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Reply to the reviewers

We thank the referees for their interest, comments and advice on our manuscprit. The reply to the reviewers follows the revision plan proposed Review Commons.

1. Description of the planned revisions

R#1 major comments :

R#1 raised three major points relative to quantitative data. For the two first, we have optimized our quantification methods, as explained in the next section, which clearly improved the results, but some data still have to be re-analyzed for figures 5,6,7.

For the third one, here is the comment and our proposed additional experiments to answer it :

c) Quantifications of lateral fraction Col IV in mosaic experiments do not support decreased lateral secretion in Rab8 OE (3G) or Dys- (S5C), which are central tenets of the study. “

We have endeavoured to detect such differences in Dys mutants and Rab8 OE and do not see any possible improvement in the quantification method and therefore propose, instead, additional experiments.

With respect to Rab8 OE, we suspect that this gain of function is not sufficiently effective under the specific conditions of the experimental setup described in Figure 3, as its effect appears to be more subtle than that of Rab10 OE in Figure 2. We therefore propose to repeat this experiment on a sensitised background in which Rab10 function is partially affected. Unpublished data indicate that this downregulation of Rab10 is not sufficient to induce significant differences in this experimental setup. However, based on the genetic interactions described in the other figures, an additive/synergistic effect between rab8 OE and Rab10 KD can be expected, which would allow to confirm the involvement of Rab8 in basal secretion.

With regard to Dys mutant, we considered another possible explanation for this observation, namely that DAPC could affect the secretion of other BM proteins but not that of collagen IV. It should be noted that Perlecan and LamininA, which are also found in BM fibrils, are ligands for Dystroglycan, which is not the case for collagen IV. Unfortunately, there is no existing transgene with a UAS promoter and a tagged version of these proteins that would allow this hypothesis to be tested within a reasonable timeframe using the same method as that described in Fig.3. Therefore, we propose an alternative approach that would determine whether the secretion of endogenous laminin and/or perlecan is affected by Dystroglycan overexpression (i.e. secreted more laterally) and test whether this effect is Dys-dependent. It should be noted that this hypothesis would be fully consistent with all our data and in particular with that shown in Fig. 5.

R#2 major comments

From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition.

We will complete fig S1 and perform ECad and Dlg staining to provide a better description of apical-basal polarity in the different Rab knock-down conditions.

R#3 comments

Results Figure 4

The authors suggest basal Rab10 expression domain near the Golgi exit point. Can the authors use a Trans-Golgi marker in order to confirm this statement other than the references stated?

Such a staining will be included in the final version with for instance golgin245 staining.

2. Description of the revisions that have already been incorporated in the transferred manuscript

R#1 major comments :

a) Rab8 KD does not significantly increase apical fraction of Collagen IV with respect to control (Fig. 1H). The image in 1C clearly shows that Col IV is present apically, something that has been shown by others and that never occurs in the wild type. Failure of the quantifying method to detect a difference can only mean the quantifying method is not adequate. A 10% average in the control when it's clear that no Col IV at all is found apically in the wild type suggests that the authors are quantifying background signal that they should not be acquiring, and, if acquired, they should be subtracting Rab8/Rab10 double knock down is said to show a synergistic effect, when an additive effect would be more consistent with alternative routes. Other problematic deductions drawn from apical fraction quantifications are found in Fig. 5J (Dys- enhancing Rab8 KD but not Rab10 KD) and Fig. 7D (Exo70- enhancing Rab10 KD but not Rab8 KD)

We agree that this quantification was not optimal. We improved it by quantifying a narrower and more precise region for each domain. The new results are shown in Figure 1H. This improvement reduces the apical signal in the control from 10% to 6% and allows us to detect a significant increase between the control and Rab8 KD, thus resolving the problem raised. After verification, we did not subtract the background because there was no electronic background in our images (i.e. black is really black and equal to zero). Thus, the remaining signal is the true cytoplasmic GFP signal and it may not be appropriate to subtract it. Other data (fig 5J and 7D, now named fig 5L and 7H) were also re-analyzed with no major change.

b) Similar to apical fraction, measurements of planar polarization (trailing/lateral ratio) show average ratios near 1 for Dg, Rab10 and Dys, which is striking given that the localization of these proteins is so clearly polarized. Ratios lower than 1, which are reported for many individual cells in these graphs, should mean reversed polarity. In light of this, I would not be too confident on the effects reported in 5O-Q. In fact, on two occasions, the authors obtain significant differences in these planar polarization measurements that they themselves disregard: Fig. 6J (Rab10 in Exo70-) and Fig. 7I (Dys in Rab8 KD).

We agree that this quantification could be improved. Our initial quantification of the planar polarised proteins, Rab10 and Dys, found at the trailing edge, was confounded by their lateral spread. We have now reported with only the front half of the lateral side. By doing this in Figure 5, we increase the ratio in the control conditions, with almost no points below the value of 1, while the conditions in which polarity is visually affected are unchanged and still close to 1. We did not have time to re-analyze all the data (Figures 6 and 7), but will do so in the final revised version.

R#1 minor comments :

All the minor points raised by R#1 have been addressed by changes in the main text and/or the figures with the exception of the following :

• Fig. S1B does not seem to make a significant point in the context of this study.

Although we understand this comment, we followed suggestion of R#2 who asked in its major comments for more details with other cell polarity markers. These data are not yet included but will be generated for the fully revised version.

• I suggest drawing a summary scheme to aid readers better assess interpretations alternative to the ones given in the text.

While we will be happy to provide such a scheme in the final version, we prefer to wait for the results of the proposed complementary experiments to be as accurate as possible.

R#2 major comments

In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.

We agree that it was not entirely appropriate to give such conclusions on the basis of the quantifications available. A new graph showing basal fluorescence intensity (new Figure 1H) (and not just the ratio of apical to apical plus basal as in Figure 1I) has been added to better support the text. A relevant statistical comparison has been added to Figure 1H (old Figure 1I). We apologize for this oversight.

R#2 minor comments :

We took in account all these comments and changed accordingly the text and the figures

R#3 comments :

Results figure 1

The authors use RNAi lines to arrive at their conclusions, however, the extent of inhibition of gene expression achieved by the RNAi, has not been justified. Also observations from only one RNAi stock may not be completely conclusive:

i) Efficiency of RNAi has not been tested or shown. No supporting data. Rab10-RNAi stock is 26289 BDSC which is in Valium10, which is a weak RNAi line and needs a Dicer.

ii) Can same observations be made using classic alleles or generate somatic clones on follicular epithelial cells?<br />

R#3 raised several questions regarding the efficiency of RNAi, the use of different lines and/or the use of classical mutants as an alternative method.

For Rab10, we tested three different lines with similar results as shown now in Figure S1A-B. These data are also consistent with those obtained by overexpression of a dominant-negative form of Rab10 (Lerner et al, 2013). Unfortunately, Rab10 is located extremely close to the X chromosome centromere and is even more proximal than the FRT transgenes. It is therefore impossible to generate somatic mutant clones.

Regarding Rab8, it is already published that Rab8 RNAi, expression of a dominant-negative form of Rab8 and Rab8 mutant cells obtained by somatic clones give similar defects (Devergne et al, 2017). The text has been modified to better illustrate the available data validating our approach.

In addition, mutant clones would not allow analysis of genetic interactions in complex genetic contexts such as double and triple KDs. Similarly, the choice of the Rab10 line was motivated by the ease of obtaining the appropriate genetic combination according to their genomic location.

iii) Intensity of Collagen IV in the basement membrane in Rab11 knock-down mutants seems to be significantly low as compared to the Rab8 and Rab10 knock downs in supplementary Fig 1B. Are the authors very sure that Rab11 has no functions in basement membrane basal organization?

Good catch! Indeed, Rab11 RNAi significantly reduces basal secretion as now shown on fig 1H. Rab11 has pleiotropic functions in epithelial cells notably for their polarity (Choubey and Roy, 2017, Fletcher et al, 2012…) and, accordingly aPKC is partially disrupted in Rab11 RNAi conditions (Fig S1). Thus, the reason for such a decrease is unclear and could be an indirect consequence of an overall abnormal epithelial structure. Thus, we now report this observation but have not taken its interpretation too far.

iv) Authors need to show where and how fluorescence intensities have been measured.

Magenta rectangles with dashed lines on figure 1A illustrate the ROIs used for this analysis and more details have been added in the ‘experimental procedures’ section.

Results Figures 2 and 3:

Texts and figures have been modified as suggested.

Results Figure 4 :

The authors suggest a UAS-Rab10-RFP transgene show same results as endogenous Rab10-YFP as compared to spatial expression pattern. This is worrisome as expression of full length functional gene tagged with a fluorophore may be an overexpression. A control experiment would be helpful in suggesting/comparing with the Rab10 OE phenotype and that will be more convincing.

We are not sure that we fully understand the reviewer's comment. However, we initially compared endogenous Rab10 and UAS-RAB10 at 25°C, a temperature at which the latter has no visible impact on BM structure (Cerqueira-Campos et al, 2020). Furthermore, even when higher expression was induced (by increasing the temperature and therefore Gal4 activity) and this had an impact on BM structure, this did not change the subcellular localization of Rab10, i.e. it was still planarly polarized, as shown in Fig 5S. The text has been modified to emphasize this point.

Result Figure 5

The authors may provide a Rab10 expression profile in DAPC null or KD mutants which would make their claims more comprehensive.

Data showing Rab10 localization in Dys mutant cells were already shown on Figure S5A-B. Of notice, we also tried similar experiments using RAB10 knock-in line. However, for unexpected reason, having one copy of the chromosome with YFP insertion in Rab10 strongly enhanced DAPC mutant phenotypes in terms of F-actin orientation and follicle elongation ((as described in Cerqueira-Campos et al, 2020). We therefore considered these data as inappropriate.

General comment

In general some immunostainings should be carried out if not in all at least in some experiments with some cell domain specific markers, more specifically PCP markers such as Flamingo/Vangl and basolateral markers such as Lgl/Dlg. This makes the positions specific claims of the authors more valid in the eyes of the reader.

We agree that this may help the reader but the pcp markers mentioned are not expressed in this tissue. However, the tissue planar orientation is now systematically indicated and consistent in all figures. We did not generally perform immunostaining for lateral markers but routinely included F-actin staining to detect cellular cortex. Our quantifications or cortical segmentations were based on the cell outline provided by this stain. On the basis of this staining, the outline of the cells was added on certain figures to facilitate understanding of the images.

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

R#3 comments :

Result Figure 6 :

Exo 70 is a versatile molecule and Rho kinases such as Cdc42 can direct Polarised exocytosis through interaction of Rab effectors with Exo 70. Have the authors considered this?

We agree that it is an interesting prospect, but we consider it as beyond the scope of this article.

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Referee #3

Evidence, reproducibility and clarity

In the present work the authors have elucidated a novel mechanistic model of basement membrane morphogenesis using Drosophila ovarian follicle cells as a model. The authors have employed extensive quantification approaches to justify the spatio-temporal expression of the molecules under study such as Collagen IV, Rab8, Rab10, DAPC, etc. The authors suggest distinct exit domains of BM protein Collagen IV facilitated by Rab8 and Rab10 via distinct routes as they interact with each other. The authors further show that DAPC plays an essential role in Rab10 mediated baso-lateral fibrillar BM synthesis whereas Rab8 functions are more Exocyst (Exo-70 dependent).

Major comments:

Result 1:

The authors use RNAi lines to arrive at their conclusions, however, the extent of inhibition of gene expression achieved by the RNAi, has not been justified. Also observations from only one RNAi stock may not be completely conclusive:

1. Efficiency of RNAi has not been tested or shown. No supporting data.<br /> Rab10-RNAi stock is 26289 BDSC which is in Valium10, which is a weak RNAi line and needs a Dicer.
2. Can same observations be made using classic alleles or generate somatic clones on follicular epithelial cells?
3. Intensity of Collagen IV in the basement membrane in Rab11 knock-down mutants seems to be significantly low as compared to the Rab8 and Rab10 knock downs in supplementary Fig 1B. Are the authors very sure that Rab11 has no functions in basement membrane basal organization?
4. Authors need to show where and how fluorescence intensities have been measured.

Result 2:

Confusing diagram. The authors should clarify whether the BM fibrils indicate lateral or planar BM components which they show to be more prominently expressed in Rab10 over-expression mutants.

A short note or an accompanying explanatory diagram on the source of the BM fibrils in the cellular context should make things less confusing.

FF calculation is an ingenious way of trying to look into functions.

The term Opposite effects/functions may be reconsidered as Rab8 and Rab10 compete with each other to deposit Collagen at spatially distinct domains. Opposite functions may give an impression that Rab8 actually represses Rab10 activity or vice versa, which may not be the case here.

Result 3:

Why was anti-GFP Ab detected with Cy3-Cy5 secondary Ab. GFP itself is green so why detect it with a Red secondary? Logic? How clone Collagen GFP and ECM collagen GFP was differentiated? Please justify

A panel with a dotted line joining the peripheral or lateral Collagen as shown in panels D' E' of Fig 3 would support the cartoon provided and link the cartoon to the actual microscopic images.

Result 4:

The authors suggest a UAS-Rab10-RFP transgene show same results as endogenous Rab10-YFP as compared to spatial expression pattern. This is worrisome as expression of full length functional gene tagged with a fluorophore may be an overexpression. A control experiment would be helpful in suggesting/comparing with the Rab10 OE phenotype and that will be more convincing.

When the authors mention back of cells, where do the authors exactly mean? A cartoon of "the back of follicle cells", wrt the entire ovarian follicle would be helpful.

The authors suggest basal Rab10 expression domain near the Golgi exit point. Can the authors use a Trans-Golgi marker in order to confirm this statement other than the references stated?

Result 5:

The authors may provide a Rab10 expression profile in DAPC null or KD mutants which would make their claims more comprehensive.

Result 6:

Exo 70 is a versatile molecule and Rho kinases such as Cdc42 can direct Polarised exocytosis through interaction of Rab effectors with Exo 70. Have the authors considered this?

In general some immunostainings should be carried out if not in all at least in some experiments with some cell domain specific markers, more specifically PCP markers such as Flamingo/Vangl and basolateral markers such as Lgl/Dlg. This makes the positions specific claims of the authors more valid in the eyes of the reader.

Significance

The findings impinge on a critical cellular process of Rab protein interactions in the genesis of the basement membrane which is of potential interest.

This falls under basic research. Since Rab molecules have emerged as molecules governing membrane morphogenesis, Cell and Molecular Biologists as well as a wide audience including clinicians will be interested on this.

Our group works on the roles of Rab11 in membrane morphogenesis in Drosophila model and we are now trying to venture with Rab11 in mammalian wound healing.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, Dennis et al. identify different secretory routes and cell exit sites involved in basement membrane secretion and diversification in epithelial cells. Using the follicular epithelium of the Drosophila ovary as their model system coupled with genetics, imaging, and image analysis approaches, they show that two previously identified RabGTPases, Rab8 and Rab10, work in parallel routes for basement membrane secretion. These two small GTPases work in a partially redundant manner, where Rab8 promotes basal secretion leading to a homogenous basement membrane, while Rab10 promotes lateral and planer-polarized secretion, leading to the formation of fibrils. The authors also show that Rab10 and the dystrophin-associated protein act together to regulate lateral secretion, and dystrophin (Dys) is necessary for dystroglycan (Dg) to recruit Rab10. Furthermore, DAPC is shown to be essential for fibril formation and is sufficient to reorient Collagen IV to the Rab10-dependent secretory route. Dys was also shown to interact directly with exocyst subunit Exo70. Using overexpression and loss of function approaches the authors claim that Exo70 limits the planer polarization of Dys, and as a result, Rab10, hence limiting basement membrane fibril formation. Finally, the authors state that the Exocyst (Exo70) is also required for the Rab8-dependent basement membrane route. Overall, the data described in this manuscript are convincing and the authors' claims are supported by the presented data. We have mainly minor comments and only a few major comments that need to be addressed.

Major Comments:

• In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.
• From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition.

Minor Comments:

General comments:

• In the text describing their data, we recommend that the authors clearly indicate which panel(s) they are referring to.
• The authors should also be consistent with the diction throughout the manuscript when referring to the cortical domain or region of the cell (back/rear/trailing edge/leading edge).
• Several references are missing in the manuscript.

The following specific comments are in order of appearance in the manuscript.

Introduction Section:

The following statements in the introduction should be supported by specific references:

• "BM is critical for tissue development, homeostasis and regeneration, as exemplified in humans by its implication in many congenital and chronic disorders."
• "BM is assembled from core components conserved throughout evolution: type IV collagen (Col IV), the heparan sulfate proteoglycan perlecan, and the glycoproteins laminin and nidogen."
• "During development, the dynamic interplay between cells and BM participates in sculpting organs and maintaining their shape."
• "BM protein secretion shows some specificities, mainly because of the large size of the protein complexes (e.g., procollagen) that must transit from the endoplasmic reticulum to the cell surface". This statement could be supported with references including specific Drosophila references. Additionally, the authors need to clarify what they mean by "some specifies".

Results section:

• In the text describing Fig. 2 (page 5), the authors describe two different basement membrane types: fibrils and homogenous. Moreover, the manuscript focuses on the role of Rab8 and Rab10 in the formation of these two structures. Thus, the authors must better describe the two different types of basement membrane structures and their known roles. This will be helpful for the readers to analyze the presented data, especially for those that are not familiar with the system. In Figure 2A, the authors describe stage 3 basement membrane as uniform BM, do they mean homogenous?
• In the text describing the data for Fig. 3 (page 6), the authors should clearly explain the reason to use anti-GFP antibodies in a non-permeabilized condition (i.e., to detect specifically the extracellular secretion of BM proteins). This will help the readers to interpret the data presented.
• On page 9, the authors stated that the precise localization of Dg in follicle cells is unknown. This statement is incorrect. It has been shown, using a Dg antibody, that Dg localizes at a high level at the basal side of the follicle cells and at a lower level at the apical side (Deng et al, 2003 and Denef et al. 2008).

Discussion Section:

• The following statement is not clear: "Thus, three different Rab proteins are targeted towards the three distinct domains of epithelial cells defined by apical basal polarity, and at least of them is also planar polarized". The authors should rephrase and describe specifically which Rabs they are talking about.
• This statement is vague: "These three Rab GTPases have been jointly involved in different processes (Knödler et al, 2010; Sato et al, 2014; Vogel et al, 2015; Eguchi et al, 2018; Häsler et al, 2020)". The authors could also mention the processes in which Rab8, 10, and 11 are involved.
• The following statements need to be supported by references. "Therefore, more investigations are required to define exactly how the DAPC allows the formation of BM fibrils. Nonetheless, given the importance of the DAPC and BM proteins in muscular dystrophies, our results will pave the way to determine whether a similar function is present also in muscle cells. Interestingly, the extracellular matrix is different between the myotendinous junction and the interjunctional sarcolemmal basement membrane and may provide another developmental context where several routes targeted to different subcellular domains may be implicated".

Experimental Procedure Section:

• In the dissection and immunostaining section (p14), there is a typo: it should be for "20 min" instead of "2for 0 min"
• For the GST pulldown experiments, the authors mention that they use a standard protocol to produce S35 Exo 70 and the GST pulldown experiments. The authors should provide references.

Figure and Figure Legend:

• General comment: The orientation of the images showing the rotation and leading and trailing edges need to be consistent in the different figures (e.g., In Figures 3 and 7, the leading edge is oriented to the top while in Figures 4, S4, 5, 6, the leading edge is oriented to the bottom). This will help the readers to analyze the data.
• In Figure 1 C-G the scale bars are missing and should be added as Fig. 1B.
• Figure S1A: The data presented in Figure S1A is convincing. However, a control panel should be added showing the absence of apical Coll IV for comparison. This information will help with the interpretation of the data.
• In Figure 3 legend: it should be "immunostained" for GFP instead of stain for f-actin and GFP.
• In Figure 4, some scale bars are missing.
• In Figure 4 legend: it should be "(A, E)" after (i.e 0.8 µm above the basal surface) instead of "(C, G)"
• In Figure 5A-E, the authors show quantification of the fibril fraction for Dys-, Rab10 OE, and Rab10OE+Dys, Rab8KD, and Rab8KD+Dys-, and images of the collagen fibril for all the conditions except Dys-, it will be informative that the authors present a representative image of the Coll IV fibril in Dys- condition for comparison. The above comment also applies to Figure 5F-J, and it will be also informative to have a representative image of Dys- condition.
• In Figure 5 legend (p23), it should be "plane" and not "plan".
• Overall, the legend for Fig. S5 is not clear and we recommend the authors to clearly described the different panels. (e.g., it should be "(D)" instead of "(H-J)")
• In Figure 6, some scale bars are missing.

Significance

Despite the important roles of the basement membrane for mechanical support, tissue and organ development, and function, the mechanisms that control the polarized deposition of basement membrane proteins are largely unknown. The contribution of Rab 8 and Rab 10 in the polarized deposition of the basement membrane was previously shown. However, by identifying two competitive secretory routes for the basal secretion of the basement membrane proteins that required these two different RabGTPases, controlled by the DAPC and the exocyst complexes, the authors make a novel contribution to our understanding of the mechanism that leads to the polarized secretion of basement membrane proteins (in that case Collagen IV). Since the basement membrane has critical roles in tissue and organ morphogenesis and functions, and its misregulation has been associated with developmental defects and pathological conditions, this research sheds light on the mechanisms important in these morphogenetic processes and will give insights into their deregulations in pathological conditions.

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Referee #1

Evidence, reproducibility and clarity

This manuscript by Dennis et al. reports a study of the polarized secretion of basement membrane Collagen IV in the Drosophila (fruit fly) follicular epithelium. Using genetic manipulations and confocal imaging, the authors show that Rab-GTPases Rab8 and Rab10, both known to be required for proper basal secretion of Collagen IV (work by the labs of Sally Horne-Badovinac and Trudi Schupbach, respectively), mediate two alternative secretion routes: Rab8 mediates basal-most secretion of soluble Collagen IV that is incorporated homogenously into the basement membrane, whereas Rab10 mediates basal-lateral secretion of Collagen IV that produces insoluble fibers. The authors additionally study the relation between Rab10 and Dystroglycan/Dystrophin (Dystrophin-associated protein complex, DAPC), which they previously showed to be essential for fibril formation (Cerqueira-Campos et al., 2020). They show here that Dystrophin and Rab10 colocalize at the basal trailing side of follicle cells and that overexpressed Dystroglycan can recruit Rab10 to the plasma membrane; however, they also show that Dystrophin mutants fail to display an effect on Rab10 localization, leaving the significance of the proposed Rab10-DAPC interaction unresolved. Finally, the authors present convincing evidence that the exocyst complex opposes fibril formation, and suggestive but comparatively weaker results pointing that this opposition is due to two independent separate exocyst roles: an inhibitory interaction exocyst-Dystrophin (Dystrophin being required for fibril formation), and a positive role in the alternative Rab8 non-fibril route.

Major comment:

• There are several instances throughout the study in which the authors seem to have problems quantifying results. This affects some assertions central to the message of the paper that are not supported by the quantifications presented. It also casts doubts on accessory points deduced from quantitative differences (or lack of difference) that do not seem fully reliable. I would urge the authors to reevaluate their quantification methods.

a) Rab8 KD does not significantly increase apical fraction of Collagen IV with respect to control (Fig. 1H). The image in 1C clearly shows that Col IV is present apically, something that has been shown by others and that never occurs in the wild type. Failure of the quantifying method to detect a difference can only mean the quantifying method is not adequate. A 10% average in the control when it's clear that no Col IV at all is found apically in the wild type suggests that the authors are quantifying background signal that they should not be acquiring, and, if acquired, they should be subtracting. Rab8/Rab10 double knock down is said to show a synergistic effect, when an additive effect would be more consistent with alternative routes. Other problematic deductions drawn from apical fraction quantifications are found in Fig. 5J (Dys- enhancing Rab8 KD but not Rab10 KD) and Fig. 7D (Exo70- enhancing Rab10 KD but not Rab8 KD).

b) Similar to apical fraction, measurements of planar polarization (trailing/lateral ratio) show average ratios near 1 for Dg, Rab10 and Dys, which is striking given that the localization of these proteins is so clearly polarized. Ratios lower than 1, which are reported for many individual cells in these graphs, should mean reversed polarity. In light of this, I would not be too confident on the effects reported in 5O-Q. In fact, on two occasions, the authors obtain significant differences in these planar polarization measurements that they themselves disregard: Fig. 6J (Rab10 in Exo70-) and Fig. 7I (Dys in Rab8 KD).

c) Quantifications of lateral fraction Col IV in mosaic experiments do not support decreased lateral secretion in Rab8 OE (3G) or Dys- (S5C), which are central tenets of the study.

Minor comments:

• It is stated that Rab10 and Dys associate with tubular endosomes, but no data here support identification as endosomes of these tubular structures, to my understanding.

• The authors call sup-basal the cell region immediately apical to the most basal. Is there sufficient reason to not call this lateral? If a new term is needed, shouldn't it be supra-basal?

• In Fig. S1A and B, Col IV is labeled as green but represented in cyan.

• Fig. S1A should present a wild type control.

• Fig. S1B does not seem to make a significant point in the context of this study.

• Fig. 3C'-E' label suggests a gradient made from multiple images, but it looks like just two images and two colors.

• Graphs in Fig. 3H-J, S5D and 7B are not legible.

• It is not clear where Y2H results in Fig 6A come from.

• I suggest drawing a summary scheme to aid readers better assess interpretations alternative to the ones given in the text.

Significance

This study reports important new information on the secretion of Collagen IV by polarized cells of the Drosophila follicular epithelium. It complements previous studies on the roles of Rab8, Rab10 and Dystroglycan/Dystrophin, additionally uncovering a role for the exocyst complex. Addressing some issues with quantitative imaging should increase confidence in its most critical conclusions.

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Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

1) It is interesting MxDnaK1 seems to prefer cytosolic proteins while Mx-DnaK2 prefers inner membrane proteins. The domain-swapping experiments seem to suggest that the NBD is important for this difference. How NBD is important is not addressed. Is it due to ATP hydrolysis, NBD-SBD interaction, or co-chaperone interactions?

Answer: Thanks for your comments. We speculate that the co-chaperone interaction might be the key factor contributing to substrate differences. According to the working principle of Hsp70, its functional diversity is largely determined by substrate differences. Co-chaperones, such as JDPs, play a crucial role in this process as they possess the ability to bind substrates and facilitate their targeted delivery. Therefore, much of the functional diversity of the HSP70s is driven by a diverse class of JDPs 1,2. We found that NBD played important roles in cochaperone recognition of MxDnaKs. Additionally, it is generally accepted that the efficiency of ATP hydrolysis does not significantly impact the substrate recognition of Hsp70. Furthermore, if the NBD-SBD interaction is crucial, the substitution of either the NBD or SBDβ domain might result in similar cell phenotypes, as both alterations disrupt the original NBD-SBDβ interaction. We believe the DnaK proteins and their cochaperones both determine the substrate spectrums. We made corresponding modifications in the revised manuscript. (Page22; Line 488-494 in the marked-up manuscript)

2) About the interactome analysis, since apyrase was added to remove ATP, it's surprising multiple Hsp40s were found in their analysis. Hsp70-Hsp40 interaction is known to require ATP. This may suggest some of the proteins found in their interactome analysis are artifacts. The authors should perform negative controls for their interactome analysis, such as using a control antibody for their CO-IP and analyze any non-specific binding to their resin.

In addition, since JDPs were pull-down, is it possible some of the substrates identified are actually substrates for JDPs, not binding directly to DnaKs?

Answer: This is an interesting question. As you correctly noted, the interaction between Hsp70 and Hsp40 requires ATP. In our experiment, we used apyrase to remove ATP in order to promote tight binding of substrate by DnaK. This methodology was initially described by Calloni, G. et al in 20123, and the authors also identified the co-chaperone protein DnaJ, but with a concentration higher than 77% of the interactors. In our opinions, the incomplete removal of ATP could be the underlying cause of this phenomenon.

We apologize for the undetailed description in Methods. Actually, we implemented negative controls for each MxDnaK in order to eliminate the potential non-specific interactions with Protein A/G beads or antibodies. Specifically, we conducted a CO-IP experiment without the presence of antibodies to assess any non-specific binding to the Protein A/G beads. To further investigate non-specific binding to the antibodies of MxDnaK2 and MxDnaK1, we utilized the mxdnak2-deleted mutant (strain YL2216) and the MxDnaK1 swapping strain with the MxDnaK2 SBDα (strain YL2204), respectively. As the SBDα of MxDnaK1 was employed as antigen to generate antibodies, and YL2204 can’t be recognized by anti-MxDnaK1 (Figure S5). We believe these controls allowed us to evaluate and exclude the non-specific interactions in our CO-IP. We have improved our description in methods. (Page 27; Line 596-607)

While one of the main functions of JDPs is to interact with unfolded substrates and facilitate their delivery to Hsp70, there may still be substrates that do not directly bind to Hsp70. It’s thus possible that some of the substrates identified only bind to JDPs. We made corresponding modifications in the revised manuscript. (Page 14; Line 290-292)

3) For Figure S7, the pull-down assay used His6-tagged JDPs. Ni resin is known to bind Hsp70s non-specifically. It's not surprising DnaK showed up in all the pull-down lanes, especially considering how much DnaK was over-expressed. For some pull-down lanes, the amount of DnaK is much more than that of JDPs, further indicating artifact. The author should include negative controls such as JDPs without His6-tag or any irrelevant protein with His6 tag.

Answer: Thanks for your suggestion. As you and another reviewer pointed out, there were some flaws in the experimental design of the pulldown assay. These include the non-specific binding of Hsp70 proteins to nickel resin, the absence of a negative control without a tag, and the inappropriate selection of the MBP tag. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between DnaK and JDP (Figure S9). While our manuscript employed nLuc to confirm protein dimerization, it is worth noting that nLuc assay was originally devised for investigating protein interactions 4.

4) For the proposed dimer formation in Fig. 4C, there are multiple bands above the monomer bands. What are these forms? It seems the majority of the Cys residues that could form disulfide bonds are in the NBD of MxDnaK2 since constructs with MxDnaK2-NBD form some sort of high-MW bands above the monomer. Does MxDnaK1-NBD also contain Cys at the analogous positions? The fact that MxDnaK1 didn't show disulfide-bonded bands doesn't mean it doesn't form dimer. It depends on where the Cys residues are.

It's nice the authors did Fig. 4D. However, the authors should include a positive control to show how strong the signal is for a true interaction before interpreting their results.

Answer: Thank you very much for your comments. In at least three independent experiments, we consistently observed two unidentified bands within the molecular weight range of 70-100 kDa during the purification process of His6-MxDnaK2. These bands appeared to be intermediate in size between the monomeric and dimeric forms of His6-MxDnaK2, and disappeared upon DTT treatment. the unidentified band compositions have been confirmed by LC/MS. The upper band included MxDnaK2 (65.3 kDa) and anti-FlhDC factor of E. coli (WP_001300634.1, 27 kDa). In the lower band, we detected the presence of MxDnaK2 and the 50S ribosomal protein L28 of E. coli (WP_000091955.1, 9 kDa). Based on these findings, we conclude that these two additional bands are the result of the interaction between His6-MxDnaK2 and these two E. coli proteins. The related explanations have been added in the legend of Figure 5. (Page 42; Line 938-942)

We analyzed the presence of Cys in MxDnaK1 and MxDnaK2. The NBD region of MxDnaK2 contains two Cys, located at positions 15 and 319. MxDnaK1-NBD contain a Cys at position of 316, which is the analogous position of 319-Cys of MxDnaK2. The analogous position of 15-Cys of MxDnaK2 is a Val in MxDnaK1, which might be an important factor contributing to the inability of MxDnaK1 to form oligomers.

Thanks for your suggestion to add the positive control. We re-performed the nLuc assays including a positive control(αSyn). According to the working principle of the nLuc assay, the amount of fluorescent substrate is limited. Therefore, even for proteins that interact with each other, the fluorescence value gradually decreases and reaches a plateau, similar to the negative control. This gradual decline in fluorescence is a significant indicator of protein interaction. In Figure 4D (Figure 5D in the revision version), we only presented the results of the first 20 minutes of detection. The complete two-hour detection results have been added in the supplementary figure (Figure S14).

5) line 48: "human HSC70 and HSP70 are 85% identical, and the phenotypes of their knockout mutants are different, which is consistent with their largely nonoverlapping substrates" The authors completely ignored that the promoters of HSC70 and HSP70 are very different.

Answer: This is our carelessness. Yes, HSC70 and HSP70 exhibit distinct expression patterns, which play important roles in their functional diversity. We modified the sentence in the new version (Page 5; Line 58)

6) Line 69: "The two PRK00290 proteins, not the other Myxococcus Hsp70s, could alternatively compensate the functions of EcDnaK (DnaK of E. coli) for growth." Please add references for this statement.

Answer: Added, thanks.

7) line 191: What's the mechanism for DnaK's role in oxidative stress? Is the disulfide bond formation in Fig. 4 related? Does disulfide-bond change the activity of DnaK?

Answer: Thanks for your pertinent comments. Honestly, we have no idea about the mechanism for MxDnaK2's role in oxidative stress. In our previous studies, we determined that the deletion of mxdnaK2 resulted in a longer lag phase after H2O2 treatment. Here, our aim was to investigate the impact of region swapping on the cellular function of MxDnaK2. In other bacteria, the critical role that DnaK plays in resistance to oxidative stress stems from the pleotropic functions of this chaperone. By shortening the dwelling time that proteins spend in the unfolded state, the DnaK/DnaJ chaperone system minimizes the risk of metal-catalyzed carbonylation of the side chains of proline, lysine, arginine, and threonine residues, but none of them linked to the dimerization characteristic of DnaK 5-7.

8) Fig. S9 seems redundant.

Answer: Deleted, thanks.

9) line 263, "but the NBD exchange was almost equal to the deletion of the gene with respect to phenotypes." But, the mutant has >50% activity in Fig. 3F.

Answer: We apologize for the confusing description. The “phenotypes” here indicates “cell phenotypes”. What we really tried to say with this sentence is that the NBD swapping strain of either MxDnaK1 or MxDnaK2 presented identical cell phenotypes with the gene-deleted strain. As we have already provided a detailed description of this result earlier, now we consider this sentence to be redundant and have therefore deleted it. (Page 17; Line 355-356)

10) line 221-226: the logic is not quite clear.

Answer: We apologize for the confusing description. In M. xanthus DK1622, MxDnaK1 is essential for cell survival, and an insertion of a second copy of mxdnaK1 in the genome is required for deletion of the in-situ gene. Thus, To verify whether the NBD region is required for the essentiality of MxDnaK1, we performed the region swapping of the in situ MxDnaK1 gene in the att::mxdnaK1 mutant (a DK1622 mutant containing a second copy of mxdnaK1 at attB site), and successfully obtained the MxDnaK1 mutant swapped with the MxDnaK2 NBD region. The experiment indicated that the NBD of MxDnaK1 is essential for the cellular functions of the chaperone. We have added the information and modified the sentences in the manuscript. (Page 15; Line 308-319)

Minor concerns:

Please check spelling. There are some typos such as "HPPES" in the Methods.

Answer: Corrected. Many thanks.

My areas of expertise are protein biochemistry, genetics, and structural biology on heat shock proteins.

Reviewer #2 (Evidence, reproducibility and clarity):

Major comments:

The manuscript is very nice and interesting, although some of the authors' conclusions are perhaps not well supported by their data. For example:

1) In the pulldown experiments the lack of interaction between 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 should be shown in order to support the conclusion made in line 197-198,

Answer: This is our carelessness. As you and another reviewer pointed out, there are some flaws in the experimental design of the pulldown assay. These include the non-specific binding of Hsp70 proteins to nickel resin, the absence of a negative control without a tag, and the inappropriate selection of the MBP tag. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between DnaK and JDP (including 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 interaction) (Figure S9). While our manuscript employed nLuc to confirm protein dimerization, it is worth noting that nLuc assay was originally devised for investigating protein interactions 4.

2) The only evidence that the NBD of MxDnaK1 is essential for bacterial growth is that this mutation couldn´t be obtained in M. xanthus. However, it could be purified in E. coli. Could the authors do some experiments with the M. xanthus strain without the chromosomal MxDnaK1 and then introduce a plasmid with the mutated gene?

Answer: We apologize for the confusing description. Actually, we determined the NBD is essential not only from the mutation couldn’t be obtained. In M. xanthus DK1622, MxDnaK1 is essential for cell survival, and in-situ deletion of the gene could be obtained after an insertion of a second copy of mxdnaK1 in the genome at the attB site. To verify whether the NBD region is required for the essentiality of MxDnaK1, we performed the region swapping of the in situ MxDnaK1 gene in the att::_mxdnaK_1 mutant (a DK1622 mutant containing a second copy of _mxdnaK_1), and successfully obtained the MxDnaK1 mutant swapped with the MxDnaK2 NBD region. The experiment indicated that the NBD of MxDnaK1 is essential for the cellular functions of the chaperone. We have added the information and modified the sentences in the manuscript. (Page 15; Line 308-319)

3) All the experiments with purified proteins were done with MxDnaKs bearing His-tags. It doesn't say explicitly its position, but as they employed a pET28A it is likely that the tag is at the N-terminus, which is close to the linker region. As this tag might interfere, it should be removed for the experiments, or at least a control done with the tag removed.

