SS.
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We thank the reviewer for raising this point. While it is true that EHEC O157:H7 strain EDL933 possesses a T6SS gene cluster in its genome, a prior study has shown that the T6SS in this strain appears to be inactivated under laboratory conditions, likely due to repression by the global regulator H-NS22. Consistent with these findings, our data indicate that the S. enterica LPS mutants did not exhibit a competitive disadvantage against EHEC EDL933. These results support the conclusion that, under the conditions tested, the truncated LPS in S. enterica does not affect its fitness against EHEC (Figure 1c), likely due to the inactivity of the EHEC T6SS22. It's not clear if the only Felix O1 and P22 phage-resistant transposon hits were in LPS-related genes, or if that pattern was observed in a more complete transposon sequencing dataset and selected for further study. A complete list of the sequence-identified hits, including the non-LPS related variants, would help clarify this and provide a useful resource to the research community.
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We thank the reviewer for the opportunity to clarify this point. For each phage, we initially isolated nine phage-resistant transposon variants, which were subsequently used for co-culture assays and transposon insertion site identification, as described in the original manuscript (Figure 1a __and Supplementary Figure 2a__). We agree with the reviewer that a broader screening approach could reveal non-LPS-related variants and provide a more comprehensive resource for the research community. To address this point, during the manuscript revision period, we followed the same procedure and isolated an additional nine phage-resistant variants for each phage (Supplementary Table 1). Interestingly, from this expanded isolation dataset, the transposon insertions were again found exclusively in LPS-related genes (Author Response Figure 1). We have now included this new dataset in the revised manuscript and believe it strengthens the robustness of our findings. This expanded data has been made available below for further reference. The fact that 8 of the 9 Felix O1 resistant variants all have transposon insertions in waaO should be stated in the results. The initial impression of showing R1-R9 is that 9 disrupted genes are being tested - in this case it's really only two. This is a minor critique because clean deletions by allelic exchange are shown for a more extensive set of genes anyway.
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We thank the reviewer for this comment. As suggested, we have revised the Results section (Lines 126-131) to explicitly state that Felix O1-resistant variants harbor transposon insertions in only two genes (waaO and dagR), which were initially tested in the competition assay (Figure 2). The S. enterica serovar Typhimurium transposon mutagenesis library could benefit from clarification on details. The results section suggests use of a pre-existing "established" transposon library, but the methods and Figure 1 seem to indicate a new library was created based on prior methods. In either case, what is the genome coverage and redundancy of the library? If this is not known or saturation is not reached, the implications of potentially missing phage resistance genes with this approach should be discussed.
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We thank the reviewer for the opportunity to clarify this point. For our study, we created a transposon library following previously established methods23. The library comprises approximately 12,000 variants, as noted in Figure 1a. While doing so provided substantial genome coverage, it did not achieve full saturation. We have now revised the Results section (Lines 93-94, and 115-117) to better describe the potential limitations of this approach, including by stating the possibility that some phage-resistance genes may have been missed during the screening. There is some variation in phenotype among the strains with transposon insertion into the same gene, such as P22 resistant strain R7 which macroscopically agglutinates while the other waaJ insertions R5 and R1 don't. Is this due to polar effects on waaO, or could it be genetic alterations at other sites driven by stringent phage selection?
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We thank the reviewer for this comment. We also suspect that the variation in the macroscopically agglutinative phenotypes among P22-resistant strains, such as strain R7 compared to R5 and R1, may be caused by polar effects on waaO. Additionally, the possibility of genetic alterations at other loci driven by stringent phage selection cannot be excluded. To address this potential variability and ensure consistency, we used clean deletions of each LPS biogenesis gene in all subsequent experiments. This approach eliminates the confounding effects of polar mutations or secondary genetic alterations, thereby providing more robust and interpretable data. Figure S1- The graphs with 12 growth curves are difficult to decipher, and the error bars would suggest maybe there are subtle growth differences among the mutants. Quantifying curve parameter(s) and applying a statistical test may clarify. The CFU counts in panel D seem to be not in log scale. Likewise in Figure S3 panel A, the authors say there are no significant growth defects, but the growth curves are modestly right-shifted for several mutants. This is a point of precision rather than a major critique, because the reversal of competitive growth phenotypes by donor T6SS inactivation indicate the potential minor growth defects aren't playing a major role in competition.
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We thank the reviewer for these suggestions and corrections. We have now revised the manuscript accordingly, including in Supplementary Figures 1 and 3. Quantitative analysis of growth curve parameters and statistical tests have been included below to clarify the observed differences (Author Response Figure 2). The slight right-shift of the growth curves for some mutants, as noted in Supplementary Figure 3, may be attributable to cell aggregation, as shown in Supplementary Figures 2e, f. The growth rate measurements were conducted in a 96-well plate with steady shaking at 200 rpm using a plate reader, which does not fully account for the aggregated cell phenotype. Despite these subtle growth differences, we agree with the reviewer that they do not appear to play a major role in the competitive growth phenotypes, as evidenced by the reversal of phenotypes upon donor T6SS inactivation (Supplementary Figure 3). Figure 3f - The authors say fepE is responsible for very long O-antigen chains, but it is not clear that the delta fepE LPS PAGE differs from wild type, which would fit with the lack of competitive disadvantage against E. cloacae in Figure 3g. The increased VL-modal O-antigen upon fepE overexpression in Figure 3h and increase protection in competition (figure 3i) are convincing. Is there another pathway(s) compensating for fepE deletion?
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We thank the reviewer for this thoughtful comment. We have repeated the experiment independently at least three times and consistently observed a reduction in the VL-modal O-antigen in the ∆fepE strain. To provide additional clarity, we have included supplementary LPS profiles and quantifications below (Author Response Figure 3). We currently do not have evidence from the literature or our experiments to identify an alternative pathway compensating for the deletion of fepE. Nonetheless, we acknowledge this as a possibility and appreciate the reviewer's insight into this topic. Lines 199-200 - I believe the conclusion from wzzB deletion would be that L-modal O-antigen is necessary for protection against T6SS, and not necessarily sufficient.
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We thank the reviewer for pointing out this important distinction. The respective sentence has now been revised in the manuscript (Line 204). Do the environmentally isolated phages As2 and As4 encode TSP homologs?
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We thank the reviewer for this question. We did not identify TSP homologs in the genome of As2 and As4 phages. The genome sequences of As1 to As4 have been uploaded to NCBI's BioProject resource under accession number PRJNA1199570 (Lines 535-544, 741-743). Reviewer #____3____ (Significance)
This manuscript provides a substantial advance in the field's understanding of how phages affect bacterial community interactions. To my knowledge, it is the first to bring together phage and T6SS defense with a strong mechanistic link. It's a conceptual advance in this regard that will stimulate more thought and experimentation on the roles of phage in bacterial communities like gut and environmental microbiomes. The manuscript's strengths include rigorous overall design, clarity of the communication, and depth of mechanistic investigation, all the way down to atomic force microscopy measurements. There are some minor revisions suggested, but these are addressable with minimal/no additional experiments.
As someone with expertise in bacterial secretion systems and interbacterial interactions, I think this work will be of interest to microbiologists generally, and specifically in the fields of phage biology, bacterial secretion systems, and microbiome research. While the phage virology components are straightforward and well described, I think a review from someone with more expertise in this specific area would be beneficial.
-
We thank the reviewer for their careful reading of our manuscript and for the suggestions to improve it. References
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- Steeghs, L., den Hartog, R., den Boer, A., Zomer, B., Roholl, P., and van der Ley, P. (1998). Meningitis bacterium is viable without endotoxin. Nature 392, 449-450. 10.1038/33046.
- Steeghs, L., de Cock, H., Evers, E., Zomer, B., Tommassen, J., and van der Ley, P. (2001). Outer membrane composition of a lipopolysaccharide-deficient Neisseria meningitidis mutant. EMBO J 20, 6937-6945. 10.1093/emboj/20.24.6937.
- Fransen, F., Heckenberg, S.G., Hamstra, H.J., Feller, M., Boog, C.J., van Putten, J.P., van de Beek, D., van der Ende, A., and van der Ley, P. (2009). Naturally occurring lipid A mutants in neisseria meningitidis from patients with invasive meningococcal disease are associated with reduced coagulopathy. PLoS Pathog 5, e1000396. 10.1371/journal.ppat.1000396.
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- Yu, J., Zhang, H., Ju, Z., Huang, J., Lin, C., Wu, J., Wu, Y., Sun, S., Wang, H., Hao, G., and Zhang, A. (2024). Increased mutations in lipopolysaccharide biosynthetic genes cause time-dependent development of phage resistance in Salmonella. Antimicrob Agents Chemother 68, e0059423. 10.1128/aac.00594-23.
- Burmeister, A.R., Fortier, A., Roush, C., Lessing, A.J., Bender, R.G., Barahman, R., Grant, R., Chan, B.K., and Turner, P.E. (2020). Pleiotropy complicates a trade-off between phage resistance and antibiotic resistance. Proc Natl Acad Sci U S A 117, 11207-11216. 10.1073/pnas.1919888117.
- Carretero-Ledesma, M., Garcia-Quintanilla, M., Martin-Pena, R., Pulido, M.R., Pachon, J., and McConnell, M.J. (2018). Phenotypic changes associated with Colistin resistance due to Lipopolysaccharide loss in Acinetobacter baumannii. Virulence 9, 930-942. 10.1080/21505594.2018.1460187.
- Aoki, S.K., Pamma, R., Hernday, A.D., Bickham, J.E., Braaten, B.A., and Low, D.A. (2005). Contact-dependent inhibition of growth in Escherichia coli. Science 309, 1245-1248. 10.1126/science.1115109.
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- Gao, Y., Widmalm, G., and Im, W. (2023). Modeling and Simulation of Bacterial Outer Membranes with Lipopolysaccharides and Capsular Polysaccharides. J Chem Inf Model 63, 1592-1601. 10.1021/acs.jcim.3c00072.
- Whitney, J.C., Beck, C.M., Goo, Y.A., Russell, A.B., Harding, B.N., De Leon, J.A., Cunningham, D.A., Tran, B.Q., Low, D.A., Goodlett, D.R., et al. (2014). Genetically distinct pathways guide effector export through the type VI secretion system. Mol Microbiol 92, 529-542. 10.1111/mmi.12571.
- Soria-Bustos, J., Ares, M.A., Gomez-Aldapa, C.A., Gonzalez, Y.M.J.A., Giron, J.A., and De la Cruz, M.A. (2020). Two Type VI Secretion Systems of Enterobacter cloacae Are Required for Bacterial Competition, Cell Adherence, and Intestinal Colonization. Front Microbiol 11, 560488. 10.3389/fmicb.2020.560488.
- Wan, B., Zhang, Q., Ni, J., Li, S., Wen, D., Li, J., Xiao, H., He, P., Ou, H.Y., Tao, J., et al. (2017). Type VI secretion system contributes to Enterohemorrhagic Escherichia coli virulence by secreting catalase against host reactive oxygen species (ROS). PLoS Pathog 13, e1006246. 10.1371/journal.ppat.1006246.
- Mandal, R.K., Jiang, T., and Kwon, Y.M. (2021). Genetic Determinants in Salmonella enterica Serotype Typhimurium Required for Overcoming In Vitro Stressors in the Mimicking Host Environment. Microbiol Spectr 9, e0015521. 10.1128/Spectrum.00155-21.
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Referee #3
Evidence, reproducibility and clarity
Summary:
Tsai et al. describe LPS biosynthesis mutants arising in selection for phage resistance that increase susceptibility to T6SS-mediated interbacterial antagonism. Phage-derived LPS degrading enzymes also contribute to T6SS susceptibility, which may be due to weakening of the physical barrier of LPS. The mechanisms of this fitness trade-off are elucidated with well-executed and presented experiments.
Major comments:
- No major critiques.
Minor comments:
- Others have described two T6SS in Enterobacter cloacae ATCC 13047 (PMID 33072020). Please clarify which of the two are inactivated by the tssM deletion in this study and either provide compelling evidence that both are inactive or change the text throughout to indicate T6SS-1 or T6SS-2 being inactivated.
- It seems the authors used EHEC EDL933, which has T6SS, in co-culture experiments (Figure 1C). Why do the authors think the S. enterica LPS mutants don't have a competitive disadvantage against EHEC? It seems to run counter to the conclusion that LPS is broadly protective against T6SS.
- It's not clear if the only Felix O1 and P22 phage-resistant transposon hits were in LPS-related genes, or if that pattern was observed in a more complete transposon sequencing dataset and selected for further study. A complete list of the sequence-identified hits, including the non-LPS related variants, would help clarify this and provide a useful resource to the research community.
- The fact that 8 of the 9 Felix O1 resistant variants all have transposon insertions in waaO should be stated in the results. The initial impression of showing R1-R9 is that 9 disrupted genes are being tested - in this case it's really only two. This is a minor critique because clean deletions by allelic exchange are shown for a more extensive set of genes anyway.
- The S. enterica serovar Typhimurium transposon mutagenesis library could benefit from clarification on details. The results section suggests use of a pre-existing "established" transposon library, but the methods and Figure 1 seem to indicate a new library was created based on prior methods. In either case, what is the genome coverage and redundancy of the library? If this is not known or saturation is not reached, the implications of potentially missing phage resistance genes with this approach should be discussed.
- There is some variation in phenotype among the strains with transposon insertion into the same gene, such as P22 resistant strain R7 which macroscopically agglutinates while the other waaJ insertions R5 and R1 don't. Is this due to polar effects on waaO, or could it be genetic alterations at other sites driven by stringent phage selection?
- Figure S1- The graphs with 12 growth curves are difficult to decipher, and the error bars would suggest maybe there are subtle growth differences among the mutants. Quantifying curve parameter(s) and applying a statistical test may clarify. The CFU counts in panel D seem to be not in log scale. Likewise in Figure S3 panel A, the authors say there are no significant growth defects, but the growth curves are modestly right-shifted for several mutants. This is a point of precision rather than a major critique, because the reversal of competitive growth phenotypes by donor T6SS inactivation indicate the potential minor growth defects aren't playing a major role in competition.
- Figure 3f - The authors say fepE is responsible for very long O-antigen chains, but it is not clear that the delta fepE LPS PAGE differs from wild type, which would fit with the lack of competitive disadvantage against E. cloacae in Figure 3g. The increased VL-modal O-antigen upon fepE overexpression in Figure 3h and increase protection in competition (figure 3i) are convincing. Is there another pathway(s) compensating for fepE deletion?
- Lines 199-200 - I believe the conclusion from wzzB deletion would be that L-modal O-antigen is necessary for protection against T6SS, and not necessarily sufficient.
- Do the environmentally isolated phages As2 and As4 encode TSP homologs?
Significance
This manuscript provides a substantial advance in the field's understanding of how phages affect bacterial community interactions. To my knowledge, it is the first to bring together phage and T6SS defense with a strong mechanistic link. It's a conceptual advance in this regard that will stimulate more thought and experimentation on the roles of phage in bacterial communities like gut and environmental microbiomes. The manuscript's strengths include rigorous overall design, clarity of the communication, and depth of mechanistic investigation, all the way down to atomic force microscopy measurements. There are some minor revisions suggested, but these are addressable with minimal/no additional experiments.
As someone with expertise in bacterial secretion systems and interbacterial interactions, I think this work will be of interest to microbiologists generally, and specifically in the fields of phage biology, bacterial secretion systems, and microbiome research. While the phage virology components are straightforward and well described, I think a review from someone with more expertise in this specific area would be beneficial.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
This work investigates the fitness trade-offs in Salmonella enterica resistant to phages. The authors performed co-culture experiments with S. enterica, E. coli, and E. cloacae and found that phage-resistant S. enterica strains displayed reduced fitness in the presence of E. cloacae. Further experiments demonstrated that phage-resistant S. enterica strains were more susceptible to the type VI secretion system (T6SS) of E. cloacae. The authors then examined the role of the O-antigen of lipopolysaccharide (LPS) in T6SS-mediated interbacterial antagonism. By constructing S. enterica mutants with varying O-antigen chain lengths, the authors demonstrated that the O-antigen protects S. enterica from T6SS attack. They then demonstrated that the O-antigen-deficient S. enterica, E. coli, and C. rodentium strains were more susceptible to T6SS attack by E. cloacae. Finally, the authors showed that phage tail spike proteins (TSPs) with endoglycosidase activity could cleave the bacterial O-antigen, thereby increasing susceptibility to T6SS attack.
The study is well-designed and the experiments are well-executed. The findings are significant and have implications for the understanding of microbial community dynamics.
Major comments:
While the study elegantly demonstrates the link between phage resistance, LPS structure, and T6SS susceptibility, we must remember that these LPS-defective strains are likely at a significant disadvantage in real-world environments without the influence of competing bacteria. Whether it's the gut or external environments, Salmonella needs its LPS for protection against a myriad of host and environmental factors. It seems a bit redundant for T6SS mediated antagonism to select for LPS structures when those structures are essential for bacterial survival outside of this very specific context. It would benefit some discussion about the likelihood of these phage-resistant, LPS-defective strains actually persisting and competing effectively in a more natural setting.
Minor comments
Figure 5 could be more effective is panels b and C are together
69 Authors should define mucoid
155 Authors should explain that this result is expected since T6SS acts on solid surface while CDI works in liquid cultures
clarify what it is meant by unicellular cultures. Should it be monocultures?
618 add to the text how much dead phage was added per bacterial cell
364 references needed for "consistent with predictions for intact LPS structures "
Significance
This study offers a new perspective on the interplay between phage resistance and bacterial fitness in the context of microbial communities. While the concept of fitness trade-offs associated with antibiotic resistance is well-established, the authors extend this paradigm to phage resistance. They demonstrate that phage-resistant Salmonella enterica strains exhibit reduced fitness in the presence of Enterobacter cloacae due to increased susceptibility to the type VI secretion system (T6SS). This finding is significant as it highlights the potential for interbacterial antagonism to shape the evolution of phage resistance. The authors further show that the O-antigen of lipopolysaccharide (LPS) plays a crucial role in protecting S. enterica from T6SS attack. This observation provides mechanistic insights into the fitness trade-offs associated with phage resistance.
The study's strength lies in its elegant experimental design and the comprehensive analysis of the interplay between phage resistance, T6SS susceptibility, and O-antigen structure. The authors employ a combination of co-culture experiments, genetic manipulations, and structural analyses to dissect the underlying mechanisms. The findings are robust and have implications for understanding the evolution of bacterial communities in the presence of phages and competing bacterial species. This research will be of interest to a broad audience, including researchers in microbiology, synthetic biology, and microbial ecology. The findings have implications for understanding the evolution of phage resistance, and the dynamics of microbial communities. The study's insights into the role of the O-antigen in T6SS susceptibility could also inform the design of novel antimicrobial strategies.
My expertise is microbial physiology
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Referee #1
Evidence, reproducibility and clarity
This manuscript by Tsai et al. shows that phage resistance mutations (LPS truncation) confer a cost during interbacterial competition. The authors show that various phage resistant mutants of S. enterica are inhibited by E. cloacae in a contact dependent manner (on a solid surface but not in liquid). Further experiments showed that this inhibition of S. enterica was mediated by T6SS in E. cloacae. The authors then dissect which parts of the LPS are required for resistance against T6SS attacks and show that a similar resistance is conferred against T6SS of B. thailandensis and C. rodentium. Moreover, the authors show that enzymatic degradation of LPS by a phage enzyme can also increase sensitivity to T6SS (including when such enzymes are on phage particles). Finally, the authors suggest that the change in the thickness of the LPS surface layer could be the reason for changes in T6SS susceptibility. Overall, the manuscript is very well-written. The experiments and controls are explained in sufficient detail and in a logical order. The figures are clear and easy to navigate. The findings are very interesting and important for the T6SS field but also for general understanding how different evolutionary pressures combine and influence each other. I believe that this manuscript will initiate further research in this direction.
The only major point that I would like to raise is that I am not generally convinced that the 2 nm difference in the thickness of LPS is the main reason for the observed differences in T6SS-mediated killing of S. enterica. Based on what we know about T6SS mode of action, we expect that it is potentially pushing effectors by up to several hundreds of nanometers. Therefore, the change in the LPS thickness by a few nanometers (as measured by AFM) seems insufficient to provide enough spacing between the attacker and the prey to significantly decrease T6SS effector delivery. While it is clear that understanding the exact reason for the LPS mediated resistance is beyond the scope of this manuscript, I would suggest that the authors consider the fact that T6SS is known to deliver proteins even to the cytoplasm of target gram-negative cells and discuss the mode of action of the machine in the context of their finding. If the T6SS was drawn to scale in the model figure, it would become apparent that 2 nm change in the distance between two cells has probably no major impact on killing by T6SS and the actual reason for the observed phenotype is likely more complicated than what is proposed.
Minor:
Specify which T6SS of B. thailandensis was tested.
Use a different naming of the two strains used in competition assays than "donor" and "recipient".
Indicate in the material and methods ODs of bacterial mixtures used in the "Bacterial competition assays".
Significance
This manuscript is interesting for researchers who study T6SS, phage predation and other evolutionary pressures shaping bacterial interactions. The work provides new and interesting insights. My expertise in LPS biology is limited.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
We thank reviewers for their comments and constructive criticisms of our study. We have implemented corrections* that were suggested for the manuscript, and we have also clarified any concerns that were raised in our responses below. *
*Reviewer #1 *
Overall technology development is good though as they claim that they are first is not true as it has been used earlier by https://doi.org/10.1128/msphere.00160-22. Hence may be that they have used to decipher the cell cycle.
The cited paper used FUCCI in the host cells and not in the parasites themselves. Our study thus reports the first FUCCI model in a unicellular *eukaryote. *
- *
The manuscript is extremely dense and at times very difficult to read and to be clear if they are focussing on the technology or cell cycle. The technology may be a better part of manuscript but the dissection of cell cycle is not very novel and at times very confusing to follow. Many of these aspects has been dissected out previously from their own group and many group in Toxoplasma and Plasmodium and it is quite known about that the cell cycle in Apicomplexa is very complex.
We adapted FUCCI to the Toxoplasma model to help dissect the organization of its cell cycle, which as the reviewer noted, is highly complex. While overlaps between some phases were anticipated based on prior data, these overlaps had not been measured. We were able to determine the extent of these overlaps in the post-G1 period and describe the organization of the non-conventional cell cycle of T. gondii.
Another aspect that most FUCCI use Geminin and CDT1 factors and since Geminin is not present it would have been better to validate that with CDT1 that is present in Apicomplexa and may be more relevant than PCNA1.
Unfortunately, the Toxoplasma ortholog of CDT1 (TgiRD1) cannot be used as a FUCCI marker for the reasons stated in lines 116-117; the expression of TgiRD1 is not limited to a specific cell cycle phase (Hawkins et al., 2024). PCNA1 can be (and has been) used as a FUCCI marker, but it was not considered an ideal marker in mammalian cells due to its relatively low expression levels. However, Toxoplasma PCNA1 is highly abundant in tachyzoites, and its expression correlates with the period of DNA replication. Furthermore, Plasmodium ortholog of PCNA1 had been used as a DNA replication sensor in the recent studies (35353560). *Altogether, it validates PCNA1 as an appropriate S-phase FUCCI probe. *
The first part of the manuscript only deals with first to identify the function and localisation of PCNA1 and then develop FUCCI technology and then go on to study cell cycle. So the focus of the manuscript is not clear. It seems three different results are just assembled together in one manuscript with out clear focus. In order to get clear focus the authors should clear set out the focus as to why they developed FUCCCI and how they decipher either replication, budding, apical or basal complex, centrosome or cytokinesis as well to be used for drug discovery The way it is organised it is not flowing well and confuses the reader who may not be aware of different compartment of Toxoplasma cell or not a molecular parasitologist.
We believe the reviewer has described the logic of our study. Our goal was to dissect the cell cycle. Consequently, we adapted a suitable technology, FUCCI. We described the relevant experiments that allowed us to produce a new molecular tool for an apicomplexan model, and illustrated how we used this tool to better understand the complicated processes of its cell division. Therefore, we organized our study accordingly and included our goal, plans, results, and conclusions that support the success of adopted technology and establishment of the cell cycle organization. We hope this brief explanation can provide some clarification for the reviewer.
Some of the conclusion on the that Replication starts at centromere region is not novel and has been studied previously.
We agree that the centromeric start of DNA replication is not a novel feature, which is stated in the text. This result was shown to demonstrate that Toxoplasma replicates its DNA according to the rules* conserved across eukaryotes. *
The manuscript needs revising by writing precisely eliminating too much literature reference in the result section with clear focus. Some of these references can be elaborated in the introduction and discussion to keep the focus.
We examined the results section, and as much as we wanted to comply with this reviewer, we found no references that could be eliminated or transferred to the introduction. We believe that to aid the reader, some foundational knowledge needs to be presented together with obtained results to support those findings.
- *
Some points with respect to figures: Generally with image panels, arrows don't stand out well
We* have adjusted the images.
*
Fig1: no scale bars and the green arrow do not stand out. So may be to make white.
*The scale bar can be found in the bottom right image, which applies to every image in the panel. We changed the color of the arrows. *
Fig 2E: state the time point in the fig without IAA treatment (-IAA)
The requested information was added to the figure legend.
Fig4: no bell shaped curve
We rephrased the description. The” bell-shape” analogy applies to the temporal dynamics of DNA replication, which starts with a single aggregate, expands to numerous replication foci, and is reduced to a few aggregates at the end of replication. We attempted to quantify aggregates, but their irregular shape makes this task impossible. Our statement is supported by steady-state images and real-time microscopy of the DNA replication included in the manuscript.
Fig 5D: it isn't obvious what the numbers on the right hand side of the graph mean. If it is size, there should be a unit given
We provided an explanation in the figure legend*.
*
Figure 6 - how do they determine that the tachyzoites have progressed through 61% of S phase? Make this clearer here.
*We examined only DNA replicating parasites (S-phase) and determined the fraction of BCC0-positive (39%) and BCC0-negative (61%) tachyzoites. Quantifications can be found in Table S4, in the S-C worksheet. *
- *
Fig7: it a strange way of ordering the figure as FigE is after Fig F hence no logical order. Thank you, we have corrected the order of these panels*. *
Fig 8H is not mentioned in the text
*Thank you, we referenced the wrong panel. Fig. 8H is now included in the text. *
Figure 9 is nice and useful but the arrows could be made proportional of time spent in each cell cycle phase. They're a little off in the conventional cell cycle at the minute
- *These schematics are intended to illustrate the dramatic difference in cell cycle organization rather than to directly describe cell cycle organization, the latter of which can be found in Figure 6.
Some comment on the text in the manuscript: Line 137: describing the expression pattern: the following papers first described the expression pattern of PCNA1 and 2 can be cited in the result. https://doi.org/10.1016/j.molbiopara.2005.03.020 We added the reference.
Line 154: Provide schematic for AID HA cloning and confirmation.
The schematics and PCR confirmations* can be found in the supplemental figure S2.
*
Line 157: Fig 2 after 4 h treatment FACS analysis shows more than 1 and less than 2n genomic content. Does this study have any -IAA treated control for 4h and 7h to compare as what should the standard genomic content to be there at this time point of development. At 4 h of development can the authors provide any statistical analysis with their 3 experiments to prove their point that the replication is actually stalled. Downregulation of TgPCNA1 as shown is western blot is still basal protein left to carry the genomic replication in 7 mins. Can authors also state that TgPCNA 2 which is although non-essential but has no redundant role in the replication machinery.
The -IAA control is indicated as 0h and is shown in blue. The statistical analysis of three independent experiments showing the increase of the S-phase population is included in Table S3. The Fig. 2 WB shows over 99% TgPCNA1 degradation, and the residual >1% would be insufficient to carry out full DNA replication. This residual signal is likely due to PCNA1 remaining in complex, which would resist *proteolysis. Unfortunately, we do not feel comfortable to make the final statement suggested. We believe that the lack of TgPCNA2 complementation with yeast PCNA1 (Guerini et al, 2005) is insufficient to draw the conclusion that TgPCNA2 plays a non-redundant role in Toxoplasma replication machinery. *
Line 178 : typing error "that that
Thank you, this has been corrected*.
*
Line 179: states the role of TgPCNA 1 in DNA1 replication, however line 159 and 160 states the TgPCNA1 deficient can fulfil DNA replication. Can author confirm this contrast in the results. Results trying to illustrate the same fact TgFUCCIs or TgPCNA1ng that TgPCNA1 first aggregates at centromeres and then distributed on many replication forks and disappears late during cytokinesis. The part of the result can be merged.
We apologize for the *confusion. We rephrased our statements and supported them by corresponding references. Although it may seem repetitive, but it was our intention to emphasize a consistent spatial-temporal expression of TgPCNA1-HA and TgPCNA1-NG. *
Line 189: Typing error, should say "such as nucleus", currently as is missing
Thank you, this has *been corrected.
*
Line 346-349: basically explaining the same thing twice.
We apologize for the confusion, the first sentence describes compartments where MORN1 is located. The second sentence describes how MORN1 localization changes during cell cycle progression, information which is used later in our quantitative IFA of cell cycle phases*. *
- *
Line 347 - immunfluorescent should be immunofluorescence
Thank you, this has been corrected*.
*
Line 395-399: does this study has any non-inhibited (-IAA control) at 4h and 7 h. for fig 7C & 7G. Can the authors provide any statistical analysis for the significance with their 3 experiments.
The untreated control (7h mock) is shown as 0h treatment (first bar in each panel). The figure also shows the results of the statistical analysis (t-test, numbers above) that can also be found in Table* S7.
*
Line 415: Why the authors have not used the TgFUUCI sc lines which expresses the TgPCNAng and IMCmch both. This could have helped to understand the real time dynamics of DNA replication and budding initiation (cytokenesis), rather then fixing and staining with TgIMC.
