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Referee #1

Evidence, reproducibility and clarity

The epithelial diffusion barrier in triangular junctions is initially formed by a protein complex of Aka, Gli and M6. Aka and M6 act upstream of Gli. GPM6a, the vertebrate homolog of M6 is palmitoylated, whose functional implications have not thoroughly been analyzed, yet. It order to better define the function of M6 and especially the role of the palmitoyl moity, the authors conducted a genetic analysis of M6 in Drosophila embryos.

• They first establish a genetic system with defined mutants (deletion and CS mutants which are not palmitoylated), tagged protein at the genetic locus and an quantitative assay for protein enrichment at triangular junctions. Secondly they provide biochemical evidence that M6 is palmitoylated at a cluster of three conserved cysteine residues in vivo. With a palmitoylation-deficient mutant, thirdly the authors investigate the function of the palmitoyl moiety for protein localization at triangular junctions and complex formation with the other proteins at triangular junctions. The authors reveal a quantitative function of the palmitoyl moiety at triangular junctions with respect to enrichment and initial accumulation but not for later functions during growth of triangular junctions. The lower enrichment of the non-palmitoylated M6 mutant are sufficient for recruitment of Aka and Gli. Importantly, reducing Aka in combination with the non-palmitoylated mutant leads to a strong phenotype with respect to Gli localization and and leads to a genetically synthetic embryonic lethality. Fourthly, on a molecular level, the palmitoyl residue mediates binding to Aka but is not required for di/oligomerization of M6 itself as shown by immunoprecipitation from embryonic lysates.

• Though the function of M6 acting together with Aka and Gli has been demonstrated previously, molecular details of the interactions and targeting of the proteins to triangular junctions have remained unclear. Similarly, although palmitoylation of the vertebrate homologue has been previously demonstrated, its functional implications have not been investigated in a physiological context with stringent genetics. The current study provides convincing data about the role of the palmitoylated moiety of M6. Importantly, the authors manage to differentiate a function of the palmitoyl residue in initial accumulation of M6 at triangular junctions versus maintenance. Also the authors manage to reveal an essential function of M6 palmitoylation when the dose of Aka is reduced. In summary, the study provides novel and interesting insights into the detailed molecular requirements of epithelial barrier formation. Although the quality of the data and analysis provides an argument for publication on its own, it may be noted that similar mechanisms may underlie barrier formation at triangular junction in vertebrates given the conservation of the protein components.

Minor comments:

1. L100: it is stated that "... is not detectable on other known TCJ components". What about Angulin-1, which is palmitoylated?

2. L122 In my understanding all M6 isoforms contain an element which is sufficient. Not "required".

3. L336. The allele designation "DeltaPalm" is misleading. A designation like "3xCS" would be more better because three defined cysteine residues are mutated.

4. L329 A reference to FLYBASE is missing. Similarly not reference to stock centers are provides. To document the importance of the community services it is essential that their services are properly cited in a way that can be automatically tracked, e. g. by a literature citation.

Significance

In summary, the study provides novel and interesting insights into the detailed molecular requirements of epithelial barrier formation. Although the quality of the data and analysis provides an argument for publication on its own, it may be noted that similar mechanisms may underlie barrier formation at triangular junction in vertebrates given the conservation of the protein components.

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Reply to the reviewers

Reviewer #1:

Comment: The author investigated the role of the stress sensor pathway in the mechanism of tumor cell survival<br /> They identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with the survival of several cancer histotypes, particularly in RCC, as a highly specific and correlated prognosis.

The abstract although not always required from the journal should be divided into methods used to reach the main findings and clear presentation of results

Response: We do not know yet to which Journal the paper will be sent. The format will be adjusted to the Journal requirements.

it is unclear whether JUNI is a positive or negative regulator of JUI (I assume the reviewer meant JUN)

Response: The text in the abstract was changed to” JUNI positively regulates the expression of its neighboring gene JUN, a key transducer of signals that regulate multiple transcriptional outputs.”

Hope it is clearer now

When the author indicates that JUNI antagonizes MAP PHOSPHATASE is not correct the term antagonism is related to receptors but the authors did not show any receptor.

Response: The term "antagonism" does not only refer to receptor drugs. In pharmacology, antagonism generally describes the interaction between a drug (or other molecule) and a receptor or biological target that results in the inhibition or blocking of the receptor's activity. However, this concept can extend beyond receptor drugs and apply to various biological interactions.

Outside of the realm of drugs and receptors, antagonism can also refer to antagonistic relationships between different biological processes, molecules, or organisms.

Overall, while antagonism is commonly discussed in the context of receptor drugs, the concept of antagonism can apply to a broader range of interactions in biology and other fields.

Response: The p values for the prognostic values of JUNI and DUSP14 in RCC were added to the abstract.

Generally, Jun oncogene correlated with poor overall survival while the table indicates promote survival so good prognosis?

Response: This manuscript describes for the first time the biological activity and cancer relevance of JUNI. It positively regulates stress induced c-Jun and can be used as prognostic marker in ccRCC.

The significance of JUNI and its interactome in ccRCC prognosis is unequivocal, according to data analysis of cancer relevant data (TCGA) regardless to its effects on c-Jun. The concern raised by reviewer 1 and 2 is whether the cancer-relevant effects are mediated by c-Jun regulation. We suggest that despite regulating stress induced c-Jun, they are not! This suggestion is based on three points: 1. We show in the manuscript that a large portion of JUNI dependent effects on cellular survival activity is c-Jun independent. 2. We describe many interacting proteins that may, in a JUN-independent manner, affect tumorigenesis. 3. In this study we examined JUNI’s functions which are cell-autonomous. However, neither the non -autonomous effects nor effects on cells that compose the tumor environment were studied. Reports that lncRNAs may have a role in immune responses and high expression of JUNI in CD8 cells may suggest this direction for future investigation (Carpenter, S et al. science, 341(6147), pp.789-792; Mickaël, M. et al https://doi.org/10.1101/2021.12.01.470587)

Therefore, we assume that direct correlations in every biological activity between JUNI and JUN is an over simplified consumption. Analogy for that can be found with another major regulator of c-Jun, JNK, which is stress induced, c-Jun regulator involved in stress-induced cell death, whereas c-Jun itself is contributing in many cases to drug resistance.

The introduction contains the main information to follow the role of JUN and renal carcinoma<br /> However, should be improved with background on the key role of stress genes in the pro-survival pathway of tumors during progression and hypoxia condition. Too many references on long noncoding compared to the JUN complex with AP-1 and transformation

Response: A section describing the major stress pathway in ccRCC, HIF 1 and its role in ccrCC was added. Due to the limitation of word count in most journals we cannot expend this section further

Results In Figure 1 the authors showed expression levels of JUNI and JUN that are clearly different after UV stimuli. they demonstrate that are both regulated by UV but the amount and the time are different. the author should comment on these data if they want to study the regulative mechanism

Response: The following comment was added at the end of the first section: Overall, these results suggested that JUNI is a stress-induced gene whose expression pattern resembles that of JUN, therefore, we investigated the potential existence of regulatory effects between the two genes, especially post exposure of cells to stress.

Figure 1 F the cellular distribution of JUNI which is the rational of this experiment to provide that is into nucleus while normally is into the cytoplasm? What adds this experiment?

Response: This is the first reported description of JUNI. We attempted to characterize it as much as possible. It’s localization was not described previously and we suggest that it is mainly nuclear. A novel important information that should be presented.

In Figure 2 the authors provided that the kinase pathway is important for Jun regulation but the effect on JUNI a Luciferase assay needs to be provided

Response: We respectfully disagree with the reviewer. We believe that examining the expression from a DNA fragment identical to the endogenous one is superior to artificial system, such as luciferase.

In Figure 3 for Migration assay is necessary to see cells on the other side of the filter by staining not a graphical representation

Response: The graphical representation is an accumulated result of at least 3 experiment. However, a figure representing a single experiment was added as a supplement figure s1.