Answer: We apologize for the lack of detailed description. As you pointed out, the His-tags are located at the N-terminus of DnaKs. The full lengths of MxDnaK1 and MxDnaK2 are 638 and 607 amino acids. The linker regions are located at amino acid positions 381-386 for MxDnaK1 and 387-392 for MxDnaK2. Therefore, we believe that the His-tag is not close to the linker regions. We have included the information in new manuscript. (Page 24; Line 544-546)

The purified His6-DnaK proteins were employed for holdase activity assays and in vitro dimerization assays. Several previous studies have utilized the same holdase activity assay method with His-tagged DnaK 8,9. We suggested that the His-tag did not interfere with the holdase activity of DnaK. To exclude the influence of His-tag on oligomerization, we conducted a control with the tag removed in the in vitro dimerization assay and the result show no difference (Figure S13).

4) The authors state that MxDnaK dimerized in vitro with the NBD, and to disrupt the dimer they used 100 mM DTT, which is a very high concentration. As the protein has the His-tag, it should be removed to corroborate that it is not interfering with the dimerization.

Answer: Thanks for your suggestion. As mentioned above, to exclude the influence of the His-tag on oligomerization, we conducted a control with the tag removed in the in vitro dimerization assay and the result show no difference (Figure S13).

5) Why were the pulldown experiments done with MBP-MxDnaKs? Can you show a negative control between the MBP and the JDPs to rule out this interaction? It will be more suitable to do the pulldown assays with the purified MxDnaK´s without the His-tags (and the His-tags JDP that were employed).

Answer: Thanks for your suggestion. As mentioned above, there are some flaws in the experimental design of the pulldown assay. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between MxDnaKs and JDPs (Figure S9).

Minor comments:

• E. coli´s DnaK is only essential in heat shock conditions and for lambda phage cycle. If MxDnaK1 is similar to this Hsp70, why the substitution of its NBD for the NBD MxDnaK2 would be lethal for bacterial growth?

Answer: Thanks for the comments. As you correctly point out, DnaK is nonessential in E. coli. But in some other bacteria, DnaK also plays an essential role in cell growth for different reasons 10-12. In our previous studies, we determined that MxDnaK1 is essential in M. xanthus DK1622, and the MxDnaK2 is nonessential. In this study, we performed region swapping and found that only the NBD of MxDnaK1 was unreplaceable. In our opinions, the result indicated that NBD play important roles in the functional diversity between MxDnaK1 and MxDnaK2.

• I think that the writing should be revised and in the supporting information the captions of the figures should include more information.

Answer: Thanks a lot for the suggestion. We revised the manuscript and added more information in the legends of supplementary figures.

Reviewer #2 (Significance):

-General assessment: This is a nice piece of work which would benefit from revision to address the comments above. The authors showed the roles and differences between two DnaK in the same organism. They track these differences to the subdomains of the MxDnaK´s and co-chaperones. It will be interesting for future works to explore more deeply the co-chaperones and their interactions.

-Advance: I think that this manuscript fills a gap regarding the role of DnaK duplicated in bacterial strains. -Audience: I would say that the audience is broad and includes scientists interested in protein folding and chaperones, as well as myxobacteria.

1. Rosenzweig, R., Nillegoda, N. B., Mayer, M. P. & Bukau, B. The Hsp70 chaperone network. Nat Rev Mol Cell Biol 20, 665-680, doi:10.1038/s41580-019-0133-3 (2019).
2. Kampinga, H. H. & Craig, E. A. The HSP70 chaperone machinery: J proteins as drivers of functional specificity. Nat Rev Mol Cell Biol 11, 579-592, doi:10.1038/nrm2941 (2010).
3. Calloni, G. et al. DnaK functions as a central hub in the E. coli chaperone network. Cell Rep 1, 251-264, doi:10.1016/j.celrep.2011.12.007 (2012).
4. Dixon, A. S. et al. NanoLuc Complementation Reporter Optimized for Accurate Measurement of Protein Interactions in Cells. ACS Chem Biol 11, 400-408, doi:10.1021/acschembio.5b00753 (2016).
5. Fredriksson, A., Ballesteros, M., Dukan, S. & Nystrom, T. Defense against protein carbonylation by DnaK/DnaJ and proteases of the heat shock regulon. J Bacteriol 187, 4207-4213, doi:10.1128/JB.187.12.4207-4213.2005 (2005).
6. Santra, M., Dill, K. A. & de Graff, A. M. R. How Do Chaperones Protect a Cell's Proteins from Oxidative Damage? Cell Syst 6, 743-751 e743, doi:10.1016/j.cels.2018.05.001 (2018).
7. Fredriksson, A., Ballesteros, M., Dukan, S. & Nystrom, T. Induction of the heat shock regulon in response to increased mistranslation requires oxidative modification of the malformed proteins. Mol Microbiol 59, 350-359, doi:10.1111/j.1365-2958.2005.04947.x (2006).
8. Chang, L., Thompson, A. D., Ung, P., Carlson, H. A. & Gestwicki, J. E. Mutagenesis reveals the complex relationships between ATPase rate and the chaperone activities of Escherichia coli heat shock protein 70 (Hsp70/DnaK). J Biol Chem 285, 21282-21291, doi:10.1074/jbc.M110.124149 (2010).
9. Thompson, A. D., Bernard, S. M., Skiniotis, G. & Gestwicki, J. E. Visualization and functional analysis of the oligomeric states of Escherichia coli heat shock protein 70 (Hsp70/DnaK). Cell Stress Chaperones 17, 313-327, doi:10.1007/s12192-011-0307-1 (2012).
10. Shonhai, A., Boshoff, A. & Blatch, G. L. The structural and functional diversity of Hsp70 proteins from Plasmodium falciparum. Protein Sci 16, 1803-1818, doi:10.1110/ps.072918107 (2007).
11. Vermeersch, L. et al. On the duration of the microbial lag phase. Curr Genet 65, 721-727, doi:10.1007/s00294-019-00938-2 (2019).
12. Burkholder, W. F. et al. Mutations in the C-terminal fragment of DnaK affecting peptide binding. Proc Natl Acad Sci U S A 93, 10632-10637, doi:10.1073/pnas.93.20.10632 (1996).

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Referee #2

Evidence, reproducibility and clarity

Summary: This manuscript describes interesting studies of two paralogues of the E. coli Hsp70, DnaK, from of M. xanthus: MxDnaK1 and MxDnaK2. MxDnaK1 is similar to E. coli DnaK in terms of heat shock response, subcellular localization, etc. while MxDnaK2 is involved with membrane proteins and does not participate in the heat shock response. The interactome of the Mx DnaK´s are larger than that of E. coli DnaK, and their subcellular localization is also different. Regarding the differences between M. xanthus DnaK´s, MxDnaK2 prefers proteins with a higher hydrophobicity score, consistent with its role associated with membrane proteins. The phenotype of diverse mutants with domain swapping showed that the substitution of the NBD of MxDnaK2 for the NBD of MxDnaK1 led to similar phenotypes as the deletion of MxDnaK2 in terms of sporulation and S motility. Consistently, the interactomes of these variants were reduced in number of substrates in comparison with the wild type enzymes. No obvious effect was observed when the SBD´s subdomains were swept. Both MxDnaK interact with JDPs and NEF cochaperones. However, MxDnaK2 interacts only with one of the NEFs, and it depends on the NBD, and has one specific JDP, whichdepends on the beta-subdomain of the SBD (no information provided regarding NBD). MxDnaK1 interacts with both NEFs and has two specific JDPs, which also seems to depend on the beta subdomain of the SBD. Finally, a phylogenetic analysis reveals that the duplication of the dnak gene in Mx is correlated with the complexity of the proteome.

Major comments:

• The manuscript is very nice and interesting, although some of the authors' conclusions are perhaps not well supported by their data. For example: 1) In the pulldown experiments the lack of interaction between 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 should be shown in order to support the conclusion made in line 197-198, 2) The only evidence that the NBD of MxDnaK1 is essential for bacterial growth is that this mutation couldn´t be obtained in M. xanthus. However, it could be purified in E. coli. Could the authors do some experiments with the M. xanthus strain without the chromosomal MxDnaK1 and then introduce a plasmid with the mutated gene?
• All the experiments with purified proteins were done with MxDnaKs bearing His-tags. It doesn't say explicitly its position, but as they employed a pET28A it is likely that the tag is at the N-terminus, which is close to the linker region. As this tag might interfere, it should be removed for the experiments, or at least a control done with the tag removed.
• The authors state that MxDnaK dimerized in vitro with the NBD, and to disrupt the dimer they used 100 mM DTT, which is a very high concentration. As the protein has the His-tag, it should be removed to corroborate that it is not interfering with the dimerization.
• Why were the pulldown experiments done with MBP-MxDnaKs? Can you show a negative control between the MBP and the JDPs to rule out this interaction? It will be more suitable to do the pulldown assays with the purified MxDnaK´s without the His-tags (and the His-tags JDP that were employed).

Minor comments:

• E. coli´s DnaK is only essential in heat shock conditions and for lambda phage cycle. If MxDnaK1 is similar to this Hsp70, why the substitution of its NBD for the NBD MxDnaK2 would be lethal for bacterial growth?
• I think that the writing should be revised and in the supporting information the captions of the figures should include more information.

Significance

General assessment: This is a nice piece of work which would benefit from revision to address the comments above. The authors showed the roles and differences between two DnaK in the same organism. They track these differences to the subdomains of the MxDnaK´s and co-chaperones. It will be interesting for future works to explore more deeply the co-chaperones and their interactions.

Advance: I think that this manuscript fills a gap regarding the role of DnaK duplicated in bacterial strains.

Audience: I would say that the audience is broad and includes scientists interested in protein folding and chaperones, as well as myxobacteria.

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Referee #1

Evidence, reproducibility and clarity

In this study, Pan et al. characterized two Hsp70 DnaKs from Myxococcus xanthus DK1622. Through determining interactomes, the authors defined the differences and similarities between these two DnaKs in interacting with co-chaperones and substrates. Using domain-swapping, the authors analyzed the domain requirements for their functions. Lastly, their bioinformatics analyses seem to suggest the presence of these two DnaKs (i.e., DnaK duplication) is due to the increase of proteomic complexity. Overall, the results are interesting although not surprising. As the authors pointed out, many organisms have multiple Hsp70s with different but overlapping functions. Although multiple experimental approaches were used, the manuscript is generally descriptive without revealing any major mechanistic insights.

1. It is interesting MxDnaK1 seems to prefer cytosolic proteins while Mx-DnaK2 prefers inner membrane proteins. The domain-swapping experiments seem to suggest that the NBD is important for this difference. How NBD is important is not addressed. Is it due to ATP hydrolysis, NBD-SBD interaction, or co-chaperone interactions?
2. About the interactome analysis, since apyrase was added to remove ATP, it's surprising multiple Hsp40s were found in their analysis. Hsp70-Hsp40 interaction is known to require ATP. This may suggest some of the proteins found in their interactome analysis are artifacts. The authors should perform negative controls for their interactome analysis, such as using a control antibody for their CO-IP and analyze any non-specific binding to their resin.<br /> In addition, since JDPs were pull-down, is it possible some of the substrates identified are actually substrates for JDPs, not binding directly to DnaKs?
3. For Figure S7, the pull-down assay used His6-tagged JDPs. Ni resin is known to bind Hsp70s non-specifically. It's not surprising DnaK showed up in all the pull-down lanes, especially considering how much DnaK was over-expressed. For some pull-down lanes, the amount of DnaK is much more than that of JDPs, further indicating artifact. The author should include negative controls such as JDPs without His6-tag or any irrelevant protein with His6 tag.
4. For the proposed dimer formation in Fig. 4C, there are multiple bands above the monomer bands. What are these forms? It seems the majority of the Cys residues that could form disulfide bonds are in the NBD of MxDnaK2 since constructs with MxDnaK2-NBD form some sort of high-MW bands above the monomer. Does MxDnaK1-NBD also contain Cys at the analogous positions? The fact that MxDnaK1 didn't show disulfide-bonded bands doesn't mean it doesn't form dimer. It depends on where the Cys residues are.<br /> It's nice the authors did Fig. 4D. However, the authors should include a positive control to show how strong the signal is for a true interaction before interpreting their results.
5. line 48: "human HSC70 and HSP70 are 85% identical, and the phenotypes of their knockout mutants are different, which is consistent with their largely nonoverlapping substrates." The authors completely ignored that the promoters of HSC70 and HSP70 are very different.
6. Line 69: "The two PRK00290 proteins, not the other Myxococcus Hsp70s, could alternatively compensate the functions of EcDnaK (DnaK of E. coli) for growth." Please add references for this statement.
7. line 191: What's the mechanism for DnaK's role in oxidative stress? Is the disulfide bond formation in Fig. 4 related? Does disulfide-bond change the activity of DnaK?
8. Fig. S9 seems redundant.
9. line 263, "but the NBD exchange was almost equal to the deletion of the gene with respect to phenotypes." But, the mutant has >50% activity in Fig. 3F.
10. line 221-226: the logic is not quite clear.

Minor concerns:

Please check spelling. There are some typos such as "HPPES" in the Methods.

Significance

In this study, Pan et al. characterized two Hsp70 DnaKs from Myxococcus xanthus DK1622. Through determining interactomes, the authors defined the differences and similarities between these two DnaKs in interacting with co-chaperones and substrates. Using domain-swapping, the authors analyzed the domain requirements for their functions. Lastly, their bioinformatics analyses seem to suggest the presence of these two DnaKs (i.e., DnaK duplication) is due to the increase of proteomic complexity. Overall, the results are interesting although not surprising. As the authors pointed out, many organisms have multiple Hsp70s with different but overlapping functions. Although multiple experimental approaches were used, the manuscript is generally descriptive without revealing any major mechanistic insights.

My areas of expertise are protein biochemistry, genetics, and structural biology on heat shock proteins.

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Reply to the reviewers

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

Summary: Kellner and Berlin present their research findings pertaining to the effect of GRIN2B variants that modify NMDA receptor function and pharmacology. While these mutations were published previously, the new manuscript provides a more thorough investigation into the effects that these variants pose when incorporated into heteromeric complexes with either wildtype GluN2B or GluN2A - NMDA receptors containing only a single mutated GluN2B subunits is more relevant to the disease cases because the associated patients are heterozygous for the variant. The authors achieved selective expression of receptor heteromeric complexes by utilising an established trafficking control system. The authors found that while a single variant subunit in the receptor complex is largely dominant in its effect on reducing glutamate potency of the NMDA receptor, it 's effect on receptor pharmacology varied. Unlike diheteromeric receptors containing mutated subunits, polyamine spermine potentiated GluN1/2B (but not GluN1/2A/2B) receptors that contained a single mutated GluN2B. In contrast, the neurosteroid, pregnenolone-sulfate (PS), was effective at potentiating the NMDA receptor currents (to varying degrees) regardless of the subunit composition. The potentiation of NMDA receptor currents by PS was also observed in neurons overexpressing the variants.

The techniques used in this study were appropriate to address the objectives and the overall effects are large, and generally convincing. I like the way the results are presented, although have a few (easily addressable) comments.

We thank the reviewer for the positive remarks on our manuscript.

Major comments:

#1 When incrementally adding drugs (e.g. traces in figures 5 and 6), it doesn't always appear like the response has plateaued before changing the solutions/drugs. Therefore, I am curious to what extent the effects observed are underestimated.

The reviewer is correct to note that some responses do not necessarily reach a plateau, despite our efforts reach steady-state (as shown in most figures, e.g., Figs. 1-4, 6b, etc.), in particular when applying pregnenolone-sulfate (PS) (Fig. 5a, all traces in middle and bottom rows). However, in several instances, this was unobtainable due the very slow effect of the neurosteroid (its mode of action is from within the membrane) and the very large size of the cell (~1 mm). For these reasons, these experiments mandated excessively long exposures (~minutes) of oocytes to glutamate and PS (see scale bar- 20 secs) to try to reach steady-state, however this also caused deterioration to some cells (which did not return to baseline- and were therefore discarded). Thus, we eventually converged on settings whereby we did not expose oocytes to more than 4 minutes of the drug. Nevertheless, to try to estimate the extent of the underestimation (if any), we fitted the currents (standard mono-exponential fit, as previously reported1–3 (Suppl. 5a). We found that our application times of PS were, on average, three time the response’s time constants (tau) (Suppl. 5b), and we found a very weak relationship (R2 = 0.09) between the response to PS and time of its application (Suppl. 5c). These are now explicitly mentioned in the text (line #203), and in the legend of Suppl. 5. These thereby suggest that the reaction reached approximately 95% (1 - 1/e^3) of the steady-state value, and we are therefore confident that we have very small, if any, underestimation the extent of PS potentiation.

2 Also, in relation to figure 6, to what extent does agonist application cause desensitization here? Looking at traces in Figure 6b it appears that there is some desensitization and it isn’t clear to what extent this persists during the solution changes.

Agonist desensitization of NMDARs-currents is a well-known phenomenon, but it is very well established that it is not always observed in cells, including neurons (e.g., 4–7). In general, we did not observe very frequent desensitization’s (we provide a larger variety of traces of desensitizing and non-desensitizing currents (Fig. 6b Suppl. 7e and Suppl. 8a). Nevertheless, we explicitly note that in neurons, currents that didn’t reach steady-state after application of 100 mM NMDA were excluded from analysis (Methods - Patch clamping of cultured neurons, line #474), and in most cases desensitization was minor (or absent) following application of 100 mM NMDA and 100 mM PS (Fig. 6b).

3 Could the authors conduct/show the controls where NMDA alone (for 50-60s), or NMDA followed by PE-S (without ifenprodil).

These recordings are now shown in Fig. 6b and Suppl. 8a, (as opposed to Suppl. 7e).

#4 Finally, figure 5 shows the effect of the neurosteroid (and ifenprodil) on NMDA-evoked currents in neurons overexpressing the GluN2B variants in neurons. However, there currents probably reflect a mixture of extrasynaptic and synaptic receptors. To what extent are synaptic NMDA receptors affected by the variants?

To show the extent of the effect of the variants over synaptic receptors, we recorded miniature NMDA-dependent EPSCs; mEPSCNMDA), as described in our previous report8. We find that the varinats completely eliminate the appearance of mEPSCs (Suppl. 7a, b). Change in minis’ frequency is not the result of a presynaptic change or a change in synapse number9, as we have shown that AMPAR-mEPSC frequency was unaffected by the variants (i.e., synapse number and probability of presynaptic release are unchanged by the variants).

  To further address this, we also explored the relative synaptic vs. extrasynaptic distribution of the variants by using the established MK-801-protocol (to block all synaptic receptors during spontaneous activity, leaving extrasynaptic receptors unblocked)10,11. In neurons overexpressing the GluN2B-*wt* subunit, we obtained an extrasynaptic fraction of 38%, highly consistent with previous reports12,13. Overexpression of the variants, however, yielded a significantly and higher fraction (~50%) of the remaining current, supposedly suggesting more variant receptors at extrasynaptic loci (__Suppl. 8b, c__). However, due to the experimental settings we have chosen, the results from this experiment represent quite the inverse when involving extreme LoF variants. Firstly, 100 mM NMDA does not saturate variant receptors (whether pure, mixed di- or tri-heteromers, see __Table 1__). Secondly, normal neurotransmission does not open synaptic receptors containing mutant GluN2B-subunits, attested by the complete absence of mEPSCs (see __Suppl. 7a, b and __8,9). Thus, during the 10 minutes exposure to MK-801, only (mostly) purely *wt* receptors are blocked by spontaneous synaptic activity, and thus the second bout of 100 mM NMDA solely exposes the remaining *wt*-receptors. An increase in the number reflects more *wt*-receptors at the extrasynapse than the synapse. Thus, the observed increase in the fraction of extrasynaptic receptors in neurons overexpressing the variants, implies that the number of *wt*-receptors is necessarily decreased from the synapse and increases at the extrasynapse. We deem this to ensue due to the incorporation of the variants at the synapse. This increase cannot be explained by an overall increase in membrane expression of *wt*-receptors in neurons overexpressing the variants, as these cells show a strong reduction in Imax  (see __Fig. 6c and Suppl. 7e__). This is now detailed in the text (lines #270-290).

Minor comments:

5 Looking at the fits in the graph of Figure 2b it appears that the slope on the concentration response curves is less steep for the mixed 2B-diheteromeric NMDA receptors. How much are the Hill coefficients changing and can this be interpreted to provide more mechanistic insight? Wouldn't it make sense to include the Hill coefficients in Table 1?

We agree with the reviewer’s observation. Actually, the mixed di-heteromers have a similar Hill coefficient (nH) as the purely di-heteromeric GluN2Bwt receptors (see Table 1), and these show the typical near nH ~1 (e.g., 14–16). The only diverging groups are the purely di-heteromeric variant-containing channels (G689C/S only containing receptors; nH~2). Although these may suggest positive cooperation between the subunits, we are less inclined to infer insights from the latter owing to the fact that we limited our examination to 10 mM glutamate (we limit exposure of oocytes to 10 mM glutamate due to artifacts arising past this concentration, as discussed in Kellner et al.8: Fig. 2—figure supplement 1). (this description is now mentioned in page lines #149, 318, 319).

6 The authors illustrate the changes in potency by the shift in the concentration response curves, but is there any change in efficacy? A simple way to illustrate this would be also present a simple graph showing the maximum current amplitudes (i.e. to 10 mM glutamate) for each of the receptor complexes.

We now provide these data in (Suppl. 2a, b). We would like to note however that the expression pattern of the tailed-receptors (i.e., subunits with carboxy-termini tagged with C1/C2 tails, see Fig. 1a) are less expressive in general when compared with the native subunits (Suppl. 2c). This description is detailed in lines# 162-166.

#7 The authors characterize the 'apparent' affinity (or potency) of the receptor using concentration-response curves, but numerous points in the manuscript refer to changes in affinity. None of the experiments shown directly measure affinity (which would require ligand-binding assays) and so the use of the word affinity is inaccurate/misleading. I suggest the authors replace the instances of the word 'affinity' with 'potency'.

We apologize for the confusion surrounding our use of the term affinity. In fact, we do initially define this term in introduction (page #4): “apparent glutamate affinity (EC50)” to differentiate from affinity (KD). Regardless, and to avoid confusion, we replaced all terms, as suggested by reviewer to potency.

#8 In the third line of the abstract, the authors wrote, 'for which there are no treatments' in relation to GRINopathies. My understanding is that there are symptomatic treatments but that there are no disease-modifying treatments.

Indeed, all current treatments are supportive, rather than provide a bona fide cure or disease-modifying. These are now better defined in the abstract.

#9 The authors have interchangeably used the terms NMDAR or GluNRs throughout the manuscript. I suggest sticking to one of these terms. I would suggest NMDARs since this is less likely to be misread as a a specific NMDA receptor subunit.

Agreed and corrected throughout manuscript.

#10 Typos: 1) Results paragraph 2 sentence one: 'We thereby produced GluN2B-wt, GluN2B-G689C and GluN2B-G689S subunits tagged with C1 or C2, co-expressed these along with the GluN1a-wt subunits in...') Results paragraph 2: '...but these were mainly noticeable when oocytes are were exposed to high (saturating) glutamate concentrations...'
3) Last sentence in the second to last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'This , PS may serve to rescue...'
4) Last sentence in last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'Despite the latter, we found no evidence for any direct effect of three different physiologically relevant concentrations of the drug on di- or tri-heteromeric receptors'

All typos corrected.

#11 Figures 1e, 2b, 3b: it would be helpful to add a legend to the graph so that the curves can be interpreted without having to read through the figure legend.

Corrected.

#12 The bar graphs in Figure 6 show individual data points but those in figures 4b and 5b don't. Can the authors please add the data points to these graph.

Individual data points have been added.

#13 It would be helpful to reviewers that future manuscripts by the authors include page numbers and line numbers.

Included.

**Referees cross-commenting**

#14 Reviewers 2 and 3 highlight an important issue concerning figure 6 and the extent to which the overexpressed variants subunits can compete and assemble with endogenous NMDA receptors (unlike the system where the surface expression of specific receptor complexes is controlled). Indeed in the recent paper by the same authors, the two variants differed in their surface expression (in HEK cells), with G689C expressing particularly poorly. With reference to the second minor comment of Reviewer 1, the maximum current amplitudes would of course need to be normalized to cell surface expression of the receptor to gain any insight into efficacy.

We provide maximal current amplitudes (Imax) as a proxy for expression level as typically done (e.g.,8,17). These are now shown in Suppl. 2a, b (and see our response to comment #6, above). We would like to emphasize that we find it challenging to gain insights about efficacy of the variants in neuronal synapses, as we purposefully express non-C1/C2 tagged subunits in neurons (as we covet assembly of the variants with endogenous subunits). Moreover, the C1/C2-tagged subunits (whether wt or variants) are less expressive compared to their non-tagged NMDAR-counterparts. For instance, tagged GluN2B-wt subunits express at ~50% compared to non-tagged GluN2B wt subunits (Suppl. 2c). Thus, we find that efficacy of the C1/C2 tagged-subunits is less relatable to the non-tagged subunits (which are used in neurons and likely more relevant to the disease).

Despite the latter, we deem that we have specifically addressed this issue by measuring miniature EPSCs (mEPSCs) (see our reply to comment #4, Suppl. 7a, b). Briefly, even though the non-tagged G689C expresses at ~40% compared to other subunits (in oocytes and mammalian cells8), in neurons it engenders a robust (and highly significant) negative effect over synaptic currents (mEPSCs), as strong as the G689S-variant which expresses much more robustly (non-tagged G689S expresses to same extent as wt subunits). This demonstrates that the reduced efficacy the tagged subunits is less relatable to the non-tagged subunits and, importantly, it does not hinder the variants’ ability to incorporate within the synapse and affect function (i.e., exert a dominant negative effect). Here, we extend these observations towards the major postnatal channel subtype, namely tri-heteromers (2A/2B*), and therefore demonstrate that the robust dominant negative effect of G689C and G689S variants is likely due to their ability to incorporate within the predominant receptor subtype at the synapse (Suppl. 8).

Reviewer #1 (Significance (Required)):

This study emphasizes the complex pattern of effects that variants can have on glutamate receptor function and pharmacology, especially considering the context of receptor subunit composition. The authors have followed up their previous findings on the same mutants (Kellner et al, 2021, Elife), but used a trafficking control system here to characterize properties of mutated receptor complexes that are most likely to exist in neurons. The authors show that the defective currents mediated by NMDA receptors containing a loss-of-function GluN2B variant can be enhanced by neurosteroids (and in the case of GluN1/2B receptors, polyamines also). Development and approval of neurosteroids for the clinic would be required for the findings to translate to a therapy for patients. Readers should also be aware that neurosteroids act on other receptors too (e.g. GABA receptors), which could complicate the outcome. The expertise of the reviewer is in glutamate receptors and synaptic transmission.

We agree with the reviewer’s comment pertaining to challenges in translating PS to the clinic. Indeed, we explicitly mentioned its inhibitory effect on GABAA receptors (see line #366-367 and reference 18), as well as note its potential negative effect over GluN2C/D-containing receptors (line #365 and reference 19). We further describe alternative neurosteroids and means to bypass the limitations of PS, for instance by use of 24(S)-hydroxycholesterol6,18 or synthetic analogues (SGE-201, SGE-301)6. Lastly, we also propose a novel therapeutic approach, for which we did not find any mentions in the literature with regard to GRINopathies, consisting of the use of the FDA-approved Efavirenz (anti-retroviral compound20) to promote activity of cytochrome P450 46A1 (CYP46A1) to increase amounts of 24-S in the brain (discussion, lines #370-383).

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

#1 The objective of this paper is to assess whether a single mutated subunit of GRIN can affect the function of various forms of NMDA receptors. In particular, this study investigates the functional consequences of a GRIN variant when assembled within tri-heteromers, containing 2 GluN1, 1 GluN2A and 1 GluN2B subunits, the major postnatal receptor type. For this purpose, the authors artificially forced the subunits to associate in predefined complexes, using chimeras of GRIN subunits fused to GABAb receptor retention control sites at the endoplasmic reticulum. This trick allows to control the stoichiometry of the channels at the membrane and thus to focus on the function of a single type of NMDA receptor.The take home message of the paper is that a single GluN2B‐variant, whether assembled with a GluN2B‐wt subunit to form mixed di‐heteromer or with a GluN2A‐wt‐subunit (tri‐heteromer), strongly impairs the receptor functioning, as reported by a decrease of the apparent glutamate affinity of the receptor.

Altogether, this is a straightforward study of great interest for the GRIN community.

We greatly appreciate the reviewer’s comment about the relevance of our work towards the GRIN-community.

2 However, the way the background and purpose of the study (title and abstract) are presented is a bit confusing for non-specialists and could be easily improved. Technical information, which is crucial to validate the conclusions drawn from data analysis, should be added to the article. Some additional experiments are suggested to consolidate the work. Finally, additional discussion points are strongly encouraged.

We apologize for not making the paper more accessible to a broader readership. We did so for the sake of brevity. Nevertheless, we have re-written major parts of the manuscript to address this issue and retitled the report: “Rescuing Tri-Heteromeric NMDA Receptor Function: The potential of Pregnenolone-Sulfate in Loss-of-Function GRIN2B Mutations”.

Specific comments

Abstract / Title:

3 This work shows that a single GRIN variant impairs the function of various forms of NMDA receptors. Several sentences in the title and the abstract are confusing for a non-specialized audience. "Two extreme Loss‐of‐Function GRIN2B‐mutations are detrimental to triheteromeric NMDAR‐function, but rescued by pregnanolone‐sulfate." "Here, we have systematically examined how two de novo GRIN2B variants (G689C and G689S) affect the function of di‐ and tri‐heteromers." The number of variants tested is not of capital importance in the title, especially because one could believe that both are tested at the same time; similarly, when variants are named in the abstract, the fact that only 1 variant is studied at a time should be clarified (G689C OR G689S). Indeed, the problem is obvious to those familiar with GRIN disorders, but if this paper is to be published in a journal reaching a large audience rather than a specialized audience, the title of the paper should be modified.

As noted in our reply to comment #1 of this reviewer, we apologize for not making the paper more accessible and have therefore changed the title and re-written major parts of the manuscript to address this issue. We would like to note that we appreciate the reviewer’s comment and intent to increase the readership of our manuscript.

#4 "We find that the inclusion of a single GluN2B‐variant within mixed di‐ or tri‐heteromeric channels is sufficient to prompt a strong reduction in the receptors' glutamate affinity, but these reductions are not as drastic as in purely di‐heterometric receptors containing two copies of the variants. This observation is supported by the ability of a GluN2B‐selective potentiator (spermine) to potentiate mixed diheteromeric channels." Please, clarify the link between the two sentences. How do spermin potentiation of mixed diheteromeric channels supports the observation that the inclusion of a single GluN2B‐variant has less effect than the inclusion of two variants?

Our intention was to highlight that mixed di-heteromeric channels (2B/2B*) are less “damaged” (this is the link) than purely di-heteromeric channels (2B*/2B*).Explicitly mixed di-heteromers show less reduction in glutamate potency AND are also spermine-responsive, whereas purely mutant di-heteromers (2B*/2B*) show reduced potency, BUT do not respond to spermine at all. We have rephrased the sentences in our current manuscript to be clearer:

For instance: The positive responses of mixed di-heteromers, compared to the null effect over pure di-heteromers, is likely the result of the restored pH-sensitivity of mixed di-heteromers (Suppl. 3). This was surprising as the minimal and essential rules of engagement for potentiation by spermine are not well established, particularly in the case of tri-heteromers21,22 (see discussion, lines #341-353).

Methods

#5 All this study is based on the use of a unique ER‐retention technique to limit expression of a desired receptor‐population at the membrane of cells. According to the ER system retention of GABAb receptor, used in this study, while C1/C1-fused subunits are retained in the ER, C2/C2 reach the cell surface and the association of C1/C2 in the ER enables cell-surface targeting of the heterodimer. However, GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, as a functionally inactive homo-dimer (doi: 10.1042/BJ20041435). If there is an experimental trick that prevents the addressing of C2/C2 to the cell membrane, it should be specified and explained. This is critically important for understanding which receptor populations the data are derived from: receptors containing C1/C2 fused subunits only as stated by the authors, or C1/C2 and C2/C2 fused subunits?

We base our experiments on two seminal reports—23,24—that have developed this unique method (which we also refer to in the text, lines# 112-116). Briefly, the method employs the binding motifs of GABAB1 (GB1) and GB2 subunits and ER-retention motifs (these are now better detailed in Methods section, line # 448). Previous reports explicitly demonstrate that C1/C1- OR C2/C2-containing receptors do not reach the plasma (or very minimally) and we have reproduced these data with our variants (C1/C1: Suppl. 1a-d; C2/C2: Fig. 1a-c).

Figures #6 NMDA-receptor current amplitude should be normalized by the membrane expression of the receptors. A preliminary experiment should measure the effective cell surface expression of each of the subunits in the different transfection conditions.

To address the effective cell surface expression, we employed Imax as a proxy for functional expression (e.g.,8,17). These are now shown in Suppl. 2a, b (and see our response to reviewer 1, comments #6 and 14). Expectedly, we find significantly reduced efficacy by the varinats compared to wt-receptors, and the purely mutant di-heteromeric receptors exhibit the weakest efficacy. We have also addressed this issue by measuring miniature EPSCs (mEPSCs) (see our reply to reviewer 1, comment #4,). We find the variants to abolish mEPSCNMDA frequency (Suppl. 7a, b). This shows that the variants’ reduced efficacy translates to elimination of synaptic activity (dominant negative effect) (also seen in Suppl. 8).

#7 Fig.1a

The scheme should include C2-C2 complexes and mention whether these complexes are expressed at the cell surface (see previous and following comments).

As noted in our reply to comment #5 of this reviewer (above), C2/C2-containing receptors do not reach the plasma membrane (Fig. 1a-c). To avoid confusion, we have now added this scheme to the cartoon presented in Fig. 1a and have provided a more detailed description of the method and clones produced in the Methods section (line # 448).

#8 Fig.1b and c

Current from cells transfected with GluN2B‐wt‐C1 and GluN2B‐wt‐C2 should be compared to current expressed in cells expressing untagged receptor subunits: GluN2B‐wt Current from cells transfected with GluN2B‐wt‐C1 alone should be shown as well (although expected to be retained in the ER) (as performed for GluN2A‐wt‐C1 GluN2B‐wt‐C1 in suppl Fig. 1a)

Current comparisons of oocytes expressing tagged GluN2B‐wt‐C1 and GluN2B‐wt‐C2 and non-tagged GluN2B‐wt are now demonstrated in Suppl. 2c. The results indicate that the “tags” (C1 and C2) affect the expression of the subunits. We have also added a sample trace of current from a cell expressing the GluN2B‐wt‐C1 alone (Fig. 1b).

9 How could you explain the null current from cells transfected with GluN2B‐wt‐C2 alone (Fig.1b middle, and 1c)? since GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, GluN2B‐wt‐C2 is supposed to reach the cell surface. This is a very important point to clarify (I am probably missing a technical detail) because if the sub-unit tagged with C2 does reach the cell surface, then all the results and conclusions drawn from the C1-C2 conditions are wrong and could be attributed to a mix of complexes containing either C1-C2 or C2-C2.

We now realize that the reviewer was missing a crucial technical detail, namely how the clones are designed. Briefly, all clones have ER retention motifs and cannot reach plasma membrane unless they necessarily assemble as C1/C223,24. Also, please see our replies to comments #5, 7 to this reviewer (and Methods section, line # 448).

My following comments are based on the assumption that only receptors containing C1-C2 tagged subunits reach the membrane (as assumed by the authors and suggested in Figure 1b middle), but explanations should absolutely be provided to convince the reader. Fig. 4a and 5a (see our above replies to comments #5, 7 and 9; and references 23,24).

#10 Please, keep the current scale constant between all current illustrations within the same figure (4a and 5a). Indeed, not only the Spermin- or SP- induced potentiation is an important data (which is presently quantified on the histograms of fig. 4b and 5b) but also knowing whether the amount of current recorded in cells expressing one mutant subunit in presence of SP (for example GluN2A‐wt‐C1 GluN2B‐G689S‐C2) is comparable to the current recorded in wt receptor-expressing cells (GluN2A‐wt‐C1 GluN2B‐wt‐C2) in absence of SP would be an excellent added value for the paper. A special figure could quantify this rescue effect of SP, measuring and comparing the mean currents recorded in these conditions (one current illustration is not sufficient given variations between similar samples). By the way 5mM glutamate might be an excessive concentration. At 1mM, the expected synaptic concentration of glutamate following action potential, according to figures 3 and Suppl1 the response of the mutated receptor is much lower than that of the WT which is already almost maximal. In these conditions, SP-induced potentiation by a factor of two of GluN2A‐wt‐C1 GluN2B‐G689S‐C2 current could be equivalent to control currents recorded in GluN2A‐wt‐C1 GluN2B‐wt‐C2 cells.