*The recent study by Gubbels lab identified the earliest known budding marker BCC0. Unfortunately, BCC0 is a low abundant factor and cannot be used in FUCCI. IMC3 emerges in the midst of budding when the daughter conoid and polar rings are assembled and thus does not signify either the beginning or the end of cytokinesis. We added IMC3 as a supporting budding marker, while our primary focus remains on the DNA replication marker PCNA1. *
Overall good technology development as FUCCI but the rest of the manuscript is extremely dense and the focus of the study is not clear after technology part. The complexity of the cell cycle is known and hence not much novelty here and extremely descriptive and hard read. Science can be simplified.
The reviewer agrees the apicomplexan cell cycle is highly complex, and the field has worked diligently to piece together what we can about it, which contributes to the density of the manuscript. We hope that the targeted audience will find it thoughtful, and we strove to provide sufficient information for those outside our field. We also respectfully disagree that our study offers little novelty; while it is known how complex the apicomplexan cell cycle is, there is still much to uncover. While overlapping cell cycle phases exist in other eukaryotes, there were no such studies that showed the degree of these overlaps across the entire T. gondii cell cycle. We believe there are valuable insights to be gained from the identification of the composite cell cycle phase, which in turn could help draw attention to other understudied features of the cell cycle in non-conventional eukaryotes*. *
*Reviewer #2 *
- It is not always clear where apical and basal ends of the parasite are. E.g. in Fig 3F are the two parasites on the right facing down with their apical end? In Fig 4 it is even harder to see. Might be helpful to turn these images with their apical end up to make comparative interpretation of figures easier. In the text it mentions that PCNA1 concludes at the 'proximal' end of the nucleus (or with the nucleus proximal, which is not clear either??). Please define clearly where the proximal site is, as it is not clear in the figures or in the movie (the 'last focus' marker in Fig 4D??). Thank you for the suggestion. We rotated images in Fig. 3 and marked the parasite ends in Fig. 4. We also indicated parasites’ polarity in the movies.
Synchrony of replication cycle. Tight synchronization depends on the retention of the cytoplasmic bridge, as mentioned by the authors. In larger vacuoles, it is very conceivable not all parasites are connected with each other (notably in large cysts with bradyzoites), which could lead to loss of tight synchrony. The results section states "One plausible explanation is that the rosette split shortens the communication path between tachyzoites". This is somewhat cryptic language: does a 'rosette split' imply the rupture of the cytoplasmic bridge? This statement should be clarified. Another factor could be centrosome maturation, with the mother centrosome ready sooner than the daughter, which is a model proposed in schizogony, where the nuclear cycles in a shared cytoplasm are even more asynchronous/independent.
Yes, by ‘rosette split’, we refer to the break of the connection, or a cytoplasmic bridge. The model based on centrosome maturation is interesting, however, it does not explain the synchronization of a vacuole of 16, unless centrosome age resets at that point*. *
Centrosome duplication. This has been documented to occur at the basal side of the nucleus (the whole nucleus rotates for centrosome duplication). The images as depicted in Fig 6 do not seem to indicate this event, possibly because it is not easy to track apical and basal end of the cell (#1 above). Please comment, as this could be an additional spatial cue to the specific stage of the cycle.
This is a very interesting suggestion, thank you. Indeed, the centrosome often duplicates away from the apical end (disconnects from the Golgi), sometimes on the side or the basal end, but quickly rotates back to the apical position to reconnect with co-segregating organelles. Centrosome traveling is an interesting feature, and it is possible that this reorientation back to the apical end signifies budding initiation. We will explore this hypothesis in future studies.
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Specific experimental issues that are easily addressable.
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The term "Apicomplexan" should be spelled with a lower case "apicomplexan", which is not consistently applied throughout the manuscript. Thank you, we have corrected the spelling*. *
* 2. Line 567 the term used in 2008 was "tightly knit" not "closely woven". We wanted to avoid the exact citation and rephrased the title of the review.
*
*Reviewer #3 *
-The authors choose to describe PCNA1 and IMC3 as FUCCI markers. The efficiency of this system in mammalian cells is based on the proof that the markers are regulated through a rapid proteolysis process. However, the data available for these markers point toward a transcriptional regulation of these markers (Toxodb and (1)). In contrast, the authors do not provide any data indicating that these proteins are true FUCCI markers. Consequently, they should not use the term FUCCI throughout the paper unless they prove that the cell cycle expression depends on proteolysis. For example, the authors could express these genes with a promoter that is not cell cycle regulated.
PCNA1 was one of the original FUCCI markers for mammalian cells, later replaced by the more abundant geminin. PCNA1 ubiquitination is well supported across all eukaryotes, and we believe there is much data to support this same turnover mechanism acts to regulate PCNA1 in Toxoplasma. Transcriptional profiles show that TgPCNA1 mRNA is constantly present in cells, never dropping below 80%, making this mRNA is among the most abundant in the cell. It also indicates that proteolysis, rather than halted transcription, controls TgPCNA1 protein levels, since TgPCNA1 protein expression drops to nearly undetectable levels in early G1 and budding (Fig. 1). In addition, TgPCNA1 is highly conserved in structure (Fig. S1) and in function (TgPCNA1 interactome, Fig. 1). The TgPCNA1 Ub sites were detected in global ubiquitome analyses (ToxoDB), supporting the fact that TgPCNA1 protein abundance is regulated by ubiquitin-dependent degradation. Furthermore, PCNA1 as a FUCCI marker in model eukaryotes was not tested for proteolysis because it was unquestionable that PCNA1 is regulated by proteolysis. In addition, Plasmodium ortholog of PCNA1 had been used as a DNA replication sensor in the recent studies (35353560), which validates PCNA1 as an appropriate S-phase FUCCI probe. The modern FUCCI probes are fragments of CDT1 and Geminin mimicking the spatiotemporal expression of the corresponding cell cycle regulators. The transcriptional profile of TgIMC3 is also largely unchanged across the cell cycle, which heavily implies that proteolysis control*s its dynamic protein expression. Therefore, we believe that the term FUCCI applies to TgPCNA1 and TgIMC3. *
-The authors show that the localization of PCNA1 change during the cell cycle and indicate that the PCNA1 aggregates observed are replication forks. They do not provide data supporting this. They should co-localize these aggregates with other markers such as ORC, MCM proteins or DNA polymerase to better characterize these aggregates. There are number of techniques that could be used to localize the origin(s) of replication. Similarly, ExM should be used to characterize the colocalization between PCNA1 aggregates and the centromeres. As such, the images provided are of poor quality and do not support the author conclusions. The few PCNA1 aggregates toward the end of the S phase are also not characterized. Are they telomeres?
Although this is an important point, such detailed analyses of the DNA replication machinery is out of the scope of the current study and will be examined in a follow-up study. Data that suggest the aggregates correspond to replication forks include proteomics analyses of chromatin-bound PCNA1 that identified replisome components such as the MCM, high conservation of TgPCNA1 sequence and structure (Fig. S1), and its conserved interactions (Fig. 1). Recent studies used Plasmodium ortholog of PCNA1 to trace DNA replication dynamics during schizogony (35353560), *Therefore, we doubt that TgPCNA1 would perform functions outside of its role as a DNA replication factor, which has been extensively studied in other eukaryotes. *
- The authors characterized the proteins associated with PCNA1. All the proteins found to potentially interact are chromatin-bound and are not naturally found in other localization (2). It is unclear why the authors insist on the fact that there are two PCNA1 complexes (one chromatin-bound and one non-chromatin bound). More concerning is the lack of verification of this dataset through reciprocal IP for example.
The PCNA IP was used to confirm its conserved function as a DNA replication factor; similarly to what was observed in other eukaryotes, we detected PCNA in both a chromatin-bound and unbound state. PCNA1 is produced in late G1 (diffuse nuclear stain) but is engaged in the replisome only upon DNA replication initiation (aggregated form). Rather than characterize the function of the highly conserved PCNA1, our primary goal was to determine the Toxoplasma cell cycle organization, which explains our choice of the experimental design.
- Quantification of some of the phenotypes is lacking. For example, the DNA content analysis are shown but the change in number are not. Similarly, there is no quantification of the PCNA1 mutant phenotypes observed by ExM. Quantification of the bell shape observed by video-microscopy in figure 4 should also be provided.
The quantifications supporting the main claims of our study are included in the five supplemental Tables S3-S8, including DNA content and microscopy analysis of the phenotype. *The U-ExM microscopy has been solely used to visualize details of the phenotype. *
- The PCNA1 mutant phenotypes are not sufficiently explored by ExM. What happen to the mitotic spindle? What happens to kinetochore (CenH3 is a centromere protein and does not represent kinetochores)? Many markers for these structures have been described, see (3).
The primary goal of our study was to examine and map out the organization of the tachyzoite cell cycle. PCNA1 deficiency was used to demonstrate that Toxoplasma PCNA1 is a conserv*ed DNA replication factor and can be used as an S-phase marker in FUCCI. Thus, we focused on the mutant-induced changes in the dynamics of DNA replication (DNA content) and arrest prior to mitosis (unresolved centrocone). *
- TgPCNA1NG strain has a number of concerns. The localization to the daughter cells conoids seems artificial since not observed in the HA-AID mutant and the expression pattern seems different as well than the previous mutant suggesting the mNG tag is affecting the localization and expression dynamics. Did the authors explore other fluorescent proteins to verify that these discrepancies where not due to this tag ?
The conoidal PCNA1 accumulation was detected only with NeonGreen-tagged PCNA1. We also built and examined tdTomato- and mCherry-tagged versions and detected minor accumulations in the conoid of tdTomato-tagged PCNA1, but not with the mCherry-tagged variant. We believe these aggregations could be attributed to the partially degraded PCNA1-NeonGreen having an affinity to conoidal proteins, thus producing this unexpected signal. Although not included in the manuscript, our quantifications, based on both PCNA1-HA and PCNA1-NeonGreen, showed similar cell cycle organization (G1, S and budding phases) of tachyzoites. The FUCCI probe is an indicator of the cell cycle phase. It does not have to be a functional protein. As we mentioned before, many FUCCI probes are fragments of the factors that mimic the spatiotemporal expression of the corresponding cell cycle regulators.
-Cytokinesis seems to be only defined by the presence of IMC3. The marker appears early during the budding process and it is not normally considered as a cytokinesis marker. The author should the text to reflect this.
We agree with the reviewer that IMC3 is not a true budding marker, which is why we used BCC0 in our quantifications. IMC3 is proven to broadly define the mid-budding stage, making it a convenient supplemental marker. We are currently exploring and testing alternative and additional FUCCI markers. It is not an easy task, since these markers are required to have high expression levels and to be localized into large organelles. For instance, BCC0 was eliminated due to low abundance.
- Throughout the manuscript, the authors seems to ignore an essential characteristic of the tachyzoite cell cycle: the nuclear cycle and the budding cycle are independently regulated. It is therefore normal they overlap as it has been shown by the authors themselves in previous studies. This should be better described and discussed in the paper to understand the peculiarities of the parasite cell cycle.
We apologize for the confusion, but the tachyzoite cell cycle does not contain a nuclear cycle, it consists of a single budding cycle. The nuclear cycle is only a feature in multinuclear cell cycles such as schizogony and endopolygeny. This is the main reason why the overlap between phases is so surprising.
- l196: "The surface of the growing buds": could the authors rephrase?
We rephrased the statement.
-L217: proximal end of the nucleus rather than "parasite ".
*We clarified the statement. It is, in fact, the shift of the nucleus to the proximal end of the parasite.
*
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Referee #3
Evidence, reproducibility and clarity
This is a manuscript from Batra et al. entitled "A FUCCI sensor reveals complex cell cycle organization of Toxoplasma endodyogeny ". It describes the characterization of PCNA1 as cell cycle marker in the parasite Toxoplasma gondii. Tachyzoite endodyogeny is a simplified division process that is crucial for the proliferation of the parasite. Some studies have used fluorescent markers to describe the segregation of organelles and the nuclear division during endodyogeny but the production of more tools to dissect the cell cycle and better characterize mutants is timely. Most of the experiments are based on characterization of PCNA1 mutant and the use of a strain expressing a PCNA1-mNG construct. Unfortunately, there are a number of concerns in this study that need to be addressed.
Major concerns:
- The authors choose to describe PCNA1 and IMC3 as FUCCI markers. The efficiency of this system in mammalian cells is based on the proof that the markers are regulated through a rapid proteolysis process. However, the data available for these markers point toward a transcriptional regulation of these markers (Toxodb and (1)). In contrast, the authors do not provide any data indicating that these proteins are true FUCCI markers. Consequently, they should not use the term FUCCI throughout the paper unless they prove that the cell cycle expression depends on proteolysis. For example, the authors could express these genes with a promoter that is not cell cycle regulated.
- The authors show that the localization of PCNA1 change during the cell cycle and indicate that the PCNA1 aggregates observed are replication forks. They do not provide data supporting this. They should co-localize these aggregates with other markers such as ORC, MCM proteins or DNA polymerase to better characterize these aggregates. There are number of techniques that could be used to localize the origin(s) of replication. Similarly, ExM should be used to characterize the colocalization between PCNA1 aggregates and the centromeres. As such, the images provided are of poor quality and do not support the author conclusions. The few PCNA1 aggregates toward the end of the S phase are also not characterized. Are they telomeres?
- The authors characterized the proteins associated with PCNA1. All the proteins found to potentially interact are chromatin-bound and are not naturally found in other localization (2). It is unclear why the authors insist on the fact that there are two PCNA1 complexes (one chromatin-bound and one non-chromatin bound). More concerning is the lack of verification of this dataset through reciprocal IP for example.
- Quantification of some of the phenotypes is lacking. For example, the DNA content analysis are shown but the change in number are not. Similarly, there is no quantification of the PCNA1 mutant phenotypes observed by ExM. Quantification of the bell shape observed by video-microscopy in figure 4 should also be provided.
- The PCNA1 mutant phenotypes are not sufficiently explored by ExM. What happen to the mitotic spindle? What happens to kinetochore (CenH3 is a centromere protein and does not represent kinetochores)? Many markers for these structures have been described, see (3).
- TgPCNA1NG strain has a number of concerns. The localization to the daughter cells conoids seems artificial since not observed in the HA-AID mutant and the expression pattern seems different as well than the previous mutant suggesting the mNG tag is affecting the localization and expression dynamics. Did the authors explore other fluorescent proteins to verify that these discrepancies where not due to this tag ? -Cytokinesis seems to be only defined by the presence of IMC3. The marker appears early during the budding process and it is not normally considered as a cytokinesis marker. The author should the text to reflect this.
- Throughout the manuscript, the authors seems to ignore an essential characteristic of the tachyzoite cell cycle: the nuclear cycle and the budding cycle are independently regulated. It is therefore normal they overlap as it has been shown by the authors themselves in previous studies. This should be better described and discussed in the paper to understand the peculiarities of the parasite cell cycle.
Minor
- l196: "The surface of the growing buds": could the authors rephrase?
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L217: proximal end of the nucleus rather than "parasite ".
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Behnke,M.S., Wootton,J.C., Lehmann,M.M., Radke,J.B., Lucas,O., Nawas,J., Sibley,L.D. and White,M.W. (2010) Coordinated progression through two subtranscriptomes underlies the tachyzoite cycle of Toxoplasma gondii. PloS One, 5, e12354.
- Barylyuk,K., Koreny,L., Ke,H., Butterworth,S., Crook,O.M., Lassadi,I., Gupta,V., Tromer,E., Mourier,T., Stevens,T.J., et al. (2020) A Comprehensive Subcellular Atlas of the Toxoplasma Proteome via hyperLOPIT Provides Spatial Context for Protein Functions. Cell Host Microbe, 28, 752-766.e9.
- L,B., N,D.S.P., Ec,T., D,S.-F. and M,B. (2022) Composition and organization of kinetochores show plasticity in apicomplexan chromosome segregation. J. Cell Biol., 221.
Significance
This study provides the characterization of a new cell cycle marker to decipher the tachyzoite cell cycle of the apicomplexan parasite Toxoplasma gondii. A better understanding of the cell cycle is needed to prevent the proliferation of this parasite. This study builds on previous works characterizing organellar segregation in T. gondii. It provides data about the overlap of each cell cycle phase and the synchronicity of the cell cycle in a single vacuole. However, it is limited by the use of a single marker and more data are needed to support the conclusions of this study. This study can be of interest to a broad audience.
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Referee #2
Evidence, reproducibility and clarity
- Are the key conclusions convincing?
The data support the new model put forward in the final figure: a composite cell cycle phase
There are couple of points that need attention:
- It is not always clear where apical and basal ends of the parasite are. E.g. in Fig 3F are the two parasites on the right facing down with their apical end? In Fig 4 it is even harder to see. Might be helpful to turn these images with their apical end up to make comparative interpretation of figures easier. In the text it mentions that PCNA1 concludes at the 'proximal' end of the nucleus (or with the nucleus proximal, which is not clear either??). Please define clearly where the proximal site is, as it is not clear in the figures or in the movie (the 'last focus' marker in Fig 4D??).
- Synchrony of replication cycle. Tight synchronization depends on the retention of the cytoplasmic bridge, as mentioned by the authors. In larger vacuoles, it is very conceivable not all parasites are connected with each other (notably in large cysts with bradyzoites), which could lead to loss of tight synchrony. The results section states "One plausible explanation is that the rosette split shortens the communication path between tachyzoites". This is somewhat cryptic language: does a 'rosette split' imply the rupture of the cytoplasmic bridge? This statement should be clarified. Another factor could be centrosome maturation, with the mother centrosome ready sooner than the daughter, which is a model proposed in schizogony, where the nuclear cycles in a shared cytoplasm are even more asynchronous/independent.
- Centrosome duplication. This has been documented to occur at the basal side of the nucleus (the whole nucleus rotates for centrosome duplication). The images as depicted in Fig 6 do not seem to indicate this event, possibly because it is not easy to track apical and basal end of the cell (#1 above). Please comment, as this could be an additional spatial cue to the specific stage of the cycle.
- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
The authors are on the conservative end of interpretations and clearly outline the limitations of their approaches and observations, while discussing alternative interpretations. - 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.
No, the presented experiments and data are very complete - 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.
n/a - 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?
yes
Minor comments:
- Specific experimental issues that are easily addressable.
- The term "Apicomplexan" should be spelled with a lower case "apicomplexan", which is not consistently applied throughout the manuscript.
- Line 567 the term used in 2008 was "tightly knit" not "closely woven".
- Are prior studies referenced appropriately?
Yes - Are the text and figures clear and accurate?
Yes, exceptional - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
See major point #1 above.
Referees cross-commenting
Comment to Rev 1: https://doi.org/10.1128/msphere.00160-22. reports on use of FUCCI in the host cell, not in the parasite itself. This comment therefore does not apply.
Comment to Rev 3: the technicality on FUCCI acting on the protein level. That is a legit concern that needs attention, and could be fixed by avoiding the term FUCCI, or putting the term in the exact context.
Looks like a shared general concern is that it is not always clear where apical and basal ends are in the presented data. This should be addressed in revision.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
The presented manuscript reports on a technical innovation in Apicomplexa: establishing a FUCCI system. However they did not stop there and added additional markers to unravel the timing and nature of S/M/G2/C overlaps that illuminated previously underappreciated or unknow details. The tools assembled here will be of great value for understanding not only T. gondii endodyogeny checkpoints and sequence of events, but also paves the way for similar studies in more complex apicomplexan cell division modes, like schizogony and endopolygeny. - Place the work in the context of the existing literature (provide references, where appropriate).
The authors very appropriately provide the wider context and completely cover where the field stands. E.g. this protein microscopy-based work fills in the fine grain details where recent advances in transcriptional profiles by single cell experiments cannot provide resolution. The authors do also an outstanding job in providing the background on the general understanding of molecular players, structures and process controls across eukaryotes that pinpoint where the Apicomplexa are different. - State what audience might be interested in and influenced by the reported findings.
The audience comprises a wide array of people with interests in cell cycle regulation, cell cycle checkpoints, DNA replication, nuclear organization across biological systems, and Apicomplexa in particular - 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.
Toxoplasma gondii cell biology - sufficient expertise across the board
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Batra et al have tried to dissect out two aspect to understand the complex cell cycle of Toxoplasma endodyogeny. One is to development of Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) technology for Toxoplasma gondii and then to use that for understanding the complex cell cycle. The authors have created ToxoFUCCIs and ToxoFUCCIsc probes using TgPCNA1 tagged with NeonGreen and TgIMC3 tagged with mCherry and used to dissect the different phases of cell cycle like S, G2, G1 and cytokinesis. Overall technology development is good though as they claim that they are first is not true as it has been used earlier by https://doi.org/10.1128/msphere.00160-22. Hence may be that they have used to decipher the cell cycle.
The manuscript is extremely dense and at times very difficult to read and to be clear if they are focussing on the technology or cell cycle. The technology may be a better part of manuscript but the dissection of cell cycle is not very novel and at times very confusing to follow. Many of these aspects has been dissected out previously from their own group and many group in Toxoplasma and Plasmodium and it is quite known about that the cell cycle in Apicomplexa is very complex. Another aspect that most FUCCI use Geminin and CDT1 factors and since Geminin is not present it would have been better to validate that with CDT1 that is present in Apicomplexa and may be more relevant than PCNA1. The first part of the manuscript only deals with first to identify the function and localisation of PCNA1 and then develop FUCCI technology and then go on to study cell cycle. So the focus of the manuscript is not clear. It seems three different results are just assembled together in one manuscript with out clear focus. Some of the conclusion on the that Replication starts at centromere region is not novel and has been studied previously.
In order to get clear focus the authors should clear set out the focus as to why they developed FUCCCI and how they decipher either replication, budding, apical or basal complex, centrosome or cytokinesisas well to be used for drug discovery The way it is organised it is not flowing well and confuses the reader who may not be aware of different compartment of Toxoplasma cell or not a molecular parasitologist.<br /> The manuscript needs revising by writing precisely eliminating too much literature reference in the result section with clear focus. Some of these references can be elaborated in the introduction and discussion to keep the focus.
Some points with respect to figures:
Generally with image panels, arrows don't stand out well
Fig1: no scale bars and the green arrow do not stand out. So may be to make white.
Fig 2E: state the time point in the fig without IAA treatment (-IAA)
Fig4: no bell shaped curve
Fig 5D: it isn't obvious what the numbers on the right hand side of the graph mean. If it is size, there should be a unit given
Figure 6 - how do they determine that the tachyzoites have progressed through 61% of S phase? Make this clearer here.
Fig7: it a strange way of ordering the figure as FigE is after Fig F hence no logical order.
Fig 8H is not mentioned in the text
Figure 9 is nice and useful but the arrows could be made proportional of time spent in each cell cycle phase. They're a little off in the conventional cell cycle at the minute
Some comment on the text in the manuscript:
Line 137: describing the expression pattern: the following papers first described the expression pattern of PCNA1 and 2 can be cited in the result. https://doi.org/10.1016/j.molbiopara.2005.03.020
Line 154: Provide schematic for AID HA cloning and confirmation.
Line 157: Fig 2 after 4 h treatment FACS analysis shows more than 1 and less than 2n genomic content. Does this study have any -IAA treated control for 4h and 7h to compare as what should the standard genomic content to be there at this time point of development. At 4 h of development can the authors provide any statistical analysis with their 3 experiments to prove their point that the replication is actually stalled. Downregulation of TgPCNA1 as shown is western blot is still basal protein left to carry the genomic replication in 7 mins. Can authors also state that TgPCNA 2 which is although non-essential but has no redundant role in the replication machinery.
Line 178 : typing error "that that
Line 179: states the role of TgPCNA 1 in DNA1 replication, however line 159 and 160 states the TgPCNA1 deficient can fulfil DNA replication. Can author confirm this contrast in the results. Results trying to illustrate the same fact TgFUCCIs or TgPCNA1ng that TgPCNA1 first aggregates at centromeres and then distributed on many replication forks and disappears late during cytokinesis. The part of the result can be merged.
Line 189: Typing error, should say "such as nucleus", currently as is missing
Line 346-349: basically explaining the same thing twice.
Line 347 - immunfluorescent should be immunofluorescence
Line 395-399: does this study has any non-inhibited (-IAA control) at 4h and 7 h. for fig 7C & 7G. Can the authors provide any statistical analysis for the significance with their 3 experiments.
Line 415: Why the authors have not used the TgFUUCI sc lines which expresses the TgPCNAng and IMCmch both. This could have helped to understand the real time dynamics of DNA replication and budding initiation (cytokenesis), rather then fixing and staining with TgIMC.
Overall good technology development as FUCCI but the rest of the manuscript is extremely dense and the focus of the study is not clear after technology part. The complexity of the cell cycle is known and hence not much novelty here and extremely descriptive and hard read. Science can be simplified.
Significance
The development of FUCCI technology is significant part of the manuscript and to understand cellcycle may be they could have used CDT1 rather than PCNA as there is another PCNA 2 that also exist. The authors have given some convincing result for some aspect of cell cycle of which most are known and only it is quite incremental.at some part. The technology may contribute to the methodology development in Apicomplexa.
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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
This study by Mordier and colleagues represents an in depth analysis to clarify the evolutionary history and processes of the rapidly evolving Schlafen gene family with a strong focus on primates and rodents.
The study is of high quality in my opinion, though I do have some minor comments:
- Fig 2 and Fig 4B present inferred phylogenetic trees of schalfens in primates and rodents - these trees appear to be unrooted or rooted on a single species rather than an outgroup/gene. I suggest that the authors consider whether an outgroup gene could be included or if an outgroup free approach could be used to estimate the position of the root. This is important because the use of an unrooted tree to make inferences on gene family evolution has important implications - for example, there are no clades in an unrooted tree (Wilkinson et al 2007, Trends Ecol Evol).
- Schlafen proteins beyond mammals are referred to as SLFN11, it is not clear why this is the case because they seem to be co-orthologous to all mammal schalfen groups (except SLFNL1) based on supplementary figure S2. In this context, perhaps this image should form part of the main text?
- For blast searches parameters should be included - what cutoffs were implied for similarity searches etc. Related to this on line 120-121 homology is described as 'significant'. Homology refers to an evolutionary relationship, sequence similarity may be significant or not based on the search performed but homology is qualitative and simply detectable or not.
- The first results section describes the results of phylogenetic analyses, however this section relies heavily on what might better be considered interpretation of these analyses, this is great and should be included but I suggest that the branching patterns in the trees and bootstrap values supporting relationships between genes are also reported in the text to link interpretations to actual results.
- Bustos 2009 included viral genes belonging to the family in their analyses and I think it may be pertinent to do so here also to determine if the results are consistent or not.
- Was a rate heterogeneity (e.g. gamma rates / +G) parameter considered in phylogenetic analyses or model testing, it is not reported here and very rare for this not to improve model fit and phylogenetic accuracy.
- The authors state that all data are available in public databases, but this is not the case for the results they generated. Making various file types produced in this study would be good - e.g. alignments, phylogenetic tree files, structures, etc.
Significance
This study is an important step forward in clarifying our understanding of schalfen evolution. I think the manuscript will be of interest to a number of research areas, including gene family evolution because of its focus on an unusually rapidly evolving gene cluster and to those working on the schalfen gene families functional importance in development and immunity. The results may also draw interest from those interested in the confluence of protein structure, function, and evolution. My expertise In the context of this study is in the phylogenetics and evolution of rapidly evolving gene families.
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Referee #2
Evidence, reproducibility and clarity
In the current manuscript, Mordier et al. combine bioinformatic searches, synteny, and phylogenetic analysis to reconstruct the duplicative history of the Schlafen Genes in rodents and primates and then use molecular evolution analyses in combination with structural modeling to make inferences regarding the role of natural selection in the evolution of this gene family. The study represents an update on Bustos et al. (2009), who had already presented evidence that Positive Darwinian selection was likely a factor in the diversification of these genes in mammals. In this context, the contribution of this paper is the identification of sites that are candidates to be evolving under natural selection, and the structural exploration of the location of these sites in the proteins. CODEML strength lies in the detection of signatures of positive selection at the codon level, but it is not that accurate when it comes to pinpointing the actual sites that might be under selection. Hence, without experimental data, these inferences remain speculative. The manuscript is well-written and represents an update on the evolution of this gene family.
Major Issues
The rationale for the choice of species included in the analyses is never presented, and some of it is hard to understand. Why do authors exclude the platypus but include non-mammalian lobe-finned vertebrates is not clear. If they are going to discuss the evolution of these genes outside mammals, the authors need to survey a much wider array of genomes. Even within mammals, there is little discussion on why some species were included and others not. I think that focusing the study on rodents and primates is OK, but I also think that providing a strong justification of the selection of species to include in the study and a tree that justifies splitting the focus on rodents and primates would also be important.
In the trees in Figures 2 and 4, several genes considered as orthologs are not in monophyletic groups. These pattern aligns well with the birth-and-death model of gene family evolution, and has implications for their molecular evolution analyses. The authors need to address this issue explicitly. I would use topology tests to evaluate whether these deviations from the expected topology are significant. In addition, the relevant tests to report here are M8 vs M7 and M8 vs M8a. The M0 vs M1a comparison does not provide evidence for positive Darwinian selection. If the M8 vs M7 and M8 vs M8a tests are not significant, the inferences about sites evolving with dN/dS>1 are not really valid.
CODEML can implements models that are designed to test patterns of gene family evolution, contrasting pre and post duplication branches, which I think would be of value in this family.
Some analyses are described very succinctly, which would make replication challenging.
Minor Issues
Could 2R be responsible for the emergence of SLFN and SLFNL1?
There are several minor issues authors should fix in a revised manuscript. In general, because results are presented before the materials and methods, I think it is easier for readers to have some of the information in the results section.
They need to be consistent in using italics for species names as well as for capitalization.
In the Alignment and maximum-likelihood phylogenies section the authors indicate that they used either Muscle or Mafft for the alignments. What was the rationale for picking one alignment over the other for a given gene? In this section, they also indicate the selected a best-fitting model of substitution using SMS, but then indicate that they used JTT for protein alignments and HKY for nucleotide alignments.