The experiment on kinase does not add any data to what is already known on jun probably should be shifted in Figure 6

Response: We apologize, this question was not fully understood as there is no experiment on kinase in figure 3. If case the reviewer was referring to kinase inhibition in Fig 2A we do think it is needed as a positive control for the kinases activity.

Table 1 is cited two times once in the context of Figure 3 and then in Figure 6 indicating that the authors go forward and back on their experimental design

Response: Table 1 is indeed referred to in two places. It is first mentioned when we investigated the potential relevance of JUNI for human cancer, given its regulatory impact on the neighboring JUN gene and its influence on motility. Later, the types of cancers described in figure 1 were further processed in order to examine relations between JUNI and DUSP14 in human cancer. We do not see it as a flaw in experimental design but rather as further evolution of the story based on data discovered in earlier stages.

in figure 4 the apoptotic cells are not clearly visible a specific staining marker is necessary to provide the phenomenon

Response: Two corrections were made to demonstrate apoptosis clearly. The pictures in Figure 4 panel A were replaced with a better-quality image with addition of DNA staining to demonstrate the cell death clearer, appearance of cell blebbing and nuclear fragmentation. Panel B demonstrating increase in cleaved caspase 3 in JUNI silenced cells after all treatment was added.

Additionally XTT assay should be reported as the percentage of survival cells not staining incorporated compared to untreated cells over time

Response: We do apologize for the legend omission, but XTT assays, colonies formation and soft agar colonies formation are presented in Figure 4 H-J and Figure S3 for all cell lines

The data on prognosis and correlation of gene expression are not clearly presented and discussed

Response: Figure S4 was replaced by table S3 to demonstrate clearer the differences in Medians survival caused by JUNI of DUSP 14. Text was changed in the last section of results.

The western blot need to be quantified

Response: All blots were quantified

Reviewer #2:

1. While the experimental data showed JUNI, like c-JUN, is pro-survival of cancer cells, the clinical sample analyses correlated it positively with patients' survival. This discrepancy casts doubts in significance of the findings. The authors need to re-evaluate their data and conclusion

Response: This manuscript describes for the first time the biological activity and cancer relevance of JUNI. It positively regulates stress induced c-Jun and can be used as prognostic marker in ccRCC.

The significance of JUNI and its interactome in ccRCC prognosis is unequivocal, according to data analysis of cancer relevant data (TCGA) regardless to its effects on c-Jun. The concern raised by reviewer 1 and 2 is whether the cancer-relevant effects are mediated by c-Jun regulation. We suggest that despite regulating stress induced c-Jun, they are not! This suggestion is based on three points: 1. We show in the manuscript that a large portion of JUNI dependent effects on cellular survival activity is c-Jun independent. 2. We describe many interacting proteins that may, in a JUN-independent manner, affect tumorigenesis. 3. In this study we examined JUNI’s functions which are cell-autonomous. However, neither the non -autonomous effects nor effects on cells that compose the tumor environment were studied. Reports that lncRNAs may have a role in immune responses and high expression of JUNI in CD8 cells may suggest this direction for future investigation (Carpenter, S et al. science, 341(6147), pp.789-792; Mickaël, M. et al https://doi.org/10.1101/2021.12.01.470587)

Therefore, we assume that direct correlations in every biological activity between JUNI and JUN is an over simplified consumption. Analogy for that can be found with another major regulator of c-Jun, JNK, which is stress induced, c-Jun regulator involved in stress-induced cell death, whereas c-Jun itself is contributing in many cases to drug resistance.

Response: The Western blotting data need at least triplicate biological experiments and quantification. This is particularly important for trivial differences, such as shown in Fig. 6.

Response: All westerns X=3. Representative experiments are depicted. Quantification was added.

The identification and gene structure of LINC01135 and its relevance to c-Jun need better clarity

Response: First result section. “According to ENCODE data, JUNI contains five main exons and has multiple isoforms. Twenty-seven different transcript isoforms were described according to LNCipedia ranging from 213 to 6213 bases {Volders, 2019 #2907}. The relevance to c-Jun was referred to in discussion: Both the effects of JUNI on c-Jun induction and cellular survival were demonstrated using under-expression conditions by targeting, the common, first, exon of JUNI. Nevertheless, this exon was also sufficient for c-Jun induction upon stress exposure, under conditions of overexpression.

Page 9-10, Line 198-199, there are no results in Fig. 1 showing that JUNI induction was dependent to serum stimulation of starved cells

Response: “ Similar to JUN, the induction was dose dependent (Fig 1C), and the rapid response to stress (Fig 1D) as well as to serum stimulation of starved cells, identified by others (36), qualifies it as an “immediate early” lncRNA.”

Serum stimulation is described in reference 36

What is the Y-axis in figures 2B, 4E-G

Response: Legend was added to Y-axis of Figures 2B and 4 E-G

In Fig. 3B, actin image is missing

Response: Actin was hidden in the graphic process. Corrected.

In Fig. 4. brightfield images are inaccurate for distinguishing apoptosis and necrosis. Additional molecular markers need to be used, such as caspase-3 cleavage and LDH release

Response: Two corrections were made to demonstrate apoptosis clearly. The pictures in Figure 4 panel A were replaced with a better-quality image with addition of DNA staining to demonstrate the cell death clearer, appearance of cell blebbing and nuclear fragmentation. Panel B demonstrating increase in cleaved caspase 3 in JUNI silenced cells after all treatment was added.

The inconsistency of using four cell types in each assay. For example, in Fig. 4A, B, E-G and Suppl Fig. 1, HMCB, MDA-MB-231 and CHL1 cells were used to test the short-term effect of JUNI knockdown on cell survival, whereas Hela, MDA-MB-231 and CHL1 cells were chosen to determine the long-term effect of JUNI knockdown. Similar case in other figures.

Response: Effects on Jun regulation and the effects on long term survival were tested in all four cell lines both by XTT and clonogenic assays whereas effects on short term survival were tested in three out of the four cell lines. It is practically impossible to perform a study of this magnitude were all assays were tested in all cell lines. Using four cell lines was applied to prove the major points.

In Fig. 5D, no difference of c-Jun expression between NS and siJUN groups

Response: Correct, the western in 5D was replaced by a more representative one

Cell survival in Fig. 5 lacked statistical analyses

Response: Error bars were mistakably omitted. The figure was corrected.

In Suppl Fig. 2C, there is no figure to show the reduced colonies formation in soft agar in MDA-MB-231 cells, contradicting to that stated in the manuscript

Response: Indeed Figure 4 J and S3 C presented colonies formation in HMCB and HeLa cells. The text was corrected.

Reviewer #3: "linc01135" - this is a human gene, should be capitalized

Response: linc01135 was capitalized

Please indicate primers in Fig1A and mention this in relevant part of Results

Response: The following section was added: “Importantly, ENCODE predicts that the first exon is shared by all, therefore, all primers to analyze JUNI’s expression as well as siRNAs to silence it, were targeted for this exon.”

Fig1C-F - please add a legend to explain the colors

Response: Legend was added into the Figure as well

Copy number: It is important to establish the approximate copy number of JUNI RNAs in the cell lines tested. FISH would be one appropriate method. This could also be referenced back to the RNA-seq TPM values. Are we talking about <1 copy /cell, or many? Quick inspection of ENCODE RNA-seq in the UCSC browser suggest an intermediate value that varies between cell lines. This value is very important when interpreting mechanistic experiments later on

Response: The copy number in HMCB and MDA-MB-231 was calculated by comparison of CT values obtained from RNAs from a known number of cells relative to calibration curve of known concentrations of JUNI. The following section was added to the first paragraph of the results: “quantitation of JUNI’s copy number in untreated HMCB and MBA-MD-231 cells revealed the presence of minimal amount of about 8 copies per cell”

Fig3 - again, no figure legends, difficult for reader

Response: Legend was added to Fig. 3A

In general, the figures could be much more clearly annotated and presented with more care. They do not do justice to the quality of the work itself. For example, Fig4E-G why not label each panel with the time course, the cell line tested etc etc to save us the work of digging through the Legends?