We have rescaled all current amplitudes in Figs. 4 and 5 to be identical in size for easier comparison.

We have added all current amplitudes to try to examine the rescue effect of the two drugs in cell overexpressing a specific channel subtype, as requested (Suppl. 4). We find that; indeed, the potentiated currents of the mutant receptors reach (or even surpass) the basal Imax (i.e., current before potentiation) of the non-mutated receptors (Suppl. 4, dashed statistics bar).

In neurons, we address this in two ways. First, we show that the total NMDA-current is reduced by expression of the variants, and this current is “normalized” by PS (Fig. 6a-c). Similar reductions in Imax (by the variants) are shown in Suppl. 7e (to provide more examples). Secondly, neurotransmission (i.e., 1 mM glutamate25,26) is not sufficient for activating mutant receptors, certainly not pre-di-heteromers (see Table1, EC50 and Suppl. 7a, b- no mEPSCs)27–29. Therefore, 5 mM was required. Together, these strongly suggest that PS may normalize the currents of different receptors that respond to PS (under physiological settings and not 1- or 5mM NMDA). As suggested by the reviewer, there are many subtypes, and some may be activated by ambient glutamate (as suggested by application of PS onto neurons without opening the receptors by NMDA; see Suppl. 7c, d).

#11 Fig. 6

Figure 6 is not convincing because cultured hippocampal neurons do express endogenous NMDA receptors. To what extent the recording currents are affected by endogenous, non-mutated GluN2B subunits? Western Blots showing an extinction of endogenous subunits expression when transfected tagged subunits are competitively expressed would be required.

We have previously shown that the two variants incorporate very efficiently within synapses, causing a very robust elimination of synaptic currents (by measuring miniature NMDA-dependent EPSCs; minis) [see Fig. 8 in Kellner et al. eLIFE, 202127, and see review by Sabo et al.9 ). Change in minis’ frequency can be interpreted as either a presynaptic change or a change in synapse number, however we observed that AMPAR-mEPSC frequency was unaffected by these variants. These imply that synapse number and probability of release are unchanged by the variants. As the experiments are performed in wild-type neurons, (which express wild-type GluN2A and -2B), the dramatic effects we observed on minis suggests a dominant-negative effect of these disease-associated GluN2B variants. These are consistent with our observations that mutant subunits can co-assemble with wild-type GluN2B and/or GluN2A subunits. We have now reproduced this experiment (in fact, we employ this strategy prior each experiment to ensure expression of the variants) (Suppl. 7a, b). This thereby shows that there are no available wt-receptors at the synapse.

As there are various pools of NMDARs at synaptic and extrasynaptic sites, we did not think that a western blot would sufficiently differentiate between the latter, and thereby would not provide insight about extinction of wt-receptors (which could be simply pushed to other sites compared to synapse). Moreover, the intracellular pool of receptors is much larger than the amount of NMDARs that can be detected at the membrane (e.g., 30,31), and therefore electrophysiology seemed to be a better means to monitor membrane receptors only:

Thus, to examine the distribution of the variants between synaptic- and extrasynaptic loci, we employed a standard procedure consisting of the use of the activity-dependent blocker MK-801 (Methods). Briefly, neurons were persistently bathed in TTX during which they were probed for Imax using 100 mM NMDA (to refrain from activating other GluRs), followed by application of MK-801 for 10 minutes to exclusively blocks synaptic receptors (that open following action-potential independent miniature neurotransmission). This thereby spares all extrasynaptic receptors from being blocked by MK-801, which are subsequently revealed by a second application of 100 mM NMDA (Suppl. 8a, inset)12. In neurons overexpressing the GluN2B-wt subunit, we obtained an extrasynaptic fraction of 38%, highly consistent with previous reports12,13. Overexpression of the variants, however, yielded a significantly and higher fraction (~50%) of remaining current (Suppl. 8b, c), but instead of reflecting a larger pool of extrasynaptic receptors, the experiment represents quite the inverse when involving LoF variants. Firstly, 100 mM NMDA does not saturate variant receptors (whether pure, mixed di- or tri-heteromers, see Table 1). Secondly, normal neurotransmission does not open synaptic receptors containing mutant GluN2B-subunits, attested by the complete absence of mEPSCs (see Suppl. 7). Thus, during the 10 minutes exposure to MK-801, only wt receptors are blocked by spontaneous synaptic activity, and thus the second bout of 100 mM NMDA solely exposes the remaining wt-receptors at the extrasynapse. Thus, the observed increase in the fraction of extrasynaptic receptors, in neurons overexpressing the variants, implies that the number of wt-receptors is necessarily decreased from the synapse and increases at the extrasynapse, most likely due to the incorporation of the variants at the synapse. This increase cannot be explained by an overall increase in membrane expression of wt-receptors in neurons overexpressing the variants, as these cells show, yet again, a strong reduction in Imax as seen above (see Fig. 6c and Suppl. 7e) (lLines #270-291). These thereby suggest that purely wt-receptors are not necessarily eliminated from the membrane (extinct), rather pushed outside of the synapse.

12 Fig.6b “PE-S” on the graph should be replaced by “PS”

Typo corrected.

Discussion #13 The authors are surprised by the fact (Fig.2) that 1 variant reduces the apparent glutamate affinity of the receptor, but not as much as 2 variants, despite the fact that "NMDARs opening requires all four subunits to be liganded (i.e., occupied by a ligand) which implies that the least affine subunit should have dominated the final affinity of the receptor". I agree that the difference is noticeable, however the glutamate affinity for receptors containing 1 variant is much closer to that of receptors containing 2 variants than that of wild-type receptors. Hence, the results obtained do not seem so surprising and could result, as rightly explained by the authors from a possible cooperativity between the subunits.

We agree with the reviewer that glutamate potency of receptors containing 1 variant subunit is much closer to that of receptors containing 2 variant subunits. However, we maintain our surprise because we expected it to equal (not just close) to the potency of the least affine subunit (the limiting factor). This is based on the notion that all four subunits need to be liganded for channel opening4,32–34. We gently raise the possibility of potential cooperativity (Table 1, see Hill-coefficient and 33,35,36), as well as mention that this may also stem from the variants’ lower proton sensitivity (Suppl. 3), which has also been shown to promote motions of the ATD (amino terminal domain) and increase open probability (positive cooperativity)36. Nevertheless, we are very careful with interpreting the Hill coefficient , as we limited exposure of oocytes to 10 mM glutamate due to artifacts arising past this concentration (see Kellner et al.8: Fig. 2—figure supplement 1). This description is now mentioned in page lines #149, 318, 319. Thus, even the slightest underestimation of the maximal reposnse would surely affect the slope.

#14 On the other hand, the data in Figure 6 are much more difficult to interpret and reconcile with the nature of the expressed receptor subunits (which this time is not controlled) nor their association within the same receptor. However, this aspect, which is essential to the understanding of the consequences of 1 variant on neuronal signalling, is not discussed: Whatever the stoichiometry of the complexes in the heterozygous disease, the mutated and wild type GluN2B subunits coexist in the same cell: Either within the same di-heteromeric complexes GluN2B-wt + GluN2B-mutant, or in separate complexes but nevertheless expressed in the same cell, in di heteromeric (GluN2B-wt + GluN2B-wt and GluN2B-mutant + GluN2B-mutant); or tri-heteromeric (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) complexes. Assuming that half of the complexes remain wild-type, e.g. (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) we would expect (Fig. 6) a small decrease in NMDA current (carried only by the half that expresses the mutated subunit, and whose function is not zero but only decreased by about 20% in response to 5 mM Glutamate, Fig. 3b). The same reasoning applies to the di-heteromeric conditions (GluN2B-wt + GluN2B-wt; GluN2B-mutant + GluN2B-mutant), here again the decrease observed Fig. 6b is difficult to reconcile with the responses measured Fig. 2b.

In other words, how to explain a 50% decrease of the currents, instead of the 10% expected by the previous reasoning. In this experiment we do not know which subunits are expressed, their proportions, nor how they are associated in functional complexes, which makes the interpretation of the data impossible. The only explanation, far-fetched, for 50 % decrease would be that the complexes were to contain all (or the vast majority) 1 wild-type subunits associated with 1 variant, then a homogeneous 50% reduction in current could be expected. But this extreme condition could only be possible in the case of di-heteromers, which is unlikely the case in Fig.6 as GluN2A currents are measured in presence of Ifenprodil. To conclude

1) the comparison of the currents in transfected and non-transfected neurons does not make sense in figure 6b which is not convincing because we do not know the nature of the currents actually measured. A comparison in controlled condition would make more sense (as I suggested in the criticism of figures 4, 5).

2) The reality of the combinations of expression and association between subunits within different complexes expressed in the same cell must be considered and taken into account in the interpretation of the data. Undoubtedly, the means of restoring the NMDA current will be different depending on the presence of mutated subunits in all functional channels or not.

Indeed, neurons express a variety of different combinations of channel stoichiometry, including following transfection with the variants. We do find find that the effect on whole-cell current is indeed ~50% (Fig. 6b, c), thereby safe to assume that 50% remain “wt”, but we do not know how they distribute between synaptic and extrasynaptic loci. Our results however argue against 50% remaining receptors at the synapse. First, mEPSCNMDA disappear (Suppl. 7a, b and see reply to comment #11 of this reviewer), but wt-receptors are still at the membrane, and they seem to be moving out of synapse (Suppl. 8). Thus, we can only state with higher certainty that the variant subunits are very efficient in incorporating within mixed or pure receptors, especially at the synapse.

  We also consider that the reduction in the whole-cell current observed in __Fig. 6b, c__ is not due to the remaining 50% GluN2B-*wt*-containing receptors, rather likely due to other variants, notably GluN2A, which are more prominent at postnatal stages37, such as in our case. In support, we see a large remaining current after saturating ifenprodil application (__Suppl. 7 e, f__)38. Thus, the variants incorporate within all 2A/2B membrane receptors, at the synapse and outside it (i.e., extrasynaptic) (see __Suppl. 8, c__).

**Referees cross-commenting**

The referees' comments are highly relevant. In particular, referee 3's comment 1 seems very interesting because it may help to better understand the discrepancy in the results observed in neurons, i.e. a 50% decrease in the currents induced by the expression of the mutant and wild type subunits in the same cells, whereas theoretically one would expect a 10% decrease of this current (cf. referee 2's 2nd comment in the discussion section). This comment 1 of referee 3 indeed stresses the fact that the control (non-transfected neurons) to which the heterozygous condition is compared is not the correct control, which should rather be neurons transfected with wild type receptor subunits. More generally, this comment underlines the importance of monitoring the effective membrane expression of the different subunits in each of the experimental conditions in order to be able to compare conditions and draw conclusions.

We initially did not perform this control as the literature paints a clear picture whereby expression of the GluN2B-subunit (without adding excess of the GluN1 subunit) does not instigate a robust increase in surface expression of NMDARs (and thus current remains the ~same) 4,39–43, and see our reply to comment #14 (above), and reviewer 3 comment 1 (below). Nevertheless, we have now performed this test by overexpressing GluN2B-wt. In support of previous reports, we do not find any statistical difference in current size between non-transfected neurons and neurons solely overexpressing the GluN2B-wt subunit (Fig. 6a, b). Furthermore, application of PS onto naïve or GluN2Bwt expressing neurons yields identical currents (Imax) and potentiation (Fig. 6c, d). These argue that we did not obtain “overexpression”.

We suggest that the 50% reduction in current size between neurons expressing the mutant and wt expressing neurons stems from the integration of mutant subunits and their dominant negative effect. Evidence for this incorporation is provided by the very strong reduction in synaptic currents (suppl 7a, b), and the supposedly higher abundance of wt-containing receptors in extrasynaptic regions (see reviewer 1 comment 4 and suppl 8). This is

Reviewer #2 (Significance required):

The novelty of the study, is to evaluate the consequences of a single mutated subunit within NMDA receptors affected by GRIN variant, to mimic the heterozygous condition of GRIN encephalopathies, this is of potential value for the field and the interest could also be extended to other genetic diseases (at least the experimental way to study the functioning of only one desired stoichiometric configuration). The strength of this paper is precisely to isolate technically and to study the functioning of a desired stoichiometric configuration only. The main limitation of the paper is the interpretation that the authors make of their data in a physio-pathological context. This work could be of interest for general audience, providing the title and summary are slightly modified. My area of expertise could not be closer to the topic of the article: Glutamate receptors; GRIN; molecular tinkering, cell culture, electrophysiology, receptor stoichiometry...

We thank the reviewer for noting the value in our work and its potential contribution and interest to the field and other diseases. Per reviewer’s suggestion, we have modified the title and text to suit a larger audience.

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

This paper is a follow up of an earlier paper published by the group (Kellner et al., eLife 2021), which aimed at characterizing the functional properties of two de novo GluN2B mutations in patients suffering from severe pediatric diseases, GluN2B-G689C and -G689S. NMDA receptors (NMDARs) are tetramers composed of two GluN1 and two GluN2 subunits. A single receptor can incorporate either two identical GluN2 subunits (di-heteromers) or two different GluN2 subunits (tri-heteromers), leading to a large diversity of NMDAR subtypes. The main NMDAR subtypes in the adult forebrain are GluN1/GluN2A and GluN1/GluN2B di-heteromers, as well as GluN1/GluN2A/GluN2B tri-heteromers. While the exact proportions of these three subtypes are still contentious, there are evidence that in the adult N1/2A/2B tri-heteromers form the major population of synaptic NMDARs in the adult forebrain. In addition, patients bearing pathogenic mutations are often heterozygous for the mutation, giving rise to mixed NMDARs incorporating one mutated and one intact GluN2 subunit. In their previous paper, Kellner et al. had shown that purely di-heteromeric GluN1/GluN2B-G689C and -G689S mutants display a drastic (> 1,000-fold) decrease of glutamate sensitivity and a decrease of surface expression. In the current paper, the authors characterize the effects of the -G689C and -G689S mutations on N1/2A/2B tri-heteromeric receptors, as well as on mixed di-heteromeric GluN1/GluN2B receptors containing one copy of the wild-type GluN2B subunit and one copy of the mutated GluN2B subunit. They show that one copy of the mutant subunit, either within mixed diheteromeric or tri-heteromeric receptors, is sufficient to decrease drastically glutamate sensitivity, although the shift in glutamate EC50 is not as strong as in pure di-heteromeric receptors (≈ 500-fold). They furthermore explore strategies to counteract the hypofunction induced by these mutations by testing the effect of positive allosteric modulators (PAMs). They show that spermine, a GluN2B-specific PAM, can potentiate the activity of mixed diheteromeric N1/2B but not N1/2A/2B tri-heteromers. However pregnenolone sulfate (a 2A/2B-specific PAM) can potentiate both the activity of mixed diheteromeric and tri-heteromeric NMDAR populations, either in oocytes or cultured neurons.I have very few major comments to make. The experiments are straightforward and the adequate controls have been made. Here are my two only major comments:

We thank the reviewer for the very detailed overview of our work and for appreciating our controls and methods.

#1 About the experiment on cultured neurons. The authors compare the currents of cultured neurons transfected with GluN2B-G689C and -G689S to non transfected neurons. The adequate control is rather neurons transfected with the wild-type GluN2B subunit to even out any phenomenon linked to transfection of the neuron. Given the overexpression that can occur after transfection, the effect of the mutations on the size of NMDAR currents might be even stronger than what the authors show. However in that case PS might not completely rescue mutant NMDAR currents to wild-type levels.

We initially did not perform this control as the literature paints a clear picture whereby expression of the GluN2B-subunit (without adding excess of the GluN1 subunit) does not instigate a robust increase in surface expression of NMDARs (and thus current remains the ~same) 4,39–43, and see our reply to comment #14 (above), and reviewer 3 comment 1 (below). Nevertheless, we have now performed this test by overexpressing GluN2B-wt. In support of previous reports, we do not find any statistical difference in current size between non-transfected neurons and neurons solely overexpressing the GluN2B-wt subunit (Fig. 6a, b). Furthermore, application of PS onto naïve or GluN2Bwt expressing neurons yields identical currents (Imax) and potentiation (Fig. 6c, d). These argue that we did not obtain “overexpression”. Thus, our results and interpretations hold true, and are therefore not underestimation of the effects of PS in neurons.

2 How come high concentrations of glutamate (>100µM) produce additional current on wt GluN1/GluN2B (with retention signals) compared to 100 µM glutamate, which is supposed to be saturating? It does not seem to stem from an osmotic effect since 10 mM glutamate does not produce any current on uninjected oocytes. Knowing that this "artefactual" effect might also occur in the mutant receptors, how do you take this effect into account when calculating the glutamate EC50s of the mutants? Given the drastic shift in EC50 produced by the mutant, taking into account this artefact is not going to change the conclusion, but the actual EC50s will be affected.

GluN1/GluN2B-wt receptors (with or without retention signals) are indeed saturated at 100 mM glutamate. However, excessively large concentrations of glutamate (>100 mM) may yield artefacts even in non-injected oocytes (in 10 mM, this occurs in ~20% of the cells, see Kellner et al 20218—Fig. 2 and Suppl. 1c, d) as well as in GluN2B-wt injected oocytes (supplementary Table 1 in 44). This is not due to osmolarity, as rightly mentioned by the reviewer (and below), rather possibly by endogenous glutamate receptors and transporters that do not readily contribute to current amplitude (these are extremely small currents), but can cause deterioration of the cell (and enhance ‘leak’) when activated for prolonged times by very large concentrations (e.g.,45). In fact, we explicitly report these to highlight potential artefacts, as these are often overlooked in the field. Regardless, most reports do no go past 100 mM glutamate, not even when describing GRIN2 mutations since most mutations do not cause such drastic shifts in potency as we observed (to the best of our knowledge only one report describes such an extreme LoF mutation for a GluN2A variant46). Of note, these effects are not seen when glycine is applied at high concentrations (supporting lack of effect by osmolarity)47. Thus, we refrained from testing concentrations past 10 mM, aware that it may yield a slight underestimation of glutamate potency (and perhaps the reason for the larger Hill coefficient, nH; see our reply to reviewer #1, comment #5). Importantly, despite the potential underestimation of the EC50, it does not change our conclusions as all groups are measured side-by-side (thus, the same underestimation equally applies to all other groups as well). We now mention this more in detail in the methods under the section – “Two Electrode Voltage Clamp recordings in Xenopus Laevis oocytes”.

Minor comments:

3 In the first paragraph of the "Results" section, when describing the design of the constructs used to force a heteromeric stoichiometry in recombinant systems, the authors do as if they had designed the constructs themselves "Briefly, we tagged...are retained in the ER (Fig. 1a)". Please rewrite this paragraph to show that you used constructs that had been previously designed by another group (Hansen et al., 2014).

We apologize. We did not mean to express that we have developed the method and indeed refer readers to the seminal works of those who did (Stroebel et al., 2014 and Hansen et al. 2014, lines #109-116). We did not go into details for the sake of brevity. We have rewritten this part to give proper acknowledgement to the method’s developers (also see Methods, line# 448).

4 I do not see any evidence of "positive cooperativity" between subunits in ref. 32. Ref. 32, to the best of my knowledge, states that in N1/2A/2B tri-heteromers, the 2A subunit sets the biophysical properties of the tri-heteromer. But there is no account of mixed di-heteromers. In addition, the cooperativity between the glutamate and glycine binding sites is negative.

The reviewer is correct, and we apologize for the mis-citation. Indeed, the cooperativity between glutamate- and glycine-binding is typically reported as negative48,49, and our intention was to highlight the strong cooperativity (whether positive or negative) observed between NMDAR-subunits and meant to cite the works of: 33,35,50 (lines . We have now rephrased the sentence: The divergence from this scenario suggests that the slight amelioration in potency could stem from positive cooperativity between the subunits50 (but see Hill coefficients in Table 1). Indeed, mixed receptors show restored proton sensitivity (Suppl. 3), which has been suggested to be coupled to other receptor features, notably increase in open probability.

5 Interpretation of spermine action within the Results section: it is striking indeed to observe that the mutations in the context of a mixed di-heteromer still allow spermine potentiation, while they abolish this potentiation in pure di-heteromers. As rightly said in the discussion, the regain of spermine potentiation in the mixed compared to the pure diheteromers is likely due to a more favorable transduction of spermine signaling to the pore, likely via a higher pH sensitivity of mixed di-heteromers compared to di-heteromers. I would thus avoid the terms of "one single intact interface" for the mixed di-heteromer, since both spermine binding sites are likely intact in this NMDAR configuration. How is pH sensitivity affected in the mixed di-heteromers?

We have performed a detailed pH dose-responses for the various channel types (Suppl 3). We find that GluN2B mixed di-heteromers exhibit similar IC­50 as pure GluN2B-wt di-heteromers, thus explaining their ability to undergo potentiation by spermine via alleviation of proton inhibition. We therefore further suggest that mixed di-heteromers’ have higher pH-sensitivity compared to pure mutant di-heteromers and this mat also contributes to their higher spermine sensitivity. Lastly, we observed that all GluN2A-wt-containing tri-heteromeric receptors were non-responsive to spermine (Fig. 4a). In fact, under our experimental conditions tri-heteromers underwent slight inhibition by spermine, regardless the identity of the GluN2B subunit (whether wt or variant) (Fig. 4b). Thus, as the tri-heteromers used here exhibit identical pH-sensitivity as 2B-di-heteromers, the only diverging aspect is the missing interface between the GluN1a and GluN2B subunits, demonstrating that potentiation by spermine requires at least one GluN2B-subunit with an intact proton sensitivity, and mandates two intact interfaces between GluN1-wt and GluN2B-wt subunits (Table 1)21.

6 In the methods section, the oocyte recording solution (likely Ringer and not Barth) does not contain any potassium. This is probably a typo. Could you correct the composition of your Ringer?

Corrected. We record NMDARs currents by use of a Barth solution containing (in mM): 100 NaCl, 0.3 BaCl2, 5 HEPES, pH 7.3 (adjusted with KOH, at ~2.5 mM) (as in 4,51).

7 There are several typos, especially in the Discussion.

We have corrected the typos throughout the publication.

**Referees cross-commenting**

I overall agree with the comments of reviewers 1 and 2. In particular, I agree that it is pointless to compare the absolute currents of non transfected neurons vs mutant-transfected neurons without an idea of receptor cell-surface expression.

We have performed this experiment (Fig. 6) and please see our reply to this reviewer’s comment #1.

I would like however to give some precisions about some comments of Reviewer 2. About the ER retention technique to express tri-heteromers: I didn't know that the C2 signal could be addressed to the membrane on its own. The lack of leak current stemming from C1-C1 or C2-C2 combinations has been demonstrated in the paper establishing the technique (Hansen et al, 2014), as well as in another paper that developed an analog technique based on GABAB retention signals (Stroebel et al., J Neurosci 2014). So it is fair to consider that the authors were not surprised by the lack of current when co-expressing two GluN2B subunits carrying the C2 signal.

We thank you for this addition and support for our observations.

About the comparison about absolute currents wt vs mutants, +/- spermine (Fig. 4a and 5a). I agree with reviewer 2 that being able to compare absolute currents of wt without spermine to mutant + spermine would be very interesting to see if spermine can actually rescue mutant hypofunction. However, to the defense of the authors, comparing absolute current values of recordings from Xenopus oocytes is meaningless. Indeed the variability of currents for the same construct and same day of experiment is too high (there can be up to a ten-fold difference between the lowest and the highest current of oocytes expressing the same construct the same experimental day). A way to investigate this aspect would be to estimate the open probability of the different constructs with or without spermine via the inhibition kinetics of an open channel blocker (e.g. MK801) and measure surface expression by Western blot, but I am not sure these experiments are worth it for the spermine experiment.

We agree with this reviewer about current size. It is quite variable among cells and would therefore introduce an additional variable and variability: the expression of these modified (C1/C2-tagged) subunits is dually affected by the mutation itself (Kellner et al. 2021) and by the introduction of the tagging (which really hampers there trafficking to membrane, Suppl. 2c); with unknown contribution of each variable. We thereby do not think these provide an added value to our conclusions, yet to grant reviewers’ no 2 request we have added __Suppl. 4 __which shows the rescue effect of the different drugs.

Reviewer #3 (Significance (Required)):

This paper is not of high significance since most of the characterization of the 2B-G689C and -G689S de novo mutants found in patients has already been published (Kellner et al., eLife 2021). However, this paper is worth publishing since it brings new data on the effect of the mutations on tri-heteromeric and mixed di-heteromeric NMDAR populations, which are likely the most abundant NMDAR populations in the patient's brain at adult stage. Tri-heteromeric and mixed NMDAR populations have often been overlooked when studying pathogenic NMDAR mutations due to the difficulty to express them specifically in recombinant systems. This paper (in addition to other papers in the field, see for instance Elmasri et al., Brain Sci. 2022; Li et al., Hum. Mutat. 2019) shows that the effect of the mutations on the receptor biophysical and pharmacological properties (but also on trafficking) differ whether the receptor contains one or two copies of the mutant subunit. This paper is of interest to scientists interested in NMDA receptor structure-function and pharmacology, as well as clinicians interested in GRINopathies (pathologies linked to NMDAR mutations).

I, the reviewer, am an expert in NMDAR structure-function and pharmacology. I believe I have sufficient expertise to evaluate the entirety of the paper.

We thank the reviewer for appreciating and acknowledging the merits of our work for publication.

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Referee #3

Evidence, reproducibility and clarity

This paper is a follow up of an earlier paper published by the group (Kellner et al., eLife 2021), which aimed at characterizing the functional properties of two de novo GluN2B mutations in patients suffering from severe pediatric diseases, GluN2B-G689C and -G689S. NMDA receptors (NMDARs) are tetramers composed of two GluN1 and two GluN2 subunits. A single receptor can incorporate either two identical GluN2 subunits (di-heteromers) or two different GluN2 subunits (tri-heteromers), leading to a large diversity of NMDAR subtypes. The main NMDAR subtypes in the adult forebrain are GluN1/GluN2A and GluN1/GluN2B di-heteromers, as well as GluN1/GluN2A/GluN2B tri-heteromers. While the exact proportions of these three subtypes are still contentious, there are evidence that in the adult N1/2A/2B tri-heteromers form the major population of synaptic NMDARs in the adult forebrain. In addition, patients bearing pathogenic mutations are often heterozygous for the mutation, giving rise to mixed NMDARs incorporating one mutated and one intact GluN2 subunit. In their previous paper, Kellner et al. had shown that purely di-heteromeric GluN1/GluN2B-G689C and -G689S mutants display a drastic (> 1,000-fold) decrease of glutamate sensitivity and a decrease of surface expression. In the current paper, the authors characterize the effects of the -G689C and -G689S mutations on N1/2A/2B tri-heteromeric receptors, as well as on mixed di-heteromeric GluN1/GluN2B receptors containing one copy of the wild-type GluN2B subunit and one copy of the mutated GluN2B subunit. They show that one copy of the mutant subunit, either within mixed diheteromeric or tri-heteromeric receptors, is sufficient to decrease drastically glutamate sensitivity, although the shift in glutamate EC50 is not as strong as in pure di-heteromeric receptors (≈ 500-fold). They furthermore explore strategies to counteract the hypofunction induced by these mutations by testing the effect of positive allosteric modulators (PAMs). They show that spermine, a GluN2B-specific PAM, can potentiate the activity of mixed diheteromeric N1/2B but not N1/2A/2B tri-heteromers. However pregnenolone sulfate (a 2A/2B-specific PAM) can potentiate both the activity of mixed diheteromeric and tri-heteromeric NMDAR populations, either in oocytes or cultured neurons.

I have very few major comments to make. The experiments are straightforward and the adequate controls have been made. Here are my two only major comments:

1. About the experiment on cultured neurons. The authors compare the currents of cultured neurons transfected with GluN2B-G689C and -G689S to non transfected neurons. The adequate control is rather neurons transfected with the wild-type GluN2B subunit to even out any phenomenon linked to transfection of the neuron. Given the overexpression that can occur after transfection, the effect of the mutations on the size of NMDAR currents might be even stronger than what the authors show. However in that case PS might not completely rescue mutant NMDAR currents to wild-type levels.
2. How come high concentrations of glutamate (>100µM) produce additional current on wt GluN1/GluN2B (with retention signals) compared to 100 µM glutamate, which is supposed to be saturating? It does not seem to stem from an osmotic effect since 10 mM glutamate does not produce any current on uninjected oocytes. Knowing that this "artefactual" effect might also occur in the mutant receptors, how do you take this effect into account when calculating the glutamate EC50s of the mutants? Given the drastic shift in EC50 produced by the mutant, taking into account this artefact is not going to change the conclusion, but the actual EC50s will be affected.

Minor comments:

1. In the first paragraph of the "Results" section, when describing the design of the constructs used to force a heteromeric stoichiometry in recombinant systems, the authors do as if they had designed the constructs themselves "Briefly, we tagged...are retained in the ER (Fig. 1a)". Please rewrite this paragraph to show that you used constructs that had been previously designed by another group (Hansen et al., 2014).
2. I do not see any evidence of "positive cooperativity" between subunits in ref. 32. Ref. 32, to the best of my knowledge, states that in N1/2A/2B tri-heteromers, the 2A subunit sets the biophysical properties of the tri-heteromer. But there is no account of mixed di-heteromers. In addition, the cooperativity between the glutamate and glycine binding sites is negative.
3. Interpretation of spermine action within the Results section: it is striking indeed to observe that the mutations in the context of a mixed di-heteromer still allow spermine potentiation, while they abolish this potentiation in pure di-heteromers. As rightly said in the discussion, the regain of spermine potentiation in the mixed compared to the pure diheteromers is likely due to a more favorable transduction of spermine signaling to the pore, likely via a higher pH sensitivity of mixed di-heteromers compared to di-heteromers. I would thus avoid the terms of "one single intact interface" for the mixed di-heteromer, since both spermine binding sites are likely intact in this NMDAR configuration. How is pH sensitivity affected in the mixed di-heteromers?
4. In the methods section, the oocyte recording solution (likely Ringer and not Barth) does not contain any potassium. This is probably a typo. Could you correct the composition of your Ringer?
5. There are several typos, especially in the Discussion.

Referees cross-commenting

I overall agree with the comments of reviewers 1 and 2. In particular, I agree that it is pointless to compare the absolute currents of non transfected neurons vs mutant-transfected neurons without an idea of receptor cell-surface expression.

I would like however to give some precisions about some comments of Reviewer 2. About the ER retention technique to express tri-heteromers: I didn't know that the C2 signal could be addressed to the membrane on its own. The lack of leak current stemming from C1-C1 or C2-C2 combinations has been demonstrated in the paper establishing the technique (Hansen et al, 2014), as well as in another paper that developed an analog technique based on GABAB retention signals (Stroebel et al., J Neurosci 2014). So it is fair to consider that the authors were not surprised by the lack of current when co-expressing two GluN2B subunits carrying the C2 signal. About the comparison about absolute currents wt vs mutants, +/- spermine (Fig. 4a and 5a). I agree with reviewer 2 that being able to compare absolute currents of wt without spermine to mutant + spermine would be very interesting to see if spermine can actually rescue mutant hypofunction. However, to the defense of the authors, comparing absolute current values of recordings from Xenopus oocytes is meaningless. Indeed the variability of currents for the same construct and same day of experiment is too high (there can be up to a ten-fold difference between the lowest and the highest current of oocytes expressing the same construct the same experimental day). A way to investigate this aspect would be to estimate the open probability of the different constructs with or without spermine via the inhibition kinetics of an open channel blocker (e.g. MK801) and measure surface expression by Western blot, but I am not sure these experiments are worth it for the spermine experiment.

Significance

This paper is not of high significance since most of the characterization of the 2B-G689C and -G689S de novo mutants found in patients has already been published (Kellner et al., eLife 2021). However, this paper is worth publishing since it brings new data on the effect of the mutations on tri-heteromeric and mixed di-heteromeric NMDAR populations, which are likely the most abundant NMDAR populations in the patient's brain at adult stage. Tri-heteromeric and mixed NMDAR populations have often been overlooked when studying pathogenic NMDAR mutations due to the difficulty to express them specifically in recombinant systems. This paper (in addition to other papers in the field, see for instance Elmasri et al., Brain Sci. 2022; Li et al., Hum. Mutat. 2019) shows that the effect of the mutations on the receptor biophysical and pharmacological properties (but also on trafficking) differ whether the receptor contains one or two copies of the mutant subunit. This paper is of interest to scientists interested in NMDA receptor structure-function and pharmacology, as well as clinicians interested in GRINopathies (pathologies linked to NMDAR mutations). I, the reviewer, am an expert in NMDAR structure-function and pharmacology. I believe I have sufficient expertise to evaluate the entirety of the paper.

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Referee #2

Evidence, reproducibility and clarity

The objective of this paper is to assess whether a single mutated subunit of GRIN can affect the function of various forms of NMDA receptors. In particular, this study investigates the functional consequences of a GRIN variant when assembled within tri-heteromers, containing 2 GluN1, 1 GluN2A and 1 GluN2B subunits, the major postnatal receptor type. For this purpose, the authors artificially forced the subunits to associate in predefined complexes, using chimeras of GRIN subunits fused to GABAb receptor retention control sites at the endoplasmic reticulum. This trick allows to control the stoichiometry of the channels at the membrane and thus to focus on the function of a single type of NMDA receptor. The take home message of the paper is that a single GluN2B‐variant, whether assembled with a GluN2B‐wt subunit to form mixed di‐heteromer or with a GluN2A‐wt‐subunit (tri‐heteromer), strongly impairs the receptor functioning, as reported by a decrease of the apparent glutamate affinity of the receptor.

Altogether, this is a straightforward study of great interest for the GRIN community. However, the way the background and purpose of the study (title and abstract) are presented is a bit confusing for non-specialists and could be easily improved. Technical information, which is crucial to validate the conclusions drawn from data analysis, should be added to the article. Some additional experiments are suggested to consolidate the work. Finally, additional discussion points are strongly encouraged.

Specific comments

Abstract / Title:

This work shows that a single GRIN variant impairs the function of various forms of NMDA receptors. Several sentences in the title and the abstract are confusing for a non-specialized audience. "Two extreme Loss‐of‐Function GRIN2B‐mutations are detrimental to triheteromeric NMDAR‐function, but rescued by pregnanolone‐sulfate." "Here, we have systematically examined how two de novo GRIN2B variants (G689C and G689S) affect the function of di‐ and tri‐heteromers." The number of variants tested is not of capital importance in the title, especially because one could believe that both are tested at the same time; similarly, when variants are named in the abstract, the fact that only 1 variant is studied at a time should be clarified (G689C OR G689S). Indeed, the problem is obvious to those familiar with GRIN disorders, but if this paper is to be published in a journal reaching a large audience rather than a specialized audience, the title of the paper should be modified.

Introduction:

"We find that the inclusion of a single GluN2B‐variant within mixed di‐ or tri‐heteromeric channels is sufficient to prompt a strong reduction in the receptors' glutamate affinity, but these reductions are not as drastic as in purely di‐heterometric receptors containing two copies of the variants. This observation is supported by the ability of a GluN2B‐selective potentiator (spermine) to potentiate mixed diheteromeric channels." Please, clarify the link between the two sentences. How do spermin potentiation of mixed diheteromeric channels supports the observation that the inclusion of a single GluN2B‐variant has less effect than the inclusion of two variants?

Methods

All this study is based on the use of a unique ER‐retention technique to limit expression of a desired receptor‐population at the membrane of cells. According to the ER system retention of GABAb receptor, used in this study, while C1/C1-fused subunits are retained in the ER, C2/C2 reach the cell surface and the association of C1/C2 in the ER enables cell-surface targeting of the heterodimer. However, GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, as a functionally inactive homo-dimer (doi: 10.1042/BJ20041435). If there is an experimental trick that prevents the addressing of C2/C2 to the cell membrane, it should be specified and explained. This is critically important for understanding which receptor populations the data are derived from: receptors containing C1/C2 fused subunits only as stated by the authors, or C1/C2 and C2/C2 fused subunits?

Figures

NMDA-receptor current amplitude should be normalized by the membrane expression of the receptors. A preliminary experiment should measure the effective cell surface expression of each of the subunits in the different transfection conditions.

Fig.1a - The scheme should include C2-C2 complexes and mention whether these complexes are expressed at the cell surface (see previous and following comments).

Fig.1b and c - Current from cells transfected with GluN2B‐wt‐C1 and GluN2B‐wt‐C2 should be compared to current expressed in cells expressing untagged receptor subunits: GluN2B‐wt - Current from cells transfected with GluN2B‐wt‐C1 alone should be shown as well (although expected to be retained in the ER) (as performed for GluN2A‐wt‐C1 GluN2B‐wt‐C1 in suppl Fig. 1a) - How could you explain the null current from cells transfected with GluN2B‐wt‐C2 alone (Fig.1b middle, and 1c)? since GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, GluN2B‐wt‐C2 is supposed to reach the cell surface. This is a very important point to clarify (I am probably missing a technical detail) because if the sub-unit tagged with C2 does reach the cell surface, then all the results and conclusions drawn from the C1-C2 conditions are wrong and could be attributed to a mix of complexes containing either C1-C2 or C2-C2. My following comments are based on the assumption that only receptors containing C1-C2 tagged subunits reach the membrane (as assumed by the authors and suggested in Figure 1b middle), but explanations should absolutely be provided to convince the reader.