How did the authors ensure that nucleotide alignments remained in frame?
Significance
I think this is a significant contribution to our understanding of the evolution of the Schlafen gene family. There are two key contributions here: the demonstration that gene conversion is a factor obscuring relationships among genes in this gene family, and the mapping of amino acids inferred be evolving under positive selection to structurally important residues of the proteins. These residues should be of interest for functional assays that evaluate the functional role of these proteins.
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Referee #1
Evidence, reproducibility and clarity
Mordier et al. used in-depth phylogenomic methods to analyze the evolution of the mammalian Schlafen gene family. They identified a novel orphan Schlafen-related gene that arose in jawed vertebrates, and they assigned orthology between Schlafen cluster paralogs. This will allow for further accurate selection studies. Throughout the entire manuscript, the authors use nomenclature predating structural and biochemical studies. The nomenclature is purely based on sequence similarities, which are sometimes very weak and not convincing, and not based on the known function of the protein. In my opinion, this causes confusion and does not help scientists in the field. Especially in Figure 3, I wouldn't call it RNAse E (AlbA); instead, tRNA recognition site,endoribonuclease domain, SLFN core domain are the correct domain designations. Since SLFN11 is not a GTPase, why do the authors name the domain GTPase domain? Actually, the SWADL domain comprises a SWAVDL instead of a SWADL sequence motif. Hence, I would name the domain SWAVDL domain instead of SWADL domain, which is, in my opinion, misleading and was wrongly chosen in initial publications.
In e.g. Figure 3 SLFN11 structure it would be better if the authors illustrated the important residues concerning the known RNase active site and ssDNA binding site. Further, a close-up of the SLFN11 interface with labeled amino acids involved in the interaction and highlighting the residues undergoing positive selection would help understand the evolutionary adaptation.
Although, according to Metzner et al., the SLFN11 dimer is built up by two interfaces (I and II), where Interface I is situated in the C-terminal helicase domain and Interface II in the N-terminal SLFN11 core domain. It would be helpful for the reader if the authors stuck to this already introduced and widely accepted nomenclature in the field.
In addition to the antiviral function, SLFN11 expression levels have been reported to show a strong positive correlation with the sensitivity of tumor cells to DNA damaging agents (DDAs). Hence, SLFN11 can serve as a biomarker to predict the response to, e.g., platinum-based drugs. It was revealed that SLFN11 exerts its function by direct recruitment to sites of DNA damage and stalled replication forks in response to replication stress induced by DDAs. Could the authors include this different molecular function of SLFN11 in their discussion of SLFN11s evolution and positive selection?
Even though it seems unclear from the genetic and evolutionary aspect (Figure 4), mouse Slfn8 and Slfn9 complement human cells lacking SLFN11 during the replication stress response and seem to resemble the function of SLFN11 (Alvi et al. 2023). The authors of this study claim that Slfn8/9 genes may share an orthologous function with SLFN11. Could the authors comment on that discrepancy?
Significance
In general, the work is well conducted and provides valuable new insights in an important and growing field of research. However, there are some limitations to the study including the disregard of known protein function (e.g. SLFN11) and the usage of a purely sequence similarity based nomenclature.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This is an interesting manuscript from two groups of experts in Notch signaling biology with complementary expertise in Drosophila genetics (Klein) and in biophysical studies of the Notch pathway (Sprinzak). The paper provides a cutting-edge structure-function dissection of the E3 ubiquitin ligase Neuralized and its mammalian homologs, Neurl1a and Neurl1a. The work is particularly relevant since the functions of mammalian Neurl1a and Neurl1b have been questioned, and more subtle altogether than those of fly Neuralized (as summarized by the authors in Fig. 1C). This is in part due to the dominant effects of the E3 ubiquitin ligase Mindbomb1 (Mib1) in Notch ligand-expressing cells from mammalian systems. The authors use careful structure-function work in fly development (mostly wing imaginal discs) and in mammalian cell culture systems, including a clever approach to study the function of mammalian Neurl1a and Neurl1b and mammalian/fly Notch ligand hybrids in Drosophila to draw new conclusions about the function of Neurl1a/b, showing that they can function as activators of Notch signaling mediated by the Notch ligands Dll1 and Jag1, and not by Dll4 and Jag2, tracing these differential effects to the recognition of a short NXXN consensus sequence in the N-terminal region of the ligand's intracellular domain.
__response: __We thank the reviewer for highlighting the novelty of our findings and experimental approach.
Specific questions: -The current title of the manuscript is not very information-rich and would not allow a reader to gather key information about the findings without reading at least the abstract. Could this be improved? For example, by referring to differential activation of individual Notch ligands, or some other more direct description of the key findings?
__Response: __We appreciate the reviewer's suggestion; however, we believe that the general nature of the title is appropriate in this case.
-The authors design most key experiments documenting agonistic effects of Neurl1a/1b in a Mib1-deficient background, both in flies and in cell culture systems. This is understandable experimentally to isolate Neurl1a/b's effects in these experimental systems. However, this leaves open questions as to the prevailing effects of Neurl1a/b in cells that also express Mib1 (which the authors comment on in the discussion based on past findings, including some suggesting that Neurl1a/1b can function as Notch inhibitors through a ligand ubiquitination mechanism that may differ from their activating function).
Do the authors actually have data that could shed light on this discussion? For example, have they performed cell coculture assays in which Neurl1a or Neurl1b is co-expressed with a Notch ligand, but in the presence of Mib1? This condition seems to be systematically omitted from all the coculture experiments that are presented. It would be interesting to evaluate the net effect of Neurl1a/Neurl1b expression in a Mib1-sufficient system as well.
Response: We have systematically removed MIB1 in our experiments because it activates all ligands, making its removal necessary to show the differential activity of Neurls. The question regarding competition between Mib1 and Neurls, as highlighted by the reviewer, is indeed intriguing. However, systematically investigating this competition would require varying the relative levels of the two proteins in a controlled manner, which is beyond the scope of this manuscript.
That said, we will perform the competition experiments suggested by the reviewers (co-expressing ligands with both Neurl1 and Mib1) and test their activity as controls. While these experiments may provide some insight into the competition, they will not comprehensively address the entire topic.
-The paper suggests important predictions about mammalian functions of Neurl1a/1b, including the neurological effects that have been reported, in double-deficient mice, namely that that there are cells that only express Neurl1a/1b and not Mib1 and do rely on Dll1 and Jag1 for signaling. Could the authors at least comment on this prediction? Are there are any single cell atlases where candidate cells like that can be identified? Or would the authors predict that Neurl1a/1b could actually function as Notch agonist even in cells expressing Mib1? (see also previous comment)
Response: This is an interesting suggestion. We will try to find if we can identify any specific expression patterns of E3 ubiquitin ligases across different tissues.
-Some minor typos: line 305 should likely read "flies homozygous for (...)". Line 408, "for providing" repeated twice.
Response: We thank the reviewer for pointing out this typo.
Reviewer #1 (Significance (Required)):
Thank you for the opportunity to review this lovely collaborative paper. As indicated in my comments to the authors, the findings provide novel structure-function information about an understudied aspect of Notch signaling and clarify conflicting past data about the mammalian homologs of fly Neuralized. The approach is elegant and multidisciplinary, notably in regards to the combination of cell co-culture systems and Drosophila as a platform to study mammalian Neuralized proteins and hybrid Notch ligand molecules. The findings will be interesting to the field and will generate discussion. I would suggest that some additional information would be a plus to substantiate predictions about mammalian functions of Neurl1a/b, and also to clarify its effects in the presence or absence of concomitant Mib1 expression.
We thank the reviewer for their positive evaluation of our work and for suggesting potential future direction regarding the concomitant expression of Mib1 and Neurls proteins.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary
The manuscript describes an analysis of specificity of functional interactions between mammalian Neuralized proteins and different human ligands for Notch. To investigate this, the authors take the approach of constructing hybrid proteins that contain the intracellular domain of the human ligands and the extracellular domain of the Drosophila Delta or Serrate, and investigate their activity in vivo, in the Drosophila wing disc. The latter is a well-established model tissue for assessing Notch ligand activity. As a second assay they express mammalian neutralized constructs in human cells for luciferase-based Notch signal reporter assays. The experiments are well presented and described and make a strong case for the conclusions that both Neurl1 and 2 can activate Notch signalling by Dll1 and Jag1 but not Dll4 and Jag2. Use of different mutant intracellular domains is used to show the importance of the NXXN motif, which in Drosophila is required for Neuralized interaction with Delta and Serrate. The use of missense mutations and in particular the reactivation of the cryptic NXXD site in Dll4 by substitution to N is convincing for establishing the importance of the motif. There is also colocalization data to support the conclusion that there is likely to be NXXN-dependent complex formation between the ligand and Neuralized proteins. This latter conclusion would be made firmer fi there were pull down data to support it, although to be fair it is most unlikely that another explanation, other than complex formation could account for the observation of both colocalization and ligand activation.
__Response: __We appreciate the reviewer's positive assessment of our manuscript and their support for the conclusions drawn from our experiments. We intend to conduct the suggested co-IP experiments with our cell culture assays to further supplement our current data.
__ Major comments__ The main limitation of the work is that it is mostly based on overexpression of constructs to activate ectopic expression rather than gene editing endogenous genes. It would be helpful if the authors could comment on the limitations of the work in discussion.
Two points of data included in the work are important in mitigating this limitation. Firstly, the experiments in the wing disc and cell culture are taking place in a mindbomb mutant background and the activation is observed is therefore a rescue of activity that has been lost.
Secondly, and importantly, the final experiment makes use of a Dl mutant Drosophila line which shows embryo lethality when homozygous, with the characteristic neurogenic phenotype. Rescue of lethality can be brought about by knock-in experiments which restore Dl function and this is also true for the ligand hybrid constructs that introduce mammalian ligand intracellular domains only when they include the NXXN motif This indicates the importance of the motif in normal development- Overall, the data presented in the paper is convincing as regards the conclusions made.
__Response: __We thank the reviewer for their very positive evaluation and his constructive suggestions, which have helped to improve the manuscript. In line with these suggestions, we will include additional data analyzing the bristle SOP selection, a process dependent on Neur. Our Results show that homozygosity of the DlattP-Dl-DLL1 allele, but not the DlattP-Dl-DLL4 allele, leads to correct Notch mediated selection. This finding provides further evidence that Neur requires the NxxN motif in the ICD of a ligand to activate DSL ligands. Notably, we previously showed that this selection relies on the NxxN motif of Dl (Troost et al., 2023). We will further emphasize in the discussion the ability of Dl-DLL4 hybrid ligands, containing a reconstructed NxxN motif, to rescue the neurogenic phenotype of Dl mutants.
Minor points In figure 1 the legend for D says that cryptic sites are substitutions of N for E or Q, but the figure and main text indicate that the substitutions are N to E or D.
Response: We thank the reviewer for pointing this out. We will correct this mistake.
In the remain figures it would be helpful to include in the figure legends and indications of the numbers of wing discs, embryos for which the images shown are representative of.
__Response: __We will quantify the experiments conducted in the wing imaginal discs of Drosophila by measuring the wing field size along the dorsal-ventral axis relative to the anterior-posterior axis. Statistical analysis will be performed to demonstrate statistical significance across n=5 experiments for each sample.
In Fg 3 The activation of Notch, by neural1 and Dl-Jag1 in B'" is stronger in the ventral side of the disc than the dorsal whereas, although activation of the same ligand by Neurl2 in C'" is weaker the majority of the ectopic wingless expression is on the dorsal compartment. Is there any reason for the switch in preference between the two neutralized proteins? Overgrowth of the wing disc seems to be similar on both sides and so am wondering if the picture is representative of the ectopic wingless distribution in this case.
Response: As discussed above we will perform quantification and statistical analysis across multiple experiments to confirm that our images are truly representative.
Reviewer #2 (Significance (Required)):
Significance
Previous work on double genetic knockouts of the two mouse Neuralized genes cast doubt as to whether Neuralized proteins play a role in Notch signal activation in mammals, unlike in Drosophila. There is, however, some genetic indications that spatial memory requires both Notch and neutralized proteins and may represent a specialised function limited to the Neuralized interaction. There are likely to be more subtle contexts waiting to be uncovered. The work is therefore showing important proof of principle for establishing the functionality of the mammalian Neurl proteins and highlights new findings indicting specialisation of the different ligands for interactions with Notch components. Elucidation of such specialisations will help understand why the diversity of different homologues of Notch and ligand have evolved and are maintained in the vertebrate genome compared to the single Notch and two ligands in Drosophila. Since Notch and it misregulation are widely involved in development, health and disease and there is much interest in developing therapeutic interactions that alter Notch activity then the work is likely of broad interest.
We thank the reviewer for the very positive evaluation and his useful suggestions which were helpful in improving the manuscript.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
**Summary**
Notch signalling is one of the major evolutionarily conserved signalling pathways involved in numerous developmental, physiological and pathological processes. Activation of the Notch receptor first requires ubiquitination of its ligands (collectively temed DSL), leading to a 'pulling force" that, upon ligand-receptor engagement, exposes Notch to intramembrane proteolysis leading to the nuclear translocation of the receptor's intracellular domain and activation of target genes with its DNA-binding co-factors.
While both Neuralized (Neurl) and Mind bomb are the E3 ubiquitin ligases for Notch ligands required for Drosphila development, in mammals, the Neur homologues Neur1 (officially Neurl1) and Neur2 (officially Neurl1B) are dispensable for development since double Neur1/2 knock-out mice have no developmental phenotype (but both Neur homologues are involved the the memory-related functions of Notch pathway in adulthood). Rather, just one of the two mammalian Mind bomb homologues, Mib1, functions as the chief E3 ligase for Notch ligands during mammalian development as evidenced by its Notch-related knockout phenotype.
Therefore, it has not been fully established whether and how the NEUR proteins regulate the mammalian Notch ligands. To clarify this issue, the authors assessed the capability of Drosophila Neur and mammalian NEUR1 and 2 proteins to activate the various hybrid Notch ligands (containing extracellularly Drosophila Delta and intracellularly the various Notch ligands' intracellular domains) in Drosophila wing dics and mammalian cell culture. The authors found that NEUR proteins only activate the Notch ligands containing a Neuralized binding motif, with the consensus sequence NxxN, that is present in DLL1 and JAG1, but not in DLL4 and JAG2. The authors also analyse the intracellular domains of mammalian Notch ligands DLL1, DLL4, JAG1 and JAG2 in Drosophila by generating knock-in alleles where endogenous Dl expression had been substituted for those of hybrid Notch ligands. This analysis showed that only in Dl-DLL1 and Dl-JAG1 flies but not in Dl-DLL4 and Dl-JAG" flies is the embryonic lethality rescued, the results being in agreement with the hybrid Dl-DLL experiments on wing dics reported earlier in this work.
The authors conclude that their findings suggest that the activation mechanism of Notch during development differs between Drosophila (where both Neur and Mib1 are required for Notch-related developmental processes ) and mammals and that this could possibly explain the apparently lesser relevance of mammalian NEUR proteins for developmental Notch signalling.
*Evidence and clarity*
The manuscript is quite laconic but clearly written. The evidence presented by the authors, given the heterologous and in vitro nature (i.e using mammalian or hybrid Notch ligands and mammalian E3 ligases thereof in Drosophila and cell cultures) of the study is generally trustworthy but limited in the sense that it probably does not allow definitive conclusions to be drawn as to the differing nature of the action of the E3 ligases of Notch ligands in flies vs mammals in vivo.
__Response: __We thank the reviewer for their positive evaluation of our work and their constructive criticism. We would like to clarify that we do not conclude that the activation mechanism differs between mammals and flies. Our findings demonstrate that the signalling mechanisms of fly Neur and mammalian Neurl's follow the same fundamental rules. Moreover, our study does not aim to provide a definitive answer to how signalling differs between species. Instead, we utilized the 'humanized fly' system to show that Neurl proteins specifically activate Dll1 and Jag1, but not Dll4 and Jag2, which lack a neuralized binding site.
*Reproducibility*
As will be mentioned a number of times, these reviewers would like to enquire as to the reasons for not providing a statistical analysis of variation in the fly wing disc-based experiments (where the readout was either resuce of Wg expression or induction of ectopic Wg expression).
Response: We thank the reviewer for raising this important point. As outlined below, we will quantify the fly experiments and conduct statistical analysis across multiple experimental datasets to further substantiate our claims.
Also, while the constructs used in the study were inserted into the same genomic landing sites to achieve comparable levels of expression of the various proteins, these reviewers would like to see data on the levels of expression of NEUR1 and 2 as well as the hybrid Notch ligands.
**Major comments**
Comment on fly wing disc experiments:
The authors study both the capability of two different mammalian E3 ubiquitin ligases, Neuralized-like 1 and 2 (mouse Neur1 and human NEUR2) to activate four different Notch receptors (DLL1 and 2, JAG1 and 2) in flies and mammalian cell culture system. In flies, they first analyse the capability of Drosophila Neur (as a positive control) and Neur1 and NEUR2 to activate the various Notch ligands (based on wingless activation as a readout) in wild-type wings (where, Mind bomb 1, or Mib1 is the only E3 ligase for Notch ligands present) and Mib1 mutant wing discs (which lack any E3 ligands of Notch receptors). The authors then test four humanised, hybrid Notch ligands (all five N ligands bar Dll3 since the latter does not transactivate the Notch receptor) - where mammalian Notch ligands' intracellular domains, or ICDs, have been attached to fly Dl (Dl-Dll1, Dl-Dll4, Dl-JAG1, Dl-JAG2) - for their capacity to mediate Mib1-dependent activation of Notch (with ectopic Wg expression in wing discs as its readout). They found that all 4 ligands can activate Nocth in wild-type wings (where Mib1 is present), with Dl-JAG2 being less effective than the other 3 hybrid ligands, implying that such hybrid, humanised ligands can be usd in studying Notch pathway activation in Drosophila (thereby constituting a mixed/heterologous experimental system). The reviewers would like to get a comment as to the reason for the weaker activity of Dl-JAG2 in this set-up?.
Response: We do not have a definitive answer as to why the ICDs differ in their activity within MIb1-dependent signalling, since this question was not addressed in the scope of this work. However, it our findings demonstrate that the hybrid ligands are functional in Drosophila and that their differential behavior in Neur-mediated signaling is not attributed to a trivial explanation, e. g. that the hybrid ligands generally display no activity. There are several potential explanations for these differences. One possibility is variations in position, arrangement, or number of targeted lysines among the ICDs. These lysines serve as substrates for ubiquitylation and determine the rate of endocytosis, which in turn impacts the signaling activity of the corresponding ligand/hybrid. Another plausible explanation is differences in affinity of the binding sites of Mib1, which would similarly result in variations in ubiquitylation and endocytosis rates. Regardless, we emphasize that resolving this question does not affect any of the conclusions of the manuscript.
Also, the reviewers would like to get a comment as to why was not a Neur mutant set-up used, only Mib1 mutant dics?
Response: Neur is only expressed at a very late stage in wing development and is restricted to specific single cells (sensory organ precursors). Consequently, even if mutants were present, their impact would be limited to these cells. Moreover, the Neur promoter has a highly complex architecture, which makes it exceedingly difficult to manipulate for experiments involving this mutation. We will address these considerations in the revised manuscript.
The authors then found that only two of these hybrid ligands - Dl-DLL1 and Dl-JAG1 but not Dl-DLL4 or Dl-JAG2 - can be used to activate Notch in the above wing assay when Mib1 was mutant. This is consistent with the fact that the NxxN-based Neuralized Binding motif (NBM) is present in DLL1 and JAG1 only. Using the wing paradigm, the authors also show by either mutating the full NBM (NxxN) in DLL1 or changing the cryptic, "weak" NBM in DLL4 (containing NxxD sequence) into "full/strong" NxxN one that the NBM in the various Notch ligands is required and sufficient for activation of the Notch pathway.
Overall, the fly experiments are convincing in showing diffrential activation of Notch ligands. However, no statistical analysis of the experimental variation in these studies - neither for the number of wing discs analysed per (hybrid) Notch ligand tested nor the extent of a given experimental manipulation's effect is included. We deem that if the images presented in Figures 2 and 3 are truly representative, this needs to be made explicitly clear.
Response: We thank the reviewer for their positive evaluation of our work and for the constructive comments, which we will consider and include into the manuscript. While we have repeated all experiments with multiple flies, we acknowledge the critique regarding the absence of statistical analysis.
To address this, we will quantify the experiments conducted in the wing imaginal discs of Drosophila. We will do that by measuring the wing field size along the dorsal-ventral axis relative to the anterior-posterior axis. We will perform statistical analysis to assess the statistical significance between experiments, using data from n=5 experiments for each sample.
Comment on fly embryonic Delta neurogenic phenotype's rescue experiments by replacing Dl with the hybrid ligands: The authors analysed the capacity of the ICDs of the mammalian ligands to rescue the Dl phenotype in Drosophila, ie. their activation capability at the organismal level. This was achieved by generating knock-in alleles expressing the hybrid ligands in place of Dl. The notion that only NBM-containing hybrid ligands was strengthened by this analysis since it showed that only NBM-containing hybrid ligands - Dl-DLL1 and Dl-JAG1 - but not Dl-DLL4 nor Dl-JAG2 rescued the Dl neurogenic embryonic lethal phenotype. Since this experimental set-up relied on the endogoneous Drosophila E3 ligases for activating the Notch ligands, the capacity of mammalian NEUR1 and 2 proteins to complement the capacity of the hybrid ligands to activate Notch to activate these ligands was not addressed. Please comment as to the reasons for this apparent omission and if such an analyis lies beyond the scope of current work, what would be the expected results of such experiment in the light of other experiments conducted in the course of this work?
Response: Testing whether mammalian Neurl1 and Neurl2 can replace Drosophila Neur in an endogenous setting is an intriguing question; however, it falls outside the focus of this study. Performing such an experiment would be highly challenging due to complex and not well understood architecture of Neur gene in Drosophila. Additionally, we believe it is highly unlikely that the mammalian NEURLs proteins would fully compensate for the loss of function in a Drosophila Neur mutant.
Journal-agnostic peer review: evaluate the paper as it stands independently from potential journal fit.
Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?
Generaly yes, put please see the above comments on the absence of statistical analysis of reproducibility/ variation (if any) in fly wing disc experiments.
**Reviewer's additional recommendations:**
To publish in a higher-ranking journal, the co-localisation analyses of Notch ligands and its various E3 ubiquitin ligases studied probably needs to be replaced by a more rigorous, ideally FRET-based approach.
Response: We thank the reviewer for the comment. The co-localization assay is quite a robust and functional approach, as it provides clear evidence that endocytosis into a different compartment has occurred with functional ligands, as opposed to non-functional ligands. This serves as a quantitative and rigorous indicator for functional differences between these ligand types.
Nevertheless, we acknowledge that co-localization is not a direct measure of molecular interactions between Neurl1 and Notch ligands. To address this, as suggested by the reviewer, we will perform co-IP to show the differential interaction between Neurl1 and specific Notch ligands. Additionally, we will attempt a proximity ligation assay (PLA), which we consider to be a more direct and suitable method for detecting interactions between NEURLs and Notch ligands in this context, compared to FRET.
Since previous studies have shown that the Notch ligands are (mostly) poly- or mono-ubiquitylated by the E3 ubiquitin ligases Mib and the NEUR proteins, ideally, this or its follow-up study would benefit from analysis of the ubiquitylation status of the various hybrid Notch ligands.
Response: We thank the reviewer for the suggestion. The ubiquitylation pattern by Neurl1 is beyond the topic of the current manuscript.
Also, it would be useful to show the strength of interaction between the hybrid Notch ligands and NEUR1 and NEUR2 by ising a co-immunoprecipitation based approach.
Response: As suggested by the reviewer, we plan to perform co-IP and/or PLA to show the differential binding of NEURL1 to the different ligands. However, due to the observed toxicity of NEURL2 in our cells, it has been excluded from our assays.
Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.
These reviewers do not strictly request any further rexperiments. However, since the mammalian NEUR2 could not be studied in cell cultures of U2OS cells due to its toxicity, we would like the auhtors to explain the choice of this cell line. Perhaps a cell line whose viability is not impaired by NEUR2 should be (or should have been) used?
Response: The decision not to use other cell lines was based on several strict experimental requirements. The most stringent requirement was the need to generate a MIB1 knockout cell line, as MIB1 strongly activates all ligands. The availability of having MIB1 KO U2OS cells enabled these experiments.
If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".
As mentioned above, the NEUR2's capacity to activate the hybrid ligands in U2OS cells could not be addressed to due to its toxicity. A more optimal cell line will have to be used in follow-up studies.
Also, these findings ultimately warrant in vivo studies using mice to definitively ascertain whether they also hold equally true there.
Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.
The suggested experiments are optional apart from statistical analysis of variation (if any) in the fly wing disc experiments. If there is no (apparently significant) variation in these data, this needs to explicitly stated.
Response: We thank the reviewer for their thoughtful assessment. We will conduct the requested statistical analysis and perform some of the suggested supporting experiments as detailed in the response.
Are the data and the methods presented in such a way that they can be reproduced?
Generally yes, but see above about the lack of statistical data on the variation (if any).
Are the experiments adequately replicated and statistical analysis adequate?
Generally yes, but again, please see above about the lack of statistical data on the variation (if any).
**Minor comments**
Comment#1 (on the abstract and introduction):
In the Abstract, the authors state that there are four Notch ligands in mammals (lines 21 and 22): "Thus, it is unclear how NEURL proteins regulate the four mammalian Notch ligands". In the Introduction, they correctly state that there are five Notch ligands in mammals (lines 38 and 39): „In mammals, there are five ligands, three from the Delta-like (Dll) family (Dll1, Dll3, Dll4), and two from the Jagged (Jag) family (Jag1 and Jag2)." There are five Notch ligands in mammals (Dll1, Dll3, Dll4, Jag1, Jag2), and it is obvious that the authors are very well aware of this (they state in lines 146-147): "We excluded the ICD of DLL3 since it is not a ligand capable of trans-activation of Notch" (the four ligands included were Dll1, Dll4, Jag1 and Jag2)." Therefore, a claricifaction is required in the part of Abstract (i.e lines 21 ansd 22) - did the authors mean the four mammalian Notch ligands they actually studied (i.e Dll1, Dll4, Jag1, Jag2) or is there an oversight and the auhtors actually intended to write "the five Nocth ligands in mammals".? In either case, a correction is required in this reviewer's opinion.
Response: We are fully aware of this point, and will address it by providing clarification in the abstract as suggested.
Specific experimental issues that are easily addressable.
NEUR2 could not be studied in mammalian cell cultures due to its toxicity in the U2OS cell line, the one used by the authors. The use of another cell line would not be probably overly time-consuming; however, if this experiment lies outside the scope of current work, we would like to hear the authors' comment on this matter.
Response: This is addressed above.
Are prior studies referenced appropriately? Generally yes, but four prior studies go unmentioned: the two 2001 mouse Neur1 knock-out studies reporting no Notch-like developmental phenotype (Ruan et al, PNAS; Vollrath et al, Mol Cell Biol), the 2002 study of mouse, rat and human NEUR1 expression, subcellular localisation (Timmusk et al, Mol Cell Neuroscience) and the 2009 cell culture-based study of NEUR2's interaction with DLL1 and DLL4 (Rullinkov et al, BBRC). The non-requirement of NEUR1 and 2 proteins in mammalian developmental Notch signalling could partly be explained by the fact that NEUR1 is not highly expressed during mouse embryonic/foetal development - its expression becomes considerably more pronounced only postnatally (Timmusk et al, 2002).
Response: We will incorporate these references into the introduction and discuss the low expression of Neurls during development as a possible reason for the non-requirement in this context.
Are the text and figures clear and accurate?
Yes. These reviewers find the cartoon-based explanations of the experimental set-up in each figure helpful for enhancing the manuscript's overall clarity.
Response: We thank the reviewers for the positive feedback!
Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Please see above about the lack of statistical data on the variation (if any) in fly wing dic experiments and referencing of the 4 papers that are currently excluded.
Response: These will be corrected in the revised version.
Reviewer #3 (Significance (Required)):
- Significance Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested. The following aspects are important: General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This study uses the amenability of Drosophila to study the mammalian NEUR proteins' (NEUR1 and NEUR2) activity upon Notch ligands using hybrid Notch ligands containing mammalian ICDs (intracellular domains) fused to the extracellular domain of Drosophila Delta (Dl). It confirms and extends prior studies showing that Notch ligands can be (strongly) activated only by the E3 ubiquitin ligases containing the Neuralized Binding Motif (NBM).
Response: We respectfully disagree with the reviewer's assessment on this point. Our study is the first to demonstrate that Neurl proteins differentially activate Dll1 and Jag1, but not Dll4 and Jag2. This findings is further supported by the significance comments of the other reviewers.
However, since this study was based on using hybrid ligands containing mammalian ICDs of Notch ligands fused to the extracellular domain of Drosophila Delta (Dl), it is somewhat artificial. While NEUR1 was also studied in mammalian cell cultures (but not NEUR2 due to its toxicity), only an in vivo study using mice expressing with systematic changes to the Notch ligands' NBM will definitively reveal whether the conclusions reached by the authors hold true in vivo in a non-heterologous system.
Response: We firmly believe that our combined 'humanized fly' model and quantitative cell culture assay represents an innovative and rigorous approach for testing humanized proteins in in-vivo settings, without the need for extensive mouse genetics. The conclusions of our experiments should not be dismissed solely on the grounds of "not being performed in mice," as this would undermine much of current scientific research.
Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...). The study's advances are chiefly mechanistic and functional since they show more definitively that the reason underlying the differing activation of four mammalian Notch ligands by mammalian NEUR1 and NEUR2 is mostly based upon the presence or otherwise of a conserved Neuralized Binding Motif, NBM. Audience: describe the type of audience ("specialised", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?