Response: We thank the reviewer for this remark. All figures were corrected, legends and proteins quantification was added.

Rescue experiments: The rescue experiments in Fig5D are nicely done and the results are interesting. However, I would request the authors to perform similar experiments with JUNI rescue. Specifically, to knock down JUNI with siRNA, and then reintroduce it from an 'immune' expression plasmid, where the siRNA site is mutated. This will further strengthen the claim that JUNI siRNA is acting through the intended target to cause observed effects on cell viability

Response: As the effects on survival are strongest in the longer term, 14 days after silencing, rescue experiments were performed to test the rescue in the survival of HMCB and HeLa cells using clonogenic assays. Results are presented in figure 4 L

IncPrint data: was Jun protein found to be an interactor? This might be mentioned in the text, whether it is yes or no

Response: c-Jun was screened and did not interact with JUNI. The text was changed as following” Interestingly, c-Jun itself does not interact with JUNI (Table S2, Normalized luciferase intensity MS2, RLU =0.44). By contrast, the dual specificity protein phosphatase 14….”

Expression: A key issue is the expression of JUNI in healthy and diseased cells and organs. Is JUNI ubiquitous (and essential to both healthy and tumor cells), or is it specific to tumor cells? Which tumor types? This would be straightforward to find out from public data. I would suggest a main figure panel. Also, is JUNI upregulated across tumors? Could find this out from GEPIA2 or other databases.

Response: Figure 7E describing the levels of JUNI in variety of normal and tumor samples was added.

Non-tumor cells: Like many studies, this one focusses on effect of LOF in transformed cells. However, therapeutic relevance is tied to specific effect in transformed cells. Therefore I believe the paper would be vastly strengthened, if knockdowns+viability assays were also performed in some non-transformed cells. Eg HEK293, immortalised fibroblasts, RPE1 etc

Response: Indeed discrimination between Normal and cancer cells is an essential point for further research and translation. We examined the affects of silencing on spontaneously immortalized keratinocytes, HaCat cells, and the results are depicted in Figure 4 K.

Alternative reagents: The siRNA experiments are well performed with two independent sequences. An important additional experiment would be to replicate these experiments with antisense oligonucleotides. This would both further strengthen the confidence in experiments, and open more lines of potential therapies. This experiment I would consider optional

Response: Stable CRISPR can not be formed. We are currently constructing inducible CRISPR but the construction consumes longer time than the scope of this revision.

Advanced models: All the present experiments are performed in monolayer cell lines. The authors will no doubt be aware that the paper would be substantially strenghtened if functional experiments could be replicated in more advanced models: spheroids, PDX, explants, mice...

Response: We examined the protective role of JUNI in Doxorubicin treated spheroids of HMCB and CHL1 cells. The results are depicted in figure 4 D and E.

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Referee #3

Evidence, reproducibility and clarity

Kumar and colleagues present an apparently novel, cancer-promoting lncRNA 'JUNI' and perform a rather thorough and careful analysis of its in vitro functions and molecular mechanisms.JUNI is located adjacent to the Jun protein coding gene, although intriguingly the two appear to be rather indepent of each other at the level of gene products. JUNI appears to be necessary for cancer cell line growth and survival (monolayer) in multiple contexts. Particularly interesting is the demonstration that JUNI appears to function in trans. Overall this is an excellent paper - work is solidly and carefully done, hypotheses are well formulated and thoroughly tested. The JUNI lncRNA is well supported in public annotations, seems to be highly expressed, and it is surprising that virtually no work has been carried out on it so far. Furthermore, the apparently essentiality of JUNI to cancer cells has potentially important therapeutic and mechanistic ramifications.

These are suggestions for improvement of the work.

"linc01135" - this is a human gene, should be capitalised.

Please indicate primers and ASOs in Fig1A and mention this in relevant part of Results.

Fig1C-F - please add a legend to explain the colors.

Copy number: It is important to establish the approximate copy number of JUNI RNAs in the cell lines tested. FISH would be one appropriate method. This could also be referenced back to the RNA-seq TPM values. Are we talking about <1 copy /cell, or many? Quick inspection of ENCODE RNA-seq in the UCSC browser suggest an intermediate value that varies between cell lines. This value is very important when interpreting mechanistic experiments later on.

Fig3 - again, no figure legends, difficult for reader.

In general, the figures could be much more clearly annotated and presented with more care. They do not do justice to the quality of the work itself. For example, Fig4E-G why not label each panel with the time course, the cell line tested etc etc to save us the work of digging through the Legends?

Rescue experiments: The rescue experiments in Fig5D are nicely done and the results are interesting. However, I would request the authors to perform similar experiments with JUNI rescue. Specifically, to knock down JUNI with siRNA, and then reintroduce it from an 'immune' expression plasmid, where the siRNA site is mutated. This will further strengthen the claim that JUNI siRNA is acting through the intended target to cause observed effects on cell viability.

IncPrint data: was Jun protein found to be an interactor? This might be mentioned in the text, whether it is yes or no.

Expression: A key issue is the expression of JUNI in healthy and diseased cells and organs. Is JUNI ubiquitous (and essential to both healthy and tumor cells), or is it specific to tumor cells? Which tumor types? This would be straightforward to find out from public data. I would suggest a main figure panel. Also, is JUNI upregulated across tumors? Could find this out from GEPIA2 or other databases.

Non-tumor cells: Like many studies, this one focusses on effect of LOF in transformed cells. However, therapeutic relevance is tied to specific effect in transformed cells. Therefore I believe the paper would be vastly strengthened, if knockdowns+viability assays were also performed in some non-transformed cells. Eg HEK293, immortalised fibroblasts, RPE1 etc.

Alternative reagents: The siRNA experiments are well performed with two independent sequences. An important additional experiment would be to replicate these experiments with antisense oligonucleotides. This would both further strengthen the confidence in experiments, and open more lines of potential therapies. This experiment I would consider optional.

Advanced models: All the present experiments are performed in monolayer cell lines. The authors will no doubt be aware that the paper would be substantially strenghtened if functional experiments could be replicated in more advanced models: spheroids, PDX, explants, mice...

Significance

This is an important advance in the cancer field. It reveals a potential new lncRNA oncogene, JUNI, which appears to be necessary for cancer cell survival in multiple contexts through mechanisms defined by the authors. Future work will be required to understand the degree to which JUNI's activity is cancer specific, and its functional effects will have to be replicated in more faithful cancer models.

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Referee #2

Evidence, reproducibility and clarity

This work identified a lncRNA JUNI located near c-JUN and investigated its relationships with c-JUN and stress response, survival, and cancer prognosis. Experiments are logically designed, and the research topic is novel. The main concern is weaknesses in data interpretation and significance. Additionally, the paper needs improvement in experimental rigor with statistical assessment of multiple data sets; data description and conclusion need better clarity.

Overall comments:

1. While the experimental data showed JUNI, like c-JUN, is pro-survival of cancer cells, the clinical sample analyses correlated it positively with patients' survival. This discrepancy casts doubts in significance of the findings. The authors need to re-evaluate their data and conclusion.
2. The Western blotting data need at least triplicate biological experiments and quantification. This is particularly important for trivial differences, such as shown in Fig. 6.

Specific comments:

1. The identification and gene structure of LINC01135 and its relevance to c-Jun need better clarity.
2. Page 9-10, Line 198-199, there are no results in Fig. 1 showing that JUNI induction was dependent to serum stimulation of starved cells.
3. What is the Y-axis in figures 2B, 4E-G
4. In Fig. 3B, actin image is missing.
5. In Fig. 4. brightfield images are inaccurate for distinguishing apoptosis and necrosis. Additional molecular markers need to be used, such as caspase-3 cleavage and LDH release.
6. The inconsistency of using four cell types in each assay. For example, in Fig. 4A, B, E-G and Suppl Fig. 1, HMCB, MDA-MB-231 and CHL1 cells were used to test the short-term effect of JUNI knockdown on cell survival, whereas Hela, MDA-MB-231 and CHL1 cells were chosen to determine the long-term effect of JUNI knockdown. Similar case in other figures.
7. In Fig. 5D, no difference of c-Jun expression between NS and siJUN groups.
8. Cell survival in Fig. 5 lacked statistical analyses
9. In Suppl Fig. 2C, there is no figure to show the reduced colonies formation in soft agar in MDA-MB-231 cells, contradicting to that stated in the manuscript.