Fig. 4a and 5a - Please, keep the current scale constant between all current illustrations within the same figure (4a and 5a). Indeed, not only the Spermin- or SP- induced potentiation is an important data (which is presently quantified on the histograms of fig. 4b and 5b) but also knowing whether the amount of current recorded in cells expressing one mutant subunit in presence of SP (for example GluN2A‐wt‐C1 GluN2B‐G689S‐C2) is comparable to the current recorded in wt receptor-expressing cells (GluN2A‐wt‐C1 GluN2B‐wt‐C2) in absence of SP would be an excellent added value for the paper. A special figure could quantify this rescue effect of SP, measuring and comparing the mean currents recorded in these conditions (one current illustration is not sufficient given variations between similar samples). By the way 5mM glutamate might be an excessive concentration. At 1mM, the expected synaptic concentration of glutamate following action potential, according to figures 3 and Suppl1 the response of the mutated receptor is much lower than that of the WT which is already almost maximal. In these conditions, SP-induced potentiation by a factor of two of GluN2A‐wt‐C1 GluN2B‐G689S‐C2 current could be equivalent to control currents recorded in GluN2A‐wt‐C1 GluN2B‐wt‐C2 cells.

Fig. 6 - Figure 6 is not convincing because cultured hippocampal neurons do express endogenous NMDA receptors. To what extent the recording currents are affected by endogenous, non-mutated GluN2B subunits? Western Blots showing an extinction of endogenous subunits expression when transfected tagged subunits are competitively expressed would be required. - Fig.6b "PE-S" on the graph should be replaced by "PS"

Discussion

The authors are surprised by the fact (Fig.2) that 1 variant reduces the apparent glutamate affinity of the receptor, but not as much as 2 variants, despite the fact that "NMDARs opening requires all four subunits to be liganded (i.e., occupied by a ligand) which implies that the least affine subunit should have dominated the final affinity of the receptor". I agree that the difference is noticeable, however the glutamate affinity for receptors containing 1 variant is much closer to that of receptors containing 2 variants than that of wild-type receptors. Hence, the results obtained do not seem so surprising and could result, as rightly explained by the authors from a possible cooperativity between the subunits.

On the other hand, the data in Figure 6 are much more difficult to interpret and reconcile with the nature of the expressed receptor subunits (which this time is not controlled) nor their association within the same receptor. However, this aspect, which is essential to the understanding of the consequences of 1 variant on neuronal signalling, is not discussed: Whatever the stoichiometry of the complexes in the heterozygous disease, the mutated and wild type GluN2B subunits coexist in the same cell: Either within the same di-heteromeric complexes GluN2B-wt + GluN2B-mutant, or in separate complexes but nevertheless expressed in the same cell, in di heteromeric (GluN2B-wt + GluN2B-wt and GluN2B-mutant + GluN2B-mutant); or tri-heteromeric (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) complexes. Assuming that half of the complexes remain wild-type, e.g. (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) we would expect (Fig. 6) a small decrease in NMDA current (carried only by the half that expresses the mutated subunit, and whose function is not zero but only decreased by about 20% in response to 5mM Glutamate, Fig. 3b). The same reasoning applies to the di-heteromeric conditions (GluN2B-wt + GluN2B-wt; GluN2B-mutant + GluN2B-mutant), here again the decrease observed Fig. 6b is difficult to reconcile with the responses measured Fig. 2b. In other words, how to explain a 50% decrease of the currents, instead of the 10% expected by the previous reasoning. In this experiment we do not know which subunits are expressed, their proportions, nor how they are associated in functional complexes, which makes the interpretation of the data impossible. The only explanation, far-fetched, for 50 % decrease would be that the complexes were to contain all (or the vast majority) 1 wild-type subunits associated with 1 variant, then a homogeneous 50% reduction in current could be expected. But this extreme condition could only be possible in the case of di-heteromers, which is unlikely the case in Fig.6 as GluN2A currents are measured in presence of Ifenprodil. To conclude 1) the comparison of the currents in transfected and non-transfected neurons does not make sense in figure 6b which is not convincing because we do not know the nature of the currents actually measured. A comparison in controlled condition would make more sense (as I suggested in the criticism of figures 4, 5). 2) The reality of the combinations of expression and association between subunits within different complexes expressed in the same cell must be considered and taken into account in the interpretation of the data. Undoubtedly, the means of restoring the NMDA current will be different depending on the presence of mutated subunits in all functional channels or not.

Referees cross-commenting

The referees' comments are highly relevant. In particular, referee 3's comment 1 seems very interesting because it may help to better understand the discrepancy in the results observed in neurons, i.e. a 50% decrease in the currents induced by the expression of the mutant and wild type subunits in the same cells, whereas theoretically one would expect a 10% decrease of this current (cf. referee 2's 2nd comment in the discussion section). This comment 1 of referee 3 indeed stresses the fact that the control (non-transfected neurons) to which the heterozygous condition is compared is not the correct control, which should rather be neurons transfected with wild type receptor subunits. More generally, this comment underlines the importance of monitoring the effective membrane expression of the different subunits in each of the experimental conditions in order to be able to compare conditions and draw conclusions.

Significance

The novelty of the study, is to evaluate the consequences of a single mutated subunit within NMDA receptors affected by GRIN variant, to mimic the heterozygous condition of GRIN encephalopathies, this is of potential value for the field and the interest could also be extended to other genetic diseases (at least the experimental way to study the functioning of only one desired stoichiometric configuration).

The strength of this paper is precisely to isolate technically and to study the functioning of a desired stoichiometric configuration only. The main limitation of the paper is the interpretation that the authors make of their data in a physio-pathological context

This work could be of interest for general audience, providing the title and summary are slightly modified My area of expertise could not be closer to the topic of the article: Glutamate receptors; GRIN; molecular tinkering, cell culture, electrophysiology, receptor stoichiometry...

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Referee #1

Evidence, reproducibility and clarity

Summary:

Kelner and Berlin present their research findings pertaining to the effect of GRIN2B variants that modify NMDA receptor function and pharmacology. While these mutations were published previously, the new manuscript provides a more thorough investigation into the effects that these variants pose when incorporated into heteromeric complexes with either wildtype GluN2B or GluN2A - NMDA receptors containing only a single mutated GluN2B subunits is more relevant to the disease cases because the associated patients are heterozygous for the variant. The authors achieved selective expression of receptor heteromeric complexes by utilising an established trafficking control system. The authors found that while a single variant subunit in the receptor complex is largely dominant in its effect on reducing glutamate potency of the NMDA receptor, it 's effect on receptor pharmacology varied. Unlike diheteromeric receptors containing mutated subunits, polyamine spermine potentiated GluN1/2B (but not GluN1/2A/2B) receptors that contained a single mutated GluN2B. In contrast, the neurosteroid, pregnenolone-sulfate (PS), was effective at potentiating the NMDA receptor currents (to varying degrees) regardless of the subunit composition. The potentiation of NMDA receptor currents by PS was also observed in neurons overexpressing the variants.

The techniques used in this study were appropriate to address the objectives and the overall effects are large, and generally convincing. I like the way the results are presented, although have a few (easily addressable) comments.

Major comments:

• When incrementally adding drugs (e.g. traces in figures 5 and 6), it doesn't always appear like the response has plateaued before changing the solutions/drugs. Therefore, I am curious to what extent the effects observed are underestimated.
• Also, in relation to figure 6, to what extent does agonist application cause desensitization here? Looking at traces in Figure 6b it appears that there is some desensitization and it isn't clear to what extent this persists during the solution changes. Could the authors conduct/show the controls where NMDA alone (for 50-60s), or NMDA followed by PE-S (without ifenprodil).
• Finally, figure 5 shows the effect of the neurosteroid (and ifenprodil) on NMDA-evoked currents in neurons overexpressing the GluN2B variants in neurons. However, there currents probably reflect a mixture of extrasynaptic and synaptic receptors. To what extent are synaptic NMDA receptors affected by the variants?

Minor comments:

• Looking at the fits in the graph of Figure 2b it appears that the slope on the concentration response curves is less steep for the mixed 2B-diheteromeric NMDA receptors. How much are the Hill coefficients changing and can this be interpreted to provide more mechanistic insight? Wouldn't it make sense to include the Hill coefficients in Table 1?
• The authors illustrate the changes in potency by the shift in the concentration response curves, but is there any change in efficacy? A simple way to illustrate this would be also present a simple graph showing the maximum current amplitudes (i.e. to 10 mM glutamate) for each of the receptor complexes.
• The authors characterize the 'apparent' affinity (or potency) of the receptor using concentration-response curves, but numerous points in the manuscript refer to changes in affinity. None of the experiments shown directly measure affinity (which would require ligand-binding assays) and so the use of the word affinity is inaccurate/misleading. I suggest the authors replace the instances of the word 'affinity' with 'potency'.
• In the third line of the abstract, the authors wrote, 'for which there are no treatments' in relation to GRINopathies. My understanding is that there are symptomatic treatments but that there are no disease-modifying treatments.
• The authors have interchangeably used the terms NMDAR or GluNRs throughout the manuscript. I suggest sticking to one of these terms. I would suggest NMDARs since this is less likely to be misread as a a specific NMDA receptor subunit..
• Typos: 
1) Results paragraph 2 sentence one: 'We thereby produced GluN2B-wt, GluN2B-G689C and GluN2B-G689S subunits tagged with C1 or C2, co-expressed these along with the GluN1a-wt subunits in...'
2) Results paragraph 2: '...but these were mainly noticeable when oocytes are were exposed to high (saturating) glutamate concentrations...'
3) Last sentence in the second to last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'This , PS may serve to rescue...'
4) Last sentence in last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'Despite the latter, we found no evidence for any direct effect of three different physiologically relevant concentrations of the drug on di- or tri-heteromeric receptors'
• Figures 1e, 2b, 3b: it would be helpful to add a legend to the graph so that the curves can be interpreted without having to read through the figure legend.
• The bar graphs in Figure 6 show individual data points but those in figures 4b and 5b don't. Can the authors please add the data points to these graph.
• It would be helpful to reviewers that future manuscripts by the authors include page numbers and line numbers

Referees cross-commenting

Reviewers 2 and 3 highlight an important issue concerning figure 6 and the extent to which the overexpressed variants subunits can compete and assemble with endogenous NMDA receptors (unlike the system where the surface expression of specific receptor complexes is controlled). Indeed in the recent paper by the same authors, the two variants differed in their surface expression (in HEK cells), with G689C expressing particularly poorly. With reference to the second minor comment of Reviewer 1, the maximum current amplitudes would of course need to be normalized to cell surface expression of the receptor to gain any insight into efficacy.

Significance

This study emphasizes the complex pattern of effects that variants can have on glutamate receptor function and pharmacology, especially considering the context of receptor subunit composition. The authors have followed up their previous findings on the same mutants (Kellner et al, 2021, Elife), but used a trafficking control system here to characterize properties of mutated receptor complexes that are most likely to exist in neurons. The authors show that the defective currents mediated by NMDA receptors containing a loss-of-function GluN2B variant can be enhanced by neurosteroids (and in the case of GluN1/2B receptors, polyamines also). Development and approval of neurosteroids for the clinic would be required for the findings to translate to a therapy for patients. Readers should also be aware that neurosteroids act on other receptors too (e.g. GABA receptors), which could complicate the outcome. The expertise of the reviewer is in glutamate receptors and synaptic transmission.

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Reply to the reviewers

Reviewer #1

Major:

- The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.

Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).

The main point we were trying to convey is that we do not think global ecdysone levels plays a role in modulating fatbody insulin or tgfb signalling, which in turn affects muscle detachment. We are not claiming that edysone levels is not changing in control vs. tumour bearing animals. In fact, we predict that 20HE levels will be different in tumour bearing vs. control animals (as tumour bearing animals undergo developmental delay), but this is not the main point of our conclusions. We believe that our conclusions are supported by the experiment demonstrating global ecdysone alterations (via feeding sterol-free food) did not affect how fatbody Akt activation altered tgfb signalling and enhanced muscle integrity (Figure S1). Therefore, we don’t think measuring 20HE helps to support our conclusions. Pharmacological inhibition via feeding ecdysone inhibitors effectively demonstrate a similar point to feeding sterol-free food which we have already performed. We are happy to try direct manipulation of 20HE related genes (eip75B-RNAi) in the fatbody to see if this affects muscle detachment or pAkt and pMad levels in tumour bearing animals.

- In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.

We will add a counterstain to these images. We will also perform qPCR in the fatbody of control and cachectic animals to assess whether Sog transcription is altered. We agree converting one of the Mimic lines to a GFP sensor would be a good option, but this experiment would require getting new fly lines into Australia, which takes at least 2 months because of quarantine laws. We don’t believe this experiment would change the general conclusions of the paper, therefore would prefer not to do this experiment.

- I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.

We will show more representative examples and fix Fig 7C.

- The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.

We agree with the reviewer that this is an important point. We will demonstrate the knockdown of sog and mad (and other RNAis) used in the study by either referring to published data or demonstrate knockdown ourselves.

- Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.

We will try this analysis.

Fig.5 M-P pMAd is not indicated in the Panels only the legend.

We will fix this error.

- Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.

We will fix this error.

- For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.

We will fix this error.

- The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

We will enlarge font size.

Minor:

1. Adjust in-figure caption alignments

2. Line 104: add comma RasV12, dlgRNAi

3. Line 114: replace little  not significant (n.s.)

4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.

5. Line 454: italicize r4>

6. Fig S4E: remove frame

7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.

8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

We will fix these minor errors.

Reviewer #2

Major comment

Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation?

From the experiments we have carried out, we do not have insights into how TGF-B signalling induce Collagen accumulation.

They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

We can address this point by assessing if Rab10 and SPARC expression is altered in cachectic fatbody.

Minor comments

Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".

we will fix this error.

Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?

The duplication of 1C and 1M, was an error, we thank the reviewer for picking this up. We will fix this error. We will use different images for 1D and 1L.

Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.

We will show more representative images of these staining.

Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.

We will show counter staining for DAPI.

Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.

This was a mistake, we will fix this error.

Figure S1~S4: These figures and their legends do not correspond to each other. We thank the reviewer in picking up this error, there was an error in inserting the images into the text. S2 and S3 were swapped.

We will fix this error.

Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.

We will include this data.

Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.

We will carry out OPP assay upon Akt overexpression in the muscle.

Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.

Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?

We are not sure, but the overexpression of Collagen proteins is at an extremely high level, therefore, it is possible that some of it can be processed and secreted despite Rab10 upregulation and SPARC downregulation. We have carried out an experiment to overexpress Collagen proteins in the muscle, in this case, this manipulation did not rescue. This indicates that processing of Collagen in the fatbody is important, however, we do not know how the processing is regulated.

Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

We have shown this data in Figure R-T. We will refer back to this data in Line 347.

Reviewer #3

Major comments:

- Are the key conclusions convincing?

Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.

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

-The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM

accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.

We will tone down the claim that the ECM accumulation is fibrotic.

- 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.

-The authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.

We will include a non-tumour control for Fig S1F.

-The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.

N=3 is generally enough to see a difference, we will include data done in parallel which shows Impl2 RNAi is sufficient to induce a reduction in Impl2 RNA levels. This will demonstrate that n=3 is sufficient to demonstrate a reduction in transcript levels if there is a reduction.

-The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.

We will include a non-tumour control for S2Q-T and show more representative pictures.

-The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'

Again, the single knockdowns and the non-tumor control are not shown in FigS3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.

We will include the single knockdowns and a wildtype control

-Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.

We will increase the n number for WT for this experiment. The reduction in muscle detachment is 10% rather than 40% here is because this experiment was done at day 6, which we will indicate in the figure legend. The 40% reduction in Fig2E is because these samples were processed at day7. Rab10RNAi experiment was carried out at day 6, because by day7, the Rab10RNAi rescue is so good, most of the tumour bearing animals have pupated, thus the experiment could only be carried out at day6.

- Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.

We will include a non-tumour control for 3W.

-The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.

We do not have an EM facility that can accommodate this experiment, thus doing EM is not an option for us. However, we can address whether the accumulation of ECM is intracellular or extracellular by performing an experiment, where we try perform antibody staining against Viking-GFP without permeabilizing the cells. If Viking is detected without permeabilization, it would indicate the accumulations are extracellular. This approach has been previously used to address this question in Zang et al., elife, 2015.

- 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.

-These suggested experiments should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.

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

-The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.

We will add the number of days in the figure legend.

-Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).

However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.

We will fix these references.

- Are the experiments adequately replicated and statistical analysis adequate?

In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

We will include a non-tumour control when appropriate to address this.

Minor comments:

- Specific experimental issues that are easily addressable.

- Are prior studies referenced appropriately?

Yes, as far as I can tell.

- Are the text and figures clear and accurate?

-In the literature, people usually call it 'fat body' rather than 'fatbody'.

We will fix this error.

-The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."

This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.

We will fix this error.

-The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.

We will reword this statement.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

This paper uses a Drosophila tumor model induced by the expression of RasV12+Scrib-IR or RasV12+Dlg-IR in the eye imaginal disc to understand how inter-organ communication affects cachexia in the fat body and muscle. The tumor has previously been shown to secrete the factors ImpL2 and Gbb which decreases insulin signalling and increases TGF-beta signalling in the fat body, respectively, and results in fat body and muscle defects. Here they dissect the role of insulin and TGF-beta signalling in the fat body in regulating muscle integrity further. They show that these two pathways converge via Sog in the fat body of tumor-bearing animals and result in aberrant ECM accumulation in the fat body which hinders ECM secretion. This then results in the muscle receiving less fat body-derived ECM which causes muscle attachment defects. Interestingly, these muscle defects can be ameliorated by activating insulin signalling or inhibiting TGF-beta signalling or even by increasing ECM secretion in the fat body. The authors also provide some evidence that the insulin and TGF-beta signalling pathways can converge in non-tumor settings.

Major comments:

• Are the key conclusions convincing?

Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.<br /> - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?<br /> - The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM<br /> accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.<br /> - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.<br /> - The authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.<br /> - The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.<br /> - The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.<br /> - The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'<br /> Again, the single knockdowns and the non-tumor control are not shown here in Fig3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.<br /> - Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.<br /> - Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.<br /> - The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.<br /> - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.<br /> - These suggested experiments should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.<br /> - Are the data and the methods presented in such a way that they can be reproduced?<br /> - The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.<br /> - Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).<br /> However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.<br /> - Are the experiments adequately replicated and statistical analysis adequate?<br /> In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

Minor comments:

Specific experimental issues that are easily addressable.

• Are prior studies referenced appropriately?

Yes, as far as I can tell.<br /> - Are the text and figures clear and accurate?<br /> - In the literature, people usually call it 'fat body' rather than 'fatbody'.<br /> - The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."<br /> This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.<br /> - The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.<br /> - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Significance

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

The field of inter-organ communication in cancer is a very interesting and trending research field. Several labs including this one have provided new insights into how the tumor, the fat body and the muscle communicate and affect each other and how this can cause cachexia. Previous work from the Chen lab already showed that the tumor secretes the factors ImpL2 and Gbb which decreases insulin signalling and increases TGF-beta signalling in the fat body, respectively and results in fat body and muscle defects. Here they dissect this role of insulin and TGF-beta signalling in the fat body in regulating muscle integrity during cachexia further. They show that these two pathways converge via Sog in the fat body of tumor-bearing animals and result in aberrant ECM accumulation in the fat body which hinders ECM secretion. As a result of this, the muscle receives less fat body-derived ECM and displays muscle attachment defects. Interestingly, the authors show that these muscle defects can be ameliorated by activating insulin signalling or inhibiting TGF-beta signalling or even by increasing ECM secretion in the fat body. This has potentially important implications for the clinic since it suggests that targeting ECM secretion or ECM remodeling in the fat tissue could be a promising treatment for cachexia.<br /> Moreover, the authors also provide some evidence that the insulin and TGF-beta signalling pathways can converge in tumor and non-tumor settings. This might also reveal new drug targets to treat cachexia.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).

The Chen lab showed previously that MMP1 secreted from the tumor induces ECM disruption in the fat body as well as muscle, ultimately causing fat body remodeling and muscle wasting (Lodge et al. 2021). They showed that this is via TGF-beta activation in the fat body. Another contributing factor is tumor-secreted Impl2 which decreases Insulin signalling in the fat body and tumor. However, it remained unknown, how ECM accumulation in the fat body might cause muscle wasting. In this paper, the authors look into this.<br /> - State what audience might be interested in and influenced by the reported findings.

This paper would be of interest for scientists and clinicians interested in inter-organ communication in cancer, particularly in the context of cachexia.<br /> - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

My expertise lies in the field of Drosophila fat body and ECM, and to some extent tumors but less so signalling pathways.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this paper, the authors show how the interaction of two signaling pathways, insulin/PI3K and TGF-β signaling, in the fatbody plays an important role in cachectic muscle detachment in tumor-bearing animals. The Drosophila tumor models and the genetic experimental tools are sophisticated and the conclusion is well supported by the data from these genetic experiments. They found that the TGF-β signaling activation (phosphorylation of Mad) is negatively regulated by insulin/PI3K signaling in the fatbody. They also identified the functional involvements of two molecules secreted from tumour tissues, Impl2 (a negative regulator of insulin signaling) and Gbb (one of the TGF-β ligands), in protein synthesis and ECM accumulation in the fatbody, respectively. They also showed that the cachectic fatbody traps ECM proteins and prevents ECM secretion to the muscle, causing muscle degradation. Finally, they identified a secreted BMP antagonist, Sog, as an important player in this process. They found that Sog is reduced in the hemolymph of tumour-bearing animals and that Sog expression is regulated by insulin signaling. Furthermore, Sog overexpression in the tumours, fatbody, and muscle rescues cachectic muscle detachment.

Major comment

Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation? They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

Minor comments

1. Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".
2. Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?
3. Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.
4. Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.
5. Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.
6. Figure S1~S4: These figures and their legends do not correspond to each other.
7. Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.
8. Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.
9. Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.
10. Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?
11. Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

Significance

I found these results from their genetic experiments described here very interesting and of high quality. Although the mechanism by which the TGF-β signaling induces ECM accumulation in fatbody is not clear, this study represents several important advances to understand the key processes in tumor-induced muscle degradation. These data will attract broad audiences not only from cancer biology but also from the research fields including interorgan interactions, systemic signaling in homeostasis, and developmental biology.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Bakopolous et al. investigated on the function of Insulin and TGF beta signaling in the converging regulation of sog (BMP antagonist) and how it controls ECM remodeling. Therefore, the authors used a Drosophila model of cachexia established in Lodge et al., 2021. The authors have shown that the tumors increase Impl2 and Gbb in the fatbody leading to the inhibition of insulin signaling and activation of TGF-β signaling respectively. This lead to the accumulation of ECM proteins that contributes to muscle ECM deficit and muscle detachment. These findings are a major advance in the field of cachexia and of broad interest. The authors demonstrate that state-of-the-art genetics in flies allows acquisition of genetically precise data along with important and complex discoveries on signaling pathways with relevance not only for basic, but for biomedical research as well.

The manuscript is concise and very well written. The experiments overt a clear logical order and are comprehensively described. The authors provide exhaustive data to support their novel claims of broad interest to the scientific community. Please find below some minor recommendations and experiments that could shed further light on some aspects of this manuscript.

Major:

• The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.<br /> Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).
• In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.
• I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.
• The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.
• Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.
• Fig.5 M-P pMAd is not indicated in the Panels only the legend.
• Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.
• For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.
• The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

Minor:

1. Adjust in-figure caption alignments
2. Line 104: add comma RasV12, dlgRNAi
3. Line 114: replace little  not significant (n.s.)
4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.
5. Line 454: italicize r4>
6. Fig S4E: remove frame
7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.
8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

Significance

In this manuscript, Bakopolous et al. investigated on the function of Insulin and TGF beta signaling in the converging regulation of sog (BMP antagonist) and how it controls ECM remodeling. Therefore, the authors used a Drosophila model of cachexia established in Lodge et al., 2021. The authors have shown that the tumors increase Impl2 and Gbb in the fatbody leading to the inhibition of insulin signaling and activation of TGF-β signaling respectively. This lead to the accumulation of ECM proteins that contributes to muscle ECM deficit and muscle detachment. These findings are a major advance in the field of cachexia and of broad interest. The authors demonstrate that state-of-the-art genetics in flies allows acquisition of genetically precise data along with important and complex discoveries on signaling pathways with relevance not only for basic, but for biomedical research as well.

The manuscript is concise and very well written. The experiments overt a clear logical order and are comprehensively described. The authors provide exhaustive data to support their novel claims of broad interest to the scientific community

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Referee #4

Evidence, reproducibility and clarity

Summary

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

Major comments

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

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

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

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

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

Minor comments:

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

Significance

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

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

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Referee #3

Evidence, reproducibility and clarity

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

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

Significance

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

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

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

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

Important comments:

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

Minor comments:

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

Significance

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

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Referee #1

Evidence, reproducibility and clarity

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

Major comments:

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

Significance

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

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

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

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

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

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

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

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

Specific points:

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

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

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

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

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

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

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

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

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

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

Other points:

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

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

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

Referee cross-commenting

I have no further comments.

Significance

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

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Referee #2

Evidence, reproducibility and clarity

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

Major comments:

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

Minor comments:

Abstract: Unclear why too much FAM111A causes cell death

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

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

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

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

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

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

Is there a homologue in other species?

Referee cross-commenting

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

Significance

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

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

My expertise is in initiation of DNA replication.

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Referee #1

Evidence, reproducibility and clarity

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

Major comments

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

Minor comments

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

Referee cross-commenting

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

Significance

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

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

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

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

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

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

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

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

Significance

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

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

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

My expertise: developmental biology, somitogenesis

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Referee #2

Evidence, reproducibility and clarity

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

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

Major comments:

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

Minor comments:

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

Significance

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

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Referee #1

Evidence, reproducibility and clarity

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

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

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

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

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

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

SOME COMMENTS ON DATA INTERPRETATION.

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

Minor points

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

A side note

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

Significance

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

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

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

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Reply to the reviewers

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

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

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

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

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

*Major comments: *

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

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

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

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

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

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

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

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

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

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

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

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

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

See above response to the first comment.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

*Minor comments and suggestions: *

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

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

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

We will rework on this graph and make necessary changes.

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

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

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

We will clarify in the revision.

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

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

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

We added this reference in no.70.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We will measure and tabulate in the revised manuscript.

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

We have corrected this reference.

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

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

MAJOR POINTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We will provide these details in the revision.

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

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

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

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

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

We will edit in the revision.

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

We will edit in the revision.

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

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

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

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

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

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

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

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

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

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

We have changed this in the current version.

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

We edited the line no.591.

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

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

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

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

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

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

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

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

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

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

*Minor comments: *

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

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

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

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

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

We now state this in the legends.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

We will add this data with the revision.

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

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

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

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

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

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

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

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

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

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

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

We will add the necessary corrections in the revision.

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

We have corrected this.

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

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

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

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

*In line 432 caudal should be italicized. *

We have edited this.

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

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Referee #4

Evidence, reproducibility and clarity

Summary:

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

Major comments:

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

Minor comments:

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

Significance

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

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

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

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Referee #3

Evidence, reproducibility and clarity

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

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

Minor comments:

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

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

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

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

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

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

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

Significance

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

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

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

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Referee #2

Evidence, reproducibility and clarity

Summary

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

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

Major points

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

Minor points

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

Optional

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

Significance

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

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

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

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Referee #1

Evidence, reproducibility and clarity

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

Major comments:

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

Minor comments and suggestions:

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

Significance

Strengths:

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

Limitations:

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

Advance:

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

Audience:

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

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Reply to the reviewers

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

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

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

Major issues:

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

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

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

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

Minor points:

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

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

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

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

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

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

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

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

Reviewer #2

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

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

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

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

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

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

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

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

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

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

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

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Referee #2

Evidence, reproducibility and clarity

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

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

Major

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

Minor

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

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

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

Significance

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

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Referee #1

Evidence, reproducibility and clarity

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

Major issues:

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

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

Minor points:

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

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

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

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

Significance

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

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Reply to the reviewers

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

Thank you very much.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thank you very much.

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

Thank you very much.

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

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

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

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

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

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

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Referee #3

Evidence, reproducibility and clarity

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

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

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

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

Referee cross-commenting

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

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

Significance

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

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Referee #2

Evidence, reproducibility and clarity

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

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

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

Significance

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

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

Major comments:

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

Minor comments:

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

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

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

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

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

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

Significance

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

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

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

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

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Referee #3

Evidence, reproducibility and clarity

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

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

Specific Concerns:

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

Significance

For strengths and limitations, see above.

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

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

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Referee #2

Evidence, reproducibility and clarity

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

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

Minor notes:

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

Significance

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

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

Major comments:

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

Minor comments:

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

Significance

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

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Reply to the reviewers

Reply to the reviewers

Manuscript number: RC-2023-01932

Corresponding author(s): Dennis KAPPEI

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

Reviewer #1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #2

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3

Reviewer #3: Strengths of the study:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Referee #3

Evidence, reproducibility and clarity

Strengths of the study:

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

Areas for improvement:

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

Significance

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

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Referee #2

Evidence, reproducibility and clarity

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

Major comments:

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

Minor comments:

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

Significance

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

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

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Referee #1

Evidence, reproducibility and clarity

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

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

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

Specific comments:

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

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

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

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

Significance

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

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Reply to the reviewers

1. General Statements

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

2. Point-by-point description of the revisions

Reviewer #1

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

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

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

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

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

Reviewer #2

Major Comments:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #3

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

The Reviewer made a number of specific comments:

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

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

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

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

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

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

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

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

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

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

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

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Referee #3

Evidence, reproducibility and clarity

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

The following comments should be addressed prior to publication:

Specific comments:

Figure 1:

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

Figure 2:

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

Figure 3:

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

Figure 4:

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

Significance

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

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

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

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Referee #2

Evidence, reproducibility and clarity

Summary

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

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

Major comments

1D continuous space coordinate

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

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

Model parameters

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

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

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

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

Minor comments

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

Significance

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

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

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

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

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Referee #1

Evidence, reproducibility and clarity

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

I have few minor concerns:

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

Significance

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

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Reply to the reviewers

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

Summary

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

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

Major comments

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

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

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

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

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

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

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

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

Revised in the manuscript:

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

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

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

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

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

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

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

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

Discussion:

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

Reference:

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

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

Minor comments

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

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

Revised in the manuscript:

Materials and Methods:

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

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

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

Abstract:

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

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

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

Reviewer #1 (Significance (Required)):

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

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

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

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

Audience: Basic research

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

Summary:

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

Major comments:

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

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

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

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

Materials and Methods:

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

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

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

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

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

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

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

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

Introduction:

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

Discussion:

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

Reference:

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

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

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

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

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

Comment 5. Regarding Figure S1:

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

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

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

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

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

Materials and Methods:

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

Comment 7. Regarding Figure 2:

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

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

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

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

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

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

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

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

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

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

In response to Figure 3:

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

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

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

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

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

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

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

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

In Figure 3 and Figure 6e and 6f:

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

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

In Figure 4:

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

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

Revised Figure 4:

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

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

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

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

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

Discussion:

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

Reference:

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

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

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

Reference:

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

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

Figure 5:

-The groups in 5g are not well defined.

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

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

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

Regarding Figure 5 and 6:

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

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

Minor comments:

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

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

Introduction:

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

Reference:

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

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

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

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

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

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

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

Reference:

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

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

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

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

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

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

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

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

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

Reviewer #2 (Significance (Required)):

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

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

Summary

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

Major comments

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

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

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

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

Reference:

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

Minor comments

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

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

Reference:

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

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

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

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

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

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

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

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

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

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

Reference:

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

Reviewer #3 (Significance (Required)):

Significance

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

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

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Reply to the Reviewers

I thank the Referees for their...

Referee #1

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

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

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

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Referee #3

Evidence, reproducibility and clarity

Summary

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

Major comments

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

Minor comments

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

Significance

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

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

Major comments:

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

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

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

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

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

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

Regarding Figure 2:

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

In response to Figure 3:

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

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

In Figure 4:

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

Figure 5:

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

Regarding Figure 5 and 6:

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

Minor comments:

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

Significance

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

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Referee #1

Evidence, reproducibility and clarity

Summary

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

Major comments

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

Minor comments

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

Significance

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

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

Audience: Basic research

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Reply to the reviewers

Manuscript number: RC-2023-01939

Corresponding authors: Jiro Toshima, Junko Y. Toshima

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

__ Point-by-point description of the revisions__

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

(Reviewers’ comments are in italics)

*Summary: *

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

Major comments:

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

Response:

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

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

Comment #1(a)

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

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

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

Comment #1(b)

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

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

Comment #1(c)

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

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

Comment #2

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

Response:

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

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

Comment #3

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

Response:

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

Comment #4

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

Response:

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

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

__Minor Comment: __

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

Response:

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

__Reviewer #1 (Significance (Required)): __

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

Response:

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

__ __

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

(Reviewers’ comments are in italics)

*Summary: *

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

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

Comment #1

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

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

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

Comment #2

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

Response:

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

Comment #3

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

Response:

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

Comment #4

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

Response:

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

Comment #5

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

Response:

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

Comment #6

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

Response:

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

Comment #7

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

Response:

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

Comment #8

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

Response:

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

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

Response:

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

__Reviewer #2 (Significance (Required)): __

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

Response:

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

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

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Referee #2

Evidence, reproducibility and clarity

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

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

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

Significance

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

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

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

Major comments:

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

Minor Comment:

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

Significance

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

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

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1. General Statements [optional]

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

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

Summary:

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

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

Major comments:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Minor comments:

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

We have included data for untreated cultures and phleomycin-only treated cultures.

1. (Figure 1D, Optional) As a ppk18D cek1D double deletion mutant is reported, the authors are advised to add and test that mutant in this experiment.

We have added the related data for the _ppk18_Δ _cek1_Δ double mutant.

1. (Figure 2) The authors need to clarify the number of cell bodies spotted (e.g., in the Figure legend).

We have modified the figure legend accordingly.

1. (Figure 3) The different number of cells in micrographs may give an (wrong) impression on the cell proliferation rate. Therefore, it is advisable to use the micrographs in which the similar number of cells are shown for conditions with the similar cell proliferation rates.

We have included data to show the cell lengths under different conditions. We find that different conditions greatly affect proliferation rates. For instance, cells do not proliferate in the presence of torin1. We initially sought to investigate if caffeine induces a phenotype in ssp1 mutants by virtue of its interaction with the DNA damage response. The micrographs were included as representative examples and have been now complemented with cell length data.

1. (Figure 4B) ssp2D, not spp2D.

The figure legend has been edited.

1. (Figure 4) The septation index of the none-treated cells should be shown as a baseline.

We have included base line data for untreated wt cells in figure 1. We have no reason to suspect any of the mutants would provide different results over the time investigated.

1. (Figure 6B, 6E) What do the black arrows indicate? Figure Legend does not seem to explain them.

The legend has been modified to indicate what the arrows refer to.

1. (Figure 6C) Indicate which part of the Maf1-PK blot corresponds to the phosphorylated species, because Maf1-PK is probed with an anti-V5 (not a phosphorylation-specific) antibody.

These experiments have been carefully repeated under different conditions and the figure is now modified accordingly.

1. (Figure 6D) gsk3Dssp1D, not gs3Dssp1D.

We have deleted this figure and have now replaced it with data we believe is more appropriate.

Reviewer #1 (Significance):

As caffeine is implicated in protective effects against diseases including cancer and improved responses to clinical therapies, the topic of the present study is of interest and importance to the broad audience.