The audience for this study is the research studying the Notch signalling pathway. Since dysregulation of this pathway is implicated in a number of devastating diseases, any improved understanding of its mechanistic underpinnings could in the long run lead to better therapeutic management of diseases with significant involvement of malfunctioning Notch signalling.
Please 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. Molecular biology, molecular neuroscience, developmental biology, cell-cell signalling, Notch signalling. All parts of the manuscript fall within our expertise.
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Referee #3
Evidence, reproducibility and clarity
Summary
Notch signalling is one of the major evolutionarily conserved signalling pathways involved in numerous developmental, physiological and pathological processes. Activation of the Notch receptor first requires ubiquitination of its ligands (collectively temed DSL), leading to a 'pulling force" that, upon ligand-receptor engagement, exposes Notch to intramembrane proteolysis leading to the nuclear translocation of the receptor's intracellular domain and activation of target genes with its DNA-binding co-factors.
While both Neuralized (Neurl) and Mind bomb are the E3 ubiquitin ligases for Notch ligands required for Drosphila development, in mammals, the Neur homologues Neur1 (officially Neurl1) and Neur2 (officially Neurl1B) are dispensable for development since double Neur1/2 knock-out mice have no developmental phenotype (but both Neur homologues are involved the the memory-related functions of Notch pathway in adulthood). Rather, just one of the two mammalian Mind bomb homologues, Mib1, functions as the chief E3 ligase for Notch ligands during mammalian development as evidenced by its Notch-related knockout phenotype.
Therefore, it has not been fully established whether and how the NEUR proteins regulate the mammalian Notch ligands. To clarify this issue, the authors assessed the capability of Drosophila Neur and mammalian NEUR1 and 2 proteins to activate the various hybrid Notch ligands (containing extracellularly Drosophila Delta and intracellularly the various Notch ligands' intracellular domains) in Drosophila wing dics and mammalian cell culture. The authors found that NEUR proteins only activate the Notch ligands containing a Neuralized binding motif, with the consensus sequence NxxN, that is present in DLL1 and JAG1, but not in DLL4 and JAG2. The authors also analyse the intracellular domains of mammalian Notch ligands DLL1, DLL4, JAG1 and JAG2 in Drosophila by generating knock-in alleles where endogenous Dl expression had been substituted for those of hybrid Notch ligands. This analysis showed that only in Dl-DLL1 and Dl-JAG1 flies but not in Dl-DLL4 and Dl-JAG" flies is the embryonic lethality rescued, the results being in agreement with the hybrid Dl-DLL experiments on wing dics reported earlier in this work. The authors conclude that their findings suggest that the activation mechanism of Notch during development differs between Drosophila (where both Neur and Mib1 are required for Notch-related developmental processes ) and mammals and that this could possibly explain the apparently lesser relevance of mammalian NEUR proteins for developmental Notch signalling.
Evidence and clarity
The manuscript is quite laconic but clearly written. The evidence presented by the authors, given the heterologous and in vitro nature (i.e using mammalian or hybrid Notch ligands and mammalian E3 ligases thereof in Drosophila and cell cultures) of the study is generally trustworthy but limited in the sense that it probably does not allow definitive conclusions to be drawn as to the differing nature of the action of the E3 ligases of Notch ligands in flies vs mammals in vivo.
Reproducibility
As will be mentioned a number of times, these reviewers would like to enquire as to the reasons for not providing a statistical analysis of variation in the fly wing disc-based experiments (where the readout was either resuce of Wg expression or induction of ectopic Wg expression). Also, while the constructs used in the study were inserted into the same genomic landing sites to achieve comparable leves of expression of the various proteins, these reviewers would like to see data on the levels of expression of NEUR1 and 2 as well as the hybrid Notch ligands.
Major comments
Comment on fly wing disc experiments:
The authors study both the capability of two different mammalian E3 ubiquitin ligases, Neuralized-like 1 and 2 (mouse Neur1 and human NEUR2) to activate four different Notch receptors (DLL1 and 2, JAG1 and 2) in flies and mammalian cell culture system. In flies, they first analyse the capability of Drosophila Neur (as a positive control) and Neur1 and NEUR2 to activate the various Notch ligands (based on wingless activation as a readout) in wild-type wings (where, Mind bomb 1, or Mib1 is the only E3 ligase for Notch ligands present) and Mib1 mutant wing discs (which lack any E3 ligands of Notch receptors). The authors then test four humanised, hybrid Notch ligands (all five N ligands bar Dll3 since the latter does not transactivate the Notch receptor) - where mammalian Notch ligands' intracellular domains, or ICDs, have been attached to fly Dl (Dl-Dll1, Dl-Dll4, Dl-JAG1, Dl-JAG2) - for their capacity to mediate Mib1-dependent activation of Notch (with ectopic Wg expression in wing discs as its readout). They found that all 4 ligands can activate Nocth in wild-type wings (where Mib1 is present), with Dl-JAG2 being less effective than the other 3 hybrid ligands, implying that such hybrid, humanised ligands can be usd in studying Notch pathway activation in Drosophila (thereby constituting a mixed/heterologous experimental system). The reviewers would like to get a comment as to the reason for the weaker activity of Dl-JAG2 in this set-up?.
Also, the reviewers would like to get a comment as to why was not a Neur mutant set-up used, only Mib1 mutant dics? The authors then found that only two of these hybrid ligands - Dl-DLL1 and Dl-JAG1 but not Dl-DLL4 or Dl-JAG2 - can be used to activate Notch in the above wing assay when Mib1 was mutant. This is consistent with the fact that the NxxN-based Neuralized Binding motif (NBM) is present in DLL1 and JAG1 only. Using the wing paradigm, the authors also show by either mutating the full NBM (NxxN) in DLL1 or changing the cryptic, "weak" NBM in DLL4 (containing NxxD sequence) into "full/strong" NxxN one that the NBM in the various Notch ligands is required and sufficient for activation of the Notch pathway.
Overall, the fly experiments are convincing in showing diffrential activation of Notch ligands. However, no statistical analysis of the experimental variation in these studies - neither for the number of wing discs analysed per (hybrid) Notch ligand tested nor the extent of a given experimental manipulation's effect is included. We deem that if the images presented in Figures 2 and 3 are truly representative, this needs to be made explicitly clear. Comment on fly embryonic Delta neurogenic phenotype's rescue experiments by replacing Dl with the hybrid ligands: The authors analysed the capacity of the ICDs of the mammalian ligands to rescue the Dl phenotype in Drosophila, ie. their activation capability at the organismal level. This was achieved by generating knock-in alleles expressing the hybrid ligands in place of Dl. The notion that only NBM-containing hybrid ligands was strengthened by this analysis since it showed that only NBM-containing hybrid ligands - Dl-DLL1 and Dl-JAG1 - but not Dl-DLL4 nor Dl-JAG2 rescued the Dl neurogenic embryonic lethal phenotype. Since this experimental set-up relied on the endogoneous Drosophila E3 ligases for activating the Notch ligands, the capacity of mammalian NEUR1 and 2 proteins to complement the capacity of the hybrid ligands to activate Notch to activate these ligands was not addressed. Please comment as to the reasons for this apparent omission and if such an analsyis lies beyond the scope of current work, what would be the expected results of such experiment in the light of other experiments conducted in the course of this work? Journal-agnostic peer review: evaluate the paper as it stands independently from potential journal fit.
Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?
Generaly yes, put please see the above comments on the absence of statistical analysis of reproducibility/ variation (if any) in fly wing disc experiments.
Reviewer's additional recommendations:
To publish in a higher-ranking journal, the co-localisation analyses of Notch ligands and its various E3 ubiquitin ligases studied probably needs to be replaced by a more rigorous, ideally FRET-based approach. Since previous studies have shown that the Notch ligands are (mostly) poly- or mono-ubiquitylated by the E3 ubiquitin ligases Mib and the NEUR proteins, ideally, this or its follow-up study would benefit from analysis of the ubiquitylation status of the various hybrid Notch ligands. Also, it would be useful to show the strength of interaction between the hybrid Notch ligands and NEUR1 and NEUR2 by ising a co-immunoprecipitation based approach. Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether. These reviewers do not strictly request any further rexperiments. However, since the mammalian NEUR2 could not be studied in cell cultures of U2OS cells due to its toxicity, we would like the auhtors to explain the choice of this cell line. Perhaps a cell line whose viability is not impaired by NEUR2 should be (or should have been) used? If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL". As mentioned above, the NEUR2's capacity to activate the hybrid ligands in U2OS cells could not be addressed to due to its toxicity. A more optimal cell line will have to be used in follow-up studies. Also, these findings ultimately warrant in vivo studies using mice to definitively ascertain whether they also hold equally true there.
Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.
The suggested experiments are optional apart from statistical analysis of variation (if any) in the fly wing disc experiments. If there is no (apparently significant) variation in these data, this needs to explicitly stated.
Are the data and the methods presented in such a way that they can be reproduced?
Generally yes, but see above about the lack of statistical data on the variation (if any).
Are the experiments adequately replicated and statistical analysis adequate?
Generally yes, but again, please see above about the lack of statistical data on the variation (if any).
Minor comments
Comment#1 (on the abstract and introduction):
In the Abstract, the authors state that there are four Notch ligands in mammals (lines 21 and 22):<br /> "Thus, it is unclear how NEURL proteins regulate the four mammalian Notch ligands". In the Introduction, they correctly state that there are five Notch ligands in mammals (lines 38 and 39): „In mammals, there are five ligands, three from the Delta-like (Dll) family (Dll1, Dll3, Dll4), and two from the Jagged (Jag) family (Jag1 and Jag2)." There are five Notch ligands in mammals (Dll1, Dll3, Dll4, Jag1, Jag2), and it is obvious that the authors are very well aware of this (they state in lines 146-147): "We excluded the ICD of DLL3 since it is not a ligand capable of trans-activation of Notch" (the four ligands included were Dll1, Dll4, Jag1 and Jag2)." Therefore, a claricifaction is required in the part of Abstract (i.e lines 21 ansd 22) - did the authors mean the four mammalian Notch ligands they actually studied (i.e Dll1, Dll4, Jag1, Jag2) or is there an oversight and the auhtors actually intended to write "the five Nocth ligands in mammals".? In either case, a correction is required in this reviewer's opinion.
Specific experimental issues that are easily addressable.
NEUR2 could not be studied in mammalian cell cultures due to its toxicity in the U2OS cell line, the one used by the authors. The use of another cell line would not be probably overly time-consuming; however, if this experiment lies outside the scope of current work, we would like to hear the authors' comment on this matter. Are prior studies referenced appropriately? Generally yes, but four prior studies go unmentioned: the two 2001 mouse Neur1 knock-out studies reporting no Notch-like developmental phenotype (Ruan et al, PNAS; Vollrath et al, Mol Cell Biol), the 2002 study of mouse, rat and human NEUR1 expression, subcellular localisation (Timmusk et al, Mol Cell Neuroscience) and the 2009 cell culture-based study of NEUR2's interaction with DLL1 and DLL4 (Rullinkov et al, BBRC). The non-requirement of NEUR1 and 2 proteins in mammalian developmental Notch signalling could partly be explained by the fact that NEUR1 is not highly expressed during mouse embryonic/foetal development - its expression becomes considerably more pronounced only postnatally (Timmusk et al, 2002).
Are the text and figures clear and accurate?
Yes. These reviewers find the cartoon-based explanations of the experimental set-up in each figure helpful for enhancing the manuscript's overall clarity.
Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
Please see above about the lack of statistical data on the variation (if any) in fly wing dic experiments and referencing of the 4 papers that are currently excluded.
Significance
Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested. The following aspects are important:
General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This study uses the amenability of Drosophila to study the mammalian NEUR proteins' (NEUR1 and NEUR2) activity upon Notch ligands using hybrid Notch ligands containing mammalian ICDs (intracellular domains) fused to the extracellular domain of Drosophila Delta (Dl). It confirms and extends prior studies showing that Notch ligands can be (strongly) activated only by the E3 ubiquitin ligases containing the Neuralized Binding Motif (NBM). However, since this study was based on using hybrid ligands containing mammalian ICDs of Notch ligands fused to the extracellular domain of Drosophila Delta (Dl), it is somewhat artificial. While NEUR1 was also studied in mammalian cell cultures (but not NEUR2 due to its toxicity), only an in vivo study using mice expressing with systematic changes to the Notch ligands' NBM will definitively reveal whether the conclusions reached by the authors hold true in vivo in a non-heterologous system.
Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...). The study's advances are chiefly mechanistic and functional since they show more definitively that the reason underlying the differing activation of four mammalian Notch ligands by mammalian NEUR1 and NEUR2 is mostly based upon the presence or otherwise of a conserved Neuralized Binding Motif, NBM.
Audience: describe the type of audience ("specialised", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field? The audience for this study is the research studying the Notch signalling pathway. Since dysregulation of this pathway is implicated in a number of devastating diseases, any improved understanding of its mechanistic underpinnings could in the long run lead to better therapeutic management of diseases with significant involvement of malfunctioning Notch signalling.
Please 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. Molecular biology, molecular neuroscience, developmental biology, cell-cell signalling, Notch signalling. All parts of the manuscript fall within our expertise.
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Referee #2
Evidence, reproducibility and clarity
Summary
The manuscript describes an analysis of specificity of functional interactions between mammalian Neuralized proteins and different human ligands for Notch. To investigate this, the authors take the approach of constructing hybrid proteins that contain the intracellular domain of the human ligands and the extracellular domain of the Drosophila Delta or Serrate, and investigate their activity in vivo, in the Drosophila wing disc. The latter is a well-established model tissue for assessing Notch ligand activity. As a second assay they express mammalian neutralized constructs in human cells for luciferase-based Notch signal reporter assays. The experiments are well presented and described and make a strong case for the conclusions that both Neurl1 and 2 can activate Notch signalling by Dll1 and Jag1 but not Dll4 and Jag2. Use of different mutant intracellular domains is used to show the importance of the NXXN motif, which in Drosophila is required for Neuralized interaction with Delta and Serrate. The use of missense mutations and in particular the reactivation of the cryptic NXXD site in Dll4 by substitution to N is convincing for establishing the importance of the motif. There is also colocalization data to support the conclusion that there is likely to be NXXN-dependent complex formation between the ligand and Neuralized proteins. This latter conclusion would be made firmer fi there were pull down data to support it, although to be fair it is most unlikely that another explanation, other than complex formation could account for the observation of both colocalization and ligand activation.
Major comments
The main limitation of the work is that it is mostly based on overexpression of constructs to activate ectopic expression rather than gene editing endogenous genes. It would be helpful if the authors could comment on the limitations of the work in discussion. Two points of data included in the work are important in mitigating this limitation. Firstly, the experiments in the wing disc and cell culture are taking place in a mindbomb mutant background and the activation is observed is therefore a rescue of activity that has been lost. Secondly, and importantly, the final experiment makes use of a Dl mutant Drosophila line which shows embryo lethality when homozygous, with the characteristic neurogenic phenotype. Rescue of lethality can be brought about by knock-in experiments which restore Dl function and this is also true for the ligand hybrid constructs that introduce mammalian ligand intracellular domains only when they include the NXXN motif This indicates the importance of the motif in normal development
Overall, the data presented in the paper is convincing as regards the conclusions made.
Minor points
In figure 1 the legend for D says that cryptic sites are substitutions of N for E or Q, but the figure and main text indicate that the substitutions are N to E or D.
In the remain figures it would be helpful to include in the figure legends and indications of the numbers of wing discs, embryos for which the images shown are representative of.
In Fg 3 The activation of Notch, by neural1 and Dl-Jag1 in B'" is stronger in the ventral side of the disc than the dorsal whereas, although activation of the same ligand by Neurl2 in C'" is weaker the majority of the ectopic wingless expression is on the dorsal compartment. Is there any reason for the switch in preference between the two neutralized proteins? Overgrowth of the wing disc seems to be similar on both sides and so am wondering if the picture is representative of the ectopic wingless distribution in this case.
Significance
Previous work on double genetic knockouts of the two mouse Neuralized genes cast doubt as to whether Neuralized proteins play a role in Notch signal activation in mammals, unlike in Drosophila. There is, however, some genetic indications that spatial memory requires both Notch and neutralized proteins and may represent a specialised function limited to the Neuralized interaction. There are likely to be more subtle contexts waiting to be uncovered. The work is therefore showing important proof of principle for establishing the functionality of the mammalian Neurl proteins and highlights new findings indicting specialisation of the different ligands for interactions with Notch components. Elucidation of such specialisations will help understand why the diversity of different homologues of Notch and ligand have evolved and are maintained in the vertebrate genome compared to the single Notch and two ligands in Drosophila. Since Notch and it misregulation are widely involved in development, health and disease and there is much interest in developing therapeutic interactions that alter Notch activity then the work is likely of broad interest.
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Referee #1
Evidence, reproducibility and clarity
This is an interesting manuscript from two groups of experts in Notch signaling biology with complementary expertise in Drosophila genetics (Klein) and in biophysical studies of the Notch pathway (Sprinzak). The paper provides a cutting-edge structure-function dissection of the E3 ubiquitin ligase Neuralized and its mammalian homologs, Neurl1a and Neurl1a. The work is particularly relevant since the functions of mammalian Neurl1a and Neurl1b have been questioned, and more subtle altogether than those of fly Neuralized (as summarized by the authors in Fig. 1C). This is in part due to the dominant effects of the E3 ubiquitin ligase Mindbomb1 (Mib1) in Notch ligand-expressing cells from mammalian systems. The authors use careful structure-function work in fly development (mostly wing imaginal discs) and in mammalian cell culture systems, including a clever approach to study the function of mammalian Neurl1a and Neurl1b and mammalian/fly Notch ligand hybrids in Drosophila to draw new conclusions about the function of Neurl1a/b, showing that they can function as activators of Notch signaling mediated by the Notch ligands Dll1 and Jag1, and not by Dll4 and Jag2, tracing these differential effects to the recognition of a short NXXN consensus sequence in the N-terminal region of the ligand's intracellular domain.
Specific questions:
- The current title of the manuscript is not very information-rich and would not allow a reader to gather key information about the findings without reading at least the abstract. Could this be improved? For example, by referring to differential activation of individual Notch ligands, or some other more direct description of the key findings?
- The authors design most key experiments documenting agonistic effects of Neurl1a/1b in a Mib1-deficient background, both in flies and in cell culture systems. This is understandable experimentally to isolate Neurl1a/b's effects in these experimental systems. However, this leaves open questions as to the prevailing effects of Neurl1a/b in cells that also express Mib1 (which the authors comment on in the discussion based on past findings, including some suggesting that Neurl1a/1b can function as Notch inhibitors through a ligand ubiquitination mechanism that may differ from their activating function). Do the authors actually have data that could shed light on this discussion? For example, have they performed cell coculture assays in which Neurl1a or Neurl1b is co-expressed with a Notch ligand, but in the presence of Mib1? This condition seems to be systematically omitted from all the coculture experiments that are presented. It would be interesting to evaluate the net effect of Neurl1a/Neurl1b expression in a Mib1-sufficient system as well.
- The paper suggests important predictions about mammalian functions of Neurl1a/1b, including the neurological effects that have been reported, in double-deficient mice, namely that that there are cells that only express Neurl1a/1b and not Mib1 and do rely on Dll1 and Jag1 for signaling. Could the authors at least comment on this prediction? Are there are any single cell atlases where candidate cells like that can be identified? Or would the authors predict that Neurl1a/1b could actually function as Notch agonist even in cells expressing Mib1? (see also previous comment)
- Some minor typos: line 305 should likely read "flies homozygous for (...)". Line 408, "for providing" repeated twice.
Significance
Thank you for the opportunity to review this lovely collaborative paper. As indicated in my comments to the authors, the findings provide novel structure-function information about an understudied aspect of Notch signaling and clarify conflicting past data about the mammalian homologs of fly Neuralized. The approach is elegant and multidisciplinary, notably in regards to the combination of cell co-culture systems and Drosophila as a platform to study mammalian Neuralized proteins and hybrid Notch ligand molecules. The findings will be interesting to the field and will generate discussion. I would suggest that some additional information would be a plus to substantiate predictions about mammalian functions of Neurl1a/b, and also to clarify its effects in the presence or absence of concomitant Mib1 expression.
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Reply to the reviewers
We thank the reviewers for their insightful comments, and we address all their comments in the detailed point-by-point responses provided below.
Reviewer #1
__Evidence, reproducibility and clarity __
*In the manuscript entitled "Inhibition of glycolysis in tuberculosis-mediated metabolic rewiring reduces HIV-1 spread across macrophages", Vahlas and colleagues investigated the hypothesis that Mtb interferes with HIV-1 infection of human macrophages, as they represent a common target cell type. In particular, they observed that a conditioned medium generated from Mtb-infected macrophages (Mtb-CM) induces tunneling nanotubes (TNT) in HIV-infected macrophages thereby facilitating viral spreading. At the same time, Mtb-CM induced a glycolytic pathway leading to ATP accumulation in HIV-infected macrophages, an essential pathway for TNT induction whereas pharmacological interference with such a metabolic switch resulted in a reduced viral production.
Experimental approach: primary human monocytes differentiated into monocyte-derived macrophages (MDM) in the presence of a TB-dominated microenvironment (Mtb-CM). The intracellular rate of ATP production was evaluated by the Seahorse technology at day 3 of MDM differentiation. The measurements of basal extracellular acidification rate (ECAR) and basal oxygen consumption rate (OCR) were used to calculate ATP production rate from glycolysis (GlycoATP) and mitochondrial OXPHOS (MitoATP).*
* This is a well-conducted, innovative study exploring the interaction of two main human pathogens, i.e. Mtb and HIV, sharing macrophages as common target cell. The manuscript is clearly written and the conclusions and hypotheses are supported by experimental evidence. I have two general points that I encourage the authors to address.*
We thank the Reviewer for his/her valuable comments and address all provided comments below.
- __ As mentioned in the Discussion, macrophage infection by HIV is characterized by the accumulation of preformed, infectious virions in VCC (Virus Containing Compartments) that can be pharmacologically modulated both in terms of accumulation and rapid release in the absence of cell cytopathicity. Although the modulation of VCC was not the objective of the present study, it would be important to discuss their role and their potential modulation by Mtb and/or metabolic modifications, if known.__ In the discussion, we mentioned that “In HIV-1 infected macrophages, ATP is also vital for the release of particles from virus-containing compartments (Graziano et al., 2015)”. Graziano et al. (PMID 26056317) showed that extracellular ATP favors the release of virions actively accumulating within the VCC of infected macrophages through its interaction with the P2X7 receptor. This study will be discussed more in detail in the revised version of the manuscript.
In addition, we fully agree with the reviewer that exploring potential modifications in the formation of virus containing compartments (VCC) following Mtb infection, CmMTB treatment or metabolic alterations is highly relevant. Importantly, VCCs are specific compartments in infected macrophages where new virions are generated and protected from the immune system and antiretroviral therapies. Interestingly, Siglec-1 was shown to be involved in VCC formation in infected macrophages (Jason E Hammonds et al., 2017; PMID 28129379), and we demonstrated that the level of expression of this lectin is increased in CmMTB-treated cells (Dupont et al., PMID: 32223897). We propose to perform new experiments during the revision process to look whether the formation of VCC is disturbed in CmMTB-treated macrophages upon HIV-1 infection, using the tetraspanin CD81 and/or Siglec-1 along with HIV-Gag to assess VCC formation (as in Reviewer Figure 1).
Reviewer Figure 1: VCC formation in multinucleated HIV-1 infected macrophages. Human macrophages were infected with HIV-1 (NLAd8-VSVG, 3 days) and stained with HIV-gag and CD81 to stain the VCC.
__ Understanding the purpose of using a VSV-g based infection system, nonetheless it would be important to know whether metabolic modulation does affect CD4 and CCR5 expression on MDM and its consequence for their susceptibility to HIV infection, in addition to the effects on TNT formation and viral transfer between cells.__
We appreciate this comment. The reviewer correctly understands that we used VSVG pseudotyped virus in this study to eliminate the effect of metabolic modulation on the expression of HIV entry receptors and potentially on virus entry. It has been previously demonstrated in CD4 T cells that the nutrient modulation does not affect HIV entry when the Blam-Vpr assay is used (Clerc et al., 2019, PMID 32373781, supplemental Figure 6).
In addition, as demonstrated in our earlier work (Souriant et al. Cell Reports, 2019), CmMTB treatment increases the levels of both CD4 and CCR5 on the surface of macrophages. However, it does not impact HIV entry, as shown using the same Blam-Vpr assay. Therefore, the exacerbation of HIV-1 infection in the TB-environment is not a consequence of increased viral entry. This will be clarified in the revised version of the manuscript.
As suggested by the reviewer, we will also conduct new experiments during the revision process. Specifically, we will assess the levels of entry receptors using flow cytometry analysis and measure virus entry using the Blam-Vpr fusion assay in CmMTB-treated cells, with or without Oxamate treatment (to inhibit glycolysis).
Specific points:
- __ "TB-PE" (pleural effusion) is neither specified in the Results nor in the Methods sections.__ We thank the reviewer for pointing out this omission. TB-PE refers to pleural effusions from TB patients, a term we had previously defined only in the introduction and figure legends. We will ensure that this definition is explicitly stated in the Result and Methods sections of the revised manuscript.
__ Figure 3A does not seem to display cell viability, but rather HIV Gag expression by IFA. __
Indeed, there is an error in the text regarding cell viability. Cell viability following drug treatments was assessed by flow cytometry, as shown in Figure S2C. In Figure 3A, we included nuclear staining (in addition to HIV Gag) to confirm that cell density is not affected. This will be corrected in the revised manuscript. Additionally, we will perform F-actin staining to evaluate cell morphology and further confirm that all key parameters, i.e., viability, cell density, and cell morphology, are unaffected by the drugs used in Figure 3.
Furthermore, Figure 3C indicates Gag expression, not "HIV infection" (see page 8, Results).
We thank the reviewer for helping us to clarify this issue. In Figure 3C, the term “infection index” refers to the percentage of HIV Gag-positive cells resulting from productive infection. This is calculated as the total number of nuclei in HIV Gag-stained cells divided by the total number of nuclei, multiplied by 100, as described in the Methods section.
We have previously used this method to estimate the HIV infection rate in our published studies (Souriant et al., 2019; Dupont et al., 2020; Mascarau et al., 2023). To further improve the clarity and interpretation of the figure, we will include a clear definition of the infection index in the figure legend in the revised version of the manuscript.
Significance
The paper addresses a poorly explored area, i.e. the interaction of Mtb and HIV during infection of macrophages. The authors focused on a specific aspect of such an interaction (I,e, the modulation of nanotubes formation and transfer of virions to target cells), but their results can be extrapolated in a broader context, particularly if the authors will be willing to address my general questions. Although specific in its experimental approach, the implication of the study will be of interest to a general audience.
We appreciate this positive comment.__ __
Reviewer #2
__Evidence, reproducibility and clarity __
The current work is based on previous observations that the abundance of lung macrophages is augmented in NHPs with active TB and exacerbated in those coinfected with SIV (Dupont et al., 2022; Dupont et al., 2020; Souriant et al., 2019). Further work with these TB-induced immunomodulatory macrophages demonstrated an increased susceptibility to HIV-1 replication and spread via the formation of tunneling nanotubes (TNTs), (Souriant et al., 2019). In the present manuscript, the authors connected these findings with the metabolic state of macrophages (glycolysis vs OXPHOS). Using a range of metabolic inhibitors coupled with seahorse assays and microscopy confirmed the role of Mtb-induced glycolytic shift in inducing the formation of TNTs and the spread of HIV. The work is well-planned and executed. However, the study is mainly correlative without any molecular insights. The knowledge generated is important and valuable for future studies to understand the molecular players in regulating immunometabolism during HIV-TB coinfection.
We thank the Reviewer for his/her valuable comments, and we address all provided comments below.
Major Comments:
There are conflicting reports about Mtb's impact on macrophage ECAR and OXPHOS, which authors have acknowledged. Therefore, including OCR and ECAR plots along with the glycoATP and MitoATP data will be useful. Similarly, OCR/ECAR plots without any conditioned medium should be included to clarify the role of Mtb infection on OCR/ECAR.
In this manuscript, we evaluated the intracellular rate of ATP production in macrophages (day 3 of differentiation) treated with either cmCTR or cmMTB using Seahorse technology. Measurements of extracellular acidification rate (ECAR) and oxygen consumption rate (OCR), both before and after the addition of oligomycin (an ATP synthase inhibitor), were used to calculate the contributions of glycolysis (GlycoATP, Figure 1B) and mitochondrial OXPHOS (MitoATP, Figure S1C) to total ATP production (Figure 1A).
We agree with the reviewer that displaying basal OCR/ECAR plots (bioenergetic profiles) would help characterize the overall energy phenotypes of macrophages. These graphs will be prepared and included in Figure S1. Furthermore, we will enhance the discussion and interpretation of these findings in the Results section of the revised manuscript.
As suggested, we will also assess ATP production using Seahorse technology for control cells (day 3 differentiated in RPMI) and provide OCR/ECAR plots for these new experiments.
__Fig 2G image is not convincing. While HIF1 alpha seems more in the nucleus, the overall morphology of the cell is more compact. Additional verification is needed. __
Regarding the specific comment on Fig. 2G, the reviewer is correct that the morphology of CmMTB-treated cells differs from that of CmCTR-treated cells. We have previously shown that CmMTB-treated macrophages display an M(IL-10) phenotype, characterized by a CD16+CD163+MerTK+PD-L1+ signature, morphological changes (cells appear rounder and form more TNTs), nuclear translocation of phosphorylated STAT3, and increased susceptibility to Mtb or HIV-1 infection (Dupont et al., 2022; Dupont et al., 2020; Lastrucci et al., 2015; Souriant et al., 2019).