Significance

Experiments are logically designed, and the research topic is novel. The main concern is weaknesses in data interpretation and significance. Additionally, the paper needs improvement in experimental rigor with statistical assessment of multiple data sets; data description and conclusion need better clarity.

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

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Referee #1

Evidence, reproducibility and clarity

The author investigated the role of the stress sensor pathway in the mechanism of tumor cell survival<br /> They identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with the survival of several cancer histotypes, particularly in RCC, as a highly specific and correlated prognosis.

The abstract although not always required from the journal should be divided into methods used to reach the main findings and clear presentation of results it is unclear whether JUNI is a positive or negative regulator of JUI. When the author indicates that JUNI antagonizes MAP PHOSPHATASE is not correct the term antagonism is related to receptors but the authors did not show any receptor. Correlated with prognosis ( negative or positive ) Statistical value should be reported in the abstract. Generally, Jun oncogene correlated with poor overall survival while the table indicates promote survival so good prognosis?

Major comments

The introduction contains the main information to follow the role of JUN and renal carcinoma<br /> However, should be improved with background on the key role of stress genes in the pro-survival pathway of tumors during progression and hypoxia condition. Too many references on long noncoding compared to the JUN complex with AP-1 and transformation.<br /> Results In Figure 1 the authors showed expression levels of JUNI and JUN that are clearly different after UV stimuli they demonstrate that are both regulated by UV but the amount and the time are different the author should comment on these data if they want to study the regulative mechanism figure 1 F the cellular distribution of JUNI which is the rational of this experiment to provide that is into nucleus while normally is into the cytoplasm? What adds this experiment?<br /> In Figure 2 the authors provided that the kinase pathway is important for Jun regulation but the effect on JUNI a Luciferase assay needs to be provided<br /> In Figure 3 for Migration assay is necessary to see cells on the other side of the filter by staining not a graphical representation the experiment on kinase does not add any data to what is already known on jun probably should be shifted in Figure 6. Table 1 is cited two times once in the context of Figure 3 and then in Figure 6 indicating that the authors go forward and back on their experimental design<br /> in figure 4 the apoptotic cells are not clearly visible a specific staining marker is necessary to provide the phenomenon additionally XTT assay should be reported as the percentage of survival cells not staining incorporated compared to untreated cells over time.<br /> The data on prognosis and correlation of gene expression are not clearly presented and discussed

Significance

The authors identified a long noncoding RNA as JUNI that regulates antagonizing MAP phosphatase and favors the JUN transcription. JUNI correlated with survival of several cancer histotypes In particular in RCC as a highly specific and correlated prognosis.

The data are not presented with a good rationale often the authors go forward and back on the experimental design. The data are not presented in the best way some data are shown as bar graph but need to be supported by cell staining of transwell staining and standard plot for survival rate The western blot need to be quantified

In general, the experimental design does not match the rational

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Reply to the reviewers

_We have underlined the important points in the reviewer's comments. All responses have been read and authorized by all authors of this manuscript. Authors would like to thank the reviewers and the editor for their valuable time. We believe that the comments and suggestions from both reviewers will significantly improve SMorph and the manuscript. _

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

First of all, I want to apologize the authors and editor for my delay. Secondly, for clarity, I want to disclose that I am the author of the Fiji's 'Sholl Analysis' plugin, that the authors cite extensively (Ferreira et al, Nat Methods, 2014).

In this study, Sethi et al introduce a software tool - SMorph - for bulk morphometric analysis of neurons and glia (astrocytes and microglia), based on the Sholl technique. The authors compare it to the state-of-the-art in a series of validation experiments (stab wound injury), to conclude that it is 1000 times faster that existing tools. Empowered by the tool, the authors show that chronic administration of a tricyclic antidepressant (DMI) leads to structural changes of astrocytes in the mouse hippocampus. The paper is well written, the description of the tool is clear, and the authors make all of the source code available, as well as most of the imagery analyzed in the manuscript. The latter on its own, makes me really appreciative of the authors work.

We thank reviewer #1 for their careful reading of the manuscript and their comments.

**Major comments:**

A major strength of SMorph is that it leverages the Python ecosystem, which allow the authors take advantage of powerful python packages such as sklearn, without the need for external packages or tools. However, I have strong criticisms for the claims that are made in terms of speed and broad-applicability of the software, including PCA.

Speed:

The 1000x speed gains, assumes - for the most part -- that the processing in Fiji cannot be automated. This is false. I read the source code of SMorph, and with exception of the PCA analysis, all aspects of SMorph can be automated in Fiji, using any of Fiji's scripting languages to make direct calls to the Fiji and Sholl Analysis plugin APIs (See https://javadoc.scijava.org/) . Now, perhaps the authors do not have experience with ImageJ scripting, or perhaps we Fiji developers failed to provide clear tutorials and examples on how to do so. Or perhaps, there is something inherently cumbersome with Fiji scripting that makes this hard (e.g., there is a current limitation with the ImageJ2 version of 'Sholl Analysis' that does not make it macro recordable). It such limitations do exist, it is perfectly fine to mention them, but do contact us at https://forum.image.sc, if something is unclear. We do strive to make our work as re-usable as possible. Unfortunately our own research does not always allow us the time required to do so. Case in point, our scripting examples (e.g., https://github.com/tferr/ASA/blob/master/scripting-examples/3D_Analysis_ImageStack.py; https://github.com/tferr/ASA/blob/master/scripting-examples/3D_Analysis_ImageStack.py) are not well advertised. That being said, I am still surprised that in their side-by-side comparisons the authors were not able to automate more the processing steps (e.g., the ImageJ1 version of 'Sholl Analysis' remains fully functional and is macro recordable). If I misunderstood what was done, please provide the ImageJ macros you used. Also, I wanted to mention that i) semi-manual tracing with Simple Neurite Tracer (now "SNT"), can also be scripted (see https://doi.org/10.1101/2020.07.13.179325); and that ii) Fiji commands and plugins can also be called in native python using pyimagej (https://pypi.org/project/pyimagej/), see e.g., https://github.com/morphonets/SNT/tree/master/notebooks#snt-notebooks). Arguably, the fact that SMorph handles blob detection and skeletonization-based metrics directly is more advantageous from a user point of view. In Fiji, blob detection, skeletonization and Strahler analysis (https://imagej.net/Strahler_Analysis) of the skeleton are handled by different plugins. However, those are also fully scriptable, and interoperate well. The point that topographic skeletonization in Fiji can originate loops is valid, however the authors should know that such cycles can be detected and pruned programmatically using e.g., pixel intensities (see https://imagej.net/AnalyzeSkeleton.html#Loop_detection_and_pruning and the original publication (https://pubmed.ncbi.nlm.nih.gov/20232465/)

We completely agree with the reviewer’s assertion that most parts of the functionality of SMorph can be automated within imageJ as well, and in such comparison, the speed gains with SMorph will not be >1000X.

However, automating the analysis in imageJ is beyond the scope of the present manuscript. In fact, imageJ analysis comparison was not a part of our original manuscript at all. Upon presubmission inquiry to one of the affiliate journals of Review Commons, we were specifically asked to include a side-by-side comparison with “already available” methods. So, we decided to use ImageJ as it is, and automation, if any, was limited to simple macros to run a series of commands sequentially on batches of images. Although it is true that this analysis could be done much more efficiently with additional scripting, it would not have met the definition of “already available” tools. The imageJ analysis was performed in a way an average biologist with no programming experience would perform it, since that group will find SMorph most useful. In no way do we intend to imply that imageJ analysis can’t be made more efficient and automated. Perhaps it was not clear from the way the text was framed in the initial version of the manuscript. We will add additional text to make this point clearer.