In the present study, the most significant finding is that caffeine- and torin1-induced hypersensitivity to phleomycin is dependent on Ssp1 and Ssp2 (Figure 2). This result may be important in chemotherapy against cancers. On the other hand, caffeine is known to activate AMPK (e.g., Jensen Am J Physiol Endocrinol, 2007). Besides, as detailed in the Major comments, many of the major conclusions are not supported by the present results. Therefore, based on my field of expertise (cell cycle, cell proliferation, and TOR signaling), I conclude that the present study hardly extends the knowledge in the field of "the cell biology of caffeine."_

We thank the reviewer for their helpful comments. We accept the constructive criticisms and have carried out extensive additional experiments to provide further roles for Ssp2 and TORC1, in mediating the cell cycle effects of caffeine. We stress that caffeine has previously been proposed its effects via inhibition of Rad3 activity. Our previous work showed that caffeine did not inhibit Rad3 mediated checkpoint signaling. As later studies suggested caffeine inhibited TORC1 activity, the major goal was to investigate if caffeine is an indirect inhibitor of TORC1 via Ssp2 which is activated by several stresses. It has never been demonstrated that caffeine signals via Ssp2. This study provides the first evidence that caffeine modulates cell cycle progression by at least partially signaling via Ssp2 and TORC1. After nearly 30 years, it is vital that its precise activity, in particular enhancing DNA damage sensitivity is properly characterized. Such work woold open the way for additional studies on how caffeine activates cell physiology. For instance, we show that caffeine at 10 mM is more effective at inhibiting Sck1 activity than Rapamycin at 100 ng/ ml. In contrast, rapamycin at this concentration is more effective at inhibiting Maf1 activity. Hence further studies on how exactly the combination of caffeine and rapamycin influences their effect on ageing and other TORC1 regulated processes.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary: In this paper, Alao and Rallis analyze the role of AMPK and TORC1 pathways, and the respective crosstalk, in regulating cell cycle progression in the presence of DNA damage in S. pombe. The authors show, almost exclusively through chemo-genetic epistasis assays, that caffeine inhibits TORC1 indirectly activating AMPK, in contrast to the specific ATP-competitive TORC1 inhibitor torin1. Specifically, it is shown that in the absence of a functional AMPK pathway caffeine is unable to revert the TORC1-inhibition-dependent override of cell-cycle arrest caused by the DNA-damaging agent phleomycin, henceforth partially suppressing the growth inhibition caused by the co-treatment.

Major comments: The overall story of the paper is convincing. However, the choice of an almost exclusively chemo-genetic approach, lack of controls in some experiments and some discrepancy in data presentation suggest that the manuscript undergoes revision before the authors claim that their conclusions are fully supported by the results. In detail:

In Figure 1, graphs of septation indexes are presented separately for each strain. This presentation prevents the reader from clearly comparing the differences of septation caused by genetic background rather than the treatment, i.e. the septation happening by treatment with torin1. I feel it would be better to group the results by drug rather than by strain/mutant. If the results are presented this way because the experiments on different strains were run separately, I further suggest that they are re-run so to always include at least the wt in every run._

We have included data for untreated and phleomycin only treated wt cells as a reference. Additionally, all experiments were repeated at least 2 times. We have used this assay for over 10 years and have found it to be reproducible and reliable. We are not able to include wt cells in every run as this would be beyond the manpower capacity and time constraints involved. It is also likely that torin1 activity is influenced by the ssp1/ 2 backgrounds due to increased basal TORC1 activity as previously reported. The main goal was to illustrate that caffeine differs from a direct inhibitor such as torin1.

Furthermore, torin1 inhibits both TORC1 and TORC2 and thus cannot be directly compared to caffeine. We do prove however, in this and other figures that in contrast to torin1 and rapamycin that caffeine signals via targets upstream of TORC1. We can therefore deduce that it functions in a manner similar to other environmental and nutrient stresses, which require with the Ssp1 and Sty1 regulated pathways to advance mitosis and other processes such as autophagy induction.

In Figure 2C-D, an inconsistency is observable between the phleo+caffeine sensitivity of ssp1Δ and ssp2Δ, the latter retaining a higher sensitivity. Provided that this is not only due to this specific replicate, how would the authors explain such a difference and fit it into their conclusion of a "cascade" signaling with Ssp1 acting upstream of Ssp2?

We agree that analyzing the different interacting pathways involved, is complex. For instance, Ssp1 is required for suppressing Srk1 following Sty1 activation independently of its effects on Ssp2 and TORC1. Furthermore, basal TORC1 activity is higher in Ssp2 mutants as previously reported. It is likely that Ssp1 exerts a more definitive role as it is required to directly reactivate Cdc25 activity following exposure to stress. In contrast Ssp2 activation eventually results in increased Cdc25 activity via inhibition of PP2A (Figure 8). These experiments are, thus, intended to compliment those in figure1 but the DNA damaging effects of caffeine must also be taken into account.

In Figure 2I, a huge discrepancy is observable compared to panel 2A in terms of phleo+caffeine (no ATP) sensitivity of wt cells. Here, cells seem to cope well with the phleomycin treatment even if co-treated with caffeine. This renders the main finding of the panel (the effect of phelo+caffeine+ATP) rather uninterpretable.

We have noted that relevant assays, at least in fission yeast, are influenced by the culture vessels (e.g., plastic type/ glass) as well as the vessel volume (probably due to different aeration, oxygen availability that affects growth and metabolism parameters). We have corrected figure 1a. In terms of ATP, these experiments are highly reproducible even if the exact mechanism remains unclear.

In Figure 3A, the simple observation of elongation is sometimes hard to assess, for example in the ATP-caused suppression of the effect of torin 1, as also acknowledge by the authors in the text. I feel it would be really necessary to quantify such results on an adequate number of cells.

We have reproducibly observed this uncharacterized effect of ATP. We have analysed the cell length in additional experiments to show that ATP influences average cell length under these conditions. It is important to note that the effects of phleomycin are pleotropic. For instance, it likely induces cell cycle arrest at various cell cycle phases as well as in early and late G2. Additionally, it may influence other cellular processes such as DNA or compete with drug targets such as TORC1 which is influenced by ATP.

In Figure 3B,C wt is missing to compare the results in the presence of the same treatments. I understand the focus on Ssp1, but the authors should show the same treatments on wt cells. Similarly, it would be better to show the drug treatments in panel C also at 30{degree sign}C. For the same reasons as in the previous point, quantifications would greatly enhance the credibility of the claims here.

Previous work by other investigators have shown that wt cells proliferate normally under these conditions. We also show in figure 1 that cell proliferation is not affected under nor cycling conditions in these assays. We have added cell length data that convincingly prove that Ssp1 is required to mediate the mitotic effects of caffeine. It appears that caffeine induces a cell cycle delay that requires Ssp1 to suppress Srk1- mediated Cdc25 inhibition. Furthermore, recent studies have demonstrated that rapamycin (which targets TORC1 downstream of Ssp1) allows cell proliferation at higher temperatures in S. pombe.

A major point is the almost complete absence of molecular data. Except for Figure 6, the data do not include a detection of the relative activation of the relevant pathways. Figure 6 could hardly fill this gap, since the samples therein analyzed are not the ones utilized in most of the other figures, but simple, single time-point treatment with a single drug. The authors usually refer in the text to previous knowledge about how a treatment influences a pathway. However, they should show it here in their experimental conditions.

We have performed extensive additional experiments including those suggested by the reviewer. These experiments conclusively show caffeine induces Ssp2 phosphorylation in an Ssp1- dependent manner. We also demonstrate that caffeine attenuates TORC1 signaling. Together with the cell cycle data, our findings strongly suggest caffeine indirectly inhibits TORC1 signaling a manner analogous to other environmental stresses. We also note that the inhibitory effect of caffeine on TORC1 has been demonstrated in several studies. What have provided further evidence for this but have for the first time demonstrated, that caffeine affects Ssp2.

Minor comments:<br /> • A different grouping of the experiments/panels would help the reader. For example, Fig. 2I would fit better together with Fig. 3A, to match the composition of the various chapters of the results.

We have performed additional experiments as suggested by the other reviewers. We believe the data is now easier to understand.

Torin 1 is sometimes referred to with a capital T or with a lowercase t, especially in the Figures. I suggest to uniform the nomenclature.

We have edited the text.

In the results, the authors state that "ATP may increase TORC1 activity or act as a competitive inhibitor towards both compounds.". It's a little bit odd to refer to ATP as a competitive inhibitor of drugs. I would rather be ATP, the physiological agonist, outcompeting two compounds which are working as ATP-competitive inhibitors.

We have modified the text accordingly.

Reviewer #2 (Significance):

The interplay between TORC1 and AMPK is of great interest in the cell signaling field, basically in every model organism.

The paper provides a conceptual advance in the field showing a genetic interaction between the two pathways using a model organism which has probably been overlooked so far, which is a pity because S. pombe is the best organism to study G2/M cell cycle/size regulation. The story would be of interest especially for an audience working in cell signaling in microorganisms, but not so much (at least at this stage) for the community working on aging, disease and chemo-/radio-sensitization, contrary to what the authors claim. Furthermore, for the above-mentioned reasons, I feel like the authors are a little bit overshooting when claiming (for example in the abstract and in the discussion), that their work provides a clear understanding of the mechanism.<br /> As requested by Review Commons, I specify that my expertise is on TORC1/AMPK/PKA pathways, on their crosstalk and their regulation by metabolic intermediates.

We believe that the additional requested experiments have adequately improved the manuscript and support our presented mechanistic model.

Caffeine is interest in cancer biology and the biogerontology field proven by recent reports on metabolic phenotyping, liver function testing, induction of autophagy and interplay with HIF-1, just to mention a few.

Reviewer #3 (Evidence, reproducibility and clarity):

Summary<br /> This manuscript examines the genetic requirements for checkpoint override by caffeine in the fission yeast model organism. The main outcome is to show that checkpoint override, which has previously been linked to the downregulation of TORC1, is dependent on on the AMPK pathway (Ssp1/Ssp2). Additional analysis of downstream factors and the cross-talking Sty1 pathway implicates Greatwall kinases and Igo1 (PP2A inhibitor - endosulfine analogue) although the pleiotropic nature of these pathways and the rather blunt endpoints of septation index and phleomycin sensitivity makes robust data interpretation difficult.

Major comments<br /> For clarity the manuscript would benefit from some restructuring. In particular it would help the reader if the diagram presented in figure 7 was presented first as this would help orientate the reader with the pathways. The mammalian equivalents should be indicated.

Figure 8 (previously figure 7) summarizes our findings schematically. We believe that it works well at the end as a conclusion to the work and the discussion. Wherever appropriate we have mentioned the mammalian equivalent (e.g., for Rad3).

For scientific accuracy and clarity the manuscript requires significant attention. For example in the abstract where Rad3 is introduced it is not made clear that this is the fission yeast gene. It would be better to introduce ATR at this point? Anther example in the abstract: 'Deletion of ssp1 and ssp2 suppresses...' should read 'Deletion of ssp1 or ssp2 suppresses...' as the two genes are not deleted in the same strain. I would recommend that the authors carefully revise the manuscript paying close attention to each statement. Fore example on page 4: 'Downstream of TORC1, caffeine failed to accelerate ppk18D but not igo1D and partially overrode DNA damage checkpoint signalling'. It is unclear what the authors mean by accelerate. I assume they mean accelerate cell cycle progression, but there is no direct analysis of cell cycle kinetics in the results. Similarly on page 5: '... ppk18D mutant displayed slower cell cycle kinetics than wild type cells exposed to phleomycin and caffeine or torin1 (Figuer 1D)'. However, the figure shows no cell cycle kinetic analysis.

We have modified the wording of the abstract according to the reviewer’s suggestions.

We refer to accelerated progression into mitosis and have edited the text where appropriate. Depending on the type of DNA damage, S. pombe cells transiently or permanently arrest cell cycle progression. It is well known that caffeine overrides these cell cycle DNA damage checkpoints. We previously proved that this was not due to Rad3 inhibition. Additionally, TORC1 (which controls the timing of mitosis) inhibition overrides checkpoint signaling. Our aim was to investigate if caffeine mimics this effect at least partially, via activation of Ssp2. We have demonstrated this is the case, although the basal state of the various mutants can complicate the data analysis in terms of cell cycle progression. Following exposure to phleomycin, this septation index peaks at 60 minutes following exposure to caffeine. In ppk18 mutants this peak was delayed by 30 minutes. Thus, wt and ppk18 mutants proceed through mitosis and cytokinesis at different rates (as determined by measuring the septation index).

The authors appear to make the assumption that 'Inhibition of DNA damage signalling by caffeine and torin1 enhanced phleomycin sensitivity...' (page 6) but then clearly go on to show that the mutants used are sensitive for other unknown reasons. To make this link it would be necessary to artificially impose a G2 delay and show how much and in which circumstances this reverses the effect on sensitivity of caffeine/torin1. The authors should thus be very clear that they cannot equate sensitivity to 'checkpoint over-ride' and adjust their wording and assumptions accordingly. Assumptions on epistasis need to use the same assay and not equate between assays. As an example F1C and F2D do not equate as phleo+caffeine would be expected to be sensitised above phleo+torin1. This is not commented on in the text. Also on page 7 '... ATP also suppressed the ability of torin1 to override DNA damage checkpoint signalling albeit to a lesser degree (Figure 2I).' However, this figure only shows sensitivity, not septation index.

We accept that these results can be difficult to interpret. Firstly, caffeine appears to modulate cell cycle progression by various means. We previously demonstrated that it stabilizes Cdc25 independently of checkpoint signaling. However, it also activates Ssp2 which subsequently affects Cdc25 activity via PP2A. Its effect on mitosis can thus differ depending on the context. For instance, igo1 mutants already have high PP2A activity which would affect the subsequent effect of caffeine on Cdc25 activity. Ssp2 on the other hand appears to regulate cell fate according to the nutritional state. Its sensing of nutritional cues is not limited to ATP/ AMP levels as it also regulates the response to amino acid quality (e.g., glutamate versus torin1).

We have carried out additional experiments on the effect of ATP. While it did affect progression into mitosis, the results were complicated and have not been shown. Instead, we have provided additional data to show that it affects cell length which is an indicator of G2 cell length. In other words, longer cells spend more time in G2 prior to septation.

We also suspect that caffeine is itself a DNA damaging agent as previously reported in the early 1970s. More recent studies have also indicated a role for Rad3 and DNA repair proteins for tolerance to caffeine. In fact, TORC1 itself has been reported to be required for DNA damage repair. Thus, TORC1 inhibition could potentially enhance DNA damage sensitivity independently of mitotic progression as shown in some of our experiments.

While we have clearly identified a role for Ssp2 in mediating the cell cycle effects of caffeine, we accept that these findings will require further studies (beyond the scope of this one); to give more insights on how these caffeine- mediated effects occur. What is clear is that caffeine overrides DNA damage checkpoint signaling by at least partially inhibiting TORC1 signaling.

All the septation index graphs require an untreated (I.e no caffeine or torin1) control.

We now show in figure 1a, that the septation index does not change over the time period studied, when cells were left untreated. These assays have been routinely used for many years now and are very reproducible. The graphs clearly show the differential effects caffeine and torin1 exert on cell cycle progression in wt and mutant strains exposed to phleomycin.

Figure 3 is not quantitative and cannot support the conclusions drawn from it. If, for example, the authors wish to demonstrate ATP can suppress checkpoint override (Figure 3A) they should use the same septation assay used before. If this is not possible, then it should be explained why not and an alternative quantitative assay should be developed. It is unclear why the authors include Figure 3B,C at all.

Ssp2, on the other hand, appears to regulate cell fate according to the nutritional state. Its sensing of nutritional cues is not limited to ATP/AMP levels as it also regulates the response to amino acid quality (e.g., glutamate versus torin1). Additionally, exposure to stress may induce a transient decline in ATP levels. We thus investigated how ATP might affect caffeine or torin1. We could not detect any major changes in the septation index (not shown). Cells exposed to ATP in the presence of caffeine and phleomycin were shorter. We cannot tell how exactly suppresses the effect of caffeine and torin1 on DNA damage sensitivity.

It is unclear to this reviewer what the significance of the data with gsk3D cells is (Figure 5). The authors should introduce the protein, why there is an expectation that it would have a role in the pathway and explain its relevance. Similarly when discussing the resulting data.

Gsk3 lies downstream of TORC2 which is inhibited by torin1 but not caffeine. Gsk3 regulates Pub1 stability which is the E3 ligase for Cdc25. We showed previously that caffeine stabilizes Cdc25, suggesting it might interfere with Pub1 activity. Additionally, we are investigating caffeine as an indirect inhibitor of TORC1 with torin1 that directly inhibits both complexes. Our data provide further evidence for a differential effect of caffeine and torin1 on TORC1 signaling. We have modified the text accordingly.

Figure 5A shows a similar response of wild type cells to phleomycin regarding checkpoint override as was shown in Figure 1A. However Figure 5C is not recognisable as equivalent to Figure 2A, yet both report sensitivity to phleomycin od wild type cells under equivalent circumstances. This is a major concern as to reproducibility of these data. It is also not possible to conclude from either Figure 5C or 5D that caffeine or torin1 treatment is, or is not, sensitising cells to phleomycin treatment, yet this conclusion is made when discussing the data.

We agree with this and other reviewers that demonstrating enhanced sensitivity to caffeine is problematic. Nonetheless, our cell cycle data clearly indicate a differential role for Gsk3 in mediating the cell cycle effects of caffeine and torin1. In terms of DNA damage sensitivity, we have reproducibly observed a lower degree of DNA damage sensitivity in gsk3 mutants relative to wt cells. Hence, while caffeine is less effective at enhancing DNA damage sensitivity relative to torin1 in wt cells; we observed that caffeine and torin1 increase DNA damage sensitivity to a similar degree in gsk3 mutants.

Figure 6A shows that caffeine, but not torin1 results in Ssp2 phosphorylation. Is this experiment reproducible and does the total level of Ssp2 increase reproducibly? This should be doe ae and the results discussed. Ideally, the bands would be quantified against actin intensity and presented as a bar graph with standard deviation.

We have repeated these experiments alone and in combination with phleomycin. This data convincingly show that caffeine but not torin1 induces Ssp2 phosphorylation. In fact, torin1 suppresses Ssp2 phosphorylation, likely due to inhibition of a feedback mechanism resulting from TORC1 inhibition. In contrast, caffeine likely activates Ssp1 via the stress response, which in turn phosphorylates Ssp2.

Figure 6B, when introduced should explain the background as to why eIF2alpha phosphorylation is a readout of TORC1 activity. Importantly, the figure should be supported by an actin control and 3 repeats quantified. Figure 6C purports to establish that caffeine moderately attenuates Maf1 phosphorylation. To be able to state this, it would be essential to quantify the gel and report repeated results relative to actin and the total levels of Maf1. Similarly Figure6D and 6E require an actin control and would benefit from proper quantification.

We have repeated the Maf1 experiments to clarify the data and show that caffeine suppresses Sck1 an additional TORC1 phosphorylation target.

Minor comments<br /> p3 'cigarette smoke and other gases'?

We have edited the statement.

P4 torin1 was dissolved in DMSO (not were)

We have edited the text.

p5 phospho not phosphor Ssp2

We have edited the text.

p6 exlpain why ppk18 deletion results are surprising. Also this result could be discussed.

It had been proposed previously, that Ppk18 is the Greatwall homologue in S. pombe and thus the major regulator of PP2A and mitosis downstream of TOCR1. Later studies suggested a redundant role for Cek1 in this pathway. While deletion of cek1 in a ppk18 background modulated the effect of torin1 on cell cycle progression, it did not interfere with the effects of caffeine. At present we cannot account for this observation. We cannot rule out that caffeine activates an additional kinase that regulates Igo1 activity.

Together our data show that caffeine advances progression into mitosis in a manner that differs from direct inhibition of TORC1 by torin1.

We have now added the relevant comments on this unexpected observation within the discussion.

Explain why Cek1 is not tested

We have now tested a ppk18 cek1 double mutant.

p6 introduce what pap1 is when first mentioned

We have introduced PP2APab1 as requested.

Reviewer #3 (Significance):

The data show that fission yeast Ssp1/2 has a role in inhibiting TORC1 in response to caffeine and this influences checkpoint override. This is an incremental, but potentially interesting, observation contributing to understanding mechanism(s) of caffeine action. The lack of quantification, the pleiotropic nature of the mutants used and the rather blunt endpoints assayed make it hard to establish to what extent the direct TORC1 inhibition by Ssp2 causes the checkpoint override, which limits is potential impact. The core observation may, however, be of interest to the wider caffeine field. The referee has the perspective of a yeast cell cycle geneticist.

We thank the reviewer for identifying the significance of the study in understanding the mechanisms of caffeine effects on the cell cycle. We have added all the suggested experiments with additional mutants and protein markers as well quantitative approaches that have appropriately improved the manuscript. We believe that the mechanism provided is of more general interest and not limited to the caffeine field: manipulating the cell cycle and understanding the interplays between growth and stress are of general interest and importance.

Reviewer #4 (Evidence, reproducibility and clarity):

The authors provide a series of genetic studies identifying a role for Ssp1-Ssp2 signaling in TORC1-dependent responses to DNA damage. The main assays are cell division (i.e. septation index) and cell viability (i.e. serial dilution spot assays) following treatment with the DNA damaging agent phleomycin. The authors perform these assays in a number of genetic mutant backgrounds to determine which genes and pathways are required for the relevant cellular response. Supporting data also include microscopy images and western blots to test protein phosphorylation. In general, the results support a role for Ssp1-Ssp2 acting upstream of TORC1. However, in several cases the data do not support a straightforward relationship, and it is confusing to parse through a number of intermediate effects, which often vary between different assays. I have provided some specific comments below that might be addressed to strengthen the technical aspects of the manuscript.

Major<br /> 1. The authors conclude "that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively" based on Figure 1. This conclusion seems quite strong given the indirect nature of assays in Figure 1, which test septation in the presence of DNA damage. The conclusion would require experiments that assay TORC1 activity itself.

Both caffeine and torin1 have previously been reported to inhibit TORC1 which controls the timing of mitosis. We sought to investigate if caffeine mediates its effects via the stress response pathway. We have conducted additional experiments which clearly demonstrate that caffeine inhibits TORC1 at least partially via the activation of Ssp2. These observations make sense as we have previously shown that caffeine actives the stress response pathway to activate Srk1 which inhibits Cdc25. More recent studies my others indicate that Ssp1 is required to suppress Srk1 to allow progression into mitosis. This accounts for the failure of ssp1 mutants to advance mitosis under stress conditions. Additionally, Ssp1 activates Ssp2 which leads to the downstream inhibition of TORC1.

1. Figure 2 needs some explanation to introduce the idea that cell growth reflects an intact DNA damage response that prevented division in the presence of phleomycin. I also felt that the conclusions were very strong given the data, and the authors should discuss each case more carefully. For example, deletion of ssp1 does not really suppress the ability of torin1 to enhance phleo sensitivity (Figure 2C).

We would not expect the deletion of ssp1 to suppress the effect of torin1 under stress conditions. We have provided further evidence to show that Ssp1 is required to facilitate progression into mitosis at least in the presence of phleomycin or heat stress.

1. Microscopy imaging in Figure 3 nicely complements some of the other assays. However, it seems important to know if the cells are actively growing in each of these cases. An example is torin and rapamycin shortening ssp1 mutants at 35 degrees: are these cells actively cycling?

Our aim was to demonstrate that caffeine exacerbates the ssp1 phenotype. This would provide further evidence to show that caffeine exerts its effects at least in part by activating Ssp1. Cells do not cycle in the presence of torin1 as it inhibits both TORC complexes. We have provided additional evidence to show that caffeine does indeed interact with Ssp1. As the primary aim of the study was to determine is caffeine overrides DNA damage via Ssp1 we have not investigated if they are cycling. Their shortened size suggests that rapamycin and torin1 affect cell division in a different manner from caffeine.

1. From Figure 6A, the authors conclude that caffeine induces phosphorylation of Ssp2. However, it appears that both Ssp2 protein levels and its phosphorylation levels are both increased, which seems an important distinction.

We have repeated these experiments several times under different conditions. Some proteins become more stable when phosphorylated as has been previously demonstrated for Srk1 for instance.

1. In Figure 6D, the authors should show separate gsk3 and ssp1 mutants. It seems likely that all phosphorylation of Ssp2 is due to Ssp1, but this should be shown.

We have replaced the figure with a ssp1 single mutant.

1. I am confused about Maf1 phosphorylation in Figure 6C. It is increased upon torin1 treatment, but it is discussed as an indicator or TORC1 activity. Does that mean that loss of its phosphorylation correlates with increased TORC1 activity? As written, I thought it was a TORC1 substrate, which led to confusion about its increased phosphorylation upon torin1 treatment.

Maf1 is phosphorylated by TORC1. Inhibition of TORC1 would thus lead to a loss of phospho-Maf1 moieties and the accumulation of the unphosphorylated form. We have conducted additional experiments and under various conditions to show that caffeine weakly inhibits Maf1 phosphorylation. We note however, that different stresses result in differential outcomes following TORC1 inhibition. As such we have included new data to show that caffeine suppresses the TORC1 target Sck1. In S. pombe Sck1 and Sck2 regulate progression into mitosis.

Minor<br /> 1. An untreated control should be shown for assays in Figure 1.

We have included this data for figure 1a.

1. An untreated control should be shown for assays in Figure 4.

We have noted in the results for figure 1, that untreated cells and phleomycin only treated cells do not show any changes in septation index over the time course studied in these experiments.

Reviewer #4 (Significance):

The study has significance in connecting several conserved and central signaling pathways including TORC1, AMPK, and PP2A. Also, the study uses caffeine and torin1 that have effects in many different cell types. The connection between caffeine and torin1 effects on phleomycin-treated cells was previously established by these researchers. The significance of the current study is providing a genetic pathway for this connection. The significance is partly limited by some of the technical points raised in the previous section, such as some inconsistencies in the strength of results from different assays. Also, the role of these pathways in DNA damage response signaling is not new. While the main significance of this work might relate to a more specialized audience, it does add to a broader body of literature regarding these conserved pathways and processes.

My expertise is yeast cell biology.

While the roles of the pathways in DNA damage has been reported usinbg genetic and pharmacological combinations we dissect their relationships and provide mechanistic connections.

We thank the reviewer for identifying the significance of this study. We believe we have now addressed the technical issues raised.

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Referee #4

Evidence, reproducibility and clarity

The authors provide a series of genetic studies identifying a role for Ssp1-Ssp2 signaling in TORC1-dependent responses to DNA damage. The main assays are cell division (i.e. septation index) and cell viability (i.e. serial dilution spot assays) following treatment with the DNA damaging agent phleomycin. The authors perform these assays in a number of genetic mutant backgrounds to determine which genes and pathways are required for the relevant cellular response. Supporting data also include microscopy images and western blots to test protein phosphorylation. In general, the results support a role for Ssp1-Ssp2 acting upstream of TORC1. However, in several cases the data do not support a straightforward relationship, and it is confusing to parse through a number of intermediate effects, which often vary between different assays. I have provided some specific comments below that might be addressed to strengthen the technical aspects of the manuscript.

Major

1. The authors conclude "that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively" based on Figure 1. This conclusion seems quite strong given the indirect nature of assays in Figure 1, which test septation in the presence of DNA damage. The conclusion would require experiments that assay TORC1 activity itself.
2. Figure 2 needs some explanation to introduce the idea that cell growth reflects an intact DNA damage response that prevented division in the presence of phleomycin. I also felt that the conclusions were very strong given the data, and the authors should discuss each case more carefully. For example, deletion of ssp1 does not really suppress the ability of torin1 to enhance phleo sensitivity (Figure 2C).
3. Microscopy imaging in Figure 3 nicely complements some of the other assays. However, it seems important to know if the cells are actively growing in each of these cases. An example is torin and rapamycin shortening ssp1 mutants at 35 degrees: are these cells actively cycling?
4. From Figure 6A, the authors conclude that caffeine induces phosphorylation of Ssp2. However, it appears that both Ssp2 protein levels and its phosphorylation levels are both increased, which seems an important distinction.
5. In Figure 6D, the authors should show separate gsk3 and ssp1 mutants. It seems likely that all phosphorylation of Ssp2 is due to Ssp1, but this should be shown.
6. I am confused about Maf1 phosphorylation in Figure 6C. It is increased upon torin1 treatment, but it is discussed as an indicator or TORC1 activity. Does that mean that loss of its phosphorylation correlates with increased TORC1 activity? As written, I thought it was a TORC1 substrate, which led to confusion about its increased phosphorylation upon torin1 treatment.

Minor

1. An untreated control should be shown for assays in Figure 1.
2. An untreated control should be shown for assays in Figure 4.

Significance

The study has significance in connecting several conserved and central signaling pathways including TORC1, AMPK, and PP2A. Also, the study uses caffeine and torin1 that have effects in many different cell types. The connection between caffeine and torin1 effects on phleomycin-treated cells was previously established by these researchers. The significance of the current study is providing a genetic pathway for this connection. The significance is partly limited by some of the technical points raised in the previous section, such as some inconsistencies in the strength of results from different assays. Also, the role of these pathways in DNA damage response signaling is not new. While the main significance of this work might relate to a more specialized audience, it does add to a broader body of literature regarding these conserved pathways and processes.

My expertise is yeast cell biology.

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Referee #3

Evidence, reproducibility and clarity

Summary

This manuscript examines the genetic requirements for checkpoint override by caffeine in the fission yeast model organism. The main outcome is to show that checkpoint override, which has previously been linked to the downregulation of TORC1, is dependent on on the AMPK pathway (Ssp1/Ssp2). Additional analysis of downstream factors and the cross-talking Sty1 pathway implicates Greatwall kinases and Igo1 (PP2A inhibitor - endosulfine analogue) although the pleiotropic nature of these pathways and the rather blunt endpoints of septation index and phleomycin sensitivity makes robust data interpretation difficult.

Major comments

For clarity the manuscript would benefit from some restructuring. In particular it would help the reader if the diagram presented in figure 7 was presented first as this would help orientate the reader with the pathways. The mammalian equivalents should be indicated.

For scientific accuracy and clarity the manuscript requires significant attention. For example in the abstract where Rad3 is introduced it is not made clear that this is the fission yeast gene. It would be better to introduce ATR at this point? Anther example in the abstract: 'Deletion of ssp1 and ssp2 suppresses...' should read 'Deletion of ssp1 or ssp2 suppresses...' as the two genes are not deleted in the same strain. I would recommend that the authors carefully revise the manuscript paying close attention to each statement. Fore example on page 4: 'Downstream of TORC1, caffeine failed to accelerate ppk18D but not igo1D and partially overrode DNA damage checkpoint signalling'. It is unclear what the authors mean by accelerate. I assume they mean accelerate cell cycle progression, but there is no direct analysis of cell cycle kinetics in the results. Similarly on page 5: '... ppk18D mutant displayed slower cell cycle kinetics than wild type cells exposed to phleomycin and caffeine or torin1 (Figuer 1D)'. However, the figure shows no cell cycle kinetic analysis.

The authors appear to make the assumption that 'Inhibition of DNA damage signalling by caffeine and torin1 enhanced phleomycin sensitivity...' (page 6) but then clearly go on to show that the mutants used are sensitive for other unknown reasons. To make this link it would be necessary to artificially impose a G2 delay and show how much and in which circumstances this reverses the effect on sensitivity of caffeine/torin1. The authors should thus be very clear that they cannot equate sensitivity to 'checkpoint over-ride' and adjust their wording and assumptions accordingly. Assumptions on epistasis need to use the same assay and not equate between assays. As an example F1C and F2D do not equate as phleo+caffeine would be expected to be sensitised above phleo+torin1. This is not commented on in the text. Also on page 7 '... ATP also suppressed the ability of torin1 to override DNA damage checkpoint signalling albeit to a lesser degree (Figure 2I).' However, this figure only shows sensitivity, not septation index.

All the septation index graphs require an untreated (I.e no caffeine or torin1) control.

Figure 3 is not quantitative and cannot support the conclusions drawn from it. If, for example, the authors wish to demonstrate ATP can suppress checkpoint override (Figure 3A) they should use the same septation assay used before. If this is not possible, then it should be explained why not and an alternative quantitative assay should be developed. It is unclear why the authors include Figure 3B,C at all.

It is unclear to this reviewer what the significance of the data with gsk3D cells is (Figure 5). The authors should introduce the protein, why there is an expectation that it would have a role in the pathway and explain its relevance. Similarly when discussing the resulting data.

Figure 5A shows a similar response of wild type cells to phleomycin regarding checkpoint override as was shown in Figure 1A. However Figure 5C is not recognisable as equivalent to Figure 2A, yet both report sensitivity to phleomycin od wild type cells under equivalent circumstances. This is a major concern as to reproducibility of these data. It is also not possible to conclude from either Figure 5C or 5D that caffeine or torin1 treatment is, or is not, sensitising cells to phleomycin treatment, yet this conclusion is made when discussing the data.

Figure 6A shows that caffeine, but not torin1 results in Ssp2 phosphorylation. Is this experiment reproducible and does the total level of Ssp2 increase reproducibly? This should be doe ae and the results discussed. Ideally, the bands would be quantified against actin intensity and presented as a bar graph with standard deviation.

Figure 6B, when introduced should explain the background as to why eIF2alpha phosphorylation is a readout of TORC1 activity. Importantly, the figure should be supported by an actin control and 3 repeats quantified. Figure 6C purports to establish that caffeine moderately attenuates Maf1 phosphorylation. To be able to state this, it would be essential to quantify the gel and report repeated results relative to actin and the total levels of Maf1. Similarly Figure6D and 6E require an actin control and would benefit from proper quantification.

Minor comments

p3 'cigarette smoke and other gases'?

P4 torin1 was dissolved in DMSO (not were)

p5 phospho not phosphor Ssp2

p6 exlain why ppk18 deletion results are surprising. Also this result could be discussed.

Explain why Cek1 is not tested

p6 introduce what pap1 is when first mentioned

Significance

The data show that fission yeast Ssp1/2 has a role in inhibiting TORC1 in response to caffeine and this influences checkpoint override. This is an incremental, but potentially interesting, observation contributing to understanding mechanism(s) of caffeine action. The lack of quantification, the pleiotropic nature of the mutants used and the rather blunt endpoints assayed make it hard to establish to what extent the direct TORC1 inhibition by Ssp2 causes the checkpoint override, which limits is potential impact. The core observation may, however, be of interest to the wider caffeine field. The referee has the perspective of a yeast cell cycle geneticist.

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Referee #2

Evidence, reproducibility and clarity

Summary: In this paper, Alao and Rallis analyze the role of AMPK and TORC1 pathways, and the respective crosstalk, in regulating cell cycle progression in the presence of DNA damage in S. pombe. The authors show, almost exclusively through chemo-genetic epistasis assays, that caffeine inhibits TORC1 indirectly activating AMPK, in contrast to the specific ATP-competitive TORC1 inhibitor torin 1. Specifically, it is shown that in the absence of a functional AMPK pathway caffeine is unable to revert the TORC1-inhibition-dependent override of cell-cycle arrest caused by the DNA-damaging agent phleomycin, henceforth partially suppressing the growth inhibition caused by the co-treatment.

Major comments: The overall story of the paper is convincing. However, the choice of an almost exclusively chemo-genetic approach, lack of controls in some experiments and some discrepancy in data presentation suggest that the manuscript undergoes revision before the authors claim that their conclusions are fully supported by the results. In detail:

• In Figure 1, graphs of septation indexes are presented separately for each strain. This presentation prevents the reader from clearly comparing the differences of septation caused by genetic background rather than the treatment, i.e. the septation happening by treatment with torin 1. I feel it would be better to group the results by drug rather than by strain/mutant. If the results are presented this way because the experiments on different strains were run separately, I further suggest that they are re-run so to always include at least the wt in every run.
• In Figure 2C-D, an inconsistency is observable between the phleo+caffeine sensitivity of ssp1Δ and ssp2Δ, the latter retaining a higher sensitivity. Provided that this is not only due to this specific replicate, how would the authors explain such a difference and fit it into their conclusion of a "cascade" signaling with Ssp1 acting upstream of Ssp2?
• In Figure 2I, a huge discrepancy is observable compared to panel 2A in terms of phleo+caffeine (no ATP) sensitivity of wt cells. Here, cells seem to cope well with the phleomycin treatment even if co-treated with caffeine. This renders the main finding of the panel (the effect of phelo+caffeine+ATP) rather uninterpretable.
• In Figure 3A, the simple observation of elongation is sometimes hard to assess, for example in the ATP-caused suppression of the effect of torin 1, as also acknowledge by the authors in the text. I feel it would be really necessary to quantify such results on an adequate number of cells.
• In Figure 3B,C wt is missing to compare the results in the presence of the same treatments. I understand the focus on Ssp1, but the authors should show the same treatments on wt cells. Similarly, it would be better to show the drug treatments in panel C also at 30{degree sign}C. For the same reasons as in the previous point, quantifications would greatly enhance the credibility of the claims here.
• A major point is the almost complete absence of molecular data. Except for Figure 6, the data do not include a detection of the relative activation of the relevant pathways. Figure 6 could hardly fill this gap, since the samples therein analyzed are not the ones utilized in most of the other figures, but simple, single time-point treatment with a single drug. The authors usually refer in the text to previous knowledge about how a treatment influences a pathway. However, they should show it here in their experimental conditions.

Minor comments:

• A different grouping of the experiments/panels would help the reader. For example, Fig. 2I would fit better together with Fig. 3A, to match the composition of the various chapters of the results.
• Torin 1 is sometimes referred to with a capital T or with a lowercase t, especially in the Figures. I suggest to uniform the nomenclature.
• In the results, the authors state that "ATP may increase TORC1 activity or act as a competitive inhibitor towards both compounds.". It's a little bit odd to refer to ATP as a competitive inhibitor of drugs. I would rather be ATP, the physiological agonist, outcompeting two compounds which are working as ATP-competitive inhibitors.