As shown in Figure 2H, HIF1-α is predominantly cytoplasmic in most control cells, whereas an increased number of cells with nuclear HIF-1α staining were observed in CmMTB-treated cells. To quantify this observation, we manually assessed the ratio of HIF-1α signal intensity between the nucleus and cytoplasm in over 50 cells from three different donors. This methodology was not adequately explained in the Methods section and will be clarified in the revised manuscript. We also propose to include more representative images of HIF-1α-stained cells to support these findings.
Furthermore, genetic evidence is required in order to confirm if HIF1 alpha is the primary regulator of glycolytic shift by cmMTB/PE-TB, leading to more HIV dissemination by the TNT formation.
We fully agree that further experiments are essential to formally demonstrate that HIF-1α activation is responsible for the observed increase in HIV-1 infection and TNT formation in CmMTB-treated cells. To address this hypothesis, we propose conducting key experiments during the revision process
We will first use pharmacological approaches to modulate HIF-1α levels, as described in our recent publication (Maio et al., eLife, PMID 38922679). Specifically, we will test the HIF-1α inhibitor PX-478 as well as dimethyloxalylglycine (DMOG), a compound that stabilizes HIF-1α expression. These drugs will be applied 24h prior to HIV-1 infection in CmMTB-treated cells, and we will quantify HIV-1 infection and TNT formation on day 6 using immunofluorescence (IF).
In parallel, though technically challenging, we will attempt to reduce HIF-1α expression (and consequently its activity) in primary human monocytes using a siRNA-mediated depletion approach. This method has been successfully employed in our previous studies to target STAT3, STAT1 and Siglec-1 (Dupont et al., 2020; Lastrucci et al., 2015; Dupont et al., 2022). Under these conditions, we will measure HIV-1 infection and TNT formation on day 6 by IF.
Also, the authors have used only one tool to measure HIV levels -microscopy. While important, another method for verifying findings is needed. This is important as the effect of inhibitors (UK5099) is marginal.
In the present manuscript, we assess HIV-1 infection levels using two methods: microscopy (Figure 3 and 4I) and flow cytometry (Figure S2H-I). To address the reviewer’s comment, we propose to complement our current analysis of HIV-1 infection by evaluating HIV-1 replication through the measurement of HIV-p24 release in the supernatant of CmMTB-treated macrophages following drug treatments, as previously performed (Dupont et al., 2020; Souriant et al., 2019; Dupont et al., 2022; Mascarau et al., 2024; Raynaud-Messina et al., 2018).
Regarding the slight increase of HIV-1 infection (Gag expression by IF, Figure 3A) upon UK5099 treatment, we appreciate the reviewer’s valuable observation. Enhancing glycolysis levels remains a considerable challenge in studies targeting metabolic pathways, as most approaches focus on inhibiting glycolysis. However, in our study, the effect UK5099 on HIV-1 infection is reproducible and statistically significant, as demonstrated by analyzes of data from more than ten donors using IF (Figure 3C) and eight donors by flow cytometry (Figure S2H-I).
We acknowledge that the specific image provided in Fig. 3A for the UK5099 condition may not be the most representative and could cause confusion. To address this, we will replace the current image with a more representative one in the revised version of the manuscript.
Authors have used oxamate to inhibit glycolysis. Inhibition of LDH could lead to inhibition of NAD/NADH regeneration, thereby slowing down glycolysis. However, lack of lactate could have wide-ranging influence on cells as lactate could regulate several post-translational modifications, including lactylation. While the authors argued against using 2-DG, several findings confirm the glycolysis inhibitory potential of 2-DG when infected with Mtb. This should be included.
We understand the reviewer’s comment regarding the glucose analog 2-DG, which is widely used to inhibit glycolysis. Notably, recent studies have used it to show that glycolytic activity is critical for reactivating HIV-1 in macrophage reservoirs (Real et al., 2022, PMID 36220814).
In our study, we did not initially use 2-DG because it also inhibits glucose contribution to OXPHOS, making it challenging to distinguish between the roles of glycolysis and OXPHOS in macrophages (Wang et al., Cell Metabolism, PMID 30184486). Unlike Oxamate or GSK 2837, which specifically target LDHA, 2-DG does not exclusively affect glycolysis. Furthermore, inhibiting glucose metabolism with 2-DG is expected to yield similar results to glucose deprivation, as demonstrated in Figures 3H-K.
To address this, we propose conducting the suggested experiments using 2-DG in CmMTB-treated macrophages during the revision process. This will allow to assess their susceptibility to HIV-1 under this treatment. We will subsequently discuss the effects of 2-DG and integrate these results into the revised version of the manuscript.
A standard glycolytic function test (glucose, oligomycin and 2-DG injection) should be performed to assess the effect of TB-PE and cmMTB on the macrophages directly.
We appreciate the reviewer’s comment and will address it by testing the ability of CmMTB to alter the glycolytic activity of macrophages using the Seahorse Glycolytic Rate Assay. This assay, a refined version of the classical Seahorse Glycolysis Stress Test (see https://www.agilent.com/en/products/cell-analysis/glycolysis-assays-using-cell-analysis-technology), relies on an algorithm that generates the Proton Efflux Rate (PER), providing a robust quantitative measurement of glycolytic function. PER is directly correlated with lactate accumulation, enabling us to calculate glycolytic parameters that will complement our existing assays aimed at characterizing the glycolytic pathway in CmMTB-treated macrophages. We plan to perform these measurements and include the results in Figure 2.
__ Depriving glucose is not the best way to show the effect of glucose on HIV infection and MGC formation, as it can affect other aspects of cellular physiology, such as redox and bioenergetics. Instead, the use of galactose in place of glucose would generate ATP only by ____OXPHOS. Some key experiments should be repeated using galactose as a sole C source.__
We agree with this comment. In M2 macrophages, it has been shown that both glucose deprivation (as demonstrated in this study, Figure 3H-K) and glucose substitution with galactose (Wang et al., Cell Metabolism, PMID 30184486) effectively suppress glycolytic activity. Galactose must first be metabolized by the Leloir pathway before entering glycolysis, resulting in a significant reduction in glycolytic flux.
As suggested by the reviewer, we will complement our study by using galactose as the carbon source instead of glucose in a new set of experiments during the revision process.
__ UK5099 and oxamate nuclei seem smaller and less bright compared to the control. Images between control and UK5099 appear marginally different (non-significant).__
Figure 3A may not clearly convey that the nuclei are unaffected by the treatment. To address this, we will adjust the images, particularly the DAPI staining settings, to ensure accurate interpretation.
Regarding the slight effect of UK5099 treatment on Gag expression (infection index), as discussed above, this effect is reproducible and significant. We will replace the current image in Figure 3A with a more representative one.
The overall impact of the study is limited as the authors provide no evidence on the mechanism of how glycolysis induces TNT formation, which needs to be more characterized.
We fully agree that understanding how glycolysis induces tunneling nanotubes (TNTs) is a crucial and challenging question. This challenge stems from the incomplete understanding of the molecular mechanisms underlying TNT formation and the contradictory results reported across different cell types.
In our study, we demonstrated that inhibiting glycolysis—using Oxamate, GSK, or glucose deprivation—reduces TNT formation, whereas promoting glycolysis with UK5099 enhances their formation. We discuss in the manuscript that glycolysis likely provides the energy required for actin cytoskeletal rearrangements, which are essential for TNT formation.
Moreover, ATP plays a critical role in supporting cellular functions depending on actin remodeling, such as cell migration and the epithelial-to-mesenchymal transition (DeWane et al., 2021, PMID__33558441).__
To try to investigate the molecular mechanisms underlying TNT formation in our model, we propose the following experiments during the revision process:
- HIF1-α and TNT formation: IF staining of HIF1-α will be performed to correlate TNT formation with the level of HIF1-α nuclear translocation (as quantified in Figure 2I). This experiment aims to demonstrate a link between HIF1-α activation and TNT formation.
- Effect of HIF1-α inhibition: TNT formation will be quantified upon inhibiting HIF1-α activity using pharmacological approaches and/or siRNA-mediated gene silencing in HIV-1-infected CmMTB-treated cells.
- GLUT-1 focalization and TNT formation: To establish a connection between glycolysis and TNT formation, we will localize the primary glucose transporter GLUT-1 in relation to TNTs in CmMTB-treated macrophages. This approach builds on previous work on microvilli, which are F-actin structures with similarities to TNTs (Hexige et al., 2015, PMID: 25561062). Confocal or super-resolution microscopy will be employed to determine whether GLUT-1 accumulates at specific TNT sites. Through these experiments, we aim to provide deeper insights into the role of glycolysis in TNT formation.
__Minor comments:
The manuscript does not clearly show how the total ATP was calculated from the ATP rate assay.__
We will ensure that the method for calculating total ATP is explicitly described in the Methods section of the revised manuscript. __ In figure 1 (and everywhere else) the units on the y-axis should be corrected to [pmol/min] instead of pmol and the Seahorse profiles should mention whether the axis represents OCR or ECAR.__
The reviewer is correct. The axes in the relevant figures for ATP rate results (Figure 1A, B, C, D and Figure S1A, B, C) will be revised in the updated version of the manuscript.
The authors have called the macrophages highly glycolytic in first set of results which is misleading. Although the glycoATP contribution is increasing, overall ATP production is still majorly through oxidative phosphorylation (70% vs 25%).
We fully agree with the reviewer’s comment. As mentioned in the Result section “Approximately 90% of ATP production in macrophages differentiated with cmCTR came from OXPHOS; this parameter was reduced to 70% when conditioned with cmMTB (Figure 1E-F).” CmMTB and TB-PE drive macrophages toward an M2/M(IL-10) phenotype (Lastrucci et al. 2015), and based on the extensive literature on metabolism of anti-inflammatory M2 macrophages, this phenotype primarily relies on OXPHOS and fatty acid oxidation (for review see Biswas and Mantovani, Cell Metabolism, 2012).
It is therefore logical that overall ATP production in these cells remains predominantly through OXPHOS. However, we observe a significant decrease in OXPHOS activity following CmMTB treatment, alongside a marked increase in glycolysis (Figure 1).
Referring to CmMTB-treated macrophages as highly glycolytic was inaccurate, indeed, and this terminology will be corrected, with a clearer explanation provided in the revised manuscript.
Fig 3: Why does the HIV gag protein signal appear as irregular large spots?
In Figure 3A, the resolution used is sufficient to quantify the number of cells positive for HIV Gag (and thus the infection index). However, it does not allow for detailed examination of the intracellular localization of Gag as “spots”. The reviewer is correct that, within macrophages, the Gag signal often appears as large and intense cytoplasmic “spots” corresponding to the VCC, as illustrated in Reviewer Figure 1 in response to Reviewer 1.
__Referees cross-commenting:
I agree with the reviewer# 1 assessment. However, I feel that mechanistically paper could be improved and by performing more experiments.__
We fully agree that additional experiments are essential to improve the manuscript. We will address all comments and perform the experiments suggested by Reviewer 2, particularly to better characterize the metabolic state of our cells, provide evidence for the role of glycolysis in HIV-1 exacerbation, and further elucidate the mechanism by which glycolysis induces TNT formation.
Significance
The knowledge generated is important and valuable for future studies to understand the molecular players in regulating immunometabolism during HIV-TB coinfection.
We appreciate this positive comment.
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Referee #2
Evidence, reproducibility and clarity
The current work is based on previous observations that the abundance of lung macrophages is augmented in NHPs with active TB and exacerbated in those coinfected with SIV (Dupont et al., 2022; Dupont et al., 2020; Souriant et al., 2019). Further work with these TB-induced immunomodulatory macrophages demonstrated an increased susceptibility to HIV-1 replication and spread via the formation of tunneling nanotubes (TNTs), (Souriant et al., 2019). In the present manuscript, the authors connected these findings with the metabolic state of macrophages (glycolysis vs OXPHOS). Using a range of metabolic inhibitors coupled with seahorse assays and microscopy confirmed the role of Mtb-induced glycolytic shift in inducing the formation of TNTs and the spread of HIV. The work is well-planned and executed. However, the study is mainly correlative without any molecular insights. The knowledge generated is important and valuable for future studies to understand the molecular players in regulating immunometabolism during HIV-TB coinfection.
Major Comments
There are conflicting reports about Mtb's impact on macrophage ECAR and OXPHOS, which authors have acknowledged. Therefore, including OCR and ECAR plots along with the glycoATP and MitoATP data will be useful. Similarly, OCR/ECAR plots without any conditioned medium should be included to clarify the role of Mtb infection on OCR/ECAR.
Fig 2G image is not convincing. While HIF1 alpha seems more in the nucleus, the overall morphology of the cell is more compact. Additional verification is needed. Furthermore, genetic evidence is required in order to confirm if HIF1 alpha is the primary regulator of glycolytic shift by cmMTB/PE-TB, leading to more HIV dissemination by the TNT formation.
Also, the authors have used only one tool to measure HIV levels -microscopy. While important, another method for verifying findings is needed. This is important as the effect of inhibitors (UK5099) is marginal.
Authors have used oxamate to inhibit glycolysis. Inhibition of LDH could lead to inhibition of NAD/NADH regeneration, thereby slowing down glycolysis. However, lack of lactate could have wide-ranging influence on cells as lactate could regulate several post-translational modifications, including lactylation. While the authors argued against using 2-DG, several findings confirm the glycolysis inhibitory potential of 2-DG when infected with Mtb. This should be included.
A standard glycolytic function test (glucose, oligomycin and 2-DG injection) should be performed to assess the effect of TB-PE and cmMTB on the macrophages directly.
Depriving glucose is not the best way to show the effect of glucose on HIV infection and MGC formation, as it can affect other aspects of cellular physiology, such as redox and bioenergetics. Instead, the use of galactose in place of glucose would generate ATP only by OXPHOS. Some key experiments should be repeated using galactose as a sole C source.
UK5099 and oxamate nuclei seem smaller and less bright compared to the control. Images between control and UK5099 appear marginally different (non-significant).
The overall impact of the study is limited as the authors provide no evidence on the mechanism of how glycolysis induces TNT formation, which needs to be more characterized.
Minor comments:
The manuscript does not clearly show how the total ATP was calculated from the ATP rate assay.
In figure 1 (and everywhere else) the units on the y-axis should be corrected to [pmol/min] instead of pmol and the Seahorse profiles should mention whether the axis represents OCR or ECAR.
The authors have called the macrophages highly glycolytic in first set of results which is misleading. Although the glycoATP contribution is increasing, overall ATP production is still majorly through oxidative phosphorylation (70% vs 25%).
Fig 3: Why does the HIV gag protein signal appear as irregular large spots?
Referees cross-commenting
I agree with the reviewer# 1 assessment. However, i feel that mechanistically paper could be improved and by performing more experiments.
Significance
The knowledge generated is important and valuable for future studies to understand the molecular players in regulating immunometabolism during HIV-TB coinfection.
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Referee #1
Evidence, reproducibility and clarity
In the manuscript entitled "Inhibition of glycolysis in tuberculosis-mediated metabolic rewiring reduces HIV-1 spread across macrophages", Vahlas and colleagues investigated the hypothesis that Mtb interferes with HIV-1 infection of human macrophages, as they represent a common target cell type. In particular, they observed that a conditioned medium generated from Mtb-infected macrophages (Mtb-CM) induces tunneling nanotubes (TNT) in HIV-infected macrophages thereby facilitating viral spreading. At the same time, Mtb-CM induced a glycolytic pathway leading to ATP accumulation in HIV-infected macrophages, an essential pathway for TNT induction whereas pharmacological interference with such a metabolic switch resulted in a reduced viral production.
Experimental approach: primary human monocytes differentiated into monocyte-derived macrophages (MDM) in the presence of a TB-dominated microenvironment (Mtb-CM). The intracellular rate of ATP production was evaluated by the Seahorse technology at day 3 of MDM differentiation. The measurements of basal extracellular acidification rate (ECAR) and basal oxygen consumption rate (OCR) were used to calculate ATP production rate from glycolysis (GlycoATP) and mitochondrial OXPHOS (MitoATP).
This is a well-conducted, innovative study exploring the interaction of two main human pathogens, i.e. Mtb and HIV, sharing macrophages as common target cell. The manuscript is clearly written and the conclusions and hypotheses are supported by experimental evidence. I have two general points that I encourage the authors to address.
- As mentioned in the Discussion, macrophage infection by HIV is characterized by the accumulation of preformed, infectious virions in VCC (Virus Containing Compartments) that can be pharmacologically modulated both in terms of accumulation and rapid release in the absence of cell cytopathicity. Although the modulation of VCC was not the objective of the present study, it would be important to discuss their role and their potential modulation by Mtb and/or metabolic modifications, if known.
- Understanding the purpose of using a VSV-g based infection system, nonetheless it would be important to know whether metabolic modulation does affect CD4 and CCR5 expression on MDM and its consequence for their susceptibility to HIV infection, in addition to the effects on TNT formation and viral transfer between cells.
Specific points:
- "TB-PE" (pleural effusion) is neither specified in the Results nor in the Methods sections.
- Figure 3A does not seem to display cell viability, but rather HIV Gag expression by IFA. Furthermore, Figure 3C indicates Gag expression, not "HIV infection" (see page 8, Results).
Significance
The paper addresses a poorly explored area, i.e. the interaction of Mtb and HIV during infection of macrophages. The authors focused on a specific aspect of such an interaction (I,e, the modulation of nanotubes formation and transfer of virions to target cells), but their results can be extrapolated in a broader context, particularly if the authors will be willing to address my general questions.
Although specific in its experimental approach, the implication of the study will be of interest to a general audience.
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Reply to the reviewers
Reply to the Reviewers
We thank all the reviewers for their time and their constructive criticism. We are encouraged by the overall positive and enthusiastic responses from the reviewers. We have taken all comments and suggestions seriously and revised the manuscript. These revisions include adding more explanation for the meaning of synaptic learning rules, language definitions, and model characteristics and limitations with more detailed figure legends. We are confident that we have addressed all the reviewer’s concerns by incorporating the reviewer’s suggestions into the revised manuscript. All changes are indicated in red font in the revised manuscript. The point-by-point response to all concerns raised by the reviewers follows. The line numbers indicated here refer to those in the revised manuscript.
Reviewer #1
Major comments:
- Introduction, line 64 and further: An important omission in the introduction is that several studies have shown that sleep deprivation, i.e., extended wakefulness, results in a loss of spines in some brain regions such as the hippocampus, which is directly opposing the SHY hypothesis (for review, see Raven et al. Sleep Med Rev 39: 3-11, 2018).
Response:
We appreciate the reviewer’s valuable comment. Indeed, as correctly pointed out, several studies have reported synaptic weakening in the hippocampus and cortical regions following sleep deprivation, which appears to contradict the SHY.
We have incorporated this point into the introduction section (lines 64-67), adding several articles, including Raven et al., the reviewer suggested.
- Introduction, line 85-87: A short explanation of what exactly the anti-Hebbian and anti-STDP rules are, is important here. It may seem obvious to the authors, but it is best to spell it out for the potential broad readership interested in this paper.
Response:
We appreciate the reviewer’s important suggestion.
Previous studies reported that Anti-Hebbian plasticity, which leads to depression when synapses are presented with correlated activity, serves critical functions in the discrimination of specific spike sequences in the cortico-striatal synapses (G. Vignoud et al., Commun. Biol, 2024) and the detection of novel stimuli in mormyrid fish (P. D. Roberts et al., Biol. Cybern, 2008; P. D. Roberts et al, Front. Comput. Neurosci, 2010).
We have added the explanations for Anti-Hebbian and Anti-STDP rules into the introduction section (lines 87-89).
- Results, line 116, 129/130, 333, 395, 400, figure captions: Pleases explain what is meant with the terms 'pre-neuronal synapse' and 'post-neuronal synapses'.
Response:
We appreciate the reviewer’s advice. We have replaced ‘pre-neuronal synapse’ and ‘post-neuronal synapse’ with ‘pre-synaptic’、’post-synaptic’, respectively, for readability in the Results section (lines 118-119, 131-133, 368, 371, 432, 436 and 437) and Figure legends.
- Results, line 121-124 say that synaptic efficacy became higher in sleep-like states than in wake-like states under Hebbian and STDP learning rules and opposite results were observed with anti-Hebbian and anti-STDP learning rules. While these relative differences are indeed visible in Figure 1H, the figure also suggests that synaptic efficacy during sleep was largely independent of the average firing frequency. In other words, synaptic efficacy seems to be dependent on firing frequency only during wakefulness. Is that correct?
Response:
The reviewer raised an important point. As shown in Fig. 1H, synaptic efficacy during sleep appears to be largely independent of mean firing rates. Here, the firing rates were adjusted by varying Down-state durations. Regarding the relationship between firing patterns and synaptic efficacy, synaptic efficacy is influenced not only by firing frequency but also by how firing patterns are generated. When firing rates are adjusted by changing ISI, synaptic efficacy during sleep also increases with higher firing rates as wake-like patterns (Fig. 5). In Fig. 2D and E, we demonstrated that the synaptic efficacy during sleep becomes higher than during wakefulness regardless of whether the spike patterns were generated with changing Down-state duration or ISI, assuming the same mean firing rates during the sleep-like and wake-like states. We have clarified this point by adding the explanation in the Discussion section (lines 318-323).
- Results, line 199 and down model the effect of differences in mean firing rate between sleep and waking, which is a crucial addition and more realistic approach for most brain regions that have lower average firing rates during sleep. It is interesting that in this case the relative effects of sleep and wakefulness can change direction, depending on the average firing frequency. Would the authors argue that this may even result in opposite effects in different brain regions after waking or sleep deprivation?
Response:
We appreciate the reviewer raising the interesting point. Our model predicted that the direction of synaptic changes depends on learning rules and firing rates. This prediction indicated that different brain regions may exhibit synaptic changes even in opposite directions after prolonged wakefulness or sleep deprivation. For example, under Hebbian and STDP, our model predicted that brain regions with firing rates increased during wakefulness or sleep deprivation compared to sleep would follow SHY, while brain regions where firing rates remain unchanged or decreased compared to sleep would follow WISE. The experimental validation of these predictions, focusing on brain regions with different activation states during wakefulness, is an interesting future work. We have clarified this point into the Discussion section (lines 260-262).
- Figure 1: The caption needs more details to help understand the different panels. some work. (B) What is a post-neuronal synapse? (C) How exactly is synaptic efficacy defined? (E) Not totally clear what the colored top panels represent.
Response:
We sincerely appreciate the reviewer’s thoughtful feedback. We agreed that Figure 1 required a more thorough explanation. In response, we have expanded the figure legend to provide more detailed information for readers to easily understand.
- Figure 5B. Since this appears to be a graphical abstract and unified framework for all the modelled parameters and learning rules, should this not be a separate figure?
__Response: __We thank the reviewer for the helpful suggestion. We have renumbered Figure 5B as Figure 6.
- Figures captions: The information provided in the figure captions is in many cases quite minimal and does not reflect the complexity of some of the figure panels. This often makes it hard for a reader to extract all the relevant information without thumbing back and forth between figures, captions and main text. I strongly suggest to add more detail to the figure captions to make them more stand-alone and self-explanatory.
__Response: __We sincerely appreciate the reviewer’s significant feedback. We have added detailed explanations in the figure legends, including Supplementary Figures, for readers to understand easily.
Reviewer #2
Major comments:
- I am not qualified to review this manuscript because I'm not sufficiently familiar with the type of modelling performed here and the specific use of terms. For example, without providing any explanation, I cannot reconstruct whether the estimates of synaptic efficacy (eq.1) are valid and applicable to the questions asked. I do have 2 general comments. I do find the premise of WISE intriguing and understand the attractiveness of the idea of opposing 'WISE' to SHY. Nevertheless, SHY is a theory that does not discount the occurrence of synaptic strengthening during sleep. It is rather that during sleep there is a net down-scaling. Therefore, the assumptions, as they are presented here, are confusing the issue.
Response:
We are deeply grateful that the reviewer found WISE intriguing and appreciate the insightful comment. We agree that SHY does not deny the occurrence of synaptic strengthening during sleep, but rather proposes a net downward scaling under the assumption of the overall synaptic homeostasis. In the present study, we assumed that SHY describes a net downscaling during sleep (and does not deny the occurrence of synaptic strengthening of some synapses during sleep) while WISE describes a net upscaling during sleep (and does not deny the occurrence of synaptic weakening of some synapses during sleep). Both SHY and WISE fulfill synaptic homeostasis. For example, SHY upscales synaptic strength during wakefulness and downscales during sleep to achieve synaptic homeostasis. On the other hand, WISE upscales synaptic strength during sleep and downscales during wakefulness __to achieve synaptic homeostasis. Our study demonstrated that __WISE is compatible with Hebbian and STDP learning rules when average neuron firing frequency is similar between sleep and wakefulness, and SHY is not compatible with Hebbian and STDP learning rules, but rather compatible with Anti-Hebbian and __Anti-STDP __learning rules.
We agreed with the reviewer that the lack of an explicit definition of SHY and WISE in the context of the present study could cause confusion for readers. Therefore, we have added a sentence to clarify SHY and WISE in the present study in the first paragraph of the Results section (lines 127-128), specifically defining them in terms of relative net synaptic changes within local neural network.
- SHY was, in part, inspired by a type of plasticity that is not considered here, namely synaptic homeostasis. Would adding such a mechanism to the model alter any of the predictions?"
__Response: __
We appreciate the reviewer raising an important point on synaptic homeostasis. In this study, we did not explicitly include synaptic homeostasis in the preposition but consider synaptic homeostasis in the definitions of SHY and WISE. For example, we assume that SHY upscales synaptic strength during wakefulness and downscales during sleep to achieve synaptic homeostasis while WISE upscales synaptic strength during sleep and downscales during wakefulness to achieve synaptic homeostasis. Importantly, since both SHY and WISE can achieve synaptic homeostasis, there are two types of synaptic homeostasis. In our study, WISE-type synaptic homeostasis is compatible with Hebbian and STDP learning rules when average neuron firing frequency is similar between sleep and wakefulness, and SHY-type synaptic homeostasis is compatible with Anti-Hebbian and __Anti-STDP __learning rules. Since our studies already consider two types of synaptic homeostasis, adding the further mechanism of synaptic homeostasis in the preposition would not alter our predictions. We described these points in the Model characteristics and limitations part in the Discussion section (lines 332-339).
Reviewer #3
Major comments:
- This is a well-written manuscript that is easily to follow and amply illustrated. The study seems very exciting but unfortunately I am not a mathematician so I cannot attest to the veracity or originality of the model. Assuming it is robust, it does appear to account for a quite a few anomalies (and inaccuracies depicted in textbooks). It would be helpful to discuss the limitations of other models that have been suggested to synaptic functions of sleep.
__Response: __
We appreciated the reviewer’s constructive suggestions. Some computational studies have investigated synaptic changes in neural networks under STDP protocols using Ca2+-based plasticity models (M. Graupner et al., PNAS, 2012; G. Chindemi et al., Nat. Commun, 2022), while other studies have examined how SWO affects synaptic plasticity under STDP conditions (T. Tadros et al., J.Neurosci, 2022). However, these previous studies were limited to a single synaptic learning rule or firing pattern. Our study is the first to comprehensively investigate synaptic dynamics during the sleep-wake cycle by integrating a Ca2+-based plasticity model to represent various types of synaptic learning rules and various simulated sleep-wake firing patterns.
We have added the sentences related to the reviewer’s comments in the Model characteristics and limitations part in the Discussion section (lines 306-312).
- Much of the neurophysiological data comes from recordings in rodents, so the model is simulating rat EEG signatures-how readily applicable is this to the human condition? Indeed, how readily can they compare between mouse and rat? The authors should expand on this in the discussion section.
Another potential weakness or limitation is the unanswered question of the model can account for sleep/wake changes in other areas of the cortex or thalamus etc.
Does this model apply equally to males and females?
__Response: __
We appreciate the reviewer for raising this significant point. As the reviewer pointed out, we generated firing patterns using parameters derived from rat firing patterns (B. O. Watson et al., Neuron, 2016), such as ISI, Up-state duration, and Down-state duration. While we started our simulations from those parameter sets, we tested a range of different values for each parameter and found consistent results (detailed in Supplementary Materials, Generation of sleep and wake-like firing patterns). The ranges of Up-state and Down-state durations during SWO in mice, rats, and cats are approximately 100-500 milliseconds (M. Steriade et al., J. Neurophysiol, 2001; V. Crunelli et al., Pflugers Arch, 2012), while in humans, Up-state durations range from 250-1000 milliseconds (B. A. Riedner et al., Sleep, 2007), all of which fall within the ranges examined in Figs. 2 D and E. Similarly, wake-state ISI across various species typically range from 2-100 milliseconds (M. Steriade et al., J. Neurophysiol, 2001; G. Maimon et al., Neuron, 2009), mostly within the scope covered in Fig. 2E. Therefore, we suppose our finding in the present study captured universal aspects of synaptic dynamic in the sleep and wake cycles regardless of species, brain region, or sex.
We have added the description in the Model characteristics and limitations part in the Discussion section (lines 312-331).
Minor comments:
Minor typo: ref. 24 is missing page and volume numbers.
__Response: __
Thank you for pointing out this typo. We corrected this by adding the page and volume numbers in Ref. 28 in the revised manuscript.
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Referee #3
Evidence, reproducibility and clarity
Understanding the functions of sleep has and remains a key question in neuroscience. A popular hypothesis is that sleep is fundamental to learning and memory and that this can be detected and measured at the level of neural networks and connections as increased synaptic weights across waking states and reduced synaptic weights or depression during sleep states. However, there are many contradictions in the literature and while it is accepted that sleep plays a role in memory consolidation, the molecular/cellular basis of this is far from clear. As considerable experimental data on synaptic function have been collected during sleep and wake states, here the authors turned to modelling how manipulating the rules of synaptic plasticity can illuminate the problem. In this manuscript, the authors report the outcomes of these simulations neuronal oscillations, firing, and synaptic plasticity across sleep-like and wake-like neural states. They report that their simulations can account for several irregularities and highlight differential involvement of spike-firing dependent plasticity (STDP) and anti-STDP in wake and NREM sleep. In particular they note that under Hebbian and STDP rules, firing patterns associated with wake lead to decreased synaptic weights, while sleep-like patterns bolster synaptic weights and collectively they describe this tendency as WISE. They also note that under Anti-Hebbian and Anti-STDP rules, synaptic depression was observed under NREM. The chief strength of this study is shows how simulation can aid in bringing together disparate observations into a well-worked study space.