On a side-note, in response to reviewer #2’s comments, we will perform the speed comparison on a per-image basis, so the speed gain (1080X) may change a little in the new comparison.

Broad applicability:

In our work, we made a significant effort to ensure that automated Sholl could be performed on any cell type: e.g., By supporting 2D and 3D images, by allowing repeated measures at each sampled distance, and by improving curve fitting. For linear profiles, we implemented the ability to perform polynomial fits of arbitrary degree, and implemented heuristics for 'best degree' determination. For normalized profiles, we implemented several normalizers, and alternatives for determining regression coefficients. We did not tackle segmentation of images directly (we did provide some accompanying scripts to aid users, see e.g. https://imagej.net/BAR) because in our case that is handled directly by ImageJ and Fiji's large collection of plugins. However, in SMorph, several of these parameters are hard-wired in the code. They may be suitable to the analyzed images, but they can be hardly generalized to other datasets. In detail: In terms of segmentation, SMorph is restricted to 2D images, scales data to a fixed 98 percentile, and uses a fixed auto-threshold method (Otsu). These settings are tethered to the authors imagery. They will give ill results for someone else using a different imaging setup, or staining method. In terms of curve fitting, the polynomial regression seems to be fixed at a 3rd order polynomial, which will not be suitable to different cell types (not even to all cells of 'radial morphology').

We have indeed hard-coded the parameters that the reviewer mentions, and we agree that we can perhaps give all options to the end-users to choose from. The decision was made to hard-code the parameters so that SMorph becomes very easy and minimalistic to use for the end-users. But the reviewer is right to point out that this may compromise the broad applicability and accuracy. We will update the code in the revised version of the manuscript to give the users control over choosing these parameters.

PCA:

The idea of making PCA analysis of Sholl-based morphometry accessible to a broader user base has merit and is welcomed. However, it has to be done carefully in a self-critic manner as opposed to a black-box solution. E.g., in the text it is mentioned that 2 principal components are used, in the tutorial notebook, 3. Why not provide intuitive scree plots that empower users with the ability to criticize choice? Also, it would be useful for users to understand which metrics correlate with each other, and their variable weights.

Reviewer #1’s suggestions would indeed make the PCA analysis more useful to the users. In the revised version of the code, we will provide additional data/plots to the user for making an informed choice of the significant principal components e.g. the elbow method, Ogive or Pareto plots, variable weights of different features in the principal components and correlation/covariance matrices.

When we showcased the utility of PCA to distinguish closely related morphology groups (as in Type-1 and Type-2 PV neurons), we had been unable to base the distinction on individual metrics, at least not in a robust manner (see Fig. S4 in Ferreira et al, 2014). A minor conundrum of the paper, is that it does not directly highlight the advantages of "analyzes in a multidimensional space". The differences between groups in the stab wound and DMI assays are such, that PCA is hardly needed: I.e., the differences depicted Fig2F,G are already significant, and already convey changes in "size and branch complexity" (as per PC1). The same argument applies to Fig. 5. The paper would profit from having this discussed.

PCA data indeed is not required to make any of the inferences we make in the paper and is superfluous. However, as mentioned in the discussion section of this manuscript, the low-dimensional PCA data can be used in future for other applications, e.g to cluster the astrocytes into morphometrically-defined subpopulations. SMorph can be further developed to perform real-time classification of these cells into morphometric clusters, which will allow the researchers to investigate clusters-specific gene expression, electrophysiology etc. Preliminary results from our lab do suggest that such clusters are differentially altered by stress and antidepressant treatments. However, these results are preliminary and are a part of a long-term future study. The data is really premature to publish at this stage, since it will require a lot of experimentation to show that these astrocyte subpopulations are indeed physiologically and functionally different. Nevertheless, we think that the utility of SMorph for such analyses may help others to come up with additional innovative ways to use the PCA data. Hence, we do believe that the community will benefit from the current release of SMorph having PCA. PCA data was shown in the figures just to demonstrate the functionality of SMorph. We will add additional text to make these points clearer.

Other:

- All metrics and parameters should be expressed in physical units (e.g.," radii increasing by 3 pixels", axes in Figure 2, 3, 5, S2) so that readers can directly interpret them.

In the revised manuscript, we will convert all units into actual physical distances.

- The paper would profit from the insights provided by Bird & Cuntz (https://pubmed.ncbi.nlm.nih.gov/31167149/)

We thank the reviewer for suggesting this paper. We will include this in the discussion of the manuscript.

**Minor comments:**

- Usage of RGB images (8-bit per channel) seems hardly justifiable. Aren't you loosing dynamic range of GFAP signal?

We agree that we could have captured the images at a higher dynamic range. However, for the changes we observe between treatment groups using GFAP immunoreactivity signal as presented in the manuscript, we do not see an advantage of using higher dynamic range. However, as the reviewer rightly pointed out, under certain conditions, imaging using a higher dynamic range may help and hence, we will include this recommendation in the materials and methods section.

- Please explain how MaxAbsScaler "prevents sub-optimal results"

Since morphometric features extracted from cell images either have different units or are scalar, we had to perform normalization before PCA. We will add further explanation in the methods section of the manuscript.

- The fact that automated batch processing can stall on a single bad 'contrast ratio' image seems rather cumbersome to deal with

This problem has been resolved in the current version of SMorph, which will be uploaded with the revised version of the manuscript.

- Please add a license to https://github.com/parulsethi/SMorph/. Without it, other projects may shy away from using SMorph

We will add a ____GPLv3 license

- "mounted on stereotax" should be "mounted on a stereotaxis device"?

We will make this change

- Ensure Schoenen is capitalized

We will make this change

Reviewer #1 (Significance (Required)):

I find the Desipramine results interesting. However, given the existing claims that DMI can modulate LTP, I regret that the authors did not look at structural modifications in hippocampal neurons (e.g., by performing the experiments in Thy1-M-eGFP animals). I understand, that doing so at this point would be a large undertaking.

Another manuscript from our lab__1, as well as work from other labs have shown that stress causes significant degenerative changes in hippocampal astrocytes__2,3__. In the light of these observations, we do believe that our observation of chronic antidepressant treatment inducing structural plasticity in astrocytes is significant. Structural alterations in neurons after DMI treatment are of interest. But in our experience, we have not seen gross morphological (dendritic arborization) changes in hippocampal neurons as a result of antidepressant drug treatments. Such changes are restricted to spine morphology and axonal varicosities, which is beyond the capabilities of SMorph. __

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

This paper addresses the challenge of automatic Sholl analysis of large dataset of multiple cell types such as neurons, astrocytes and microglia. The developed approach should improve the speed of morphology analysis compared to the state of the art without compromising on the accuracy. The authors present an interesting application of their tool to the morphological analysis of astrocytes following chronic antidepressant treatment. The paper is well written, and the tool presented could be beneficial for different applications and context. However, some major aspects should be addressed by the author concerning the description of the algorithms used and the quantification of the results.

We thank reviewer #2 for their careful reading of the paper and their comments.

**Major comments/Questions:**

1. In the Results and/or Methods sections, the author should better describe how their approach is different from state-of-the-art approaches in terms of algorithms used and how these difference impacts on the speed and accuracy of the analysis.

We will add these descriptions in the methods section in response to this comment as well as some comments from reviewer #1.

Imaging was performed on a Zeiss LSM 880 airyscan confocal microscope. Is this method robust to other types of imaging techniques, other microscopes, variable levels of signal-to-noise? This should be tested and quantified.

We will demonstrate the results obtained from images taken using different microscopes and imaging techniques, and quantify the outcome.

Manual cropping of the cells with ImageJ was used. However, in the methods section, the authors mention that other machine learning tools could be used for this task. Why were these tools not implemented in this paper in order to propose a fully automated analysis approach in combination with SMorph?