Significance

The interplay between TORC1 and AMPK is of great interest in the cell signaling field, basically in every model organism. The paper provides a conceptual advance in the field showing a genetic interaction between the two pathways using a model organism which has probably been overlooked so far, which is a pity because S. pombe is the best organism to study G2/M cell cycle/size regulation. The story would be of interest especially for an audience working in cell signaling in microorganisms, but not so much (at least at this stage) for the community working on aging, disease and chemo-/radio-sensitization, contrary to what the authors claim. Furthermore, for the above-mentioned reasons, I feel like the authors are a little bit overshooting when claiming (for example in the abstract and in the discussion), that their work provides a clear understanding of the mechanism.

As requested by Review Commons, I specify that my expertise is on TORC1/AMPK/PKA pathways, on their crosstalk and their regulation my metabolic intermediates.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In the manuscript "The AMPK-TORC1 signalling axis regulates caffeine-mediated DNA damage checkpoint override and cell cycle effects in fission yeast," the authors studied the role of genes that are potentially involved in the caffeine-mediated override of a cell cycle arrest caused by activation of the DNA damage checkpoint. The methylxanthine substance caffeine has been known to override the DNA damage checkpoint arrest and enhance sensitivity to DNA damaging agents. While caffeine was reported to target the ATM ortholog Rad3, the authors previously reported that caffeine targets TORC1 (Rallis et al, Aging Cell, 2013). Inhibition of TORC1, like caffeine, was also reported to override DNA damage checkpoint signaling. Therefore, in the present study, the authors compared the effects of caffeine and torin1 (a potent inhibitor for TORC1 and TORC2) on cell cycle arrest caused by phleomycin, a DNA damaging agent, using various gene deletion S. pombe mutants.

The authors concluded that they identified a novel role of Ssp1 (calcium/calmodulin-dependent protein kinase) and Ssp2 (catalytic subunit of AMP-activated kinase) in the cell cycle effects caused by caffeine, based on the following findings; (1) the caffeine-mediated DNA damage checkpoint override requires Ssp1 and Ssp2; (2) Ssp1 and Ssp2 are required for caffeine-induced hypersensitivity against phleomycin; (3) under normal growth conditions, caffeine leads to a sustained increase of the septation index in a Ssp2-dependent manner; (4) Caffeine activates Ssp2 and partially inhibits TORC1.

Major comments:

I do not think that many of the authors' claims are supported by the results of the present study. The corresponding parts are detailed below.

1. The conclusion of the first paragraph in the Results (top in page 6; Our findings indicate that caffeine and torin1 indirectly and directly inhibit TORC1 activity respectively.) is not supported by the data in Figure 1. The result that caffeine, but not torin1, requires Ssp1 and Ssp2 to override the phleomycin-induced cell cycle arrest does not necessarily indicate that caffeine indirectly inhibits TORC1 via Ssp1 and Ssp2. Rather, the authors should mention that this conclusion is based on the authors' previous reports by citing them (e.g., Rallis et al, Sci Rep, 2017).

To add to Figure 1, an additional experiment using a constitutively active AMPK mutant, a temperature-sensitive TORC1 mutant, and a srk1 deletion mutant will help the authors claim their original conclusion as one possibility.<br /> 2. The conclusion of the second paragraph in the Results (lower-middle in page 6; Our results indicate that caffeine induces the activation of Ssp2.) is not based on the results of Figure 2. Figure 2 simply illustrates that both caffeine and torin1 cause hypersensitivity to phleomycin dependent on Ssp1 and Ssp2.<br /> 3. The conclusion of the fourth paragraph in the Results (middle in page 7) is not clearly supported by the result, due to an insufficient data analysis. As the cell length and the progress through mitosis are the key assay parameters in Figure 3, the average cell length should be shown next to each micrograph of Figure 3A and 3B. In Figure 3C, a mitotic index and the average cell length should be shown next to each micrograph. A statistical analysis is necessary for the authors to compare the measurements and to claim as the headline (Caffeine exacerbates the ssp1D phenotype under environmental stress conditions), as the effect of caffeine was not evident.<br /> 4. In the middle of page 8, the statement "Accordingly, the effect of caffeine and torin1 on DNA damage sensitivity was attenuated in gsk3D mutants (Figure 5C and 5D)." is not supported by the corresponding results. Rather, Figure 5C and 5D look almost same.<br /> 5. The description and the conclusion of the last paragraph in the Results (bottom in page 8 - page 9) are not supported by the results of Figure 6, due to an insufficient data analysis. The extent of phosphorylation must be quantified as a ratio of the phosphorylated species (e.g., pSsp2) to all species of the protein (e.g., Ssp2).

From Figure 6, the authors claim that caffeine (10 mM) partially inhibits TORC1 signaling. However, the authors previously showed that the same concentration of caffeine inhibited phosphorylation of ribosome S6 kinase as strongly as rapamycin, the potent TOR inhibitor (Rallis et al, Aging Cell, 2013). The authors are advised to assess phosphorylation of S6 kinase again in the present study and compare to the results of the present results in Figure 6, because addition of that data may allow the authors to discuss that caffeine affects TORC1 downstream pathways at different intensities.

Also, immunoblotting of the same proteins looks somehow different from panel to panel (e.g., pSsp2 in panel A and D; Actin in panel A, C, and D). Therefore, the blotting result before clipping had better be shown as a supplementary material.

Minor comments:

1. (Figure 1) The septation index of the phleomycin-treated cells (without any further additional drugs) should be shown, as a baseline.
2. (Figure 1D, Optional) As a ppk18D cek1D double deletion mutant is reported, the authors are advised to add and test that mutant in this experiment.
3. (Figure 2) The authors need to clarify the number of cell bodies spotted (e.g., in the Figure legend).
4. (Figure 3) The different number of cells in micrographs may give an (wrong) impression on the cell proliferation rate. Therefore, it is advisable to use the micrographs in which the similar number of cells are shown for conditions with the similar cell proliferation rates.
5. (Figure 4B) ssp2D, not spp2D.
6. (Figure 4) The septation index of the none-treated cells should be shown as a baseline.
7. (Figure 6B, 6E) What do the black arrows indicate? Figure Legend does not seem to explain them.
8. (Figure 6C) Indicate which part of the Maf1-PK blot corresponds to the phosphorylated species, because Maf1-PK is probed with an anti-V5 (not a phosphorylation-specific) antibody.
9. (Figure 6D) gsk3Dssp1D, not gs3Dssp1D.

Significance

As caffeine is implicated in protective effects against diseases including cancer and improved responses to clinical therapies, the topic of the present study is of interest and importance to the broad audience.

In the present study, the most significant finding is that caffeine- and torin1-induced hypersensitivity to phleomycin is dependent on Ssp1 and Ssp2 (Figure 2). This result may be important in chemotherapy against cancers. On the other hand, caffeine is known to activate AMPK (e.g., Jensen Am J Physiol Endocrinol, 2007). Besides, as detailed in the Major comments, many of the major conclusions are not supported by the present results. Therefore, based on my field of expertise (cell cycle, cell proliferation, and TOR signaling), I conclude that the present study hardly extends the knowledge in the field of "the cell biology of caffeine."

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Referee #3

Evidence, reproducibility and clarity

Summary

Cheng et al. use single-cell sequencing to determine how Nodal signaling influences endodermal and prechordal plate fate specification in zebrafish. Much data and data analyses are presented, but the conclusions that can be drawn remain vague and do not go far beyond what previous studies have already established. While the datasets are a potentially useful resource, the conceptual impact is limited.

Major comments

1. The major weakness of the paper is that previous studies have already shown that differential Nodal signaling (and additional mechanisms) can induce anterior endoderm versus prechordal plate. In particular, the studies of Barone et al. (2017) and Sako et al. (2016) have provided much more convincing insights, because they combine genetic manipulations with in vivo imaging. In contrast, the current study mostly infers fate specification from scRNA-seq data. This approach is fraught with artifacts, because pseudotime trajectories are only a proxy for developmental processes, and UMAPs can misrepresent relationships between different cell states and types. The potentially more novel findings (roles for ripply; role of chromatin accessibility) are quite preliminary. Therefore, the conceptual advances provided by the study are minor.
2. The study attempts to distinguish between anterior endoderm and prechordal plate, but there is little evidence that anterior endoderm versus most/all endoderm is studied. Clear markers for anterior endoderm would be needed (or live imaging as in Barone et al.).
3. The claim that prechordal plate gives rise to prechordal plate and endoderm is confusing. The initial prechordal plate is different from the later prechordal plate. Please use a more precise nomenclature.
4. Gsc is described to be expressed highly in anterior endoderm progenitors but Figures 1C and 1J do not support this.
5. I am not sure what to make of the Nodal and Lefty manipulations. There is plenty of data but previous studies by the Heisenberg lab have provided much more definitive insihgts into the role Nodal signaling in this fate decision. Please put your results into the context of these studies.
6. The chromatin accessibility results and conclusions seem trivial in light on previous observations that Nodal signaling (and many other signaling pathways) activate gene expression via enhancers, a hallmark of which is increased accessibility upon activation.
7. The ripply1 overexpression result is potentially interesting, but needs to be complemented with a loss of function analysis.

Referee cross-commenting

It is gratifying to see that all three reviewers appreciate the potential of the data, but they find the results not as conclusive as one might wish, and they question the conceptual novelty of the claims when compared to previous studies. I share their suggestions and concerns.

Significance

The study provides new single-cell data and analyses but does not provide major conceptual advances when compared to previous studies (e.g. Barone et al. (2017); Sako et al. (2016)). In its current form a small group of researchers in the zebrafish Nodal field might be interested in further exploring the data in this paper and combine it with in situ gene expression analyses and fate mapping.

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Referee #2

Evidence, reproducibility and clarity

In this study, Chen and colleagues examine the molecular basis for the segregation of mesodermal and endodermal fates during zebrafish gastrulation. For this, they focused on cell fate separation within the anterior mesendoderm, which gives rise to prechordal plate (pp) and anterior endoderm (endo) progenitors. Using a combination of "-omics" approaches and live imaging in both embryos and explants, the authors find that endo progenitors derive from the pp and are specified by comparatively lower levels of Nodal signaling. Mechanistically, higher Nodal signaling levels in pp cells correlates with chromatin openness. Furthermore, the authors provided evidence that gsc and ripply1 activity can repress endo specification. Overall, the authors suggest a model whereby different Nodal signaling levels promote cell fate diversification through modulation of epigenetic states.

The paper is well written, and the data presented in this study nicely supports the author's interpretation that anterior endo cells derive from the pp and that it requires lower Nodal signaling levels. However, it is unclear what are the core differences between this model and previous work (Sako, et al. 2016; Barone, et al. 2017) and how to interpret these findings in light of recent work in the field, implicating FGF signaling as a critical regulator of the segregation between mesoderm and endoderm in zebrafish (Economou et al. 2023). To reinforce their findings, it would thus be important for the authors to use their datasets to investigate further between these distinct models and explore the role of FGF signaling in this process. For instance, can they observe differential activation of FGF targets in early progenitors cells with higher vs. lower Nodal signaling?

Similarly, the link between Nodal signaling and chromatin openness is interesting, however, it is still unclear how causative these differences are for the cell fate segregation investigated in this study (this is in line with the way the authors describe these findings). However, given my previous point, I think it would be important to dissect this link further to strengthen the novelty of the study.

Finally, to prove that gsc and ripply1 cooperate in wild-type embryos for the segregation between pp and endo progenitors, it would be important to include further functional data. For instance, describe the respective loss-of-function phenotypes, as well as whether, at endogenous levels, they can partially compensate for each other's loss-of-function. A similar analysis should be included for osr1 to validate the need for cooperation between these distinct transcriptional repressors in anterior endo specification (a hypothesis nicely raised by the authors in the discussion of this study). Finally, is the expression of these transcriptional repressors restricted to the ppl? If so, why? Would they require peak levels of Nodal signaling, only present in the pp, for their induction? What are the expression levels of these regulators in the morphants for ndr1 and lefty1 (which show differences in Nodal signaling levels)?

Significance

This study tackles an important question in vertebrate gastrulation, which has been under intense investigation over the last years. By integrating sequencing datasets from previously published studies, as well as newly-generated datasets, the authors provide evidence that anterior endo progenitors derive from the pp, which is nicely confirmed using live imaging. The findings that anterior endo progenitors are specified by comparatively lower levels of Nodal signaling than pp constitutes a major part of this manuscript. However, it is not clear i) what is necessarily new compared to previous studies implicating Nodal signalling in this process and, ii) how to interpret these findings in the light of recent work in the field disputing this more Nodal-based model. Accordingly, it was previously shown that the duration of Nodal signaling, partially through the action of gsc, played a key role in the differentiation between pp and endo progenitors (Sako, et al. 2016). Furthermore, previous work showed that endo cells leaving the ppl showed shorter-lived cell-cell contacts and, thus, on average lower Nodal signaling (Barone, et al. 2017). Since a recent model challenged the idea that differences in Nodal signaling are sufficient to account for the segregation between mesoderm and endoderm progenitors and instead suggested that an interplay between Nodal and FGF is necessary for the stochastic switch between these two cell fates (Economou et al. 2023), it would be important for the authors to use their datasets to investigate further these distinct models. This would synthetize both previous and current findings into a conceptual framework explaining how endoderm progenitors are specified.

Audience: This study would be relevant for a broad audience of cell and developmental biologists, interested in morphogen signaling, cell fate specification and pattern formation.

Expertise in zebrafish development, gastrulation, morphogen signaling and morphogenesis.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In zebrafish embryos, progenitor cells for both the prechordal plate and anterior endoderm reside at the dorsal margin in early gastrulation. Both cell populations are induced via signaling through the Nodal signaling pathway, however the mechanisms that send Nodal-exposed cells to one fate versus the other remain a matter of debate. Cheng et al use single-cell RNA sequencing to investigate the mechanistic origins of this developmental decision. They argue that both populations emerge from a common progenitor pool marked by the prechordal-plate marker gene goosecoid (gsc). By adding single-cell ATACseq analysis, they go on to argue that Nodal signaling encourages open chromatin states at target genes, and that this may underly the distinction between prechordal plate and endodermal fates. Finally, they suggest two potential regulators (gsc and ripply1) that may repress commitment to the endodermal fate.

Major Comments:

1. In lines 128-136, the authors describe a live imaging experiment to support the argument that anterior endodermal cells emerge from a gsc+ progenitor pool. The claim is that sox17+ cells (marked by RFP fluorescence) arise in gsc+ cells (marked by GFP fluorescence). From the presented data, I find it very hard to evaluate this claim. The GFP signal appears quite close to background in the highlighted cell. Additionally, the argument- as presented-turns on the behavior of a single highlighted cell. I think that this analysis should be clarified and extended to support the claim.

I suggest that the authors (1) plot average cell fluorescence over time rather than a 'line scan' across the cell, (2) draw cell borders from the mask used in each frame for clarity of presentation, and (3) plot the trajectories of gsc+/sox17+, gsc-/sox17- and gsc+/sox17-cells for comparison.

Alternatively, it could be helpful to extract fluorescence intensities for each cell in the field of view and scatter the RFP vs. GFP intensity for each cell. If the claim is true, three distinct subpopulations should be visible (i.e. gsc+/sox17-, gsc-/sox17- and gsc+/sox17+). Statistical analysis supporting the significance of these differences (e.g. comparing the means of each reporter within the populations) would be clarifying.

OPTIONAL: The live imaging experiment the authors present is quite ambitious, but perhaps overly difficult for the task at hand. I think this point could be more easily and clearly demonstrated by using two-color fluorescent in situs or HCR staining for gsc and sox17. Using an endpoint measurement would allow for deeper sampling across multiple embryos, and would likely yield clearer signals for cell type quantifications.<br /> 2. In the same section, I suggest that the authors address the possibility that the sox17+ cells observed don't go on to become part of the anterior endoderm. I commend the authors experimental work to support their scRNA-Seq data, however observation of the expression of a reporter gene (injected on a plasmid) is not equivalent to demonstrating that those cells adopt a given fate in the end. Is it not possible that the sox17 expression is transient, and these cells revert to prechordal plate fate? This point would be sealed by a formal fate mapping study (e.g. photoconversion of sox17::kaede cells), but I don't think this is a necessary bar for publication.<br /> 3. In Figure 1 M, the explant data does not seem to clearly support the claim that higher Nodal signaling intensities favor prechordal plate over endoderm. It appears that, for the endodermal panel, 2/3 replicates for 6 pg and 10 pg injections resulted in no endodermal cells observed. Could the authors clarify how this reflects the certainty of the conclusion? No statistical analysis is indicated on this panel or the one below.<br /> 4. OPTIONAL: The analysis presented in Fig. 1M strikes me as rather indirect (i.e. deconvolution of bulk RNA-Seq data to infer cell population proportions), and not strongly compelling. I think a stronger support of this point would be to inject Nodal into embryos and measure positive cell counts for gsc and an endodermal marker (e.g. sox32 or sox17) via HCR or in situ hybridization. This would yield a direct measurement of the cell counts in question. I think this would greatly support the claim, but I don't think should be considered a requirement for publication.<br /> 5. In Fig. 2H, the authors analyze responses to ectopic Nodal gradients in order to corroborate the results of their LIANA analysis. This experiment is a welcome addition to the argument, but has weak points that should be addressed.<br /> - a. The description of image analysis procedures used to construct the quantification plots are inadequate. It seems likely that the nuclei were segmented from the DAPI images, but this was not clear from the methods section. The authors should completely describe the segmentation pipeline and include sample code in the supplementary material.<br /> - b. The methods section seems to suggest that the analysis was performed exclusively on maximum intensity projections. I think this procedure may make the data hard to interpret and should be modified/support with additional analysis. For example, there is no reason that, at any given position in the image, the brightest DAPI and pSmad2 channel pixels occur in the same plane. Segmentation boundaries may therefore not reliably match between channels in the maximum intensity projection. The segmentation should be performed using the full Z-stack images. This can be done using widely-available software packages (e.g. CellProfiler).<br /> - c. The fluorescence images in 2H (specifically for the pSmad2 channel) look like they may contain some artifacts that carry through into the quantification. Specifically, there appears to be substantial non-specific background (both hazy and punctate) in the lft1 mutant that may artificially elevate the quantified intensity. This is evident in the quantification as a larger 'offset' to which the gradient decays than in the other presented images. This may be another explanation for the observation that pSmad2 staining is stronger in this background. I suggest that the authors (a) present all fluorescence images from the dataset in the supplement to allow for visual inspection, and (b) estimate the effect of fluorescence background on their quantifications to ensure that this artifact is not the source of the claimed difference.<br /> 6. In lines 267-284 and Fig. 4 L, the authors make the argument that ripply1 acts as a cell-autonomous repressor of endodermal fate. I find the argument for the cell autonomous character of its function hard to follow. Specifically, the authors lean on the experiment in which a plasmid with a sox17 promoter-ripply1 construct is injected, resulting in a decrease in endodermal cell count. Could the authors elaborate on how this proves a cell autonomous effect? Is it not possible that ripply1 expressed from this construct induces a signal that influences neighboring cells?<br /> 7. The suggestion that prechordal plate fate is favored (over endodermal fate) by higher Nodal signaling levels is interesting. This claim is supported by the derivation of a 'Nodal score' from RNAseq data. However, I don't see where the score is defined in the Methods section or in the supplementary materials. If this was accidentally omitted (my apologies if I am just missing it), it should be added. Additionally, I found the description in the main text to be opaque, and the paper would benefit from a more intuitive/friendly explanation of this metric.

Additionally, could the authors comment on what they believe-in terms of Nodal signaling history for a given cell- this score represents? Does it correlate with integrated Nodal exposure? Nodal exposure duration? Peak Nodal exposure? Given the results of Sako et al-that Nodal exposure duration is a critical determinant of prechordal plate fate- it would be useful to know if the authors believe their Nodal score findings point toward a different mechanism.

Minor Comments:

1. Line 84: The authors refer to the prechordal plate cells being 'more mature' than endoderm. It is unclear what the claim is here; some elaboration would be helpful.
2. The fluorescence images in Fig. S2 are virtually invisible in the PDF. The images should be rescaled to make them visible.
3. Fig. 2H would be easier to make sense of if the image panels were labeled. Please indicate which color corresponds to which stain.

Significance

I believe that this study fills in some details on the process of anterior endoderm specification that will be of interest to specialists in zebrafish Nodal signaling. I believe that the strongest and most novel section is the combined scRNA-Seq/ATAC-Seq analysis. This dataset is likely to be of interest to researchers who want to dig into potential mechanisms for the separation anterior endoderm and prechordal plate. Further, the singling out of ripply1 as a potential regulator of endodermal specification is interesting, and I hope that the authors follow this promising lead in future work.

While this study does provide a useful single-cell view of the specification of anterior endoderm, I didn't feel that it came to a concrete conclusion about the mechanism of separation of the anterior endoderm and prechordal plate. A few interesting processes/players are suggested by the findings- for example, Nodal/Lefty signaling between the populations or ripply1 expression could tip the balance- but I don't believe these hypotheses were tested clearly. The authors correctly point out that models for Nodal-driven endoderm/mesoderm separation have recently emerged in the literature, however the findings presented here don't rule out either of these models or compellingly support an alternative. I don't believe that this should preclude publication, however I do think it will limit the reach of the paper. Experiments that more concretely test the possible mechanisms hinted at here- for example, studying the separation of the two lineages in ripply1 mutants- would strengthen the paper's reach.

My enthusiasm for the paper is also somewhat reduced by the fact that some key findings of the paper can be found in earlier work. Acknowledgement of this prior work in the relevant sections could be improved. Specifically:

1. The finding that anterior endoderm cells emerge from a gsc-expressing population in the dorsal margin was strongly suggested in the classic Warga et al paper on the origin of zebrafish endoderm. There, fate mapping experiments demonstrate that dorsal marginal cells (in the first two cell tiers) in the late blastula can go on to form both endoderm and mesoderm. This strongly implies that anterior endoderm cells emerge from a gsc+ population, given that these cells are firmly within the gsc expression domain. I also note that the scRNAseq data from Fig. 2 in Farrell et al directly demonstrates that some sox17+ endoderm cells express gsc in their developmental trajectory. The findings in this paper are a welcome confirmation of these earlier observations, however this context should be discussed.
2. The observation that squint and lefty single mutants (either lefty1 or lefty2) can alter the propensity to adopt endodermal or mesodermal fates has also been observed previously. See for example Fig.1 in Norris et al, Figs 3 and 4 in Rogers et al, or Fig.1 in Chen et al. Acknowledging some of these earlier findings would benefit the paper.

As a reviewer, I feel most qualified to comment on the embryological aspects of the presented work. While I am generally familiar with the single-cell genomics toolkit, I am not in a position to rigorously assess the technical merit of that side of this work. Accordingly, I have tried to restrict my comments to the embryology side.

References:

1. Warga, R.M. and Nüsslein-Volhard, C., 1999. Origin and development of the zebrafish endoderm. Development, 126(4), pp.827-838.
2. Farrell, J.A., Wang, Y., Riesenfeld, S.J., Shekhar, K., Regev, A. and Schier, A.F., 2018. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science, 360(6392), p.eaar3131.
3. Norris, M.L., Pauli, A., Gagnon, J.A., Lord, N.D., Rogers, K.W., Mosimann, C., Zon, L.I. and Schier, A.F., 2017. Toddler signaling regulates mesodermal cell migration downstream of Nodal signaling. Elife, 6, p.e22626.
4. Rogers, K.W., Lord, N.D., Gagnon, J.A., Pauli, A., Zimmerman, S., Aksel, D.C., Reyon, D., Tsai, S.Q., Joung, J.K. and Schier, A.F., 2017. Nodal patterning without Lefty inhibitory feedback is functional but fragile. Elife, 6, p.e28785.
5. Chen, Y. and Schier, A.F., 2002. Lefty proteins are long-range inhibitors of squint-mediated nodal signaling. Current Biology, 12(24), pp.2124-2128.
6. Sako, K., Pradhan, S.J., Barone, V., Ingles-Prieto, A., Müller, P., Ruprecht, V., Čapek, D., Galande, S., Janovjak, H. and Heisenberg, C.P., 2016. Optogenetic control of nodal signaling reveals a temporal pattern of nodal signaling regulating cell fate specification during gastrulation. Cell reports, 16(3), pp.866-877.

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Reply to the reviewers

__Reviewer #1 (Evidence, reproducibility and clarity (Required)):____

Summary: In this manuscript by Berg et al the authors demonstrate that RNA polymerase activity is important for the formation of nuclear blebs. This is an interesting and significant finding because prior work has suggested nuclear bleb formation is a result of changes in nuclear rigidity (lamins) or chromatin (via histone modifications). Overall I thought the manuscript was quite interesting and the data well presented. I think the inclusion of multiple mechanisms of blebbing (VPA treatment, as well as lamin B KO) helps to further support the importance of RNA polymerase/transcription activity in the blebbing process. However, I do have some concerns regarding the conclusions of the data that I think should be addressed as a revision.__

We appreciate that Reviewer states that “the manuscript was quite interesting and the data well presented”, it is a “significant advancement”, and “the first report of this phenomena, and thus will be impactful to the nuclear mechanics field.”

In the points below, the Reviewer specifically suggests that we: 1) clarify possible contributions from RNA pol III, 2) address how global vs. local chromatin motion might contribute to our findings, and 3) discuss the force production capabilities of RNA pol II. We also appreciate the feedback regarding the conclusions and have made the specific changes requested in the revision.

Major Comments:____ 1. One concern I have is that the alpha-amanitin inhibitor has been shown to also inhibit RNA polymerase III. In an old study (1974 Weinmann PNAS) it appears that the inhibitor starting at 1 to 10 ug/ml. In this study the authors are using 10 uM alpha-amanitin, which is ~ 9 ug/ml and within the range of inhibiting some RNA polymerase III. Additionally, the other drug (actinomycin D) is even less specific for RNA polymerase II. I would suggest that the authors consider one of the following approaches 1) acknowledge in the manuscript the potential for RNA polymerase III to be important in the blebbing process 2) try a 10-fold lower dose of alpha-amanitin and see if that also inhibits blebbing, 3) try to find a way to demonstrate that RNA polymerase III activity is not inhibited at the 10 uM alpha-amanitin dosage, or 4) consider an alternate method to perturb RNA polymerase II activity (see Zhang Science Advances 2021 for an auxin-based approach to downregulate RNA polymerase II).

The Reviewer raises the point that alpha-amanitin inhibits both RNA pol II and III. In the revised manuscript, we provide new data to further support that the observed effects arise from RNA pol II. We now include new data from cells treated with the transcription inhibitors flavopiridol (which inhibits RNA pol II elongation) and triptolide (which inhibits RNA pol I and II initiation). These transcription inhibitors also suppress nuclear blebbing in VPA-treated nuclei (Figure 2C) as well as three other nuclear blebbing perturbations in chromatin and lamins (Supplemental Figure 1A). These new experiments directly show that nuclear bleb suppression by transcription inhibitors can be observed without possible inhibition of RNA pol III by alpha-amanitin.

__ A second concern I have is that the inhibition of RNA polymerase is global. Thus it is difficult to know for sure the biophysical function of the polymerase occurs immediately at the bleb, or instead is somehow affecting the overall chromatin state throughout the entire nucleus. I agree that figure 3 does provide some evidence that major mechanical and biophysical properties of the nuclei are not changed in response to the inhibition of the polymerase. However, micromanipulation experiments are done with isolated nuclei, which may be somehow mechanically altered already by isolation from cells. I feel that there still must be given some consideration in the discussion of the possibility that RNA polymerase activity outside of the bleb may be having some role in the stabilization of the chromatin and blebbing propensity.__

We appreciate the Reviewer’s insightful comments and we have revised the manuscript to clarify that we do not attribute blebbing purely to local effects. Instead, we argue that global changes in chromatin motion driven by transcription could contribute to nuclear blebs.

We did not intend to communicate that alterations to chromatin or its dynamics were necessarily only local. Indeed, we found that relative levels in RNAP Ser2 and Ser5 phosphorylation were different inside the blebs (Figure 6). Nonetheless, transcription was perturbed globally in our experiments, so we realized that blebbing could be driven by global changes (Figure 1). We hypothesize that global regulation of transcription can stimulate nuclear blebbing since transcription and its inhibition can, respectively, drive and suppress correlated chromatin motion throughout the entire nucleus (as previously observed by Zidovska et al. (PNAS 2013) and Shaban et al. (NAR 2018, Genome Biol. 2020), among others). We have revised the manuscript to clarify this point (Discussion section, page 15). We have also added new simulation snapshots showing global chromatin motions and how these motions are coupled to nuclear morphology (Figure 7C).

In response to the concern that isolated nuclei exhibit different mechanical properties than nuclei inside of cells, we refer to our previously published micromanipulation measurements (Stephens et al. MBoC 2017). There, we found that nuclei within the cell and outside of the cell have quantitatively similar spring constants and qualitatively similar force-extension curves. Therefore, we are confident that the lack of change in nuclear stiffness measured by micromanipulation accurately reflects the mechanics of nuclei inside of cells across different perturbations.

__ While I lack expertise to evaluate the basis of the model, I appreciate the model can show that motor activity can influence bulge. But it is not clear in the manuscript that RNA polymerase can generate these kinds of forces. The Liu citation is a model, and does not provide direct evidence that the RNA polymerase can generate force, or forces large enough to be meaningful. To me the model in this paper (Figure 7) felt as if it was only a possible hypothesis of why the RNA polymerase has an effect on blebbing, but I imagine there could be other hypotheses that would cause the same effect. The authors state (in the abstract) that RNA pol II can generate active forces, but I am concerned this is not sufficiently established. Since this motor/force activity of RNA polymerase is not experimentally demonstrated in this paper the authors should either do a better job of including evidence of this from the literature or consider removing this part of the manuscript.__

RNA polymerase is capable of exerting forces in excess of 10 pN (e.g., see Wang et al. Science 1998; Herbert et al., Annu Rev Biochem 2008). The collective activity of many motors (10’s of thousands, e.g., see Zhao et al. Proc. Natl. Acad. Sci. 2014) may generate even larger forces. As discussed in our earlier modeling paper, this force scale is consistent with the motor strengths studied in our simulations (Liu et al. Phys. Rev. Lett. 2021); in the present work, we present simulation results for motors that generate 0.14 pN forces. Thus, transcription, in principle, could generate forces even larger than the ones we considered in the model.

Additional experiments indicate that at larger length scales, RNA polymerase activity appears to drive coherent motions of chromatin throughout the cell nucleus (Zidovska et al. PNAS 2013; Shaban et al. NAR 2018; Shaban et al. Genome Biol 2020). It is these motions, driven by motors, that appear to drive the formation of nuclear bulges in our model (please see new panel Figure 7C).

Therefore, the aim of the model is to build on established and new results to better understand how transcription could alter nuclear morphology. Our model is adapted from earlier models, which could reproduce observations of chromatin-based nuclear rigidity, (Stephens et al. MBoC 2017, Banigan et al. Biophys J 2017, Strom et al. eLife 2021), some aspects of nuclear morphology (Banigan et al. Biophys J 2017, Lionetti et al. Biophys J 2020), and possibly explain how nonequilibrium motor activity (such as RNA pol II) can drive coherent chromatin dynamics (Liu et al. PRL 2021), which have been observed in live-cell imaging experiments (e.g., Zidovska et al. PNAS 2013; Shaban et al. NAR 2018; Shaban et al. Genome Biol. 2020, among others). The precise form of the motor activity is not the focus of our model (or the previous motor model in Liu et al. PRL 2021). Instead, our simulation result indicates that the relatively small motor forces that generate coherent chromatin dynamics could explain the surprising observation that transcription is a critical component of nuclear blebbing.

To address the Reviewer’s comment, we have added additional text to the Introduction and the Results sections to support the inclusion of motors to model the possible effects of transcription on chromatin dynamics and nuclear shape.

In the Introduction (page 4), we now write:

Simulations suggest that chromatin connectivity combined with the forces generated by polymerase motor activity (~10 pN per polymerase (Herbert et al. 2008)) could generate these dynamics (Liu et al., 2021).

In the Results section (page 10), we write:

We consider motors that generate sub-pN forces, well below the 10 pN forces that may be generated by individual RNA polymerases (Herbert et al. 2008).

Additionally, we have updated Table 1 to include the simulated motor strength.__ __

__ Minor Comments: 1. Did the authors do any analysis to see if the increased RNA transcription with VPA treatment (Figure 1B) has any spatial relationship to where the bleb occurs? Could an analysis of this be done similar to Figure 6 (with a bleb/body ratio)?__

The Reviewer raises an interesting point about measuring RNA localization relative to the bleb. We measured RNA intensity in the bleb and the nuclear body for wild type cells only. We find that RNA levels are significantly decreased in the bleb (80% of body signal, p

__ Is there anything known about lamin B1 KO cells as to whether or not they have increased transcription? Or could the authors do an analysis like they did with VPA treatment to check this?____ If they were to have increased transcription this would further support the authors' proposed mechanism of transcription itself (or RNA polymerase activity) driving blebbing).__

In the revised manuscript, we show that several nuclear perturbations that are known to decrease nuclear stiffness and cause increased nuclear blebbing also rely on active transcription. Lamin B1 knockout or knockdown cells have been shown to result in changes in transcription. However, it was difficult to find data that shows whether the overall level of transcription changes. Collaborators of ours have unpublished data that indicates that twice as many genes are upregulated as downregulated upon lamin B1 knockdown, but this still does not assess the total level of transcription within the nucleus. Alternatively, increasing transcription via other means is fraught with off-target effects, which would require many additional complementary experiments. We thank the Reviewer for this interesting suggestion, but we believe this is beyond the scope of this manuscript, in which we have focused on showing that transcription inhibition suppresses bleb formation.

__ Figure 1D, the VPA ser2 image appears much brighter than the untreated image. Yet the graph shows they are similar. Perhaps a more representative image should be used?__

The image used reflects the data that Ser2 signal is brighter (by ~10%) in VPA-treated cells but is not significantly altered compared to wild type (unt), and thus it is an accurate reflection of the data.

__ Can the authors comment if there is less DNA at the bleb site? In Figure 6 A this appears to be the case (based on the VPA image). If true, is the alpha-amanitin treatment rescuing this such that there is more DNA at the bleb (maybe causing the bleb to be smaller?).__

We find that there is less DNA signal intensity per unit area in the nuclear bleb as compared to the nuclear body (bleb has ~60% the signal of the body; see teal dots/data in Figure 6B). This agrees with previously published work from our lab (Stephens et al. 2018 MBoC).

Alpha-amanitin treatment does not rescue this effect. Decreased DNA enrichment in the bleb remains with alpha-amanitin treatment (p > 0.05, comparing across all 4 conditions in Figure 6B).

__ What is the significance of bleb vs non-bleb nuclear rupture? Is there anything known in the literature as to how these ruptures may be different in terms of biophysics, impact to DNA, repair? It would be helpful to have some context, as well as to understand if non-bleb rupture is something that may have been previously missed in other contexts.__

The Reviewer asks a valid and interesting question that this manuscript only begins to address. In general, we believe that ruptures occurring with blebs vs. without blebs may reflect aspects of the underlying mechanism(s) of blebbing and rupture, in the presence or absence of transcription. We offer a few further thoughts below.

1) Non-bleb nuclear ruptures have been reported in a few papers by our group (Stephens et al., 2019 MBoC) and others (Chen et al., 2018 PNAS), but much is still unknown.

2) Non-bleb nuclear rupture is part of normal nuclear behavior, as it accounts for ~20% of nuclear ruptures in wild type and perturbed cells (VPA and LMNB1-/-).

3) Overall, we think that bleb-based and non-bleb-based ruptures may occur through different mechanisms. The simplest difference is that bleb-based nuclear ruptures follow the nucleus’ ability to form blebs, whereas non-bleb-based nuclear rupture occurs in cases where there is less bleb formation, suggesting that factors other than the ability to form blebs may also be important for rupture. In the current study, we observed that bleb-based nuclear ruptures (and bleb formation) require transcription. In another manuscript from our lab under review, bleb-based nuclear ruptures (and nuclear blebbing) can be suppressed by actin contraction inhibition and increased by increased actin contraction (Pho et al., biorxiv 2022).

Additionally, we note it was reported that non-bleb-based nuclear ruptures, at least some of which are driven by microtubule prodding, result in increased levels of DNA damage (Earle et al. Nat Mater 2020), as has been observed for bleb-based ruptures (Stephens et al., 2019 MBoC; Xia et al. J Cell Bio 2018). Thus, nuclear rupture in general is thought to lead to DNA damage. However, total levels of DNA damage due to rupture may be controlled by different cellular processes.

In the revision, we have clarified our motivation for quantifying ruptures with and without blebs. We have also added a few remarks, drawn from the above comments, to the Discussion section (pages 11-14).

Reviewer #1 (Significance (Required)):____ General assessment: This study is a careful analysis of how RNA polymerase inhibition reduces nuclear blebbing. The study demonstrates this very well, using a variety of approaches. However, some limitations are the overstatement of some conclusions (specifically that it is RNA polymerase II when the inhibitor may also affect RNA polymerase III; that the RNA polymerase activity is important at the bleb and involves motor activity). Advance: This paper is a significant advancement because it shows the role of transcription in the biophysics of the nuclear shape. To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field. Audience: I think the findings are of broad interest, including beyond the nuclear mechanics field. I think the audience would be the entire cell biology community. Expertise: My expertise is in cell mechanics, including forces at the the nuclear LINC complex. While I do not work in the field of nuclear blebbing and rupture, I follow this field quite closely.