This is a well-written manuscript that is easily to follow and amply illustrated. The study seems very exciting but unfortunately I am not a mathematician so I cannot attest to the veracity or originality of the model. Assuming it is robust, it does appear to account for a quite a few anomalies (and inaccuracies depicted in textbooks). It would be helpful to discuss the limitations of other models that have been suggested to synaptic functions of sleep.
Much of the neurophysiological data comes from recordings in rodents, so the model is simulating rat EEG signatures-how readily applicable is this to the human condition? Indeed how readily can they compare between mouse and rat? The authors should expand on this in the discussion section.
Another potential weakness or limitation is the unanswered question of the model can account for sleep/wake changes in other areas of the cortex or thalamus etc.
Does this model apply equally to males and females? Minor typo: ref. 24 is missing page and volume numbers.
Significance
As noted above, there are discrepancies in the literature regarding synaptic plasticity and its mechanisms across the sleep-wake cycle. This model appears to answer some of the reasons for these and provides a framework for further experimental research to interrogate these mechanisms.
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Referee #2
Evidence, reproducibility and clarity
I am not qualified to review this manuscript because I'm not sufficiently familiar with the type of modelling performed here and the specific use of terms. For example, without providing any explanation, I cannot reconstruct whether the estimates of synaptic efficacy (eq.1) are valid and applicable to the questions asked.
I do have 2 general comments. I do find the premise of WISE intriguing and understand the attractiveness of the idea of opposing 'WISE' to SHY. Nevertheless, SHY is a theory that does not discount the occurrence of synaptic strengthening during sleep. It is rather that during sleep there is a net down-scaling. Therefore, the assumptions, as they are presented here, are confusing the issue. SHY was, in part, inspired by a type of plasticity that is not considered here, namely synaptic homeostasis. Would adding such a mechanism to the model alter any of the predictions?"
Significance
I do find the premise of WISE intriguing and understand the attractiveness of the idea of opposing 'WISE' to SHY. Nevertheless, SHY is a theory that does not discount the occurrence of synaptic strengthening during sleep. It is rather that during sleep there is a net down-scaling. Therefore, the assumptions, as they are presented here, are confusing the issue. SHY was, in part, inspired by a type of plasticity that is not considered here, namely synaptic homeostasis. Would adding such a mechanism to the model alter any of the predictions?"
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Referee #1
Evidence, reproducibility and clarity
Summary:
While the function of sleep is still an unresolved mystery, some of the most influential theories propose that sleep serves a crucial role in regulating neuronal plasticity and synaptic strength. However, the exact way how synaptic strength is affected by sleep and impaired by sleep deprivation is a topic of much controversy and ongoing debate in the field of sleep research (SHY vs WISE). Using computation models, the manuscript illustrates that opposite effects of sleep on synaptic efficacy can be found, depending on the firing patterns and learning rules. Specifically, sleep promotes synaptic strength and efficacy under Hebbian and spike-timing dependent plasticity rules and it resulted in synaptic depression under anti-Hebbian and anti-STDP rules.
Major comments:
Introduction, line 64 and further: An important omission in the introduction is that several studies have shown that sleep deprivation, i.e., extended wakefulness, results in a loss of spines in some brain regions such as the hippocampus, which is directly opposing the SHY hypothesis (for review, see Raven et al. Sleep Med Rev 39: 3-11, 2018).
Introduction, line 85-87: A short explanation of what exactly the anti-Hebbian and anti-STDP rules are, is important here. It may seem obvious to the authors, but it is best to spell it out for the potential broad readership interested in this paper.
Results, line 116, 129/130, 333, 395, 400, figure captions: Pleases explain what is meant with the terms 'pre-neuronal synapse' and 'post-neuronal synapses'.
Results, line 121-124 say that synaptic efficacy became higher in sleep-like states than in wake-like states under Hebbian and STDP learning rules and opposite results were observed with anti-Hebbian and anti-STDP learning rules. While these relative differences are indeed visible in Figure 1H, the figure also suggests that synaptic efficacy during sleep was largely independent of the average firing frequency. In other words, synaptic efficacy seems to be dependent on firing frequency only during wakefulness. Is that correct?
Results, line 199 and down model the effect of differences in mean firing rate between sleep and waking, which is a crucial addition and more realistic approach for most brain regions that have lower average firing rates during sleep. It is interesting that in this case the relative effects of sleep and wakefulness can change direction, depending on the average firing frequency. Would the authors argue that this may even result in opposite effects in different brain regions after waking or sleep deprivation?
Figure 1: The caption needs more details to help understand the different panels. some work. (B) What is a post-neuronal synapse? (C) How exactly is synaptic efficacy defined? (E) Not totally clear what the colored top panels represent.
Figure 5B. Since this appears to be a graphical abstract and unified framework for all the modelled parameters and learning rules, should this not be a separate figure?
Figures captions: The information provided in the figure captions is in many cases quite minimal and does not reflect the complexity of some of the figure panels. This often makes it hard for a reader to extract all the relevant information without thumbing back and forth between figures, captions and main text. I strongly suggest to add more detail to the figure captions to make them more stand-alone and self-explanatory.
Significance
This paper addresses a major controversy in the field of sleep research: does sleep strengthen neuronal connections in the brain or does it downscale and weaken them (Raven et al. 2018)? Using computation models, the current paper shows that both options are possible and it does an admirable job in bridging the different views on sleep and synaptic strength. As such, the conceptual value of this paper can hardly be overestimated and provides an important framework for future experimental studies.
This paper is of interest for most everybody interested in sleep and brain function, as well as neuroscientist with a broader interest in brain plasticity.
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Reply to the reviewers
Manuscript number: RC-2024-02465
Corresponding author(s): Saravanan, Palani
1. General Statements
We would like to thank the Review Commons Team for handling our manuscript and the Reviewers for their constructive feedback and suggestions. In our revised manuscript, we have addressed and incorporated all the major suggestions of the reviewers, and we have also added new significant data on the role of Tropomyosin in regulation of endocytosis through its control over actin monomer pool maintenance and actin network homeostasis. We believe that with all these additions, our study has significantly gained in quality, strength of conclusions made, and scope for future work.
2. Point-by-point description of the revisions
Reviewer #1
Evidence, reproducibility and clarity
There are 2 Major issues -
Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.
Response: We would like to clarify that all mNG-Tpm constructs used in our study contain a 40 amino-acid (aa) flexible linker between the N-terminal mNG fluorescent protein and the Tpm protein as per our earlier published study (Hatano et al., 2022). During initial optimization, we have also experimented with linker length and the 40aa-linker length works optimally for clear visualization of Tpm onto actin cable structures in budding yeast, fission yeast (both S. pombe and S. japonicus), and mammalian cells (Hatano et al., 2022). These constructs have also been used since in other studies (Wirshing et al., 2023; Wirshing and Goode, 2024) and currently represents the best possible strategy to visualize Tpm isoforms in live cells. In our study, we characterized these proteins for functionality and found that both mNG-Tpm1 and mNG-Tpm2 were functional and can rescue the synthetic lethality observed in Dtpm1Dtpm2 cells. During our study, we observed that mNG-Tpm1 expression from a single-copy integration vector did not restore full length actin cables in Dtpm1 cells (Fig. 1B, 1C). We hypothesized that this could be a result of reduced binding affinity of the tagged tropomyosin due to lack of normal N-terminal acetylation which stabilizes the N-terminus. The 40aa linker is unstructured and may not be able to neutralize the charge on the N-terminal Methionine, thus, we tried to insert -Ala-Ser- dipeptide which has been routinely used in vitro biochemical studies to stabilize the N-terminal helix and impart a similar effect as the N-terminal acetylation (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) by restoring normal binding affinity of Tpm to F-actin (Monteiro et al., 1994; Greenfield et al., 1994). We observed that addition of the -Ala-Ser- dipeptide to mNG-Tpm fusion, indeed, restored full length actin cables when expressed in Dtpm1 cells, performing significantly better in our in vivo experiments (Fig. 1B, 1C). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may stabilize the N-terminus of Tpm and allow normal head-to-tail dimer formation (Greenfield et al., 1994; Monteiro et al., 1994; Frye et al., 2010). We have discussed this in our new Discussion section (Lines 350-372). Since, the addition of -AS- dipeptide was referred to as "acetyl-mimic (am)" in a previous study (Alioto et al., 2016), we continued to use the same nomenclature in our study. Now as per your suggestions and to be more accurate, we have renamed "mNG-amTpm" constructs as "mNG-ASTpm" throughout the study to not confuse or claim that -AS- addition mimics acetylation. In any case, we have not seen any other ill effect of -AS- dipeptide introduction in addition to our 40 amino acid linker suggesting that it can also be considered part of the linker. Although, we agree with the reviewer that biochemical characterization of the effect of linker would be important to determine, we strongly believe that it is currently outside the scope of this study and should be taken up for future work with these proteins. Our study has majorly aimed to understand the functionality and utility of these mNG-Tpm fusion proteins for cell biological experiments in vivo, which was not done earlier in any other model system.
My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.
Response: __ We agree with the reviewer that N-terminal tagging of tropomyosin may have effects on its function, but these constructs represent the only fluorescently tagged functional tropomyosin constructs available currently while C-terminal fusions are either non-functional (we were unable to construct strains with endogenous Tpm1 gene fused C-terminally to GFP) or do not localize clearly to actin structures (See __Figure R1 showing endogenous C-terminally tagged Tpm2-yeGFP that shows almost no localization to actin cables). To our knowledge, our study represents a first effort to understand the question of spatial sorting of Tpm isoforms, Tpm1 and Tpm2, in S. cerevisiae and any future developments with better visualization strategies for Tpm isoforms without compromising native N-terminal modifications and function will help improve our understanding of these proteins in vivo. We have also discussed these possibilities in our new Discussion section (Lines 391-396).
Significance
This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly. The significance of this study, given the above, and the concerns raised is not clear to this reviewer.
Response: __Our study explores multiple facets of Tropomyosin (Tpm) biology. The lack of functional tagged Tpm has been a major bottleneck in understanding Tpm isoform diversity and function across eukaryotes. In our study, we characterize the first functional tagged Tpm proteins (Fig. 1, Fig. S1) and use them to answer long-standing questions about localization and spatial sorting of Tpm isoforms in the model organism S. cerevisiae (Fig. 2, Fig. 3, Fig. S2, Fig. S3). We also discover that the dual Tpm isoforms, Tpm1 and Tpm2, are functionally redundant for actin cable organization and function, while having gained divergent functions in Retrograde Actin Cable Flow (RACF) (Fig. 4, Fig. 5A-D, Fig. S4, Fig. S5, Fig. S6). We have now added new data on role of global Tpm levels controlling endocytosis via maintenance of normal linear-to-branched actin network homeostasis in S. cerevisiae (Fig. 5E-G)__. We respectfully differ with the reviewer on their assessment of our study and request the reviewer to read our revised manuscript which discusses the significance, limitations, and future perspectives of our study in detail.
Reviewer #2
Evidence, reproducibility and clarity
This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.
Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.
1. Functionality of the acetyl-mimic tagged tropomyosin constructs: The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.
__Response: __We would like to thank the reviewer for his feedback and suggestions. Based on the suggestions, we have now more accurately described the growth rescue observed by expression of mNG-ASTpm1 in Dtpm1 cells in the revised text. We have also removed the use of "completely functional" to describe mNG-Tpm functionality and corrected any errors in Figure citations in the revised manuscript.
As per reviewers' suggestion, we have now tested rescue of synthetic lethality of Dtpm1Dtpm2 cells by expression of all mNG-Tpm variants and we find that all of them are capable of restoring the viability of Dtpm1Dtpm2 cells when expressed under their native promoters via a high-copy plasmid (pRS425) (Fig. S1E) but only mNG-Tpm1 and mNG-ASTpm1 restored viability of Dtpm1Dtpm2 cells when expressed under their native promoters via an integration plasmid (pRS305) (Fig. S1F). These results clearly suggest that while both mNG-Tpm1 and mNG-Tpm2 constructs are functional, Tpm1 tolerates the presence of the N-terminal fluorescent tag better than Tpm2. These observations now enhance our understanding of the functionality of these mNG-Tpm fusion proteins and will be a useful resource for their usage and experimental design in future studies in vivo.
It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.
Response: __We agree with the reviewer's observation and for the sake of clarity and accuracy, we have now renamed "mNG-amTpm" with "mNG-ASTpm". The use of -AS- dipeptide is very routine in studies with Tpm (Alioto et al., 2016; Palani et al., 2019; Christensen et al., 2017) and its addition restores normal binding affinities to Tpm proteins purified from E. coli (Monteiro et al., 1994). We agree with the reviewer that the -AS- dipeptide addition may not mimic N-terminal acetylation structurally but as per previous studies, it may help neutralize the impact of a freely protonated Met on the alpha-helical structure and stabilize the N-terminus helix of Tpm and allow normal head-to-tail dimer formation (Monteiro et al., 1994; Frye et al., 2010; Greenfield et al., 1994). Consistent with this, we also observe a highly significant improvement in actin cable length when expressing mNG-ASTpm as compared to mNG-Tpm in Dtpm1 cells, suggesting an improvement in function probably due to increased binding affinity (Fig. 1B, 1C). We have also discussed this in our answer to Question 1 of Reviewer 1 and the revised manuscript (Lines 350-372)__.
__ Localization of Tpm1 and Tpm2:__Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.
Response: __We thank the reviewer for this observation and their suggestions. We agree that relative concentrations of functional Tpm1 and Tpm2 in cells may influence the extent of their localizations. As per the reviewer's suggestion, we have now conducted our quantitative analysis in cells lacking endogenous Tpm1 and only expressing mNG-ASTpm1 from an integrated plasmid copy at the leu2 locus and the data is presented in new __Figure S3. We compared Tpm-bound cable length (Fig. S3A, S3B) __and Tpm-bound cable number (Fig. S3A, S3C) along with actin cable length (Fig. S3D, S3E) and actin cable number (Fig. S3D, S3F) in wildtype, Dbnr1, and Dbni1 cells. Our analysis revealed that mNG-ASTpm1 localized to actin cable structures in wildtype, Dbnr1, and Dbni1 cells and the decrease observed in Tpm-bound cable length and number upon loss of either Bnr1 or Bni1, was accompanied by a corresponding decrease in actin cable length and number upon loss of either Bnr1 or Bni1. Thus, this analysis reached the same conclusion as our earlier analysis (Fig. 2) that mNG-ASTpm1 does not show preference between Bnr1 and Bni1-made actin cables. mNG-ASTpm2 did not restore functionality, when expressed as single integrated copy, in Dtpm1Dtpm2 cells (new results in __Fig. S1E, S1F, S5A) thus, we could not conduct a similar analysis for mNG-ASTpm2. This suggests that use of mNG-ASTpm2 would be more meaningful in the presence of endogenous Tpm2 as previously done in Fig. 2D-F.
We have now also performed additional yeast mating experiments with cells lacking bnr1 gene and expressing either mNG-ASTpm1 or mNG-ASTpm2 and the data is shown in new Figure 3. From these observations, we observe that both mNG-ASTpm1 and mNG-ASTpm2 localize to the mating fusion focus in a Bnr1-independent manner (Fig. 3B, 3D) and suggests that they bind to Bni1-made actin cables that are involved in polarized growth of the mating projection. These results also add strength to our conclusion that Tpm1 and Tpm2 localize to actin cables irrespective of which formin nucleates them. Overall, these new results highlight and reiterate our model of formin-isoform independent binding of Tpm1 and Tpm2 in S. cerevisiae.
In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.
Response: __We thank the reviewer for pointing this out. Our data and analysis do not suggest that Tpm1 and Tpm2 show any preference for decoration of cables in either mother or bud compartment. As per the reviewer's suggestion, we have now quantified the ratio of mean mNG fluorescence in the bud to the mother (Bud/Mother) and the data is shown in __Figure. S2G. The bud-to-mother ratio was similar for mNG-ASTpm1 and mNG-ASTpm2 in wildtype cells, and the ratio increased in Dbnr1 cells and decreased in Dbni1 cells for both mNG-ASTpm1 and mNG-ASTpm2 (Fig. S2G). __This is consistent with the decreased actin cable signal in the mother compartment in Dbnr1 cells and decreased actin cable signal in the bud compartment in Dbni1 cells (Fig. S2A-D). Thus, our new analysis shows that both mNG-ASTpm1 and mNG-ASTpm2 have similar changes in their concentration (mean fluorescence) upon loss of either formins Bnr1 and Bni1 and show similar ratios in wildtype cells as well, suggesting no preference for binding to actin cables in either bud or mother compartment. The preference inferred by the reviewer seems to be a bias of the current representative images and thus, we have replaced the images in __Fig. 2A, 2D to more accurately represent the population.
The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?
__Response: __We did not use LifeAct for our analysis as LifeAct is known to cause expression-dependent artefacts in cells (Courtemanche et al., 2016; Flores et al., 2019; Xu and Du, 2021) and it also competes with proteins that regulate normal cable organization like cofilin. Use of LifeAct would necessitate standardization of expression to avoid such artefacts in vivo. Also, phalloidin staining provides the best staining of actin cables and allows for better quantitative results in our experiments. The use of LifeAct along with mNG-Tpm would also require optimization with a red fluorescent protein which usually tend to have lower brightness and photostability. However, during the revision of our study, a new study from Prof. Goode's lab has developed and optimized expression of new LifeAct-3xmNeonGreen constructs for use in S. cerevisiae (Wirshing and Goode, 2024). Thus, a similar strategy of using tandem copies of bright and photostable red fluorescent proteins can be explored for use in combination with mNG-Tpm in the future studies.
__ Complementation of tpm1∆ by Tpm2:__
I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue.
The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.
Response: __We thank the reviewer for pointing this out. We would like to clarify that in our RT-qPCR experiments, the primers were chosen within the Tpm1 and Tpm2 gene and do not distinguish between transcripts from endogenous or plasmid copy. We have now mentioned this in the Materials and Methods section of the revised manuscript. So, they represent a relative estimate of the total mRNA of these genes present in cells. We were consistently able to detect ~19 fold increase in Tpm2 total mRNA levels as compared to wildtype and ∆tpm1 cells (Fig. S4D) when tpm2 was expressed from a high-copy plasmid (pRS425). This increase in Tpm2 mRNA levels was accompanied by a rescue in growth (Fig. S4A) and actin cable organization (Fig. S4B) of ∆tpm1 cells containing pRS425-ptpm2TPM2. When tpm2 was expressed from a low-copy number centromeric plasmid (pRS316), we detected a ~2 fold increase in Tpm2 transcript levels when using the tpm1 promoter and no significant change was detected when using tpm2 promoter (Fig. S4E)__. We have made sure that these results are accurately described in the revised manuscript.
As per the reviewer's suggestion, we have now conducted a more extensive analysis to ascertain the expression levels of Tpm2 in our experiments and the data is now presented in new Figure S5. We used mNG-ASTpm1 and mNG-ASTpm2 to rescue growth of ∆tpm1 (Fig. S5A) and correlated growth rescue with protein levels using quantified fluorescence intensity (Fig. S5B, S5C) and western blotting (anti-mNG) (Fig. S5D, S5E). We find that ∆tpm1 cells containing pRS425-ptpm1mNG-ASTpm1 had the highest protein level followed by pRS425-ptpm2 mNG-ASTpm2, pRS305-ptpm1mNG-ASTpm1, and the least protein levels were found in pRS305-ptpm2 mNG-ASTpm2 containing ∆tpm1 cells in both fluorescence intensity and western blotting quantifications (Fig. S5C, S5E). Surprisingly, we were not able to detect any protein levels in ∆tpm1 cells containing pRS305-ptpm2 mNG-ASTpm2 with western blotting (Fig. S5D) which was also accompanied by a lack of growth rescue (Fig. S5A). This most likely due to weak expression from the native Tpm2 promoter which is consistent with previous literature (Drees et al., 1995). Taken together, this data clearly shows that the rescue observed in ∆tpm1 cells is caused due to increased expression of mNG-ASTpm2 in cells and supports our conclusion that increase in Tpm2 expression leads to restoration of normal growth and actin cables in ∆tpm1 cells.
__ Specific function of Tpm2:__
The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.
Response: __We agree with the reviewer and as per the reviewer's suggestion, we have performed another experiment which include wildtype, ∆tpm2 cells containing empty pRS316 vector or pRS316-ptpm2TPM1 or pRS316-ptpm1TPM1. We find that RACF rate increased in ∆tpm2 cells as compared to wildtype and was restored to wildtype levels by exogenous expression of Tpm2 but not Tpm1 (Fig. S6E, S6F). Since, actin cables were not detectable in ∆tpm1 cells, we measured RACF rates in ∆tpm1 cells expressing Tpm1 or Tpm2 from a plasmid copy, which restored actin cables as shown previously in __Fig. 5A-C. We observed that RACF rates were similar to wildtype in ∆tpm1 cells expressing either Tpm1 or Tpm2 (Fig. S6E, S6F), suggesting that Tpm1 is not involved in RACF regulation. Taken together, these results suggest a specific role for Tpm2, but not Tpm1, in RACF regulation in S. cerevisiae, consistent with previous literature (Huckaba et al., 2006).
Minor comments: __1.__The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).
Response: __ We thank the reviewer for pointing this out. We have now repeated the drop test multiple times (__Fig. R2), but we see similar growth rates as the drop test already presented in Fig. S4A. __At this point, it would be difficult to ascertain the basis of this difference observed at 23{degree sign}C and 30{degree sign}C, but a recent study that links leucine levels to actin cable stability (Sing et al., 2022) might explain the faster growth of these ∆tpm1 cells containing a leu2 gene carrying high-copy plasmid. However, there is no effect on growth rate at 37{degree sign}C which is consistent with other spot assays shown in __Fig. S1D, S4F, S5A.
Significance
I am a cell biologist with expertise in both yeast and actin cytoskeleton.
The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.
We thank the reviewer for their positive assessment of our work and the constructive feedback that has greatly improved the quality of our study. After addressing the points raised by the reviewer, we believe that our study has significantly gained in consolidating the major conclusions of our work.
**Referees cross-commenting**
Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.
Response: __We acknowledge the reviewer's point about the effect of Ala-Ser dipeptide and would request the reviewer to refer to our response to Reviewer 1 (Question 1) for a more detailed discussion on this. We have also extensively addressed the question of Tpm2 expression levels as suggested by the reviewer (new data in __Figure S5) which has further strengthened the conclusions of our study.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary:__ The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.
Major and Minor Comments: 1. The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.
Response: __We agree with the reviewer's suggestions and have now updated our Materials and Methods section to describe the image analysis pipelines used in more detail. We have also added examples of quantification procedure for actin cable length/number and mitochondrial morphology as an additional Supplementary __Figure S7. Briefly, the following pipelines were used:
- Actin cable length and number analysis: This was done exactly as mentioned in McInally et al., 2021, McInally et al., 2022. Actin cables were manually traced in Fiji as shown in __ S7A__, and then the traces files for each cell were run through a Python script (adapted from McInally et al., 2022) that outputs mean actin cable length and number per cell.
- Mitochondria morphology: Mitochondria Analyzer plug-in in Fiji was used to segment out the mitochondrial fragments. The parameters used for 2D segmentation of mitochondria were first optimized using "2D Threshold Optimize" to find the most accurate segmentation and then the same parameters were run on all images. After segmentation of the mitochondrial network, measurements of fragment number were done using "Analyze Particles" function in Fiji. An example of the overall process is shown in __ S7B.__ As per the reviewer's suggestion, we have now included the description of the statistical test used in the Figure Legends of each Figure in the revised manuscript. We have used One-Way Anova with Tukey's Multiple Comparison test, Kruskal-Wallis test with Dunn's Multiple Comparisons, and Unpaired Two-tailed t-test using the in-built functions in GraphPad Prism (v.6.04).
**Referees cross-commenting**
I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.
Response: __We acknowledge the reviewer's concern about the Ala-Ser dipeptide and would request them to refer our earlier discussion on this in response to Reviewer 1 (Question 1) and Reviewer 2 (Question 2). We would also request the reviewer to refer to our answer to Reviewer 2 (Question 6) where we have extensively addressed the question of Tpm2 expression levels and their effect on rescue of Dtpm1 cells. This data is now presented as new __Figure S5 in our revised manuscript.
Reviewer#3 (Significance (Required)):
The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.
The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.
As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.
We thank the reviewer for their positive outlook and assessment of our study. We have incorporated all their suggestions, and we are confident that the revised manuscript has significantly improved in quality due to these additions.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The study presents the first fully functional fluorescently tagged Tpm proteins, enabling detailed probing of Tpm isoform localization and functions in live cells. The authors created a modified fusion protein, mNG-amTpm, which mimicked native N-terminal acetylation and restored both normal growth and full-length actin cables in yeast cells lacking native Tpm proteins, demonstrating the constructs' full functionality. They also show that Tpm1 and Tpm2 do not have a preference for actin cables nucleated by different formins (Bnr1 and Bni1). Contrary to previous reports, the study found that overexpressing Tpm2 in Δtpm1 cells could restore growth rates and actin cable formation. Furthermore, it is shown that despite its evolutionary divergence, Tpm2 retains actin-protective functions and can compensate for the loss of Tpm1, contributing to cellular robustness.
Major and Minor Comments:
The key conclusions of this paper are convincing. However, I suggest that more detail be provided regarding the image analysis used in this study. Specifically, since threshold settings can impact the quality of the generated data and, therefore, its interpretation, it would be useful to see a representative example of the quantification methods used for actin cable length/number (as in refs. 80 and 81) and mitochondria morphology. These could be presented as Supplemental Figures. Additionally, it would help to interpret the results if the authors could be more specific about the statistical tests that were used.
Referees cross-commenting
I agree with both reviewers 1 and 2 regarding the issues with the Ala-Ser acetylation mimic and Tpm2 expression levels, respectively. I think the authors should be more careful in how they frame the results, but I consider that these issues do not invalidate the main conclusions of this study.
Significance
The finding that Tpm2 can compensate for the loss of Tpm1, restoring actin cable organization and normal growth rates, challenges previous assumptions about the non-redundant functions of these isoforms in Saccharomyces cerevisiae (ref. 16). It also supports a concentration-dependent and formin-independent localization of Tpm isoforms to actin cables in this species. The development of fully functional fluorescently tagged Tpm proteins is a significant methodological advancement. This advancement overcomes previous visualization challenges and allows for accurate in vivo studies of Tpm function and regulation in S. cerevisiae.
The findings will be of particular interest to researchers in the field of cellular and molecular biology who study actin cytoskeleton dynamics. Additionally, it will be relevant for those utilizing advanced microscopy and live-cell imaging techniques.
As a researcher, my experience lies in cytoskeleton dynamics and protein interactions, though I do not have specific experience related to tropomyosin. I use different yeast species as models and routinely employ live-cell imaging as a tool.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
Learn more at Review Commons
Referee #2
Evidence, reproducibility and clarity
This manuscript by Dhar, Bagyashree, Palani and colleagues examines the function of the two tropomyosins, Tpm1 and Tpm2, in the budding yeast S. cerevisiae. Previous work had shown that deletion of tpm1 and tpm2 causes synthetic lethality, indicating overlapping function, but also proposed that the two tropomyosins have distinct functions, based on the observation that strong overexpression of Tpm2 causes defects in bud placement and fails to rescue tpm1∆ phenotypes (Drees et al, JCB 1995). The manuscript first describes very functional mNeonGreen tagged version of Tpm1 and Tpm2, where an alanine-serine dipeptide is inserted before the first methionine to mimic acetylation. It then proposes that the Tpm1 and Tpm2 exhibit indistinguishable localization and that low level overexpression (?) of Tpm2 can replace Tpm1 for stabilization of actin cables and cell polarization, suggesting almost completely redundant functions. They also propose on specific function of Tpm2 in regulating retrograde actin cable flow.
Overall, the data are very clean, well presented and quantified, but in several places are not fully convincing of the claims. Because the claims that Tpm1 and Tpm2 have largely overlapping function and localization are in contradiction to previous publication in S. cerevisiae and also different from data published in other organisms, it is important to consolidate them. There are fairly simple experiments that should be done to consolidate the claims of indistinguishable localization, and levels of expression, for which the authors have excellent reagents at their disposal.
Functionality of the acetyl-mimic tagged tropomyosin constructs:
The overall very good functionality of the tagged Tpm constructs is convincing, but the authors should be more accurate in their description, as their data show that they are not perfectly functional. For instance, the use of "completely functional" in the discussion is excessive. In the results, the statement that mNG-Tpm1 expression restores normal growth (page 3, line 69) is inaccurate. Fig S1C shows that tpm1∆ cells expressing mNG-Tpm1 grow more slowly than WT cells. (The next part of the same sentence, stating it only partially restores length of actin cables should cite only Fig S1E, not S1F.) Similarly, the growth curve in Fig S1C suggests that mNG-amTpm1, while better than mNG-Tpm1 does not fully restore the growth defect observed in tpm1∆ (in contrast to what is stated on p. 4 line 81). A more stringent test of functionality would be to probe whether mNG-amTpm1 can rescue the synthetic lethality of the tpm1∆ tpm2∆ double mutant, which would also allow to test the functionality of mNG-amTpm2.