We have tried both the machine learning tools cited in this paper (one for DAB images and other for confocal images). However, in our experience, we do not get robust performance from these tools with our datasets, and these tools will perhaps need more optimization for broad applicability. We are developing an auto-cropping tool in-house, but that is beyond the scope of the current study. Another point is that these tools are tailor-made for astrocytes, and their integration into SMorph will restrict its applicability to just one cell type.

In the methods section you state that cropped cells need to have a good contrast ratio for automated batch processing. Could you define what a good contrast ratio is and characterize the performance of your approach for different contrast ratio?

In the revised manuscript, we will compare the images taken from multiple microscopes and quantify the outcome. We will change the text accordingly. As such, the comment on rejected cells referred to really poor quality images. In the revised manuscript, we will make specific recommendations on imaging parameters so that this should not be an issue at all.

It is mentioned that the analysis routine can be interupted by a cell with lower contrast ratio. This is a major drawback of the approach (but I think that it could be easily improved), as such interruptions may not be= practicable for many applications that need to rely on automated processing.

We have already rectified this problem and the updated version of SMorph will be uploaded with the revised manuscript.

Also, you should precise how the contrast ratio should be enhanced without modifying raw data in order to be processed with your approach. You suggest removing cells with lower contrast ratio from the analysis, but can this impact on the findings especially if some treatments impact on the detected fluorescence signal? Can you propose ways to improve the robustness of your approach to variable signal ratios?

It is indeed possible that removing cells from analysis, may in certain cases, affect the results. To rectify this, we are testing the method on images obtained from different microscopes and under different imaging conditions. From these analyses, we will deduce minimum recommendations for imaging conditions so that images don’t have to be edited/altogether removed from analysis for the software to work. In the materials and methods section, we will add these recommendations to the users on the optimal range of imaging parameters. This way, rejection/modification of images should not be an issue.

In the Results section, you describe the time necessary to perform different analysis. However, giving a total time in hours is not very informative as this will likely vary a lot depending on the size of the dataset, complexity of the images, etc. You should compare the average time per image for both methods and types of analysis.

We compared the total time required for the entire dataset, since SMorph is meant for batch-processing all the images at once. However, we can change the comparisons to time taken per image. We can divide the total time taken by SMorph by the number of images analysed. However, in our opinion, the time taken to initiate SMorph will make these comparisons inaccurate.

You state that for the number of branch point, the lower value of the measured slope when comparing SMorph and ImageJ was related to a constant overestimation of this parameter with ImageJ. How was this quantified? I think you should stress out more the comparison of both approaches with the manually annotated dataset.

In the revised version of this manuscript, we will include some examples of skeletonized images that overestimate the number of forks. We have observed this to be a recurring problem with the skeletonization tools we have tried in imageJ. This can be rectified in imageJ itself as pointed out by reviewer #1. However, that’s beyond the scope of the present study and will not fit the definition of comparison with “already available” methods.

How can you explain the differences in the 2D-projected Area, total skeleton length and convex hull between SMorph and ImageJ, which all show a slope around 0.83? Can you quantify the performance of both methods by comparing them with your manually annotated dataset?

In the revised version, we will include the correlation data between completely manual and SMorph comparisons. We will discuss these comparisons further in the manuscript and make specific conclusions about the accuracy.

In the introduction and discussion, you mention that you present a method that works on neurons, astrocytes and microglia. However, I don't see in the paper the comparison between the accuracy for all these cell types as you seem to have analyzed only the morphology of astrocytes.

In the revised manuscript, we will include the Sholl analysis comparison (imageJ vs SMorph) from images of neurons and microglia.

You mention that your method is quite sensitive to variation in contrast ratio. You should quantify the contrast ratio throughout the experiments and ensure that this is not biasing the SMorph analysis for some of the treatments.

We thank both reviewers for highlighting this issue in the initial version of SMorph. As mentioned in our response to point #6, we will perform additional analyses to make specific recommendations to the end users regarding imaging parameters so that SMorph can work on images as they are. As such, our comments on contrast ratio applied only to very poor quality images. If images are acquired conforming to the imaging parameters we will recommend in the revised manuscript, images can be analysed without any issues.

**Minor Points :**

1. Precise the exact inclusion and exclusion criteria for Soma detection and rephrase: "The high-intensity blobs were detected as a position of soma..." & "Boundary blobs coming from adjacent cells...".

We will add a complete explanation of blob detection and the exclusion criterion in the methods section.

Throughout the text, make sure to always refer to an analysis time per image or per cell and not only include absolute duration values without reference to the task at hand (e.g. in the discussion : SMorph took 40 second to complete the analysis... please state to which analysis you are exactly referring to and if applicable if it varies from cell to cell).

We will change all comparisons to time taken per cell. Text will be added to mention which datasets were used when any claims of speed are made.

When you state in the discussion that "Although some methods do allow Sholl analysis without manual neurite tracing, they still work on one cell at a time", please precise if the only aspect that is missing from this type of analysis is batch processing (looping through the data) or if there is a major obstacle to automate this technique. This is important a SMorph does proceed with the analysis one cell at a time but can work in a loop/batch.

We will elaborate further on our assertion regarding the challenges of using imageJ plugins for sholl analysis in large batches of cells.

Reviewer #2 (Significance (Required)):

This tool could very useful to researchers in the field of cellular neuroscience working with high-throughput analysis of microscopy data. The authors show some interesting improvements over existing methods. An improved quantitative characterization of the robustness of their approach would be of great importance to ensure the significance of this tool to a large community of researchers using different types of microscopes or studying different cell types.

My expertise is in the field of optical microscopy and high-throughput (automated) image analysis for neuroscience. My expertise to evaluate the biological findings in this study is very limited.

We thank reviewer #2 for their careful reading of the manuscript and their insightful comments. Growing evidence (clinical and preclinical) shows a significant reduction in astrocyte density in key limbic brain regions as a result of depression. We believe that the structural plasticity induced by chronic antidepressant treatment, as demonstrated in this manuscript, is an interesting novel plasticity mechanism that can negate deleterious effects of stress on astrocytes.

The improvements suggested by both reviewers will help us to greatly improve SMorph in the revised version of this manuscript.

References:

1. Virmani, G., D’almeida, P., Nandi, A. & Marathe, S. Subfield-specific Effects of Chronic Mild Unpredictable Stress on Hippocampal Astrocytes. doi:10.1101/2020.02.07.938472.
2. Czéh, B., Simon, M., Schmelting, B., Hiemke, C. & Fuchs, E. Astroglial plasticity in the hippocampus is affected by chronic psychosocial stress and concomitant fluoxetine treatment. Neuropsychopharmacology 31, 1616–1626 (2006).
3. Musholt, K. et al. Neonatal separation stress reduces glial fibrillary acidic protein- and S100beta-immunoreactive astrocytes in the rat medial precentral cortex. Dev. Neurobiol. 69, 203–211 (2009).

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Reply to the reviewers

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

**Summary:**

-The authors have carried out an extensive survey of dorso-ventral axis determination in the cricket Gryllus bimaculatus. They did this through analysing and knocking down key components of the two main pathways involved in D/V patterning, the toll pathway and BMP signalling. This analysis was placed in a comparative context, looking at published data on four other insect species, with the aim of contributing to our understanding of the evolution of D/V patterning.

-The authors find significant similarities between D/V patterning in Gryllus and in Drosophila - These similarities are both in the relative contributions of toll and BMP to D/V polarization and in the early ovarian activation of the toll pathway. Despite these similarities, a closer analyses of the molecular interactions uncovers some significant differences, most notably, the absence of several key modulators of BMP activity. These results lead the authors to conclude that the similarities in D/V patterning between Gryllus and Drosophila are due to convergence and not due to the conservation in Drosophila of an ancestral patterning mechanism that has been lost in almost all other lineages studied.