We greatly appreciate the Reviewer’s statement that “To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field.__” __We thank the Reviewer for their thoughtful comments and suggestions, which have helped to improve the manuscript. __

__

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells

Reviewer #2’s greatest concern throughout the review was that we focused on the use of VPA as a model for generating increased nuclear blebbing and 24-hour treatment with alpha-amanitin as a transcription inhibitor. In the revised manuscript, we provide new data to show that nuclear blebbing generated by a variety of different nuclear perturbations (VPA, DZNep, LMNB1-/-, and LA KD Figure 2D __and __Supplemental Figure 1A) is reliant on active transcription in two different cell lines (MEF and HT1080, Figure 2 A and B). This is supported by use of four different transcription inhibition drugs, which work over varying time periods (24 hrs in alpha-amanitin, triptolide, or flavopiridol; actinomycin D for 1.5 hrs Figure 2C). We also timelapse imaged during drug treatment to show that transcription inhibitors for which we used 24-hour incubation times, can suppress nuclear blebs within 8 hours (Supplemental Figure 1B). __We also show that nuclear bleb formation and stability in wild type is transcription dependent (__Figure 5). We believe the new data added in our revised manuscript addresses the concerns of the Reviewer that the findings were specific to VPA and alpha-amanitin together only.

__Reviewer #2 (Significance (Required)):____

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells. __

While somewhat interesting, this is a rather specific condition that is further restricted by the limited use of experimental approaches. For example, the only deacetylase inhibitor used is VPA. Is this because VPA is the only one to trigger the effect? The authors should expand their approach to include additional inhibitors or, preferably, a directed knockdown tactic that targets the specific HDACs driving their phenomena.

The Reviewer is concerned that we have used limited experimental approaches by focusing on VPA treatment to induce nuclear blebs and alpha-amanitin overnight treatment to suppress nuclear blebbing. VPA treatment is a well-established perturbation to induce nuclear blebbing via HDAC inhibition, and it is similar to a variety of other nuclear perturbations that also induce blebs (Stephens et al. MBoC 2018, 2019; Kalinin et al. MBoC 2021; Pho et al. biorxiv 2022).

Nonetheless, to clearly address the Reviewer’s concerns we have provided new data which shows that four different nuclear perturbations are suppressed by transcription inhibition and that four different transcription inhibitors suppress nuclear blebbing. In addition to these perturbations, we also note that transcription inhibition affects bleb formation and stability in wild type cells. Below we outline the diverse experimental approaches that support the major conclusion of our manuscript.

Our data shows that transcription inhibition suppresses nuclear blebbing through data for:

1. Multiple cell lines (MEF and HT1080, Figure 2, A and B) – original data
2. Multiple transcription inhibitors (Figure 2C __and Supplemental Figure 1__):
3. Alpha-amanitin (RNA pol II and III degradation) – original data
4. Triptolide (RNA pol I and II initiation inhibition) – new data
5. Flavopiridol (RNA pol II elongation inhibition) – new data

6. Actinomycin D (DNA intercalation) – original data

7. Multiple perturbations that cause nuclear blebbing (Figure 2D ____and Supplemental Figure 1):

8. VPA histone deacetylase inhibitor, which increases euchromatin and chromatin decompaction; used because it is the most highly studied treatment by our lab (Stephens et al., 2017, 2018, 2019 MBoC; Pho et al., 2022 biorxiv) – original data
9. DZNep histone methyltransferase inhibitor, which decreases heterochromatin and chromatin decompaction (Stephens et al., 2018, 2019 MBoC) – new data
10. Lamin B1 null cells (LMNB1-/- or LB1-/-) (many previous works, including Stephens et al. MBoC 2018) – original data

11. Lamin A constitutive knockdown cells (LA KD) (Vahabikashi et al., 2022 PNAS) – new data

12. Nuclear bleb formation and stabilization in wild type cells is dependent on transcription in addition to VPA (Figure 5). – original data

13. Time dependence of suppression of nuclear blebbing requested by Reviewers 2 & 3:
14. Actinomycin D treatment of 1.5 hrs is sufficient to suppress nuclear blebs (Figure 2C) – original data
15. Transcription inhibition with alpha-amanitin, triptolide, and flavopiridol all show an increased rate of nuclear bleb reabsorption in the first 8 hrs of treatment for both VPA and LMNB1-/- perturbations (Supplemental Figure 1B) – new data.
16. This new data indicates that even formed blebs require active transcription to remain blebbed for long times
17. This new data also shows that the effect of transcription inhibition on nuclear blebbing does not require 24 hours of treatment.

__Moreover, the authors imply that VPA works through histone deacetylation yet do not provide direct evidence. It is equally likely that the application of VPA alters the acetylation pattern of a non-histone protein that eventually alters nuclear blebbing. __

The Reviewer questions whether histone deacetylation due to VPA treatment is responsible for nuclear blebbing. As the Reviewer notes in their next point below, histone deacetylation (e.g., by VPA or TSA treatment) as a mechanism for nuclear blebbing was previously established by work from our lab (Stephens et al., 2018 and 2019 MBoC) and others (Kalinin et al. MBoC 2021). This was described and referenced in the original manuscript’s introduction.

To summarize previous work, inhibition of histone deacetylation by VPA induces chromatin decompaction (Stypula-Cyrus et al. PLoS One 2013, Lleres et al. J Cell Bio 2009), increasing histone acetylation/euchromatin (Göttlicher et al. EMBO J 2001; Krämer et al. EMBO J 2003). In turn, this softens the nucleus (Stephens et al. MBoC 2017; Shimamoto et al. MBoC 2017), which succumbs to nuclear blebbing (Stephens et al., MBoC 2018). Softening and blebbing effect can also be induced by histone hyperacetylation via TSA or histone demethylation via DZNep (Stephens et al., MBoC 2018). This effect can be reversed by chromatin compaction via increased histone methylation/heterochromatin formation (Stephens et al. MBoC 2019).

In the present work, we measured histone acetylation (H3K9ac) in both VPA and VPA+alpha-amanitin perturbations to ensure that alpha-amanitin does not simply reverse the increase in VPA-based histone acetylation and thereby decrease nuclear blebbing, which it does not (Figure 3, A and B).

Altogether, inhibition of histone deacetylation by VPA as a mechanism for nuclear blebbing is established by the previous literature. The present work builds on those results to uncover a surprising new driver of nuclear blebbing which is transcirption. Therefore, we consider it to be unnecessary to provide further confirmatory measurements of VPA-treated cells beyond what is already provided in the manuscript. Finally, we point to the inclusion of new data from three other nuclear perturbations that cause nuclear blebbing that can be suppressed by transcription inhibition (Figure 2).

__Regardless, the reported findings with VPA were previously reported (Stephens et al. 2018) and the influence of alpha amanitin only represents an incremental advancement in our understanding of nuclear blebs. __

The finding that alpha-amanitin inhibits nuclear blebbing implies that a previously unknown mechanism/pathway, involving an essential genomic process, is critical to nuclear shape regulation. We therefore strongly disagree with the Reviewer that bleb inhibition upon alpha-amanitin treatment represents an incremental advance.

Moreover, the existing literature generally argues that nuclear blebbing is caused by actin-based compression and confinement. It is widely believed that the cytoskeleton deforms the nucleus, which can herniate a nuclear bleb in softer nuclei. Here, we show that with transcription inhibition there are no overt changes to actin contraction (Supplemental Figure 2), actin confinement (Figure 3E), and nuclear mechanics (Figure 3G). However, levels of blebbing change anyway! This will be a new and surprising result to those who believe the current prevailing narrative from the literature. We have now shown for the first time that transcription is also needed to form and stabilize nuclear blebs; to our knowledge, this was almost entirely unknown until now.

Further supporting our belief in the significance of our findings, Reviewer #1 and Reviewer #3 clearly state that our work is novel and important:

Reviewer #1 “To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field.”

Reviewer #3 “This is an interesting study that shows, for the first time, that inhibition of transcription reduces the occurrence of nuclear blebs in cells that have been pre-treated with valproic acid.”

To address the Reviewer’s concern, we have revised the manuscript to clarify that active transcription is required to form nuclear blebs across all of the perturbations now presented in this manuscript. Furthermore, we have clarified that transcription inhibition appears to suppress blebbing without altering other cellular components and properties (actin, nuclear stiffness) that are widely believed to control blebbing (see Results page 7, Results page 10, Discussion page 14).

Adding to the concern is that actinomycin D does not have the same level of influence as alpha amanitin (Figure 2), which suggests the alpha amanitin is having a pleotropic impact on blebbing. To validate that the changes in blebbing in the presence of VPA are dependent upon active transcription, the authors should use the anchor-away technique to remove RNAP from the nucleus thereby avoiding any indirect effects of the drugs (i.e., alpha amanitin) in use. Further adding concern that it is an indirect outcome is the prolonged incubation period (16-24 hours) that is apparently needed to observe the changes (page 5 paragraph 4). If it is active transcription that is causing the change in blebbing, then this should be apparent in a much shorter time frame (The Reviewer is worried about possible differences between transcription inhibitors actinomycin D and alpha amanitin. To further address these concerns in the revised manuscript, we now present new data for VPA without transcription inhibitor and VPA with transcription inhibition vy four different transcription inhibitors (__Figure 2C). Inhibitors include alpha-amanitin (RNA pol II degradation), triptolide (transcription initiation inhibition), flavopiridol (transcription elongation inhibition), and actinomycin D (DNA intercalation). All VPA plus transcription inhibitor treatments result in a significant decrease in nuclear blebbing relative to VPA treatment alone (p (p > 0.05, Figure 2C). Thus, there is no significant difference in the degree of nuclear blebbing suppression between the four different transcription inhibitors used.

Furthermore, the Reviewer raises concerns about the time interval from the start of transcription inhibitor treatment to suppression of nuclear blebbing. We agree that considering this time interval is valuable. However, we need to consider that the time interval for each of the different transcription inhibitors to take effect is different (Bensaude 2011 Transcription). Alpha-amanitin inhibits transcription in 4-8 hours (10 µM, Nguyen et al., 1996 NAR), triptolide (1 µM, Chen et al. 2014 Genes Dev) and flavopiridol (0.5 µM, Chen et al., 2005 Blood) work in 2-4 hours, and actinomycin D works in about 1 hour (10 mg/mL, Lai et al. 2019 Methods). These times are now mentioned in the manuscript (Figure 2 legend and Methods section).

It was not, however, known in advance how long it would take for transcription inhibition to have an effect on nuclear morphology. Therefore, the time to observe bleb suppression could have been longer than these treatment durations. As mentioned above, treatment with actinomycin D for 1.5 hours results in a similar decrease in nuclear blebbing as compared to the other inhibitors with 24-hour treatment (Figure 2C). To further address these concerns, we provide new data in the revised manuscript showing tracking of nuclear bleb reabsorption during the first 8 hours of treatment with alpha amanitin, triptolide, and flavopiridol via live cell imaging. Nuclear bleb reabsorption for both VPA and LMNB1-/- perturbations goes from ~5 % to 30% or greater during the first 8 hours of treatment with each of the transcription inhibitors (Supplemental Figure 1B), consistent with the time required to fully inhibit transcription. This supports our conclusion that transcription is essential to stabilizing nuclear blebs.

__In addition to these issues, the authors rely on immunofluorescence signals to measure the levels of various factors including the Ser5 and Ser2 phosphorylation, which is capturing the total levels of these factors and not the DNA bound forms. If the changes in blebbing actually involve transcription initiation, then the authors should include measurements on the DNA-bound factors. __

We are measuring Ser5 and Ser2 phosphorylation of RNA polymerase to track the actively DNA transcribing population. These markers appear on DNA-bound RNAP. Ser5 and Ser7 of RNAP are phosphorylated during initiation, and subsequently dephosphorylated during transcription elongation, while Ser2 is added at that time (Hsin and Manley 2012 Genes Dev). Ser2 is removed at transcription termination. Therefore, we expect immunofluorescence to measure DNA-bound RNAP.

__As reported the authors conclude that there is no changes in Ser2 and Ser5 phosphorylation yet they report that total RNA levels rise (Figure 1). How is the disconnect between RNA levels and Ser2 and Ser5 phosphorylation occurring? __

The Reviewer raises a question about how VPA treatment increases RNA levels but not levels of active RNA pol Ser2 and Ser5. While this is an interesting question, without a dedicated investigation, we can only speculate, at best; this question is beyond the scope of the paper focused on how transcription inhibition suppresses nuclear blebbing. The point of this data is to show that treatment with alpha-amanitin alone and along with VPA causes decreases in both RNA and RNA pol II Ser2 and 5 confirming transcription inhibition.

__Comparably, they use H3K9ac immunofluorescence as a measure of euchromatin. While the authors might be gaining a view on the total levels of H3K9ac under these experimental conditions, it is not clear whether this is DNA associated or not. Minimally, the authors should perform ATAC-Seq to judge the changes in euchromatin. __

The Reviewer questions the use of H3K9ac immunofluorescence as measurement of euchromatin levels, particularly in VPA-treated cells. The relationship between VPA and chromatin decompaction / euchromatin levels has been previously established (e.g., Stypula-Cyrus et al. PLoS One 2013, Felisbino et al. J Cell Biochem 2014, Lleres et al. J Cell Bio 2009). New data in Figure 3B shows that heterochromatin marker H3K9me2,3 also is not altered by alpha-amanitin treatment. In the case VPA + alpha-amanitin treatment, micromanipulation and nuclear height measurements provide further evidence that chromatin decompaction remains, since chromatin-based force response is unchanged from VPA treatment alone (Figure 3, E and G).

Again, we note that our manuscript focuses on the effects of transcription on nuclear blebbing and rupture, which were not previously reported and differ from the current understanding in the literature. Furthermore, ATAC-seq is a major undertaking that is simply not appropriate for further proving an auxiliary point about a previously established effect.

In summary, the original manuscript addresses this point. The specific experiment requested by the Reviewer is not necessary and is far beyond the scope of this study.

A final major concern is the lack of a correlation between the blebbing and nuclear ruptures (page 7 paragraph 3; Figure 4). If ruptures are not correlating with the blebbing, what is the relevance of the blebbing?

The Reviewer is asking for a clarification of the importance of nuclear blebbing in relation to nuclear ruptures. We have revised the manuscript to add new text to the Figure 4 legend clarifying the measurements and to the Discussion section describing the importance of this data (Discussion pages 12-13 and page 14). We discuss this in more detail below.

We would like to clarify that blebbing and nuclear rupture are not uncorrelated, as suggested by the Reviewer. We and others have shown that nuclear blebs are sites of high curvature that result in nuclear ruptures. In the present manuscript, timelapse imaging of nuclear bleb formation has been observed to result in nuclear rupture within minutes in all imaged cases (Figure 5). This data in the manuscript agrees with previous published data from our lab of bleb formation to rupture in >95% of the time (Stephens et al., 2019 MBoC). Furthermore, stabilized nuclear blebs persist for hours (Supplemental Figure 1B) and undergo more rupture, as shown in Figure 4D. Therefore, ruptures remain correlated with nuclear blebs in our study.

What we have shown, however, is that the percentage of cells that undergo at least one nuclear rupture during the time lapse is not statistically significantly decreased from VPA-treated levels by the addition of alpha-amanitin (Figure 4B). This appears to be due to two factors: 1) a basal level of nuclear rupture (see wild type data in Figure 4) and 2) an increase in the level of non-bleb-based nuclear rupture. However, importantly, non-bleb-based ruptures appear to occur less frequently for cells that undergo nuclear ruptures. Of the cells that exhibit nuclear rupture, those with non-bleb-based ruptures on average undergo only a single rupture over a 3-hour timelapse whereas those undergoing bleb-based rupture undergo an average of > 2 ruptures over the same time (Figure 4D).

Altogether, these data point to a correlation between blebbing and rupture, where blebbing can promote nuclear rupture, but is not essential for rupture. Therefore, observations of blebs are important in that they correspond to increases in nuclear rupture and corresponding nuclear dysfunction, such as DNA damage. The observation of non-bleb-based rupture, while not entirely a new (Chen et al. PNAS 2018, Stephens et al. MBoC 2019, Pho et al. bioRxiv 2022), is interesting because it may be driven by a different mechanism; transcription is not essential for nuclear ruptures in the absence of nuclear blebs but promotes rupture in the presence of blebs. These results add to our knowledge of the factors regulating nuclear integrity and shape, and we anticipate that they will be further investigated in future studies.

Finally, beyond these findings, we speculate that blebbing itself may be harmful to cell nuclear function. Previous studies have observed that nuclear deformations can cause DNA damage (Shah et al. Curr Biol 2021), chromatin reorganization (Jacobson et al. BMC Biol 2018, Golloshi et al. EMBO J 2022), and alterations to mechanotransduction (reviewed in Kalukula et al. Nat Rev Mol Cell Biol 2022). The extent to which the changes associated with these “nuclear deformations” require blebbing, rupture, or both is under investigation by various labs. Furthermore, previous studies (Shimi et al. Genes Dev 2008; Pfleghaar et al. Nucleus 2015) along with the present study (RNA Pol Ser2 and Ser5; Figure 6) have shown that chromatin content and, possibly, functionality is different within the nuclear bleb. Data in another manuscript in preparation from our lab, further suggests that there is limited exchange of biomolecular content between the nuclear body and bleb. Therefore, while we cannot conclusively claim that blebs are themselves deleterious to function, there is a growing body of suggestive evidence that this is the case.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

This is an interesting study that shows, for the first time, that inhibition of transcription reduces the occurrence of nuclear blebs in cells that have been pre-treated with valproic acid. The data that supports this is in Figure 2, collected in two different cell types (MEFs and HT1080 cells). The effect appears robust. New data is also provided that a marker of initiation of transcription but not transcriptional elongation is enriched in valproic acid-induced blebs.

We thank Reviewer #3 for positive comments that our study is “interesting”, “reproducible”, and data that shows the effect of transcription on nuclear blebbing “for the first time”.

This Reviewer asks for clarifications on 1) how transcription is a new mechanism for nuclear bleb formation and not part of the traditional view, 2) the generality of our conclusions (similar to Reviewer #2) since we report “on the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin”, and 3) the insight the modeling provides. We have provided new data and made changes to the manuscript to address all the Reviewer’s comments.

__ Major comments

1. The paper makes general claims about transcription and nuclear shape, when in reality, it is only reporting on the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin. This scenario under which the experiments were performed, for which there is no obvious physiological counterpart, ought not to be construed to challenge or contrast with the current understanding that the nucleus maintains its shape by resisting cytoskeletal forces. Cytoskeletal forces are well-known to establish nuclear shape; nuclear shape in this context, is generally taken to refer to the gross shape of the nucleus (e.g. elliptical, circular, etc.), and not small local blebs that may form due to F-actin based confinement or other mechanisms. Thus, this interpretation is overstated:

"Surprisingly, we find that while nuclear stiffness largely controls nuclear rupture, it is not the sole determinant of nuclear shape. This contrasts with previous studies, which suggested that the nucleus maintains its shape by resisting cytoskeletal and/or other external antagonistic forces (Khatau et al., 2009; Le Berre et al., 2012; Hatch and Hetzer, 2016; Stephens et al., 2018; Earle 12 et al., 2020)."

__

The Reviewer appears to be concerned with two issues in this comment. First, the Reviewer is concerned about our use of the word shape, which could be interpreted too generally, rather than as categorizing the blebbing and rupture phenomena that we observe in this study. We appreciate the Reviewer’s feedback and have made changes to this sentence as well as the paper in general to clarify that we are focused on nuclear blebs. Second, there is the issue of to what degree our results modify our understanding of the role of nuclear stiffness in nuclear blebbing and rupture. We discuss this below.

To address the Reviewer’s comment that the results are limited to “the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin” we provide new data and context for our results. The revised manuscript includes 1) new data using four transcription inhibitors and four nuclear blebbing perturbations and 2) original data showing that nuclear blebs are persistent rather than small and transient, and they alter gross nuclear shape. Our results are relevant to a wider range of blebbing/rupture and bleb/rupture suppression scenarios, as exemplified by the different nuclear perturbations, transcription inhibitors, cell types tested in our experiments, and long lifetimes for nuclear blebs. More specifically:

1) The Reviewer notes that our original studies were done with VPA and alpha-amanitin, similar to Reviewer #2 concerns. We provide new data to now show that 4 different transcription inhibitors can suppress nuclear blebbing across 2 chromatin and 2 lamin perturbations (Figure 2 and Supplemental Figure 1). Thus, our new data supports the idea that transcription is broadly required for nuclear blebbing.

2) The Reviewer states that blebs are small and transient, and that “shape” is meant to reflect the gross shape (e.g., circular). In fact blebs are long-lived as we show with new data that most (>95%) of VPA and LMNB1-/- blebs, remain at the end of an 8-hour timelapse (Supplemental Figure 1B). Furthermore, on average, nuclear blebs account for 15% of the nuclear size in VPA-treated cells (Figure 6E). While not measured in this paper, many studies have shown that nuclear blebs cause gross circularity to decrease significantly and that changes in circularity are associated with nuclear rupture (e.g., Stephens et al. MBoC 2018, Xia et al. JCB 2018). Most recently, we show nuclear blebs decreased nuclear circularity significantly in another manuscript under review (Pho et al., 2022 biorxiv).

The Reviewer also argues that our data showing the importance of transcription in nuclear blebbing “ought not to be construed to challenge or contrast with the current understanding that the nucleus maintains its shape by resisting cytoskeletal forces.” We acknowledge that our results are not sufficient to rule out the broad assertion made by the Reviewer. However, our data shows for the first time that nuclear blebbing relies on transcriptional activity, while we measure no change in actin contraction or confinement or nuclear stiffness (respectively, Supplemental Figure 2 and Figure 3, C-E). Consequently, these results are a challenge to the current understanding, which must be updated by our results and future experiments. At the same time, we note that this manuscript’s Discussion section acknowledges that we have data in another preprint in which inhibition of actin contraction decreases nuclear blebbing to near 0% in wild type and perturbations (Pho et al., 2022 biorxiv). Together, these observations suggest a complicated picture in which multiple factors are jointly responsible for regulating nuclear blebbing and rupture.

__ As an aside, the data in the paper does not appear to support the interpretation that "nuclear stiffness largely controls nuclear rupture". It is unclear what the authors mean by this statement.__

We originally intended that comment to state the previous understanding in the literature, but we realize it was unclear. We appreciate the Reviewer’s feedback and have revised the text.__ __

__ 2. Further to point 2, treatment with alpha-amanitin does nothing to the occurrence of blebbing in normal cells. Thus, the data are specifically applicable to valproic acid-treated cells. As such, the broad interpretations related to nuclear shape and mechanics should be tempered.__

The Reviewer is concerned that we cannot support the claim that this effect is broad and general; these concerns are also raised by Reviewer #2. We have provided new data and highlight original data to support that this effect is in fact broad and general, and moreover, that the data supports a role for transcription in nuclear blebbing.

We specifically address the Reviewer’s statement: “treatment with alpha-amanitin does nothing to the occurrence of blebbing in normal cells”. In the original manuscript, we provided data that showed that wild type nuclear bleb formation and stability are suppressed upon transcription inhibition (Figure 5) even though the percentage of wild type nuclei exhibiting a bleb is not changed by alpha-amanitin treatment (Figure 2). We also provided data showing that the predominant type of nuclear rupture changes with alpha-amanitin treatment, including in wild type cells (blebbed vs. not, Figure 4C). Thus, while the effects of transcription inhibition are most easily visible in VPA-treated cells, they are also present in wild type cells in how blebs are formed and stabilized (Figure 5). We have revised the manuscript to better highlight this important point.

In addition, we again emphasize that our results extend beyond VPA-induced blebs. Our revised manuscript now includes new data of 4 different perturbations (to chromatin histone modifications and lamins A and B) that induce nuclear blebs, which can be suppressed by 4 different transcription inhibitors (Figure 2 and Supplemental Figure 1). As previously noted by both Reviewers 1 and 3, this effect is reproducible in different cell lines. This new data directly addresses the concern that the effect is only applicable to VPA and alpha amanitin.

Nonetheless, we agree with the Reviewer that we cannot support broader claims that nuclear mechanical properties are unaltered by transcription inhibitors across all scenarios, as we only measured this change in VPA-treated cells. Micromanipulation force experiments are detailed and time consuming, making it difficult to include data for multiple perturbations. We chose VPA because we have the most measurements of this perturbation which have remained consistent over the life of micromanipulation force measurements. Therefore, we have revised our statements on nuclear mechanics in the revised manuscript (page 14).

__ T____he motor model for RNA pol II activity assumes that the motor 'repels' nearby chromatin units. It is not clear how this is related to the mechanism of motor action of RNA pol II on chromatin during transcription.__

The point of the model is not to precisely reproduce the manner in which transcribing RNA pol II exerts forces on the chromatin fiber. Instead, we have developed a coarse-grained model to study how the collective activity of molecular motors might drive chromatin dynamics and consequently, changes in nuclear shape, either global or local.

The model itself is based on our earlier models, which were used to recapitulate and understand how changes to chromatin mechanical properties governed nuclear rigidity (Stephens et al. MBoC 2017, Banigan et al. Biophys J 2017, Strom et al. eLife 2021; also see a similar model by Lionetti et al. Biophys J 2020) and how nonequilibrium activity due to molecular motors, such as RNA pol II, can drive coherent chromatin dynamics (Liu et al. PRL 2021), which have been observed in live-cell imaging experiments (e.g., Zidovska et al. PNAS 2013; Shaban et al. NAR 2018; Shaban et al. Genome Biol. 2020, among others). The current model therefore explores how the newly observed connection between transcription and nuclear blebbing could be explained by known phenomena.

We note that the "repelling” motors used to model RNA pol II activity in the present work are in many ways qualitatively similar to the dipolar “extensile” motors used by other researchers to model motor-driven chromatin dynamics (e.g., see Saintillan et al. PNAS 2018). More generally, study of “active matter” over the last 20-30 years (and statistical physics over the last century) has shown that precise details of active molecular agents are often unimportant to the larger-scale behavior of the system (e.g., see Marchetti et al. Rev Mod Phys 2013). Thus, we view the repulsive motors as modeling the effective behavior of many RNA pol II within a sub-micron region of chromatin. Better establishing the differences between different choices of motor activities is the subject of a modeling paper in preparation.

To address the Reviewer’s concern, we have more clearly stated the scientific foundations of the model, and we have revised our description of the model to clarify that we do not intend to model the behavior of individual RNA pol II by individual repulsive motors (see Results section, page 10).

__The motor model also does not seem to add conclusive insight to the manuscript, as the nuclear shapes predicted are not directly comparable to the experimental shapes which are flat and smooth with only an occasional, single, local bleb. __

The Reviewer raises two related points with this comment: that bulges and blebs are not directly comparable, and therefore, that the model “does not seem to add conclusive insight to the manuscript.”

We agree with the Reviewer that bulges in the simulations are not blebs as they are observed in the experiments. However, it seems likely to us that bulges are necessary precursors to bleb formation; it is difficult to envision how a large local nuclear protrusion could form without first bulging outward from the nuclear body. Furthermore, we disagree with the assertion that nuclei are generally flat and smooth, as qualitative and quantitative analysis of imaging data reveals that nuclei exhibit shape fluctuations and irregularities across multiple scales (see, for example, Chu et al. PNAS 2017, Patteson et al. JCB 2019, Stephens et al. MBoC 2019, Liu et al. PRL 2021).

Nonetheless, the observation of bulges but not blebs is a shortcoming of the simulation model. We believe this shortcoming reflects a tradeoff made in developing this model; we chose to develop and study a model with relative simplicity compared to a real cell nucleus. A more complicated model might better capture some aspects of nuclear blebbing at the expense of additional complexity. For example, the current model does not allow lamin-lamin or chromatin-lamin bonds to rupture, either stochastically or due to high forces. This effect, which is likely present in vivo, might be necessary for generating more bleb-like structures in simulations. Developing and refining such a model is an active pursuit within our collaboration, but for the moment, it is beyond the present purpose of the model.

Instead, the purpose of the model is to determine whether the observed effect of transcription inhibition on nuclear blebbing / localized shape deformations can be understood through known biophysical phenomena. Established models – to the extent that they exist – were insufficient because they typically relied on nuclear mechanics, which our experiments provide data that transcription is not changing nuclear mechanical rigidity. The current model demonstrates how motor activity within chromatin can alter the structure and dynamics of the lamina. The simulations are certainly not proof that transcription affects nuclear blebbing through the proposed mechanism. However, they are a first-of-their kind demonstration of how nonequilibrium biophysical activity (such as that generated by transcription) within a biopolymer system (chromatin) can emergently alter the geometry of the confining boundary (the lamina). This new result provides a plausible interpretation for the experiments in the manuscript.

In the revised manuscript, we have clarified our modeling approach and objectives in the Results and Discussion sections, and we have more clearly identified and discussed the limitations of the model (Results pages 10-11, Discussion page 15).

The model offers 'proof of principle', but is not capable of ruling out alternative mechanisms (such as nuclear pressurization by confinement, chromatin decompaction, or changes to osmotic pressure). It may be more appropriate to include the model in the discussion as opposed to presenting it as a new result that can be reliably interpreted through comparisons with experiment.

We respectfully disagree with the suggestion to include the model in the Discussion section instead of the Results. As discussed above, the model is new biophysics research and the simulations produced new scientific results, even if the overall interpretation remains open.

However, we have some thoughts about the alternatives suggested by the Reviewer. This is discussed in detail below, but briefly: experimental data, rather than the model itself, suggests that the alternative mechanisms mentioned by the Reviewer do not explain the effects of transcription. After treatment with alpha-amanitin, we do not observe changes to actin-based confinement or contraction (Figure 3E, Supplemental Figure 2), and there are no changes to chromatin histone modifications or nuclear rigidity (Figure 3). We also are skeptical of osmotic pressure arguments since 1) fluid, ions, and small biomolecules should freely flow through nuclear pores to maintain osmotic pressure balance between the nucleus and the cytoplasm, especially on hours-long time scales, and 2) increasing the osmotic pressure by fragmenting chromatin has previously been observed to have either no effect or a suppressive effect on nuclear stiffness (Stephens et al. MBoC 2017, Belaghzal et al. Nat Genet 2021), which would potentially increase blebbing (the opposite of the effect suggested by the Reviewer). We have addressed this further in the revised Results section (page 10) and below.__ __

__ 4. The data in the paper is not strong enough to rule out the more conventional mechanism of nuclear pressurization, which could be caused by F-actin based confinement or chromatin decompaction, or changes to osmotic pressure. Immunostaining of myosin is not a reliable way to compare myosin activity across conditions. It is possible that the long treatment with alpha-amanitin (unto 24 h, Fig. 2) relieves the pressure in the nucleus without measurable changes in the already established cell shape and hence the nuclear shape (height changes in spread cells are small at best -- valproic acid appears to reduce height by ~0.5 microns in Figure 3E which is smaller than the optical resolution along the z-axis of a typical confocal microscope).__

The Reviewer has proposed several alternative mechanisms and questioned the use of immunostaining and nuclear height measurements in the manuscript. We address each of these below.

Specifically, the Reviewer is concerned that we cannot rule out the more conventionally believed mechanisms of 1) actin confinement, 2) actin contraction 3) chromatin decompaction and/or 4) osmotic pressure. We have revised the text to clarify that our data and data from others strongly supports that these four “conventional” mechanisms are not responsible for transcription inhibition-based nuclear blebbing suppression (revisions on pages 7, 10, 14).

1) Actin confinement, as measured by nuclear height does not change upon transcription inhibition (Figure 3, C-E). Thus, our data supports the idea that transcription inhibition suppresses nuclear blebbing through a different mechanism. The Reviewer objects to this measurement on the basis that even the 0.5 µm change observed for VPA-treated cells is below optical resolution. However, optical resolution is not relevant to this measurement because we are not resolving two objects; rather, we are measuring the size of one object, the nucleus.

When two dots/objects are separated in the same frame or in different z slices, one needs to clearly distinguish two gaussians point spreads from the two objects a distance X apart. That is resolution and that is not the relevant limitation here. We measure the size of one object (the nucleus) using full-width half-maximum, which can quantify changes in nuclear height at scales finer than the optical resolution. For example, the FWHM of a fluorescence bead can be observed to change by just 10’s of nm depending on the light emitted; with small wavelengths, one has smaller FWHM (from the Rayleigh criterion, θ = 1.22λ/D, where λ is the wavelength of the light). Our measurements are through a z-stack at 200 nm steps, thus the change in distance from wild type to VPA-treated of 0.5 µm is 2.5 z steps (not smaller than one z step). Finally, we have additional data showing our ability to measure these differences many times over (Pho et al. 2022 biorxiv).

Image left is from: https://en.wikipedia.org/wiki/Full_width_at_half_maximum

Image right is a crop of Figure 3D from the manuscript.

2) Actin contraction, as measured by γMLC2, does not change either (Supplemental Figure 2). However, we know that actin contraction is a major determinant of nuclear blebbing (Mistriotis et al., 2019 JCB and Pho et al., 2022 biorxiv). Therefore, our data support that transcription affects blebbing in some other way than actin contraction.

The Reviewer disputes this finding by stating that “immunostaining of myosin is not a reliable way to compare myosin activity across conditions.” Published reports show that γMLC2 immunostaining is a reliable way to measure actin contractility changes (Wan et al. MBoC 2012; Ramachandran et al. Mol Vision 2011; Duan et al. Cell Cycle 2016; Nishimura et al. PLOS One 2020). We have another preprint showing that alterations to actin contraction as measured by immunostaining of phosphorylated myosin light chain 2 (γMLC2) determine nuclear blebbing, independent of changes in actin confinement (Pho et al., 2022 biorxiv). There, we clearly show that changes in γMLC2 immunostaining can measure changes in actin contraction due to well-established modulators. Similarly, the ROCK inhibitor Y27632 in Supplemental Figure 2 can be viewed as a positive control in that γMLC2 immunostaining is clearly decreased after treatment with the inhibitor.

3) Chromatin decompaction via H3K9ac and chromatin-based nuclear rigidity are not rescued by transcription inhibition. New data also shows that levels of heterochromatin H3K9me2,3 does not change upon transcription inhibition (Figure 3B). The new data presented in this manuscript shows that transcription inhibition also suppresses blebbing in DZNep-treated cells (Figure 2D), where chromatin compaction by heterochromatin formation is inhibited (Stephens et al. MBoC 2019). Together, these experiments suggest that transcription inhibition is not suppressing nuclear blebs through increases in heterochromatin-based chromatin compaction.

Furthermore, the lack of change in the measurement of nuclear stiffness via micromanipulation (Figure 3G) provides a complementary metric suggesting that chromatin compaction is unchanged, at least in the case of VPA + alpha-amanitin.

Altogether, these results are inconsistent with transcription inhibition suppressing blebs through alterations to chromatin compaction.

4) Osmotic pressure is the least or not at all established of the four “traditional” mechanisms. The Reviewer proposes that transcription inhibitors, such as alpha-amanitin, could relieve osmotic pressure within the nucleus. We disagree with this explanation in that it is implausible for the nucleus to maintain an osmotic pressure imbalance in VPA-treated cells over long periods of time. Fluid, ions, and small biomolecules likely can flow through nuclear pores to maintain osmotic balance between the nucleoplasm and cytoplasm, especially over the hours long duration of VPA treatment. Furthermore, we are skeptical that VPA treatment, even with its chromatin-decompacting effects, significantly increases osmotic pressure because nuclear stiffness actually decreases after VPA treatment (Stephens et al. MBoC 2017, 2018, 2019; Krause et al. Phys Bio 2013; Shimamoto et al. MBoC 2017; Hobson et al. MBoC 2020) . Increased osmotic pressure should cause the nucleus to be stiffer. Moreover, nuclei in VPA-treated cells consistently undergo blebbing and rupture, which would naturally relieve any pressure imbalance. Thus, the notion that the measurements after hours VPA or VPA+aam treatment (Figures 2-5) are the result of a steady-state change in osmotic pressure is simply inconsistent with the experimental data.

We note that in cases of acute osmotic shock, where the osmotic pressure balance of the nucleus may be altered, the nucleus changes in size (e.g., see Finan et al., 2009 Ann Biomed Eng), which we do not observe in our experiments. Our measurements of nuclear area (Figure 6C) and height (Figure 3E) show no change nuclear size upon transcription inhibition (for more on the issue of height measurement, see the previous point).

To further address concerns about overnight treatment causing off-target effects, we have provided new data from a shorter treatment duration in the manuscript. The new data shows that within 8 hours, blebs exhibit more reabsorption after alpha-amanitin, triptolide, and flavopiridol treatment in both VPA-treated and LMNB1-/- cells (Supplemental Figure 1B). Additionally, we note that actinomycin D decreased nuclear blebbing in 1.5 hours, and thus did not require overnight treatment.