It would also be nice to comment on whether the mNG-amTpm constructs really mimicking acetylation. Given the Ala-Ser peptide ahead of the starting Met is linked N-terminally to mNG, it is not immediately clear it will have the same effect as a free acetyl group decorating the N-terminal Met.
Localization of Tpm1 and Tpm2:
Given the claimed full functionality of mNG-amTpm constructs and the conclusion from this section of the paper that relative local concentrations may be the major factor in determining tropomyosin localization to actin filament networks, I am concerned that the analysis of localization was done in strains expressing the mNG-amTpm construct in addition to the endogenous untagged genes. (This is not expressly stated in the manuscript, but it is my understanding from reading the strain list.) This means that there is a roughly two-fold overexpression of either tropomyosin, which may affect localization. A comparison of localization in strains where the tagged copy is the sole Tpm1 (respectively Tpm2) source would be much more conclusive. This is important as the results are making a claim in opposition to previous work and observation in other organisms.
In fact, although the authors conclude that the tropomyosins do not exhibit preference for certain actin structures, in the images shown in Fig 2A and 2D, there seems to be a clear bias for Tpm1 to decorate cables preferentially in the bud, while Tpm2 appears to decorate them more in the mother cell. Is that a bias of these chosen images, or does this reflect a more general trend? A quantification of relative fluorescence levels in bud/mother may be indicative.
The difficulty in preserving mNG-amTpm after fixation means that authors could not quantify relative Tpm/actin cable directly in single fixed cells. Did they try to label actin cables with Lifeact instead of using phalloidin, and thus perform the analysis in live cells?
Complementation of tpm1∆ by Tpm2:
I am confused about the quantification of Tpm2 expression by RT-PCR shown in Fig S3F. This figure shows that tpm2 mRNA expression levels are identical in cells with an empty plasmid or with a tpm2-encoding plasmid. In both strains (which lack tpm1), as well as in the WT control, one tpm2 copy is in the genome, but only one strain has a second tpm2 copy expressed from a centromeric plasmid, yet the results of the RT-PCR are not significantly different. (If anything, the levels are lower in the tpm2 plasmid-containing strain.) The methods state that the primers were chosen in the gene, so likely do not distinguish the genomic from the plasmid allele. However, the text claims a 1-fold increase in expression, and functional experiments show a near-complete rescue of the tpm1∆ phenotype. This is surprising and confusing and should be resolved to understand whether higher levels of Tpm2 are really the cause of the observed phenotypic rescue. The authors could for instance probe for protein levels. I believe they have specific nanobodies against tropomyosin. If not, they could use expression of functional mNG-amTpm2 to rescue tpm1∆. Here, the expression of the protein can be directly visualized.
Specific function of Tpm2:
The data about the retrograde actin flow is interpreted as a specific function of Tpm2, but there is no evidence that Tpm1 does not also share this function. To reach this conclusion one would have to investigate retrograde actin flow in tpm1∆ (difficult as cables are weak) or for instance test whether Tpm1 expression restores normal retrograde flow to tpm2∆ cells.
Minor comments:
The growth of tpm1∆ with empty plasmid in Fig S3A is strangely strong (different from other figures).
Referees cross-commenting
Having read the other reviewers' comments, I do agree with reviewer 1 that it is not clear whether the Ala-Ser linker really mimics acetylation. I am less convinced than reviewer 3 that the key conclusions of the study are well supported, notably the issue of Tpm2 expression levels is not convincing to me.
Significance
I am a cell biologist with expertise in both yeast and actin cytoskeleton.
The question of how tropomyosin localizes to specific actin networks is still open and a current avenue of study. Studies in other organisms have shown that different tropomyosin isoforms, or their acetylated vs non-acetylated versions, localize to distinct actin structures. Proposed mechanisms include competition with other ABPs and preference imposed by the formin nucleator. The current study re-examines the function and localization of the two tropomyosin proteins from the budding yeast and reaches the conclusion that they co-decorate all formin-assembled structures and also share most functions, leading to the simple conclusion that the more important contribution of Tpm1 is simply linked to its higher expression. Once consolidated, the study will appeal to researchers working on the actin cytoskeleton.
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Referee #1
Evidence, reproducibility and clarity
There are 2 Major issues:
- Having an -ala-ser- linker between the GFP and tropomyosin mimics acetylation. This is not the case, and more likely the this linker acts as a spacer that allows tropomyosin polymers to form on the actin, and without it there is steric hindrance. A similar result would be seen with a simple flexible uncharged linker. It has been shown in a number of labs that the GFP itself masks the effect of the charge on the amino terminal methionine. This is consistent with NMR, crystallographic and cryo structural studies. Biochemical studies should be presented to demonstrate that the impact of a linker for the conclusions stated to be made, which provide the basis of a major part of this study.
- My major issue however is making the conclusions stated here, using an amino-terminal fluorescent protein tag that s likely to impact any type of isoform selection at the end of the actin polymer. Carboxyl terminal tagging may have a reduced effect, but modifying the ends of the tropomyosin, which are integral in stabilising end to end interactions with itself on the actin filament, never mind any section systems that may/maynot be present in the cell, is not appropriate.
Significance
This paper explores the role of formin in determining the localisation of different tropomyosins to different actin polymers and cellular locations within budding yeast. Previous studies have indicated a role for the actin nucleating proteins in recruiting different forms of tropomyosin within fission yeast. In mammalian cells there is variation in the role of formins in affiecting tropomyosin localisation - variation between cell type. There is also evidence that other actin binding proteins, and tropomyosin abundance play roles in regulating the tropomyosin-actin association according to cell type. Biochemical studies have previously been undertaken using budding yeast and fission yeast that the core actin polymerisation domain of formins do not interact with tropomyosin directly.
The significance of this study, given the above, and the concerns raised is not clear to this reviewer.
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Reply to the reviewers
Response to reviewers’ comments for Isbilir et al
We thank the reviewers for their insightful comments and advice. In light of the reviewers’ constructive suggestions, we have revised our manuscript as detailed below.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: In this manuscript, the authors investigate the unique Mycobacteriaceae cell envelope using cryo-tomography/cryo-electron microscopy with Corynebacterium glutamicum as a model organism. Cryo-EM images of C. glutamicum cells successfully resolved previously observed densities corresponding to the MM, arabinogalactan, peptidoglycan, and inner membrane layers of the cell envelope along with the S-layer. The authors found that the S-layer is patchy in a manner dependent on growth phase (i.e. liquid versus solid growth). Intriguingly, when the S-layer was present, the leaflets of the MM appeared to be disrupted. The authors solved the structure of purified S-layer protein PS2 by cryo-EM, however they could not resolve the C-terminal membrane interaction domain. The authors found that PS2 is hexameric and different hexamers are linked by trimeric interface to create a porous structure. Phylogenetic analysis showed conservation of PS2 within corynebacteria and suggested a signature for MM-association.
Major comments:
(1) The S-layer structure is porous and the authors suggest that it may function as a molecular sieve or permeability barrier. This hypothesis should either be tested experimentally, or further discussion is needed regarding what small molecules (chemical features, size) would be able to penetrate.
This is a misunderstanding; we rather expect the opposite scenario in which the dimensions of the PS2 S-layer pores are too large to act as a molecular sieve. We are sorry for the confusion and have further clarified this part of the results and discussion.
Line 258: “The combination of hexameric and trimeric interfaces results in varying pores sizes of 6 Å, 27 Å, and 81 Å within the lattice (Fig. 3A). Some of these pores are relatively large and are reminiscent of the porous S-layer of Deinococcus radiodurans, which is also patchy on the cell surface (von Kügelgen et al., 2023). This suggests that C. glutamicum S-layer likely does not function as a molecular sieve, i.e. it has no protective role due to large pore dimensions and patchy cellular coating of the S-layer.”
and
Line 470: “The large pores (especially the 27 Å- and 81 Å-pores) in the S-layer suggest that its role is not to protect the cells from invading molecules or phages.”
(2) The authors show cryo-EM images of dividing C. glutamicum cells but don't make any statements as to the presence, morphology, and measurements of the different cell envelope layers. This analysis should be included.
We thank the reviewer for pointing this out. As suggested, we modified Figure S1 to highlight further details, and we have added the sentences below into the manuscript text.
Line 175: “To probe the plasticity of the cell envelope during the cell cycle, we analysed the cell envelope layers within the dividing septum (Fig. S1E). The thickness of the septum (~55 nm) was found to be greater than the usual thickness of the cell envelope (~42 nm on the same cell, see also Fig. 1A). The septum is composed of unseparated cell envelopes of the daughter cells that appear to contain a single ‘outer’ membrane, which is likely composed of mycolic acids. Presumably, this membrane will form the future MM once division is completed. Notably, the putative mycolic acid-containing bilayer within the septum was not connected to the MM on the other parts of the cell, whereas the remaining cell envelope layers appeared to be continuous with the rest of the cell. While IM and the putative future MM were clearly distinguishable, PG and AG could not be differentially identified in the dividing septum.”
and
Line 422: “In addition to cell envelopes of non-dividing cells, the dividing C. glutamicum septum shows two daughter cell envelopes separated by a bilayer likely containing mycolic acids. Notably, this bilayer was not connected to the MM on the rest of the cell (Fig. S1E). This observation is in line with the previous studies showing that at septal junctions, a contiguous PG layer acts as a diffusion barrier for the MM, and during separation of daughter cells, the PG in the septal junctions is displaced, allowing the bilayer at the septum to merge with the rest of the MM (Zhou et al., 2019).”
__Figure S1. Cryo-FIB milling of C. glutamicum cells. __
… E) Septum of a dividing C. glutamicum cell. Ten 0.85 nm thick-slices of the tomogram were averaged and bandpass-filtered to boost contrast. Zoomed view of the septum is shown on the right.
(3) The authors should include more discussion as to the patchiness or "wavy" MM near sites of PS2 contact. Cryo-EM of cells that express a variant of PS2 that lack the membrane anchoring domain would demonstrate that this is specific to PS2-membrane contacts. Minimally, providing some quantification for this phenotype would strengthen the claim (for instance, does the spacing between the perturbations match the expected scale of distance between S-layer membrane contacts).
We agree with reviewer that demonstrating the “wavy” nature of the MM requires further analysis. While it is our strong impression that the wavy nature is increased underneath the PS2 S-layer, we could not find a suitable metric to show this convincingly, i.e. all our analyses (real space averaging or averaging of power spectra) did not give clear-cut results. This is probably due to the inherent variability in the MM around the cell. In line with this, we have decided to tone down the relevant text in the manuscript.
Line 151: “Although we cannot be certain given the existing data, we suppose that this perturbation of the MM directly beneath the patchy S-layer could arise due to the interaction of the S-layer anchoring domain with the MM, which has been predicted to be present in the coiled coil part of the PS2 protein forming the S-layer using bioinformatics (Johnston et al., 2024).”
(4) The authors speculate on complete conservation of certain residues in the C-terminal domain of PS2 and hypothesize that they may be important for maturation or targeting of MM-associated proteins. Two additional examples of proteins with this motif are mentioned as evidence. Authors should search for this motif in pre-existing lists of MM proteins in the literature to test if this hypothesis is robust. Experiments to test if the conserved C-terminal residues of PS2 are required for export or assembly into an S-layer are feasible but optional given the scope of the paper.
We thank the reviewer for raising this point. Upon thoroughly re-examining the literature, we identified a previous study by Marchand et al. (J Bacteriol., 2012) that characterized MM-associated proteins in C. glutamicum. The proteins reported in this study as associated with the inner leaflet of the MM, including the mycoloyltransferases MytA and MytB, as well as those involved in pore formation, such as PorA and PorB, do not possess a phenylalanine as their terminal residue. This observation suggests that the invariant phenylalanine in PS2 does not represent a universal mechanism for targeting proteins to the MM. However, we also noted that several putative cell-surface proteins identified in this study, which feature a PS2-like C-terminal hydrophobic anchor preceded by a disordered segment, harbor a phenylalanine, proline, or lysine at their C-terminus. Additionally, the targeting of porins such as PorA, PorH, PorB, and PorC to the MM in C. glutamicum is known to depend on posttranslational O-mycoloylation. Based on these findings, we speculate that the conserved phenylalanine in PS2 may contribute to its anchoring and stabilization within the MM, rather than functioning as a universal targeting signal—a hypothesis we plan to investigate in future studies. We have revised the manuscript to incorporate these points and provide additional context.
Line 377: “To explore this hypothesis, we analysed MM-associated proteins of C. glutamicum identified in a previous study (Marchand et al., 2012). Proteins associated with the inner leaflet of the MM, such as the mycoloyltransferases MytA, MytB, MytC, MytD, and MytF, or those involved in pore formation, such as PorA and PorB, do not possess a phenylalanine as their terminal residue, suggesting that the invariant phenylalanine in PS2 does not represent a general mechanism for targeting proteins to the MM. However, several putative cell-surface proteins with a PS2-like C-terminal hydrophobic anchor preceded by a disordered segment were found to harbor a phenylalanine, proline, or lysine at their C-terminus. Examples include a prenyltransferase/squalene oxidase repeat-containing protein (NCBI: WP_011013715.1) and a metallophosphoesterase family protein (WP_011015494.1) (Fig. S8). Based on this conservation, we identified additional putative MM-associated cell-surface proteins in C. glutamicum (Fig. S8), such as an ExeM/NucH family extracellular endonuclease (WP_003854007.1) and a lamin tail domain-containing protein (WP_004567709.1). Interestingly, the targeting of porins PorA, PorH, PorB, and PorC to the MM in C. glutamicum has been shown to depend on posttranslational O-mycoloylation, which facilitates their proper localization and integration into the mycomembrane (Carel et al., 2017). Whether O-mycoloylation is also involved in the targeting of PS2 remains an open question and warrants further investigation. We speculate that terminal residues such as phenylalanine, proline, and lysine may contribute to anchoring cell-surface proteins within the MM by stabilizing interactions with the hydrophobic membrane environment or acting as signals for specific sorting or assembly mechanisms.”
(5) The authors do not draw the distinction between MM-associated and integral MM proteins (that contain a transmembrane domain). Is the C-terminal membrane anchoring domain of PS2 likely to span the entire bilayer or just be associated by a few amino acids?
The MM-anchoring hydrophobic segment is approximately 25 residues long across PS2 homologs, corresponding to a ~3.75 nm α-helix. In comparison, the MM has a thickness of 4–5 nm. This suggests that, while the MM-anchoring segment may not strictly qualify as a transmembrane domain integral to the MM, it is sufficiently long to embed deeply into the membrane and potentially span much of its bilayer thickness. To address this, we have added the following clarification to the manuscript:
Line 363: “The MM-binding segment is predicted by AlphaFold2 models to comprise an N-terminal hydrophobic a-helix and a short C-terminal amphipathic a-helix; however, in the MM, these may function as a single continuous helix. The MM-binding segment of PS2 homologs in Corynebacterium is consistently approximately 25 amino acid residues long, corresponding to a ~3.75 nm α-helix—sufficiently long to nearly traverse the 4–5 nm thickness of the MM.”
Minor comments:
(1) The authors comment that the thickness of the MM both with and without the S-layer is the similar and conclude that there is no change in mycolic acid length. The resolution of the technique is not sufficient to make this statement.
We agree with the reviewer in this point, while we can only measure the thickness of bilayer, we cannot comment on the thickness of each leaflet of the mycomembrane. Therefore, we have revised the text accordingly.
Line 144: “In 2D projection images of FIB-milled cells, the two leaflets of the MM were clearly resolved (Figs. 1C-D). The thickness of the MM in both cell envelopes with and without S-layer was between 4-5 nm (Table S1).”
(2) It would be helpful if the authors could comment if their membrane dimension measurements agree with previously published results in the main text of the manuscript. It is currently only included in the legend of Table S1.
Specifically regarding the MM, the measurements from both studies are quite similar; compare 4-5 nm from our study with 4.7 nm from Zuber et al., 2008. As the reviewer suggested, we have revised the discussion to include the comparison of the measurements with Zuber et al., 2008.
Line 413: “Our measurements are largely consistent with previous results (Zuber et al., 2008), except that in our data the IWZ was significantly thinner (~9.8 nm in this study vs. ~18 nm in Zuber et al., 2008), which is possibly due to strain differences. Moreover, our measurement of MWZ was slightly different because we could resolve OWZ as a separate layer, which was included into the MWZ measurement in the previous study (~15nm in this study vs. ~20.9 nm in Zuber et al., 2008) (Zuber et al., 2008).”
Reviewer #1 (Significance (Required)):
The manuscript provides compelling images and structures of the C. glutamicum cell envelope and S-layer protein PS2, respectively. These cryo-EM images of the cell envelope appear to agree nicely with pre-existing studies in the field. The introduction of the manuscript was well-written and the data in the manuscript is of broad interest to those who study the Mycobacteriaceae cell envelope. There is a lot of compelling data included in the paper, but the study would be strengthened by further analysis of the data as well as additional experiments to support some of the hypotheses suggested.
Thank you.
Reviewer expertise: bacterial genetics, bacterial cell envelope, protein transport
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Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Corynebacterium glutamicum is an organism with important industrial application, and it shares its complex cell-envelop architecture with organism of great relevance in human health such Corynebacterium diphtheriae and pathogenic mycobacteria. Using a cryo-EM and cryo-ET approaches together with phylogenetic studies, the authors provide of an in-deep structural characterization of the cell envelop of C. glutamicum. The authors map the different components of the cell envelope using high-resolution tomography, revealing unseen details of the outer wall zone, previously unsolved and attributed to the AG molecule. They provide with an atomic model of the PS2 S-layer at 3.1 A global resolution. The later discloses key features of the S-layer architecture, consisting of a hexagonal scaffold built by the PS2 protein, and its interaction with the mycolic membrane. The phylogenetic and bioinformatic studies show PS2 S-layer to be exclusively found within the Corynebacterium genus, although sporadically, and a correlation of PS2 presence/absence with other genetic differences. Despite PS2 homologues are shown to share common regions, which suggests all PS2 S-layers to exhibit a hexagonal lattice like the described in this study, but with divergent lattice parameters.
Major comments:
The authors provide with solid data supporting the structural models and conclusions stated. Text and figures are clear and nicely presented. I have however an important question regarding a the cryo-EM model. In Figure 3 B-C and Figure S3D-H, the authors depict protein details including hydrogen atoms, which make me question if the PS2 S-layer structure has been modeled including hydrogen atoms. The resolution of the cryo-EM data does not enable to model hydrogens that, if were included in the structure, should be removed of the coordinate file of the S-layer model and figures.
We agree with the reviewer that the current resolution of the cryo-EM map is not sufficient to model hydrogen atoms. The hydrogens were added to PS2 S-layer model during refinement in ISOLDE (Croll, 2018), and retained during Phenix real space refinement (Afonine et al., 2018; Liebschner et al., 2019). We agree with the reviewer that hydrogens should not be shown in the figures, since their positions have not been determined experimentally in our cryo-EM map. We have therefore removed these atoms from Figures 3 and S4.
__ “Figure 3. The PS2 S-layer Lattice. …“__
“Figure S4. Features of the PS2 S-layer lattice”
Minor comments
- Regarding the proposed calcium atoms at the S-Layer. The authors should provide further analysis to support the presence of calcium/divalent atoms proposed. Please show how is the coordination around the blobs spotted as potential calcium (or any other potential divalent that might be interacting at those positions). Does the coordination observed fit with the expected for a calcium/divalent binding site? Are the residues coordinating to those blobs well defined in density? Are the blobs of density of the potential cations observed across all the protomers of the PS2 S-layer? Figure 3D-F depicting the proposed cation-binding sites are too busy and unclear, they should focus on the proposed binding sites showing the interacting side/main-chains involved in the proposed coordination.
This is an interesting point. To investigate, we performed EDTA/EGTA treatment of the purified PS2 S-layer to see whether there would be any observable effect on the S-layer. We observed that S-layer lattices were still intact after EDTA or EGTA treatment. Therefore, we concluded that either cations do not play a role in stabilizing this S-layer or they are not accessible for chelation by EDTA or EGTA. This experiment unfortunately did not allow us to identify the ionic species. About the coordination: in the unknown densities 1 and 2 in the new Fig. S4, the coordination is clearer when compared to unknown density 3, however we cannot say for certain that these ions are calcium ions. Considering this, we have changed the text accordingly.
Line 237: “At the sequence level, the PS2 protein is enriched in acidic amino acid residues, giving it an overall negative charge, with an estimated isoelectric point of 4.25 (Fig. S4B-C). Consistent with this overall negative charge, we observed putative cationic densities at various locations along the PS2 sequence in the cryo-EM map, which are surrounded and stabilized by negatively charged amino acid residues (Figs. S4D-F). The identity of these cations cannot be ascertained at the current resolution of our cryo-EM map; however, previous studies on other bacterial S-layers suggest that they may correspond to calcium (Baranova et al., 2012; Herdman et al., 2022; Sogues et al., 2023). These cations may further stabilize the lattice, similar to other S-layers where cations were found to be essential for lattice formation (Baranova et al., 2012; Herdman et al., 2022; Sogues et al., 2023; von Kügelgen et al., 2021). To probe this further, we incubated purified PS2 S-layers with either 10 mM EDTA or 10 mM EGTA and examined the effect on the treated S-layers. Following the chemical treatment, S-layer lattices were still intact, with no observable differences under both conditions (Fig. S4I). This suggests that either these putative cations do not play a major role in stabilizing the PS2 S-layer or they are not accessible for chelation by EDTA or EGTA under the chosen experimental conditions”
and
“Figure S4. Features of the PS2 S-layer lattice… D, E, F) __Putative densities possibly corresponding to cations and G) SDS detergent molecules are shown, with the respective sigma values of the maps shown in the bottom right. The potential densities are denoted with an “*”, and the surrounding residues also labelled. H) __The coiled-coil segment (residues 405-445) is shown in side view (left) and bottom view (right). __I) __Purified PS2 S-layer sheets incubated with EDTA (middle) and EGTA (right) show no discernible differences from native S-layers (left).”
- Regarding the potential SDS density. Looking at Figure 3G, it is not clear how the morphology of the density shown (with a T-shape) would fit a linear molecule of SDS (could be the view selected?). Have the authors performed any attempt of modelling the SDS molecule to assess this and/or those PS2 residues contributing to stabilize the SDS? Is this density consistently observed across the other interfaces of the hexamer? That would support their hypothesis.
This density is observed in the other interfaces of the hexamer as well, and it is also seen in maps that were produced from refinements without any symmetry applied, i.e. when the processing was performed in C1. Nevertheless, taking on board the criticism about the ambiguity of both the putative SDS and calcium densities, combined with the inconclusive results of our EDTA/EGTA treatment, we have changed the panel titles of Fig. S4D-G to “Unknown density 1-4” in revised the manuscript (see above), making sure to not claim more than what is revealed by the density.
Reviewer #2 (Significance (Required)):
As structural biologist I consider that this study constitutes an important advance in our understanding of the complex architecture and function of the cell-envelop of C. glutamicum. Knowledge that can help to better understand this intricate envelop present in other Mycobacteriaceae relatives, which include important human pathogen such as Mycobacterium tuberculosis or Corynebacterium diphtheriae. This study is most relevant for the scientific community investigating on the bacterial cell envelop (structure, evolution and function) as well as in host-pathogen interactions. Moreover, the cell envelop constitutes a target for bacteriostatics and thus, this study may be relevant for the scientific community working on antimicrobial development.
Thank you.
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Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary: In the manuscript from Isbilir et al, the authors investigate the cell envelope of Corynebacterium glutamicum, a bacterium extensively used in biotechnological applications, using state-of-the-art cryo-electron microscopy methodologies as well as bioinformatics. They convincingly demonstrate that the C. glutamicum S-layer consists of hexagonal PS2 arrays and provide the underlying structural basis of this intriguing assembly. Bioinformatic analysis further revealed conserved and divergent elements of PS2 across Corynebacteria.
Major comments:
- My main point of criticism relates to the first part of the results, in which the authors attempt to characterize the cell envelope using cryo-electron tomography. From my own experience, plunge-freezing bacterial lawns often results in bad ice quality (crystalline ice) between the bacterial cells. This seems to be also the case here, looking at the 2D images in Fig. S1D revealing clear Bragg reflections. While often not a problem if interested in intracellular features, the authors are drawing conclusions on the cell envelope, which is in direct contact with these ice crystals, known to be destructive for ultrastructural features. For example, this could be the reason for the "wavy" mycomembrane in Fig. 1A and 1B as well as Fig. S1C. On top, it also might affect their observations of interrupted and discontinuous mycomembranes covered by an S-layer in Fig. 1D. The authors should discuss this limitation, and I would highly recommend rephrasing their conclusions made from this data more carefully.
We would like to thank the reviewer for their constructive criticism. We agree that it is difficult to vitrify a lawn of bacteria without formation of crystalline ice in all areas of the specimen. In our lamellae, we have primarily vitreous ice (see Fig. S1B, lower right panel for example) but the reviewer has correctly pointed out observed crystalline ice in some areas on the edges of the lamellae. As suggested, we included the following text in the legend to Fig. S1B to warn the readers about this potential shortcoming.
Line 562: “After milling, lamellae with a 150-200 nm thickness were retained for cryo-ET investigations. Each lamella contained multiple cells suitable for imaging. Although vitreous ice was observed in most lamellae, the edges of some lamellae showed signs of crystalline ice formation…”
The reviewer’s comment about the MM perturbations is well taken, this was also raised by reviewer 1. Although we attempted to quantify this effect by various image analysis tools, in the end we feel that it is not possible to make clear-cut conclusions about the MM-waviness based on our data. We have therefore toned down our interpretations about the “wavy” nature of the MM in the manuscript text (see also our response to reviewer 1 above).
Line 151: “Although we cannot be certain given the existing data, we suppose that this perturbation of the MM directly beneath the patchy S-layer could arise due to the interaction of the S-layer anchoring domain with the MM, which has been predicted to be present in the coiled coil part of the PS2 protein forming the S-layer using bioinformatics (Johnston et al., 2024).”
- The single-particle cryoEM data and the bioinformatic analysis are very well presented, analyzed in much detail, and convincing. While the authors state that the S-layer most probably does not serve to protect the cells from invading molecules or phages, additional experiments to figure out the function of the S-layer would be desirable. However, this might be beyond the scope of this paper but the authors should at least include a clearer discussion about potential function(s).
As suggested by the reviewer, we have extended the discussion about the potential function of the PS2 S-layer in C. glutamicum.
Line 465: “We also observed that S-layer coverage appeared to increase when C. glutamicum cells were grown on solid media (Fig. S2A-B). This suggests that the S-layer could be useful for the bacteria to grow in in a colony or in a surface-attached biofilm community, as shown for other bacteria including Clostridium difficile and Tannerella forsythia (Ðapa et al., 2013; Honma et al., 2007; Wong et al., 2023).”
and
Line 474: “…Slightly at odds with the large pores, it has been shown that the presence of the PS2 S-layer renders cells more resistant towards lysozyme (Sogues et al., 2024; Theresia et al., 2018). Although lysozyme is much smaller than the pore sizes, it is possible that the S-layer might biochemically sequester such undesirable molecules.”
- The authors speculate about cations stabilizing the S-layer. To provide further evidence, an optional but straightforward experiment would be to treat the purified S-layer with EDTA and subsequently analyze it with negative stain EM or cryoEM.
As suggested, we incubated the purified PS2 S-layer with 10 mM EDTA or 10 mM EGTA and imaged the resulting specimens with cryoEM. We found intact S-layers in these treated samples, therefore, we have concluded that either cations do not play a role in stabilizing this S-layer or they are not accessible for chelation by EDTA or EGTA -
Line 246: “To probe this further, we incubated purified PS2 S-layers with either 10 mM EDTA or 10 mM EGTA and examined the effect on the treated S-layers. Following the chemical treatment, S-layer lattices were still intact, with no observable differences under both conditions (Fig. S4I). This suggests that either these putative cations do not play a major role in stabilizing the PS2 S-layer or they are not accessible for chelation by EDTA or EGTA under the chosen experimental conditions.”
and
Figure S4. Cryo-EM of C. glutamicum cells. … I) Purified PS2 S-layer sheets incubated with EDTA (middle) and EGTA (right) show no discernible differences from native S-layers (left).
- The anchoring of the S-layer to the characteristic mycomembrane is only discussed very briefly. As this is a unique feature, it would be of high interest to understand how the anchoring is different from other S-layer carrying Gram-positive/negative bacteria.
We agree with the reviewer and have extended our discussion of this unique feature of the PS2 S-layer.
Line 359: “…the length of the coiled-coil stalk and the MM-binding segment is highly conserved among PS2 homologs across species (Figs S5-S6). This is in line with the fact that the underlying cell envelope architecture, including the MM, is preserved among different Corynebacterium species, necessitating the conservation of the MM anchoring segments in PS2. The MM-binding segment is predicted by AlphaFold2 models to comprise an N-terminal hydrophobic α-helix and a short C-terminal amphipathic α-helix; however, in the MM, these may function as a single continuous helix. The MM-binding segment of PS2 homologs in Corynebacterium is consistently approximately 25 amino acid residues long, corresponding to a ~3.75 nm α-helix—sufficiently long to nearly traverse the 4–5 nm thickness of the MM. Notably, this segment includes the last residue of PS2, a phenylalanine (F), which is remarkably conserved across all PS2 homologs (Figs S5-S6). While the functional significance of this invariant phenylalanine residue remains unclear, the conservation of the preceding residues, particularly the penultimate residue, which is typically either a proline (P) or lysine (K), suggests a potential functional role. It is plausible that these terminal residues collectively contribute to the sorting, export, and insertion of PS2 into the MM or help ensure its stable anchoring within the lipid-rich MM.”
and
Line 444: “The PS2 S-layer protein has a distinctive mode of attachment to the prokaryotic cell envelope. In most archaea, S-layers are directly attached to the cytoplasmic membrane (Bharat et al., 2021), either through lipid modification of the SLP (von Kügelgen et al., 2021) or through the action of a secondary protein (von Kügelgen et al., 2024). In Gram-negative bacteria such as C. crescentus, S-layers are non-covalently attached to the O-antigen of lipopolysaccharide layer covering the outer membrane (von Kügelgen et al., 2020). In turn, in Gram-positive bacterial S-layers are non-covalently anchored via SLH domains to the PG-linked secondary cell wall polymers (Blackler et al., 2018). In other diderm bacteria that are positive for Gram-staining such as Deinococcus radiodurans, the SLP HPI (Bharat et al., 2023) is lipidated at its N-terminus (von Kügelgen et al., 2023), allowing the protein to interact with the cell membrane. In the case of C. glutamicum, the attachment of the PS2 S-layer is achieved through the insertion of the C-terminal hydrophobic helix into the MM, which is a distinctive feature for bacterial S-layers that have been studied in detail using structural biology.”