**Major comments:**

•All in all this is an excellent paper. There is a huge amount of data in here, and everything is done very meticulously and carefully. There is a good balance between mostly descriptive work (gene expression patterns, cell movements in WT embryos) and experimental work. I could find no obvious flaws with any of the results or methods, and I think the authors have made a convincing case to support their conclusions, without being too dogmatic.

•I don't see a need for any additional experiments beyond what the authors have done. They have covered all relevant aspects of D/V patterning, and make a convincing case with the data they have.

**Minor comments:**

The few comments I have are very minor and technical:

•Missing taxonomic names (families) in Fig. 1

•Missing label in Fig. 6 Panel A.

•Punctuation could be improved. There are several instances of missing commas, and other places with unnecessary commas.

*Reviewer #1 (Significance (Required)):

•The manuscript represents an admirable amount of work. One can say that in a single paper, the authors have provided nearly as much information about Gryllus D/V patterning as is available for other "second-order" insect model species such as Oncopeltus or Nasonia. A such, it provides an additional major phylogenetic anchor point for understanding the evolution of early patterning.

•In terms of significance to advancing our knowledge, the data in the manuscript is, as stated above, an anchor point. It does not on its own provide any major novel insight, but fits into an ever-expanding body of comparative knowledge, whose importance is greater than the sum of its parts. Perhaps the most interesting conclusion, is indeed the one the authors have chosen as the selling-point of their paper, the fact that there is functional convergence in certain aspects of D/V patterning between two widely diverged insect species, with very different oogenesis and early development. This is again, not a major advance on its own, but an important additional piece of the comparative picture of early insect development.

•This paper will be of significant interest to the research community of comparative insect development (the community to which this reviewer belongs). It will also be of interest to those interested in examples of convergence at the functional and molecular level, to those interested in the evolution of gene families and to those interested specifically in the signalling pathways discussed (even in a non-comparative context).*

Response

We thank the reviewer for the very positive response to our paper.

We added missing taxonomic names and labels in Figure 6A and improved the punctuation throughout the manuscript.

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

In this paper Pechmann and colleagues investigate the molecular mechanisms of dorso-ventral patterning in Gryllus bimaculatus. As a basis for their study they carry out thorough RNAseq analyses of various embryonic stages. Gryllus is a member of the hemimetabolous insects and therefore of interest for comparison with holometabolous insects such as Drosophila, Tribolium and Nasonia. Previous work has shown that there are significant differences in the use of Toll and Sog in establishing the dorso-ventral gradient of BMP signaling among Drosophila and Nasonia. Pechmann et al find that in Gryllus Toll has a similar role as in Drosophila and is regulated via Pipe, so far only found in Drosophila. Furthermore, they show by RNAi knockdown studies that loss of BMP signaling has little impact on the differentiation of mesoderm in Gryllus, like in Drosophila, hence, BMP signaling has largely a role in dorsal fates. Ventral fates are under direct control of the Toll gradient. Surprisingly, they also find that the key antagonist of BMP signaling and shuttle for BMPs, Sog, has been lost in Ensifera, the lineage leading to Gryllus.

This is a thorough and detailed study involving a series of functional experiments, which highlights the flexibility and evolvability of GRN of the dorso-ventral body axis formation in insects. The major finding that Gryllus is more similar to Drosophila than is Nasonia and Tribolium is interesting and even somewhat unexpected, since Drosophila is often regarded as the derived odd ball. The authors discuss two obvious explanations: the situation found in Gryllus and Drosophila reflects the ancestral condition, or, alternatively, it is the result of convergent evolution. They tend to favor the latter hypothesis. This study is an important advancement to our understanding, as it shows the constraints and the evolvability of a key patterning system to establish a body axis.

Even though the authors show nicely that Toll signaling is required to establish the BMP signaling gradient, the loss of Sog in Gryllus leaves the question unanswered how the long range BMP gradient and its shape is established. In Drosophila and vertebrates, Sog/Chordin acts both as an antagonist close to its source and as a shuttling factor, promoting BMP signaling at a distance, which is crucially important for the long range and the shape of the BMP signaling gradient. It would be desirable to test the function of other potential BMP antagonists (follistatin, gremlin, noggin) or competing BMPs (BMP3, ADAMP) in this context.

As a minor suggestion, I would recommend to summarize the findings in a synthetic picture depicting the evolutionary scenarios of the two hypotheses.

Reviewer #2 (Significance (Required)):

This study is an important advancement to our understanding, as it shows the constraints and the evolvability of a key patterning system to establish a body axis.

Response

We thank the reviewer for the very positive response to our paper.

As the reviewer suggested we added a schematic representation (Figure 11) depicting the two scenarios, which explain the evolution of DV patterning.

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

**Summary**

This manuscript continues a series of beautiful papers from Roth, Pechman, Lynch and colleagues analysing D/V patterning in a range of insects. The work started with Drosophila and has extended to other holometabolous and now hemimetabolous insect species.

This paper is in many ways one of the most remarkable of the series, for it shows that the mechanisms of D/V patterning in the cricket Gryllus are, in several striking respects, very similar to those known from Drosophila - much more so than in some of the other insects studied to date, even though Gryllus is phylogenetically the most distant from Drosophila.

Specifically, the authors present compelling data to show that the roles of Toll and dpp, as inferred from their knockdown phenotypes, are remarkably similar in Gryllus and Drosophila. This is very different from the consequences of toll and dpp knockdown in the hemipteran Oncopeltus, a species which almost certainly shares a more recent common ancestor with Drosophila.

The discussion, after summarising the results, addresses the interpretation of this surprising observation. The authors favour the hypothesis that the similarity between Drosophila and Gryllus is the result of convergence in the roles and regulation of Toll and dpp signalling, rather than an ancestral trait that has been lost to a greater or lesser extent in Oncopeltus, and in the two other insects previously studied. The argument for this interpretation is carefully made, on the basis of a thorough knowledge of the comparative embryological literature (including highly relevant recent work).

**Major comments**

The work depends on an analysis of candidate genes, not de novo functional searches. However, it builds on the well established understanding of the relevant genetic machinery in Drosophila, and on extensive knowledge of the genome and transcriptome of Gryllus, a dataset that has been substantially extended by new work reported in this paper, on ovary and embryonic transcriptomes. These data are sufficiently complete to give confidence that all orthologues of most of the known candidate genes have been identified, and to highlight the apparent absence from the Gryllus genome of any sog/chordin orthologue - a key dpp inhibitor widely involved in D/v patterning.

The embryology is beautifully described. The early stages of these very yolky eggs are not easy to handle, but the stainings reported here are almost all of high quality, as are the movies of live development using a nuclear GFP marked line.

The gene knockdowns appear to have been carried out carefully with due regard for the potential biases caused by sterility following parental RNAi. Phenotypes have been documented effectively by the expression of marker genes in fixed embryos, and by live imaging of development in knockdown embryos. Tables in the supplementary data show that sufficient numbers have been obtained. The work is carefully interpreted, and where inferences are less than certain, they are carefully phrased.

I find the results convincing, and therefore accept the conclusion of fundamental similarity between the roles of Toll and dpp in Drosophila and Gryllus.

Time will tell whether or not the authors favoured interpretation of these data as convergent is correct, but I certainly believe that the argument as here presented in the discussion is appropriate for publication in its current form. The abstract is, appropriately, more non-committal than the discussion itself on the interpretation of these results.

The paper is well written.

**Minor points**

Videos - please state orientation of the embryos, especially in videos 2 &4

Page 23 bottom "The early dorsal-to-ventral gradient of pMad (Figure 5AB) indicates that BMP signalling plays an important role ...." suggests would be better than indicates here, until functional data is considered.

Reviewer #3 (Significance (Required)):

The gene networks mediating patterning of the D/V body axis are related across the whole range of animals, with in particular the involvement of TGFb/dpp signalling being almost universal in this process. However, there are a great many variations on this theme. Even within the insects, the mechanisms that have been described for establishing localised TGFb and Toll signalling span the range from self organisation to effective maternal prelocalisation. This has made the GRN underlying D/V patterning a key model for studies of the evolution of gene regulatory networks.