In summary, our original and new data clearly show that transcription contributes to nuclear blebbing. Transcription inhibition does not change other factors (such as actin-based confinement or contraction, changes in chromatin compaction, or osmotic pressure), that have been shown or may be thought to contribute to nuclear blebbing. The revised manuscript addresses this issue through the inclusion of new data, as discussed above.

__

Further to point 4, the data in Figure 4B and 4D both show a decrease in the mean of the % of ruptured nuclei and rupture frequency (please provide units for this frequency on the Y-axis). With more experiments, perhaps the data would have reached statistical significance?__

The Reviewer is asking for clarification on the data included in Figure 4 B and D reporting the percentage of cells that display a nuclear rupture.

We have revised the manuscript to clarify that Figure 4B is the percentage of all nuclei that show at least one nuclear rupture. The measurement unit, percent (listed as “[%]”), is shown on the y-axis. The revised manuscript also clarifies that Figure 4D reports, for the nuclei that rupture, the average number of times a nucleus ruptures during the 3-hour time-lapse.

The Reviewer stats that “with more experiments, perhaps the data would reach statistical significance?” To address this comment, we have altered the text to explain that % of all nuclei that rupture at least once does not significantly decrease by t-test but does show a non-statistically significant decrease. The data in Figure 4B shows that VPA causes 18.5 +/- 2.7 % rupture and VPA+alpha-amanitin causes 12.4 +/- 1.5 % rupture. Student’s t-test is p = 0.08 which is not statistically significant (p > 0.05) for six biological replicates each consists of n = 100-300 cells. We feel the data speaks for itself without us doing more experiments with the sole purpose of getting a lower p value. The stronger data is in Figure 4D, which clearly shows less nuclear ruptures per nucleus. We appreciate the Reviewer’s perspective and have modified the text in the Results and Discussion sections to reflect these important points (pages 8 and 14). __ __

__ Minor comments.

1. Confirmatory data, which has already been published in the same cell line in the past, could be moved if possible to supplemental information. Figure 1 seems to be a characterization of the efficacy of alpha-amanitin which is well-known, and therefore does not represent an original finding. It should perhaps be in supplemental information.__

We understand the Reviewer’s point but would like to leave Figure 1 as a main text figure to provide a clearer story for all readers of our manuscript.__ __

__ 2. Did the counting method used to collect data in Figure 4B exclude nuclei that rupture multiple times? This should be specified in the manuscript.__

No, Figure 4B is the percentage of nuclei that rupture, which includes nuclei that rupture any number of times as a single nucleus that ruptures. We have revised the Figure 4 legend to clarify this point. __ __

__ 3. This statement should be rephrased: "Since transcription is needed to form and stabilize nuclear blebs, at least some aspect of nuclear shape deformations appears to be non-mechanical" - deformation in the model in Figure 7 is clearly 'mechanical' - driven by motor force.__

We appreciate the Reviewer’s feedback and have rewritten the text changes this to “independent of the bulk mechanical strength of the nucleus”. __ __

__ 4. It is important to specify the times for which cells were treated with the various drugs in each figure (and not just in figure 2).__

We appreciate the Reviewer’s feedback and have added this information to each figure legend.__

__

__

Reviewer #3 (Significance (Required)):

This paper reports new data that nuclear blebbing induced by treatment with valproic acid can be inhibited by co-treatment with alpha-amanitin. The data provided are reproducible across different cell lines. The data suggest that inhibition of transcription inhibits blebs which are induced by valproic acid treatment, but it does not inhibit blebs in cells untreated with valproic acid. Immunostaining reveals some enrichment of RNA pol II phosphorylated at Ser5 in valproic acid-induced blebs, suggesting an enhancement of transcription-initiation (but not transcriptional elongation) in the bleb. Alpha-amanitin treatment reduces bleb formation and bleb lifetime.

While the data are clearly presented, and interesting in terms of relating transcription to blebbing, the proposed interpretation in terms of a new mechanism of blebbing is not strongly supported by the data or by the computational model. More definitive evidence is required to rule out that blebbing in valproic acid treated cells is not caused by a pressurization of the nucleus due to valproic acid treatment, which could be released by treatment with alpha-amanitin treatment for upto 24 h. The manuscript generalizes the findings to 'nuclear shape', and interprets them as suggestive of an alternative mechanism of establishment of nuclear shape; this generalization seems unsupported by the data.__

Overall, the data provided is novel and interesting to cell biologists, provided more definitive evidence can be provided to rule out other models and to establish the new proposed model for nuclear blebbing. Else, the claims of an alternative mechanism for blebbing could be toned down, and the data on the relation between transcription and blebbing, which is the novel and interesting finding in this paper, could be presented in a more focused way.

We appreciate that the Reviewer points out that “the data are clearly presented and interesting” and “reproducible across different cell lines.” The Reviewer’s main concerns appear to be with: 1) the effect of transcription inhibition on blebbing that is not induced by VPA, 2) alternatives or limitations to our proposed interpretation of the results, and 3) describing our results as applicable to “nuclear shape” in general.

We have addressed each of these concerns in detail in the above response and the revised manuscript. To summarize:

• We have included new data to show that four different transcription inhibitors combined with four different nuclear perturbations exhibit the same effects (Figure 2 and Supplemental Figure 1). Furthermore, we have clarified in the revised manuscript that even wild type (“untreated”) nuclei exhibit changes to blebbing dynamics (decreased stability, increased reabsorption) after transcription inhibition (Figure 5). Furthermore, concerns about time intervals was addressed by time lapse imaging showing that bleb reabsorption (return to normal shape) increases six-fold in the first 8 hours of transcription inhibitor treatment (Supplemental Figure 1B).
• The original manuscript, new data, and previous data from the literature provides evidence that alternative mechanisms involving “pressurization” (discussed above), the actin cytoskeleton (Figure 3E and Supplemental Figure 2), and chromatin and nuclear rigidity (Figure 3) do not explain the observed effects of transcription inhibition. We discuss this in detail in the revised manuscript and the above response. Furthermore, we have revised our presentation and discussion of the simulation model to describe its relevance more clearly to the results, support its inclusion in the manuscript, and provide appropriate caveats on our computational findings.
• We have revised the manuscript to clarify that our results primarily concern nuclear blebbing and rupture. The Reviewer is correct that the current investigation does not particularly focus on larger-scale shape such as circularity/ellipticity. In summary, our data clearly indicate that transcription contributes to nuclear blebbing and rupture. Previously suggested mechanisms of blebbing are generally inconsistent with the observed effect in combination with our other measurements. The model investigates a plausible new, complementary mechanism, which in itself represents an advance in biophysical modeling and ties the manuscript together.

We thank the Reviewer for their thorough critique, which we have now addressed. We believe that the new experimental data and analysis and computational modeling in our manuscript significantly advances our overall understanding of nuclear blebbing, even as it raises new questions to be addressed by future work.

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Referee #3

Evidence, reproducibility and clarity

This is an interesting study that shows, for the first time, that inhibition of transcription reduces the occurrence of nuclear blebs in cells that have been pre-treated with valproic acid. The data that supports this is in Figure 2, collected in two different cell types (MEFs and HT1080 cells). The effect appears robust. New data is also provided that a marker of initiation of transcription but not transcriptional elongation is enriched in valproic acid-induced blebs.

Major comments

1. The paper makes general claims about transcription and nuclear shape, when in reality, it is only reporting on the inhibition of transient, small, valproic acid-induced blebs by alpha-amanitin. This scenario under which the experiments were performed, for which there is no obvious physiological counterpart, ought not to be construed to challenge or contrast with the current understanding that the nucleus maintains its shape by resisting cytoskeletal forces. Cytoskeletal forces are well-known to establish nuclear shape; nuclear shape in this context, is generally taken to refer to the gross shape of the nucleus (e.g. elliptical, circular, etc.), and not small local blebs that may form due to F-actin based confinement or other mechanisms. Thus, this interpretation is overstated:

"Surprisingly, we find that while nuclear stiffness largely controls nuclear rupture, it is not the sole determinant of nuclear shape. This contrasts with previous studies, which suggested that the nucleus maintains its shape by resisting cytoskeletal and/or other external antagonistic forces (Khatau et al., 2009; Le Berre et al., 2012; Hatch and Hetzer, 2016; Stephens et al., 2018; Earle 12 et al., 2020)."

As an aside, the data in the paper does not appear to support the interpretation that "nuclear stiffness largely controls nuclear rupture". It is unclear what the authors mean by this statement. 2. Further to point 2, treatment with alpha-amanitin does nothing to the occurrence of blebbing in normal cells. Thus, the data are specifically applicable to valproic acid-treated cells. As such, the broad interpretations related to nuclear shape and mechanics should be tempered. 3. The motor model for RNA pol II activity assumes that the motor 'repels' nearby chromatin units. It is not clear how this is related to the mechanism of motor action of RNA pol II on chromatin during transcription. The motor model also does not seem to add conclusive insight to the manuscript, as the nuclear shapes predicted are not directly comparable to the experimental shapes which are flat and smooth with only an occasional, single, local bleb. The model offers 'proof of principle', but is not capable of ruling out alternative mechanisms (such as nuclear pressurization by confinement, chromatin decompaction, or changes to osmotic pressure). It may be more appropriate to include the model in the discussion as opposed to presenting it as a new result that can be reliably interpreted through comparisons with experiment. 4. The data in the paper is not strong enough to rule out the more conventional mechanism of nuclear pressurization, which could be caused by F-actin based confinement or chromatin decompaction, or changes to osmotic pressure. Immunostaining of myosin is not a reliable way to compare myosin activity across conditions. It is possible that the long treatment with alpha-amanitin (unto 24 h, Fig. 2) relieves the pressure in the nucleus without measurable changes in the already established cell shape and hence the nuclear shape (height changes in spread cells are small at best -- valproic acid appears to reduce height by ~0.5 microns in Figure 3E which is smaller than the optical resolution along the z-axis of a typical confocal microscope). 5. Further to point 4, the data in Figure 4B and 4D both show a decrease in the mean of the % of ruptured nuclei and rupture frequency (please provide units for this frequency on the Y-axis). With more experiments, perhaps the data would have reached statistical significance?

Minor comments.

1. Confirmatory data, which has already been published in the same cell line in the past, could be moved if possible to supplemental information. Figure 1 seems to be a characterization of the efficacy of alpha-amanitin which is well-known, and therefore does not represent an original finding. It should perhaps be in supplemental information.
2. Did the counting method used to collect data in Figure 4B exclude nuclei that rupture multiple times? This should be specified in the manuscript.
3. This statement should be rephrased: "Since transcription is needed to form and stabilize nuclear blebs, at least some aspect of nuclear shape deformations appears to be non-mechanical" - deformation in the model in Figure 7 is clearly 'mechanical' - driven by motor force.
4. It is important to specify the times for which cells were treated with the various drugs in each figure (and not just in figure 2).

Significance

This paper reports new data that nuclear blebbing induced by treatment with valproic acid can be inhibited by co-treatment with alpha-amanitin. The data provided are reproducible across different cell lines. The data suggest that inhibition of transcription inhibits blebs which are induced by valproic acid treatment, but it does not inhibit blebs in cells untreated with valproic acid. Immunostaining reveals some enrichment of RNA pol II phosphorylated at Ser5 in valproic acid-induced blebs, suggesting an enhancement of transcription-initiation (but not transcriptional elongation) in the bleb. Alpha-amanitin treatment reduces bleb formation and bleb lifetime.

While the data are clearly presented, and interesting in terms of relating transcription to blebbing, the proposed interpretation in terms of a new mechanism of blebbing is not strongly supported by the data or by the computational model. More definitive evidence is required to rule out that blebbing in valproic acid treated cells is not caused by a pressurization of the nucleus due to valproic acid treatment, which could be released by treatment with alpha-amanitin treatment for upto 24 h. The manuscript generalizes the findings to 'nuclear shape', and interprets them as suggestive of an alternative mechanism of establishment of nuclear shape; this generalization seems unsupported by the data.

Overall, the data provided is novel and interesting to cell biologists, provided more definitive evidence can be provided to rule out other models and to establish the new proposed model for nuclear blebbing. Else, the claims of an alternative mechanism for blebbing could be toned down, and the data on the relation between transcription and blebbing, which is the novel and interesting finding in this paper, could be presented in a more focused way.

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Referee #2

Evidence, reproducibility and clarity

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells

Significance

The authors present data supporting the potential involvement of active transcription in the formation of nuclear blebs when the global deacetylase inhibitor valproic acid (VPA) has been applied to cells. While somewhat interesting, this is a rather specific condition that is further restricted by the limited use of experimental approaches. For example, the only deacetylase inhibitor used is VPA. Is this because VPA is the only one to trigger the effect? The authors should expand their approach to include additional inhibitors or, preferably, a directed knockdown tactic that targets the specific HDACs driving their phenomena. Moreover, the authors imply that VPA works through histone deacetylation yet do not provide direct evidence. It is equally likely that the application of VPA alters the acetylation pattern of a non-histone protein that eventually alters nuclear blebbing. Regardless, the reported findings with VPA were previously reported (Stephens et al. 2018) and the influence of alpha amanitin only represents an incremental advancement in our understanding of nuclear blebs. Adding to the concern is that actinomycin D does not have the same level of influence as alpha amanitin (Figure 2), which suggests the alpha amanitin is having a pleotropic impact on blebbing. To validate that the changes in blebbing in the presence of VPA are dependent upon active transcription, the authors should use the anchor-away technique to remove RNAP from the nucleus thereby avoiding any indirect effects of the drugs (i.e., alpha amanitin) in use. Further adding concern that it is an indirect outcome is the prolonged incubation period (16-24 hours) that is apparently needed to observe the changes (page 5 paragraph 4). If it is active transcription that is causing the change in blebbing, then this should be apparent in a much shorter time frame (<1 hour). In addition to these issues, the authors rely on immunofluorescence signals to measure the levels of various factors including the Ser5 and Ser2 phosphorylation, which is capturing the total levels of these factors and not the DNA bound forms. If the changes in blebbing actually involve transcription initiation, then the authors should include measurements on the DNA-bound factors. As reported the authors conclude that there is no changes in Ser2 and Ser5 phosphorylation yet they report that total RNA levels rise (Figure 1). How is the disconnect between RNA levels and Ser2 and Ser5 phosphorylation occurring? Comparably, they use H3K9ac immunofluorescence as a measure of euchromatin. While the authors might be gaining a view on the total levels of H3K9ac under these experimental conditions, it is not clear whether this is DNA associated or not. Minimally, the authors should perform ATAC-Seq to judge the changes in euchromatin. A final major concern is the lack of a correlation between the blebbing and nuclear ruptures (page 7 paragraph 3; Figure 4). If ruptures are not correlating with the blebbing, what is the relevance of the blebbing?

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Referee #1

Evidence, reproducibility and clarity

Summary: In this manuscript by Berg et al the authors demonstrate that RNA polymerase activity is important for the formation of nuclear blebs. This is an interesting and significant finding because prior work has suggested nuclear bleb formation is a result of changes in nuclear rigidity (lamins) or chromatin (via histone modifications). Overall I thought the manuscript was quite interesting and the data well presented. I think the inclusion of multiple mechanisms of blebbing (VPA treatment, as well as lamin B KO) helps to further support the importance of RNA polymerase/transcription activity in the blebbing process. However, I do have some concerns regarding the conclusions of the data that I think should be addressed as a revision.

Major Comments:

1. One concern I have is that the alpha-amanitin inhibitor has been shown to also inhibit RNA polymerase III. In an old study (1974 Weinmann PNAS) it appears that the inhibitor starting at 1 to 10 ug/ml. In this study the authors are using 10 uM alpha-amanitin, which is ~ 9 ug/ml and within the range of inhibiting some RNA polymerase III. Additionally, the other drug (actinomycin D) is even less specific for RNA polymerase II. I would suggest that the authors consider one of the following approaches 1) acknowledge in the manuscript the potential for RNA polymerase III to be important in the blebbing process 2) try a 10-fold lower dose of alpha-amanitin and see if that also inhibits blebbing, 3) try to find a way to demonstrate that RNA polymerase III activity is not inhibited at the 10 uM alpha-amanitin dosage, or 4) consider an alternate method to perturb RNA polymerase II activity (see Zhang Science Advances 2021 for an auxin-based approach to downregulate RNA polymerase II).
2. A second concern I have is that the inhibition of RNA polymerase is global. Thus it is difficult to know for sure the biophysical function of the polymerase occurs immediately at the bleb, or instead is somehow affecting the overall chromatin state throughout the entire nucleus. I agree that figure 3 does provide some evidence that major mechanical and biophysical properties of the nuclei are not changed in response to the inhibition of the polymerase. However, micromanipulation experiments are done with isolated nuclei, which may be somehow mechanically altered already by isolation from cells. I feel that there still must be given some consideration in the discussion of the possibility that RNA polymerase activity outside of the bleb may be having some role in the stabilization of the chromatin and blebbing propensity.
3. While I lack expertise to evaluate the basis of the model, I appreciate the model can show that motor activity can influence bulge. But it is not clear in the manuscript that RNA polymerase can generate these kinds of forces. The Liu citation is a model, and does not provide direct evidence that the RNA polymerase can generate force, or forces large enough to be meaningful. To me the model in this paper (Figure 7) felt as if it was only a possible hypothesis of why the RNA polymerase has an effect on blebbing, but I imagine there could be other hypotheses that would cause the same effect. The authors state (in the abstract) that RNA pol II can generate active forces, but I am concerned this is not sufficiently established. Since this motor/force activity of RNA polymerase is not not experimentally demonstrated in this paper the authors should either do a better job of including evidence of this from the literature or consider removing this part of the manuscript.

Minor Comments:

1. Did the authors do any analysis to see if the increased RNA transcription with VPA treatment (Figure 1B) has any spatial relationship to where the bleb occurs? Could an analysis of this be done similar to Figure 6 (with a bleb/body ratio)?
2. Is there anything known about lamin B1 KO cells as to whether or not they have increased transcription? Or could the authors do an analysis like they did with VPA treatment to check this? If they were to have increased transcription this would further support the authors' proposed mechanism of transcription itself (or RNA polymerase activity) driving blebbing).
3. Figure 1D, the VPA ser2 image appears much brighter than the untreated image. Yet the graph shows they are similar. Perhaps a more representative image should be used?
4. Can the authors comment if there is less DNA at the bleb site? In Figure 6 A this appears to be the case (based on the VPA image). If true, is the alpha-amanitin treatment rescuing this such that there is more DNA at the bleb (maybe causing the bleb to be smaller?).
5. What is the significance of bleb vs non-bleb nuclear rupture? Is there anything known in the literature as to how these ruptures may be different in terms of biophysics, impact to DNA, repair? It would be helpful to have some context, as well as to understand if non-bleb rupture is something that may have been previously missed in other contexts.

Significance

General assessment:

This study is a careful analysis of how RNA polymerase inhibition reduces nuclear blebbing. The study demonstrates this very well, using a variety of approaches. However, some limitations are the overstatement of some conclusions (specifically that it is RNA polymerase II when the inhibitor may also affect RNA polymerase III; that the RNA polymerase activity is important at the bleb and involves motor activity).<br /> Advance: This paper is a significant advancement because it shows the role of transcription in the biophysics of the nuclear shape. To my knowledge this is the first report of this phenomena, and thus will be impactful to the nuclear mechanics field. Audience: I think the findings are of broad interest, including beyond the nuclear mechanics field. I think the audience would be the entire cell biology community. Expertise: My expertise is in cell mechanics, including forces at the the nuclear LINC complex. While I do not work in the field of nuclear blebbing and rupture, I follow this field quite closely.

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Reply to the reviewers

1. General Statements

We thank the reviewers for their positive statement and the significance of our work.

2. Point-by-point description of the revisions

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Reviewer #1 (Evidence, reproducibility and clarity (Required)):

This paper contains a set of highly valuable information on the physicochemical parameteters of betain lipids - which are synthesized in microalgae and some other lower eukaryotic organisms.

The authors, using advanced biophysical techniques - neutron diffraction and small-angle scattering (SANS) as well as molecular dynamics (MD) simulations - established key physicochemical parameters of synthetic betaine lipid DP-DGTS, and compared it with those of the DPPC phospholipid. They "show that DP-DGTS bilayers are thicker, more rigid, and mutually more repulsive than DPPC bilayers". These are important findings.

The authors also analyzed the phylogenetic tree of the appearance and disappearance of DGTS biosynthesis enzymes, which - together with the observed "different properties and hydration response of PC and DGTS" led them to explain "the diversity of betaine lipids observed in marine organisms and for their disappearance in seed plants". The authors tentatively suggest "A physicochemical cause of betaine lipid evolutionary loss in seed plants" (Title with "?")

We put a question mark because our work suggests that the difference of sensitivity to hydration between DGTS and PC bilayers could be an explanation for the betaine lipid disappearance in seed plants due to the dry stage of the seed. In our hands, we never managed to obtain 35S-BTA1 overexpressing plant that produce seed. However, we do not have a formal evidence for this fact. We propose to change the title into: “The possible role of lipid bilayer properties in the evolutionary disappearance of betaine lipids in seed plants.”

May major concerns with this suggestion are:

• In thylakoid membranes (TMs) the only phospholipid, PG, plays key roles in PSII and PSI functions (Wada and Murata 2007 Photosynth Res, Hagio et al. Plant Physiol 2000, Domonkos et al. 2004 Plant Physiol; it is difficult to explain how these roles would be overtaken by betaine lipids. In fact, data of Huang et al. (https://www.sciencedirect.com/science/article/pii/S2211926418309366) indicate betaine lipids constitute the major compounds of non-plastidial membranes" and compensation mechanism operate according to which "by the increase of PG in thylakoid membranes, suggesting a transfer of P from non-plastidial membranes to chloroplasts that would maintain a stable lipid composition of thylakoid membranes".
• Although neutron diffraction and SANS data, as well as MD simulationa might indicate important differences, the behavior of membranes (e.g. stacking interactions, overall structure and structural dynamics of TMs, protein embedding conditions / membrane thickness etc), TMs are more dominantly determined by protein-protein interactions, mainly because these membranes, contain only small areas occupied by the bilayer phase. Similar arguments hold true for the inner mitochondrial membranes (IMMs). I suggest to take into account these severe limitations when extrapolating the data and trying to reach general conclusions. In general, I suggest a more cautious interpretation of data.

We fully agree with the reviewer’s comments. We indeed wrote in the introduction: “In algae, under phosphate starvation, a situation commonly met in the environment, betaine lipids replace phospholipids in extraplastidic membranes. Because betaine lipids are localized in these membranes [11, 12] and share a common structural fragment with the main extraplastidic phospholipid phosphatidylcholine (PC) (Figure 1A and B), it can be speculated that these two lipid classes are interchangeable, but this was never demonstrated.”

Plastidial membranes are mainly composed of the non-phosphorus glycerolipids MGDG, DGDG and SQDG. It is well known that in phosphate starvation, in plants and algae, the main phospholipid present in thylakoid membranes, PG, is replaced by SQDG because they are both anionic and bilayer forming lipids (Hölzl G, Dörmann P. Chloroplast Lipids and Their Biosynthesis. Annu Rev Plant Biol. 2019 Apr 29;70:51-81. doi: 10.1146/annurev-arplant-050718-100202; Endo K, Kobayashi K, Wada H. Sulfoquinovosyldiacylglycerol has an Essential Role in Thermosynechococcus elongatus BP-1 Under Phosphate-Deficient Conditions. Plant Cell Physiol. 2016 Dec;57(12):2461-2471; Van Mooy BA, Rocap G, Fredricks HF, Evans CT, Devol AH. Sulfolipids dramatically decrease phosphorus demand by picocyanobacteria in oligotrophic marine environments. Proc Natl Acad Sci U S A. 2006 Jun 6;103(23):8607-12.; Kobayashi K, Fujii S, Sato M, Toyooka K, Wada H. Specific role of phosphatidylglycerol and functional overlaps with other thylakoid lipids in Arabidopsis chloroplast biogenesis. Plant Cell Rep. 2015 Apr;34(4):631-42.). We recently showed by the same kind of neutron diffraction approaches that PG and SQDG share similar physicochemical properties that can explain their conserved replacement by each other in plastidial membranes (Bolik S, Albrieux C, Schneck E, Demé B, Jouhet J. Sulfoquinovosyldiacylglycerol and phosphatidylglycerol bilayers share biophysical properties and are good mutual substitutes in photosynthetic membranes. Biochim Biophys Acta Biomembr. 2022 Dec 1;1864(12):184037. ). However, nothing is known about mitochondrial membranes and DGTS localization. Because PC is a major lipid component of mitochondria in plants and fungi and PC is absent in Chlamydomonas reinhardtii, mitochondria membranes could contain DGTS at least in Chlamydomonas.

To clarify this statement, we added in the introduction the sentences: “Betaine lipid synthesis is located in the ER [13,14] and betaine lipids are expected to be absent in photosynthetic membranes [12]. Therefore, this PC-betaine lipid replacement is not expected to occur in photosynthetic membranes. However, it might occur at the surface of the chloroplast envelope where PC might be present [15–17]. Nothing is known about the composition of mitochondrial membranes in algae but because PC is a major lipid component in plant and fungal mitochondria, this replacement might also occur in mitochondria.” In the discussion, we replaced “cellular membrane” with “extraplastidial membrane”.

A minor point - just to avoid possible misunderstanding: betaine can be present in large quantities in many photosynthetic organisms. A short statement on betaine would help.

To avoid any confusion with betaine as a soluble molecule and betaine lipid, we added this sentence in the introduction: “The presence of betaine lipids is not linked to the synthesis of betaine, a soluble compound present in almost every organism including most animals, plants, and microorganisms, acting as protectant against osmotic stress [22].”

**Referee cross-commenting**

I agree with the evaluation of Reviewer #2 - while keeping mine

Reviewer #1 (Significance (Required)):

The physico-chemical properties of betaine lipids have not been established. These lipids - under P starvation of microalgae - accummulate in large quentites. Thus, their detailed characterization and comparison to (otherwise similar) phospolipids are of high importance and advance our knowledge about the roles of these lipids and the organization and structural / functional plasticity of biological membranes.

As outlined above, I suggest a more cautious interpretation of the data and conclusions regarding e.g. the energy-converting membranes.

I think the audience is relatively broad: (i) basic research of lipid models and (ii) methodology as well as calling the attention of membrane biologists to the scarcely studied betaine lipids.

My field is the biophysics photosynthesis - the stability and plasticity of the oxygenic photosynthetic machinery at different levels of complexity; the and closest to this topic is the polymorphic lipid phase behavior of plant TMs.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

This manuscript nicely presents the effect of phosphate depletion on how betaine lipids function as effective replacements in a water-rich environment. The mix of computational and wet lab experiments provides details on membrane structure and general effects when phospholipids are changed to betaine lipids. I found this manuscript easy to read and understand and is worthy of publishing. However, I do have a few minor comments below to improve the manuscript.

Minor Comments:

1. Phases in PC lipids with saturated tails: The authors present a gel to liquid crystalline phase change for DPPC at 40oC. However, this is at the ripple-liquid crystalline phase transition and the gel doesn't occur until about 34-35oC. This should be noted in the manuscript.

We indeed completed the sentence in the first result section by : “The DSC data show a sharp phase transition at 40.2 ± 0.1°C for DPPC corresponding to the transition between the ripple phase and the fluid phase, which is consistent with earlier reports on DPPC large unilamellar vesicles [25].”

Page 4: I am confused with the following phase: "indicating either weak cooperativity between lipid bilayers or that phase co-existence is not a thermodynamic disadvantage, while this phenomenon is not observed for DPPC bilayers." What is meant by phase co-existence is not a thermodynamic disadvantage? Could this also be due to some frustration in phase coexistence and the presence of a ripple phase that kinetically is inhibited and thus a sharp transition is not observed?

We did not observe a ripple phase in DP-DGTS as it is defined in DPPC bilayer either by DSC, neutron diffraction or SANS experiments. We don’t know if it exists in DP-DGTS bilayers. What we observe in neutron diffraction is a coexistence of gel phase and fluid phase domains in oriented multilayer films of DP-DGTS over a wide range of humidity whereas for DPPC we observe only a gel phase or a fluid phase. Because the thicknesses of the DP-DGTS bilayers are not so different between the gel phase and the fluid phase, we suppose that the free energy difference between the two phases is very small over a wide osmotic pressure range and that could explain the broad phase transition.

To further clarify our point, we have reworded the sentence in the following way: “As seen in Figure 2A , by increasing the humidity, DPPC molecules transit from the gel to the fluid phase via a ripple phase through a narrow window of osmotic pressures as previously reported [30,31]. In contrast, DP-DGTS bilayers show a phase coexistence that can be observed over a wide P-range and without the appearance of a third phase that could be attributed to a distinct ripple phase (Figure 2B) before forming a single fluid phase at high humidity (i.e., at low P). Based on DSC and neutron diffraction as two independent techniques, we can safely conclude that the phase transition for DP-DGTS is broad. This observation indicates that the free energy difference between the two phases is very small over a wide osmotic pressure range and may be connected to the shapes of the pressure-distance relations in the two phases, which are discussed further below.” We also added in the legend of figure 4 (SANS experiment): “No ripple phase Pb was detected for DP-DGTS bilayers.”

DOI for computational methods: The DOI listed computational files (https://doi.org/10.18419/darus-2360) does not work.

Unfortunately, we did not ask for publication of the URL upon submission of the manuscript and thank the reviewer for carefully checking this. Since DaRUS is a peer-reviewed repository ensuring high quality data sets according to the FAIR principle, peer review is still ongoing. The provided link will work definitely only when the manuscript will be published. In the meantime, we provide a temporary link for reviewing :

https://darus.uni-stuttgart.de/privateurl.xhtml?token=cbfac341-0e4a-4403-8f73-87bce31ca805

Reviewer #2 (Significance (Required)):

This work has broad significance and would be of general interest to those in membrane biophysics to plant biology and evolution. The work nicely touches on all these topics, and I find this fills a gap in details of these betaine lipids structure and relation to evolution in terrestrial vs. marine plants.

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Referee #2

Evidence, reproducibility and clarity

This manuscript nicely presents the effect of phosphate depletion on how betaine lipids function as effective replacements in a water-rich environment. The mix of computational and wet lab experiments provides details on membrane structure and general effects when phospholipids are changed to betaine lipids. I found this manuscript easy to read and understand and is worthy of publishing. However, I do have a few minor comments below to improve the manuscript.

Minor Comments:

1. Phases in PC lipids with saturated tails: The authors present a gel to liquid crystalline phase change for DPPC at 40oC. However, this is at the ripple-liquid crystalline phase transition and the gel doesn't occur until about 34-35oC. This should be noted in the manuscript.
2. Page 4: I am confused with the following phase: "indicating either weak cooperativity between lipid bilayers or that phase co-existence is not a thermodynamic disadvantage, while this phenomenon is not observed for DPPC bilayers." What is meant by phase co-existence is not a thermodynamic disadvantage? Could this also be due to some frustration in phase coexistence and the presence of a ripple phase that kinetically is inhibited and thus a sharp transition is not observed?
3. DOI for computational methods: The DOI listed computational files (https://doi.org/10.18419/darus-2360) does not work.

Significance

This work has broad significance and would be of general interest to those in membrane biophysics to plant biology and evolution. The work nicely touches on all these topics, and I find this fills a gap in details of these betaine lipids structure and relation to evolution in terrestrial vs. marine plants.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

This paper contains a set of highly valuable information on the physicochemical parameteters of betain lipids - which are synthesized in microalgae and some other lower eukaryotic organisms.

The authors, using advanced biophysical techniques - neutron diffraction and small-angle scattering (SANS) as well as molecular dynamics (MD) simulations - established key physicochemical parameters of synthetic betaine lipid DP-DGTS, and compared it with those of the DPPC phospholipid. They "show that DP-DGTS bilayers are thicker, more rigid, and mutually more repulsive than DPPC bilayers". These are important findings.

The authors also analyzed the phylogenetic tree of the appearance and disappearance of DGTS biosynthesis enzymes, which - together with the observed "different properties and hydration response of PC and DGTS" led them to explain "the diversity of betaine lipids observed in marine organisms and for their disappearance in seed plants". The authors tentatively suggest "A physicochemical cause of betaine lipid evolutionary loss in seed plants" (Title with "?")

May major concerns with this suggestion are:

• (i) In thylakoid membranes (TMs) the only phospholipid, PG, plays key roles in PSII and PSI functions (Wada and Murata 2007 Photosynth Res, Hagio et al. Plant Physiol 2000, Domonkos et al. 2004 Plant Physiol; it is difficult to explain how these roles would be overtaken by betaine lipids. In fact, data of Huang et al. (https://www.sciencedirect.com/science/article/pii/S2211926418309366) indicate betaine lipids constitute the major compounds of non-plastidial membranes" and compensation mechanism operate according to which "by the increase of PG in thylakoid membranes, suggesting a transfer of P from non-plastidial membranes to chloroplasts that would maintain a stable lipid composition of thylakoid membranes"
• (ii) Although neutron diffraction and SANS data, as well as MD simulationa might indicate important differences, the behavior of membranes (e.g. stacking interactions, overall structure and structural dynamics of TMs, protein embedding conditions / membrane thickness etc), TMs are more dominantly determined by protein-protein interactions, mainly because these membranes, contain only small areas occupied by the bilayer phase. Similar arguments hold true for the inner mitochondrial membranes (IMMs).

I suggest to take into account these severe limitations when extrapolating the data and trying to reach general conclusions. In general, I suggest a more cautious interpretation of data.

A minor point - just to avoid possible misunderstanding: betaine can be present in large quantities in many photosynthetic organisms. A short statement on betaine would help.

Referee cross-commenting

I agree with the evaluation of Reviewer #2 - while keeping mine

Significance

The physico-chemical properties of betaine lipids have not been established. These lipids - under P starvation of microalgae - accummulate in large quentites. Thus, their detailed characterization and comparison to (otherwise similar) phospolipids are of high importance and advance our knowledge about the roles of these lipids and the organization and structural / functional plasticity of biological membranes.

As outlined above, I suggest a more cautious interpretation of the data and conclusions regarding e.g. the energy-converting membranes.

I think the audience is relatively broad: (i) basic research of lipid models and (ii) methodology as well as calling the attention of membrane biologists to the scarcely studied betaine lipids.

My field is the biophysics photosynthesis - the stability and plasticity of the oxygenic photosynthetic machinery at different levels of complexity; the and closest to this topic is the polymorphic lipid phase behavior of plant TMs.

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Our reply to reviewers contain figures that has been uploaded as a word file with the rest of the files. Here, we cannot past figures into the reply box, and because we don't wish to submit an incomplete response, we cannot provide a response in this format.

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Referee #2

Evidence, reproducibility and clarity

Lateral root production is a process regulated by auxin, among others. The expression of auxin-dependent genes requires the activity of transcription factors of the ARF family. In this study by Ebstrup et al., the authors suggest that selective autophagy would be involved in the degradation of the ARF7 factor involved in lateral root initiation and production in Arabidopsis thaliana, even though the accumulation of ARF7 in autophagy-deficiency mutant may not affect lateral root initiation.

Major remarks and comments:

1. In general, some experimental data do not facilitate appropriate comparisons due to lack of statistical analysis. This is particularly the case for Figures 1-a,b,c and 4-a,b,c,d.
2. Confocal microscopy images are not always convincing, due to a lack of necessary controls and also qualitatively. It would be useful, for example, to clearly indicate the objects of interest that the reader can use for comparisons. It is for example difficult to understand that chlorophyll fluorescence and GFP fluorescence (from the BiFC signal) colocalize almost in the same organelles (fig. 2c). The parent lines expressing the Venus and mCherry fusions should also serve as controls for figure EV3. Another point concerning fig. 2 a, b (IP): how do the authors explain the "GFP" signal, especially the apparent size and the doublet present only in one of the "YFP" controls after IP?
3. It would be important for the authors to clarify whether the different fluorescent fusions used are indeed functional or not. This is particularly important in the context of the proteins being studied and the possible regulatory process(es).
4. Apparently ARF7 would be degraded by the UPS system and the selective autophagy pathway. Would autophagy-deficient mutants, including atg2-1 and atg5-1 be more or less sensitive to MG132 (relative levels of ARF7 accumulation)? This is not clear from the data and its discussion.
5. The authors seem to insist that NBR1-mediated degradation of ARF7 by selective autophagy would be observable only preferentially in mature root tissues (probably to prevent them from forming lateral roots?). If this is the case, the title of their paper should reflect this conclusion. The authors have the tools (described in their manuscript) to unambiguously clarify this important point. Just as it would be important to demonstrate that the ARF7 proteins that accumulate would indeed be ubiquitylated.

Minor comments:

1. Some of the figures would benefit from qualitative improvement, especially the photographs and micrographs.
2. The authors' attention is drawn to the existence of several typos in the text and the absence of certain references cited in the bibliography.

Significance

Although the biological question is of unquestionable interest and importance, the data presented in this manuscript unfortunately do not allow us to rightly assess the contribution of this work to the state of our knowledge.