- Remove the word "accurately" in the second sentence of the second paragraph in the abstract.
Changed as requested.
Line 28: “Our cellular imaging allowed us to map the different components of the cell envelope onto the tomographic density.”
- Remove the word "strong" in the last sentence of the abstract.
Done.
Line 41: “This study, therefore, provides an experimental framework for understanding cell envelopes that contain mycolic acids.”
- As this is a back-to-back submission, the manuscript from Sogues et al. should be cited.
Done, as requested.
Line 191: “Purified S-layers were deposited on cryo-EM grids and vitrified using methods previously described for other S-layers (von Kügelgen et al., 2023, 2024), and specifically for the C. glutamicum S-layer concurrently with this study (Johnston et al., 2024; Sogues et al., 2024).”
and
Line 474: “…Slightly at odds with the large pores, it has been shown that the presence of the PS2 S-layer renders cells more resistant towards lysozyme (Sogues et al., 2024; Theresia et al., 2018). Although lysozyme is much smaller than the pore sizes, it is possible that the S-layer might biochemically sequester such undesirable molecules.”
Minor comments:
- Line numbers are missing, making the manuscript more complicated to review.
Sorry about that, the updated version of the manuscript has line numbers included.
- In the abstract, in the last paragraph of the introduction, and in the first sentence of the discussion, the authors use the term "high-resolution" in conjunction with their cryo-electron tomography imaging. This might be correct if you compare the data to light microscopy or conventional EM imaging. However, given the fact that the authors also used single-particle cryoEM, their cryoET data cannot be called "high-resolution," and they should remove this term as used here.
We agree with the reviewer and change the text accordingly:
Line 28: “Our cellular imaging allowed us to map the different components of the cell envelope onto the tomographic density.”
and
Line 39: “Our structural and cellular data collectively provide a topography of the unusual C. glutamicum cell surface, features of which are shared by many pathogenic and microbiome-associated bacteria, as well as by several industrially significant bacterial species.”
and
Line 102: “Building on these foundational studies, we have used C. glutamicum as a model for MM-containing organisms to perform characterisation of this unusual cell envelope.”
and
Line 110: “By combining our S-layer structure with cryo-ET of the cell envelope and bioinformatics analyses, we provide further clues regarding the MM-anchoring mechanisms of the S-layer and offer insights into its conservation and evolution in corynebacteria.”
and
Line 124: “To overcome this limitation, we employed FIB milling to create thin sections of the cells, which allowed us to obtain images with enhanced contrast of the cell envelope.”
and
Line 401: “In this study, we visualized the C. glutamicum cell envelope by imaging FIB-milled cells using...”
Reviewer #3 (Significance (Required)):
The single-particle cryoEM and bioinformatics analysis are convincing, but this manuscript resides at a rather descriptive level on the S-layer of C. glutamicum and some major comments should be addressed.
The findings in this manuscript are exciting for a specialized audience interested in bacterial cell surfaces/surface appendages and S-layers. On top, as C. glutamicum is widely used in biotechnological applications, the results have clear significance within this field.
Contrary to what the authors claimed, the general insights gained on cell envelopes containing mycolic acids are limited. Only very few insights reported here will advance our understanding of the cell envelope of important human pathogens such as Mycobacterium tuberculosis, as this manuscript focuses on the S-layer, which is absent from these strains.
Thank you for your comments, we have reworded the discussion section with more cautionary statements to present a balanced picture to readers of this manuscript.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In the manuscript from Isbilir et al, the authors investigate the cell envelope of Corynebacterium glutamicum, a bacterium extensively used in biotechnological applications, using state-of-the-art cryo-electron microscopy methodologies as well as bioinformatics. They convincingly demonstrate that the C. glutamicum S-layer consists of hexagonal PS2 arrays and provide the underlying structural basis of this intriguing assembly. Bioinformatic analysis further revealed conserved and divergent elements of PS2 across Corynebacteria.
Major comments:
- My main point of criticism relates to the first part of the results, in which the authors attempt to characterize the cell envelope using cryo-electron tomography. From my own experience, plunge-freezing bacterial lawns often results in bad ice quality (crystalline ice) between the bacterial cells. This seems to be also the case here, looking at the 2D images in Fig. S1D revealing clear Bragg reflections. While often not a problem if interested in intracellular features, the authors are drawing conclusions on the cell envelope, which is in direct contact with these ice crystals, known to be destructive for ultrastructural features. For example, this could be the reason for the "wavy" mycomembrane in Fig. 1A and 1B as well as Fig. S1C. On top, it also might affect their observations of interrupted and discontinuous mycomembranes covered by an S-layer in Fig. 1D. The authors should discuss this limitation, and I would highly recommend rephrasing their conclusions made from this data more carefully.
- The single-particle cryoEM data and the bioinformatic analysis are very well presented, analyzed in much detail, and convincing. While the authors state that the S-layer most probably does not serve to protect the cells from invading molecules or phages, additional experiments to figure out the function of the S-layer would be desirable. However, this might be beyond the scope of this paper but the authors should at least include a clearer discussion about potential function(s).
- The authors speculate about cations stabilizing the S-layer. To provide further evidence, an optional but straightforward experiment would be to treat the purified S-layer with EDTA and subsequently analyze it with negative stain EM or cryoEM.
- The anchoring of the S-layer to the characteristic mycomembrane is only discussed very briefly. As this is a unique feature, it would be of high interest to understand how the anchoring is different from other S-layer carrying Gram-positive/negative bacteria.
- Remove the word "accurately" in the second sentence of the second paragraph in the abstract.
- Remove the word "strong" in the last sentence of the abstract.
- As this is a back-to-back submission, the manuscript from Sogues et al. should be cited.
Minor comments:
- Line numbers are missing, making the manuscript more complicated to review.
- In the abstract, in the last paragraph of the introduction, and in the first sentence of the discussion, the authors use the term "high-resolution" in conjunction with their cryo-electron tomography imaging. This might be correct if you compare the data to light microscopy or conventional EM imaging. However, given the fact that the authors also used single-particle cryoEM, their cryoET data cannot be called "high-resolution," and they should remove this term as used here.
Significance
The single-particle cryoEM and bioinformatics analysis are convincing, but this manuscript resides at a rather descriptive level on the S-layer of C. glutamicum and some major comments should be addressed.
The findings in this manuscript are exciting for a specialized audience interested in bacterial cell surfaces/surface appendages and S-layers. On top, as C. glutamicum is widely used in biotechnological applications, the results have clear significance within this field.
Contrary to what the authors claimed, the general insights gained on cell envelopes containing mycolic acids are limited. Only very few insights reported here will advance our understanding of the cell envelope of important human pathogens such as Mycobacterium tuberculosis, as this manuscript focuses on the S-layer, which is absent from these strains.
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Referee #2
Evidence, reproducibility and clarity
Corynebacterium glutamicum is an organism with important industrial application, and it shares its complex cell-envelop architecture with organism of great relevance in human health such Corynebacterium diphtheriae and pathogenic mycobacteria. Using a cryo-EM and cryo-ET approaches together with phylogenetic studies, the authors provide of an in-deep structural characterization of the cell envelop of C. glutamicum. The authors map the different components of the cell envelope using high-resolution tomography, revealing unseen details of the outer wall zone, previously unsolved and attributed to the AG molecule. They provide with an atomic model of the PS2 S-layer at 3.1 A global resolution. The later discloses key features of the S-layer architecture, consisting of a hexagonal scaffold built by the PS2 protein, and its interaction with the mycolic membrane. The phylogenetic and bioinformatic studies show PS2 S-layer to be exclusively found within the Corynebacterium genus, although sporadically, and a correlation of PS2 presence/absence with other genetic differences. Despite PS2 homologues are shown to share common regions, which suggests all PS2 S-layers to exhibit a hexagonal lattice like the described in this study, but with divergent lattice parameters.
Major comments:
The authors provide with solid data supporting the structural models and conclusions stated. Text and figures are clear and nicely presented. I have however an important question regarding a the cryo-EM model. In Figure 3 B-C and Figure S3D-H, the authors depict protein details including hydrogen atoms, which make me question if the PS2 S-layer structure has been modeled including hydrogen atoms. The resolution of the cryo-EM data does not enable to model hydrogens that, if were included in the structure, should be removed of the coordinate file of the S-layer model and figures.
Minor comments
- Regarding the proposed calcium atoms at the S-Layer. The authors should provide further analysis to support the presence of calcium/divalent atoms proposed. Please show how is the coordination around the blobs spotted as potential calcium (or any other potential divalent that might be interacting at those positions). Does the coordination observed fit with the expected for a calcium/divalent binding site? Are the residues coordinating to those blobs well defined in density? Are the blobs of density of the potential cations observed across all the protomers of the PS2 S-layer? Figure 3D-F depicting the proposed cation-binding sites are too busy and unclear, they should focus on the proposed binding sites showing the interacting side/main-chains involved in the proposed coordination.
- Regarding the potential SDS density. Looking at Figure 3G, it is not clear how the morphology of the density shown (with a T-shape) would fit a linear molecule of SDS (could be the view selected?). Have the authors performed any attempt of modelling the SDS molecule to assess this and/or those PS2 residues contributing to stabilize the SDS? Is this density consistently observed across the other interfaces of the hexamer? That would support their hypothesis.
Significance
As structural biologist I consider that this study constitutes an important advance in our understanding of the complex architecture and function of the cell-envelop of C. glutamicum. Knowledge that can help to better understand this intricate envelop present in other Mycobacteriaceae relatives, which include important human pathogen such as Mycobacterium tuberculosis or Corynebacterium diphtheriae. This study is most relevant for the scientific community investigating on the bacterial cell envelop (structure, evolution and function) as well as in host-pathogen interactions. Moreover, the cell envelop constitutes a target for bacteriostatics and thus, this study may be relevant for the scientific community working on antimicrobial development.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this manuscript, the authors investigate the unique Mycobacteriaceae cell envelope using cryo-tomography/cryo-electron microscopy with Corynebacterium glutamicum as a model organism. Cryo-EM images of C. glutamicum cells successfully resolved previously observed densities corresponding to the MM, arabinogalactan, peptidoglycan, and inner membrane layers of the cell envelope along with the S-layer. The authors found that the S-layer is patchy in a manner dependent on growth phase (i.e. liquid versus solid growth). Intriguingly, when the S-layer was present, the leaflets of the MM appeared to be disrupted. The authors solved the structure of purified S-layer protein PS2 by cryo-EM, however they could not resolve the C-terminal membrane interaction domain. The authors found that PS2 is hexameric and different hexamers are linked by trimeric interface to create a porous structure. Phylogenetic analysis showed conservation of PS2 within corynebacteria and suggested a signature for MM-association.
Major comments:
- The S-layer structure is porous and the authors suggest that it may function as a molecular sieve or permeability barrier. This hypothesis should either be tested experimentally, or further discussion is needed regarding what small molecules (chemical features, size) would be able to penetrate.
- The authors show cryo-EM images of dividing C. glutamicum cells but don't make any statements as to the presence, morphology, and measurements of the different cell envelope layers. This analysis should be included.
- The authors should include more discussion as to the patchiness or "wavy" MM near sites of PS2 contact. Cryo-EM of cells that express a variant of PS2 that lack the membrane anchoring domain would demonstrate that this is specific to PS2-membrane contacts. Minimally, providing some quantification for this phenotype would strengthen the claim (for instance, does the spacing between the perturbations match the expected scale of distance between S-layer membrane contacts).
- The authors speculate on complete conservation of certain residues in the C-terminal domain of PS2 and hypothesize that they may be important for maturation or targeting of MM-associated proteins. Two additional examples of proteins with this motif are mentioned as evidence. Authors should search for this motif in pre-existing lists of MM proteins in the literature to test if this hypothesis is robust. Experiments to test if the conserved C-terminal residues of PS2 are required for export or assembly into an S-layer are feasible but optional given the scope of the paper.
- The authors do not draw the distinction between MM-associated and integral MM proteins (that contain a transmembrane domain). Is the C-terminal membrane anchoring domain of PS2 likely to span the entire bilayer or just be associated by a few amino acids?
Minor comments:
- The authors comment that the thickness of the MM both with and without the S-layer is the similar and conclude that there is no change in mycolic acid length. The resolution of the technique is not sufficient to make this statement.
- It would be helpful if the authors could comment if their membrane dimension measurements agree with previously published results in the main text of the manuscript. It is currently only included in the legend of Table S1.
Significance
The manuscript provides compelling images and structures of the C. glutamicum cell envelope and S-layer protein PS2, respectively. These cryo-EM images of the cell envelope appear to agree nicely with pre-existing studies in the field. The introduction of the manuscript was well-written and the data in the manuscript is of broad interest to those who study the Mycobacteriaceae cell envelope. There is a lot of compelling data included in the paper, but the study would be strengthened by further analysis of the data as well as additional experiments to support some of the hypotheses suggested.
Reviewer expertise: bacterial genetics, bacterial cell envelope, protein transport
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Reply to the reviewers
Response to Reviewers
We thank all three reviewers for their time and engagement, for their generally supportive comments, and for raising some important concerns. We are pleased to submit a significantly revised manuscript where we tried to accommodate all suggested changes and extensions. Importantly, we have included additional experiments that support the relevance of FACT for the overall stability of the inner kinetochore. Below is a detailed point-to-point response. Changes to the manuscript relative to the original submission have been highlighted at the end of this response.
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __
Summary: The authors investigated molecular interactions between CCAN and FACT complexes. They revealed contact domains in FACT and the cognate subcomplexes of CCAN by in vitro reconstitution from recombinant proteins followed by SEC and pull-down assay.
They also revealed a couple of potential means to control interactions between FACT and the CCAN. They conclude that phosphorylation of FACT by CK2 is essential for binding to the CCAN; and CENP-A nucleosomes or DNA prevent CCAN from interacting with FACT.
Major comments:
The authors show that phosphorylation of FACT is essential for interaction with CCAN.
They argue that this phosphorylation is partly catalysed by CK2.
My concerns are:
-1- The authors assume that the sites phosphorylated in insect cell are also phosphorylated in human cells. However, it is not demonstrated which residues are phosphorylated in human cells and whether they match those from insect cells. Whether phosphorylation of recombinant proteins in insect cells is physiologically relevant to mammalian is uncertain. Kinetochore components are not very well conserved evolutionarily, thus their regulation may be different.
We thank the reviewer for these remarks, which we answer together with point 2 below.
-2- They identify several residues which are phosphorylated by CK2 in vitro. However, these are not necessarily the same sites as those phosphorylated in insect cells or more importantly in human cells. The in vitro phosphorylation by CK2 did not restore binding affinity in full, suggesting phosphorylation at other sites may be critical for interaction with CCAN. Further evidence is required to support the claim that those sites are phosphorylated in vivo and important for integrity of kinetochores in mitosis.
Our analysis of FACT phosphorylation represents a relatively small part of a very data-rich paper, and was not meant to be exhaustive. Nonetheless, the reviewer's comments are important and well received. We agree that we have no definitive evidence that the same sites are phosphorylated in insect cells, in vitro, and in human cells. However, it is quite remarkable, and supports specificity, that the interaction with FACT, lost after dephosphorylation in vitro, is restored with CK2 and not with three additional mitotic kinases (CDK1, Aurora B, and PLK1 - Figure S8D). We also note that S437, S444 and S667 of SSRP1, which were phosphorylated by CK2 in vitro, were also detected as phosphorylated sites on recombinant FACT purified from insect cells (Table S1). So collectively, while we agree with the reviewer that the analysis of FACT phosphorylation is not complete, it does significantly add to the manuscript and more generally to the FACT field.
Minor comments:
Figure 1H
I am confused with 4 stars shown at the top of the right plot. If the 4 stars are meant to show a significant difference, then the statement in the text (line 123) is not correct.
"SSRP1 localization was also largely unaffected ..."
Similar discrepancies are found in Figures 3H (line 212), Figures S2 (line 122), S5I (line 197), and S6I (line209). Figure S6H is not referred to anywhere.
There is no description for the numbers at the top. Are they mean values? Do red bars represent S.D.?
We thank the reviewer for these comments. In this revised version of the manuscript, we have substantially improved the quantification and statistical analysis. The main problem with the previous automated analysis is that the non-circular shape of the CREST-staining led to inconsistencies with the statistical analysis and the statement. In contrast, the same analysis works well when the CENP-C signal was used for KT identification (e.g. in Figure 3), as CENP-C staining yields well separated circular signals ideally suited for our automated identification of individual KTs and subsequent retrieval of fluorescence intensities. We have therefore modified our analysis macro for all experiments where CREST was used as a reference. We used Othsu-thresholding of the DAPI signal for generating a segmentation mask per each cell. Then, integrated cell intensities were calculated for each fluorescence channel based on the DAPI reference mask. With these adjustments, the statistical analyses (Figures 1, S2, S3) support the claim presented. We have updated the Methods and Results sections to reflect the revised analysis.
The numbers on top of the graphs are median values, bars represent interquartile ranges. We have now included the description in figure legends.
We appreciate your feedback, which prompted us to clarify and enhance the rigor of our approach.
We are now referring to Fig. S6H in the text.
Figure 1D
There is no description of R* to the right of gels.
We have added a description of R* to the relevant figure legend.
Figure S2
A 4 hour nocodazole treatment is too short to drive all cells into mitosis. Is the data taken from mitotic cells only?
Yes, the data are taken only from the mitotic population. We have now clarified this in the figure legend.
Reviewer #1 (Significance (Required)):
The interaction of FACT with kinetochore components has been known for several years. However how FACT contributes to architecture or function of kinetochore is not very well understood. How the FACT complex, which is known for its established role as a histone chaperone, is involved in kinetochore assembly/architecture will attract interest in several fields of basic research including epigenetics, mitosis, structural biology.
We are grateful to the reviewer for this supportive statement that recognizes the broad potential interest of the manuscript.
Identification of CCAN subunits that interact with FACT is important for future analysis to understand the kinetochore function of FACT. The authors identified OPQRU and CHIKM subcomplex in addition to TW as FACT-interacting domains. These subcomplexes are geographically scattered in a 3D model of CCAN holocomplex. Stoichiometry of CCAN and FACT might be informative whether a single or multiple FACT binds to the multiple sites of CCAN. The authors do not address whether these multiple sites are occupied simultaneously, separately or sequentially.
We thank the reviewer for raising this point. As mentioned in the discussion, we have not yet been able to perform a structural analysis of the FACT/CCAN complex to determine its stoichiometry. However, we have now added a newexperiment (Figure S1B,C) where we quantified in-gel tryptophan fluorescence after analytical size-exclusion chromatography. This strongly suggests that FACT and CCAN form a complex with a 1:1 stoichiometry. Nevertheless, we cannot comment on which sites are occupied.
The statement at the end of Abstract (lines 23-25) is a speculative hypothesis without evidence for "a pool of CCAN that is not stably integrated into chromatin", "chaperoning CCAN", and "stabilisation of CCAN".
We agree with the reviewer that this is speculative, and have therefore modified the Abstract to clearly indicate this point.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
FACT is a histone chaperone and is involved in various events on chromatin such as transcription and replication. In addition, FACT interacts with various kinetochore components, suggesting potential functions at the kinetochore. However, it is largely unclear how FACT functions in the kinetochore. Authors of this MS took the biochemical approach to understand roles of FACT in the kinetochore.
Authors demonstrated that FACT forms a complex with the constitutive centromere associated network (CCAN), which contains 16 subunits on centromeric chromatin, using multiple binding sites. They also showed that casein kinase II (CK2) phosphorylated FACT and dephosphorylated FACT did not bind to CCAN. Furthermore, they displayed that DNA addition disrupt the stable FACT-CCAN complex.
Overall, while authors have done solid and high-quality biochemical analyses (these are elegant), it is still unclear how FACT plays its roles in the kinetochore. Simple knockout or knockdown study on FACT might be complicated, because FACT has multi-functions. If authors can identify specific regions of FACT for interaction with CCAN, they would put specific mutations into FACT to analyze phenotype. Although they did not reach a high-resolution structure for the FACT-CCAN complex, they can utilize AlphaFold and test specific interaction regions, biochemically. Then, using such information, significance of FACT-CCAN interaction might be testable in cells. Such a kind of study would be expected. In summary, biochemical parts are beautiful, but the paper did not address significance of FACT-CCAN interaction.
We thank the reviewer for praising the biochemical work in our manuscript. The reviewer, however, also underscored the limits of our functional analysis. The reviewer proposes generating separation-of-function mutants in a minimal kinetochore-binding region. Indeed, we have identified the minimal domain for the interaction of FACT with kinetochores. However, this information is insufficient for a reliable functional analysis at this stage, as the region we identified encompasses the AIDs and the phosphorylation-rich region, both of which have been previously shown to be important for transcription and other functions. Furthermore, any suitable mutant should be tested in cells devoid of endogenous FACT, raising the concern that the resulting phenotype may be indirect.
Nonetheless, as we wanted to provide at least an initial answer to the reviewer's concern, we enriched the manuscript by adding experiments in a recently published cell line (K562-SSRP1-dTAG) where FACT levels can be controlled with a small molecule (Žumer et al. Mol Cell., 2024) and that the authors generously shared with us. In this line, which grows in suspension and that we had to adapt to grow on a substrate for imaging, we were able to deplete FACT while cells were arrested in mitosis. We are glad to report that we found a significant reduction in the kinetochore levels of CENP-TW after this treatment, which is consistent with other conclusions from our study. These experiments add an initial functional characterization of the interaction of FACT with kinetochores, and extend the significance of the manuscript. We refer to these results again below in response to specific point 5.
Specific point
Authors showed nice mitotic localization of FACT. Can they observe this localization by a usual IF? Using GFP fusion, do they observe kinetochore localization like IF experiments?
The localization of FACT was observed using pre-extraction and fixation followed by antibody staining. We have now added a panel demonstrating mitotic localization of GFP-SSRP1 at the kinetochore in transiently transfected RPE-1 cells (Fig. S2A).
On page 7, authors tested CENP-C binding to FACT and they conclude that C-teminal region of CENP-C preferentially binds to FACT. However, they used N-terminal region of CENP-C (2-545) for CCAN-FACT complex formation in entire MS. therefore, this is complicated, and story on CENP-C N-terminal region can be removed from this MS.
We were only able to purify full-length CENP-C with tags at the N- and C-terminus, including an MBP tag with a stabilizing effect. At the time of our first successful purification of full-length CENP-C, we had already established the solid phase assay using MBPFACT as a bait on amylose beads and CENP-C2-545HIKM as one of the preys. As we cannot obtain stable full-length CENP-C without MBP, this form of CENP-C is incompatible with our pull-down assay. Nevertheless, CENP-C2-545 still has low affinity for FACT, influencing the FACT/CCAN interaction independent of the PEST-rich region. We, therefore, opted for keeping this information in the manuscript.
On page 9, authors suddenly focus on N-terminal tails of CENP-Q and CENP-U. Why did they focus on this region. They should explain this. If they perform a structural prediction, they should describe this point.
Thanks for raising this point. We focused on the N-terminal tails of CENP-QU because they are known interaction hubs. We have now added a sentence to introduce this concept and citing the appropriate literature.
I agree the fact that FACT phosphorylation is required for FACT-CCAN interaction. They may explain how the phosphorylation contributes to stable FACT-CCAN interaction.
We have added a sentence explaining that FACT is known to mimic DNA, and negative charges due to phosphorylation could drive this effect. A more detailed mechanistic understanding will require identifying specific phosphorylation sites required for the interaction.
Readers really want to know phenotype, if FACT-CCAN interaction was compromised without disruption pf CCAN assembly in cells. Although I agree that FACT has some functions in the kinetochore, it is still unclear what FACT does in the kinetochore.
We wholeheartedly agree with the reviewer. As depletion of FACT by RNAi required 48 h, an unreasonably long time for this multifunctional protein. We therefore turned to engineering RPE-1 cells for rapid degradation of SSRP1. While these attempts were unsucessful, earlier this year, Žumer et al. Mol Cell., 2024 reported generating a K562-SSRP1-dTAG cell line growing in suspension. As already reported, this cell line now allowed to demonstrate a significant effect on the kinetochore stability of CENP-TW upon mitotic depletion of FACT.
Reviewer #2 (Significance (Required)):
As mentioned above, biochemical parts are beautiful, but the paper did not address significance of FACT-CCAN interaction.
We thank the reviewer for this positive assessment. In this revision, we have obtained initial evidence that FACT contributes to kinetochore stability.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __
Main findings:
The major findings of this paper are:
Detailed dissection of CCAN subunit interactions and requirements to bind the FACT complex using in vitro reconstituted components Binding of FACT and nucleosomes to CENP-C are mutually exclusive FACT phosphorylation by CK2 enhances interaction with CCAN FACT localization in mitosis depends on the CCAN CCAN binding to FACT is outcompeted by DNA and CENP-A nucleosomes The claims and conclusions of the paper are supported by the data and do not require additional experiments. All experiments include biological replicates and appropriate controls.
We are thankful to the reviewer for this very positive assessment of our work.
Minor comments
Intro: • Line 81: In humans [...], here it is worth mentioning that in Drosophila, FACT subunits have been shown to interact directly with the CENP-A assembly factor CAL1 (Ref 61). This paper is perfunctorily cited once in the context of its implication of FACT in CENP-A deposition, but it merits more consideration when setting up the foundational context for the present work.
We have extended the Introduction and discuss the specified paper more thoroughly.
Figure 1:
1F: Add insets.
Done.
1G and all other figures containing IFs: Avoid red/green color scheme (red-green colorblindness is fairly common, affecting about 8% of men).
Done.
1E: Please add a table summarizing interactions.
We have included this table as Fig. S1E.
Results: • It's fine to direct readers to previous work in which you reconstituted the CCAN, but the text should mention how proteins are exogenously expressed and purified (as done for FACT in line 247).
Done.
Line 113: FACT has been shown to localize to the mitotic kinetochore also in Drosophila (Ref 61).
We have included this information now.
Line 132: The authors should cite work from the Drosophila system as well when they mention centromere transcriptional activity in mitosis (e.g. https://doi.org/10.1083/jcb.201404097; https://doi.org/10.1083/jcb.201611087; and Ref 61).
We have added these citations.
Figure 2F: The authors could use a line to mark the region interacting with FACT and that interacting with CENP-A to help summarize the data in this diagram.
Done.
Figure 4: Highlight constructs n.2 (FACT^TRUNC) since these are sufficient for interaction (e.g., use a box around them).
Done.
Line 276: "CCAN decodes CENP-A^NCP..." What do the authors mean by "decodes"? This whole sentence would benefit from clearer language.
We thank the reviewer for this suggestion and have aimed for clearer language.
Figure 6: There's a lot of information in these experiments that would benefit from two schematics, one showing the mechanism of FACT + CCAN binding with DNA and one with CENP-A nucleosomes.
Done.
Discussion: The authors discuss FACT localization at kinetochores in mitosis. In Drosophila Schneider cells, FACT is observed enriched at the centromeres in both mitosis and interphase (Ref 61). The authors mention their inability to detect FACT in interphase in the discussion, but I did not find this mentioned in the results. The authors state that FACT "redistributes to the entire chromosome" upon entry into interphase. They cite Figure 1F in reference to this statement, but the staining in the early G1 panel is difficult to interpret with the low signal/noise scaling of CENP-C and the lack of zoom insets. Their protocol uses a pre-extraction step with Triton prior to fixation. Apparently, this was not enough to reveal FACT in interphase, but better images and a brief description are warranted.
We have now added a staining of SSRP1 in interphase in the panel.
It is unlikely that FACT would change its localization pattern in mitosis. A more likely possibility is that in mitosis FACT is not redistributed, but rather more tightly bound (and thus less easily extracted by Triton treatment) at kinetochores, while along the arms FACT is more readily removed by extraction because at this time transcription is repressed and FACT is likely less engaged in transcription-mediated histone destabilization.
We thank the reviewer for these remarks and have updated the Discussion.
Given the well-known function of FACT in transcription and the many studies linking transcription to centromere maintenance, including with the involvement of FACT, the model that "the localization of FACT at the kinetochore coincides with active centromeric transcription in mitosis and interphase" is very tempting. A speculative model would go a long way to help the reader visualize all these complex aspects of FACT's interactions and possible functions.
We agree with the reviewer that such a model is tempting. However, we also feel that it would be rather speculative at this stage and we feel that the manuscript does not provide sufficient data to support the model.
Reviewer #3 (Significance (Required)):
The strongest aspect of the study is the detailed characterization of protein-protein interactions, as well as competition with DNA and CENP-A nucleosomes. The siRNA experiments in cells complement this largely in vitro study. However, a limitation of the study is that it does not shed light on what FACT might be doing at the centromere. Additionally, it does not sufficiently provide context for these findings in relation to previous studies that have demonstrated the roles of FACT at the centromere in budding yeast, fission yeast, and Drosophila. Nonetheless, this study provides valuable insights into the details of FACT interactions at the kinetochore and will be of interest to readers interested in centromeres and kinetochore. I am a centromere biologist with molecular and cell biology expertise.
We are very grateful to the reviewer for his/her support.
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