This paper adds an interesting and important twist to the story. It is certainly not the result that any of us would have expected, based on prior published work from Oncopeltus.

If indeed it does turn out to be a case of convergence, a more detailed mechanistic analysis of that convergence will provide considerable insight into the reproducibility of evolution.

Other published work: There is no comparable work on D/V patterning in any other polyneopteran insect, to my knowledge.

Audience: Insect developmental biologists, evolutionary developmental biologists and others interested in the evolution of gene regulatory networks.

My expertise: Arthropod embryology, axial patterning, evolutionary developmental biology.

I have not reviewed in detail the presentation of the transcriptomic data and the phylogenetic analysis of gene sequences as presented in the supplementary info.

Response

We thank the reviewer for the very positive response to our paper.

We made the small textual corrections suggested by the reviewer.

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Reply to the reviewers

Overall, we were pleased that the reviewers found our study carefully designed and interesting. We have addressed their comments below.

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

The manuscript by Kern, et al., demonstrates that phagocytosis in macrophages is regulated in part by the intermolecular distance of phagocytosis-promoting receptors engaging phagocytic targets. Cells expressing chimeric receptors containing cytosolic domains of Fc receptors (FcR) and defined ligand-binding DNA domains were used to drive phagocytosis of opsonized glass beads coated with complementary DNA ligands of defined spacing and number. These so-called origami ligands allowed manipulation of receptor spacing following engagement, which allowed the demonstration that tight spacing of ligands (7 nm or 3.5 nm) optimized signaling for phagocytosis. The study is carefully performed and convincing. I have a few technical concerns and minor suggestions.

1. __ It is assumed that the origami preparations were entirely uniform. How much variation was there? Is that supported by TIRF microscopy of origami preparations? Was the TIRF microscopy calibrated for uniformity of fluorescence (ie., shade correction)?__ Our laboratory, Dong et al., has extensively characterized the origami uniformity and robustness of these exact pegboards. This paper was just posted on bioRxiv (Dong et. al, 2021). We have also cited this paper in our revised manuscript in reference to the characterization of the DNA origami (Line 117).

We did not use any shade correction. Instead we only collected data from a central ROI in our TIRF field. To check for uniformity of illumination, we plotted the origami pegboard fluorescent intensity along the x and y axis. We observed very modest drop off in signal - the average signal intensity of origamis within 100 pixels of the edge is 76 ± 6% the intensity of origamis in a 100 pixel square in the center of the ROI. Fitting this data with a Gaussian model resulted in very poor R values. While this may account for some of the variation in signal intensity at individual points, we expect the normalized averages of each condition to be unaffected. We have amended the methods to describe this strategy (Lines 851-854).

(Image could not be uploaded)

__ Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?__

We thank the reviewer for bringing up this point. We confirmed comparable receptor expression levels at the cell cortex of the DNA CAR-𝛾 and the DNA CAR-adhesion used throughout the paper. We also have confirmed that receptor levels at the cell cortex were similar for the large DNA CAR constructs used in Figure 6C-D. This data is now included in Figures S5 and S7. We have also altered the text to include this (lines 169-172):

Expression of the various DNA CARs at the cell cortex was comparable, and engulfment of beads functionalized with both the 4T and the 4S origami platforms was dependent on the Fc𝛾R signaling domain (Figure S5).

When quantifying bead engulfment, cells were selected for analysis based on a threshold of GFP fluorescence, which was held constant throughout analysis for each individual experiment. We have amended the “Quantification of engulfment” methods section to convey this (lines 921-923).

__ The scale of the origami relative to the cells is difficult to discern in Figures 2C and D. Additional text would be helpful to indicate, for example, that the spots on the Fig. 2D inset indicate entire origami rather than ligand spots on individual origami particles.__

Thank you for pointing this out, we see how the legend was unclear and have corrected it (lines 453-454), including specifically noting “Each diffraction limited magenta spot represents an origami pegboard.” We have also outlined the cell boundary in yellow to make the cell size more clear.

__ Figure 5 legend, line 482: How was macrophage membrane visualized for these measurements?__

We have added the following clarification (line 535-536): “The macrophage membrane was visualized using the DNA CAR𝛾, which was present throughout the cell cortex.”

__ line 265: "our data suggest that there may be a local density-dependent trigger for receptor phosphorylation and downstream signaling". This threshold-dependent trigger response was also indicated in the study of Zhang, et al. 2010. PNAS.__

The Zhang et al. study was influential in our study design, and we wish to give the appropriate credit. Zhang et al. found that a sufficient amount of IgG is necessary to activate late (but not early) steps in the phagocytic signaling pathway. In contrast, our study addresses IgG concentration in small nanoclusters. We find that this nanoscale density affects receptor phosphorylation. Thus, we think these two studies are distinct and complementary.

Lines 283-287 now read:

While this model has largely fallen out of favor, more recent studies have found that a critical IgG threshold is needed to activate the final stages of phagocytosis (Zhang et al., 2010). Our data suggest that there may also be a nanoscale density-dependent trigger for receptor phosphorylation and downstream signaling.

__ line 55: Rephrase, “we found that a minimum threshold of 8 ligands per cluster maximized FcgR-driven engulfment.” It is difficult to picture how a minimum threshold maximizes something.__

We now state “we found that 8 or more ligands per cluster maximized FcgR-driven engulfment.”

__ line 184: Rephrase, "we created... pegboards with very high-affinity DNA ligands that are predicted not to dissociate on a time scale of >7 hr". Remove "not".__

Thank you for pointing this out, it is now correct.

Reviewer #1 (Significance (Required)):

This study provides a significant advance in understanding about the molecular mechanisms of signaling for particle ingestion by phagocytosis.

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

The manuscript on “Tight nanoscale clustering of Fcg-receptors using DNA origami promotes phagocytosis" studies how clustering and nanoscale spacing of ligand molecules for a chimeric Fcg-receptors influence the phagocytosis of functionalized silicon beads by macrophage cell lines. The basis of this study is the design of a chimeric Fc-receptor (DNA-CARg) comprising an extracellular SNAP-tag domain that can be loaded with single-stranded (ss) DNA, the transmembrane part of CD86 and the cytosolic part of the Fc-receptor g-chain containing an immunoreceptor tyrosine-based activation motif (ITAM) as well as a C-terminal green fluorescent protein (GFP). As control the authors used a similar designed DNA-CAR that is lacking the intracellular ITAM-containing FCg tail. The chosen target for this chimeric DNA-CAR, are silicon beads covered by a lipid bilayer that contains biotin-labelled lipids that, via Neutravidin, can be loaded with a biotinylated DNA origami pegboard displaying complimentary ss-DNA as ligand for the DNA-CAR. The DNA origami pegboard contains four ATTO647N fluorescence for visualization and the ssDNA ligand in different quantities and spacing. Using these principles, the authors study how ligand affinity, concentration and spacing influence the activation of the DNA-CARg and the engulfment of the loaded beads.

The authors show that bead engulfment is increased between 2 till 8 ssDNA ligands on the pegboard. After this, ligand numbers do not play a role anymore in the engulfment. They then study the role of the ligand spacing using pegboards that either contain 4 single strand DNA ligands in close (7nm/3,5nm) proximity or a more spaced version using 21/17,5 nm or 35/38,5 nm. The authors find that the bead engulfment is maximally and positively affected by the close spacing of the ssDNA ligands. In their final experiments the authors vary the design of the DNA-CARs by tetramerization of the ITAM-containing Fcg-signaling subunit. In their discussion the authors mention different possibilities for the effect of spacing on the engulfment process.

I think that, in general, this is an interesting study. However, it has some caveats and open issues that should be clarified before its publication.

**Major comments**

1. __ As a general comment, it is somewhat a pity that the authors did not use the endogenous FcR as a control. It would have been quite easy for the authors to place the SNAP-tag domain on the Fcg extracellular domain which would allow to do all their experiments in parallel, not only with the DNA-CAR, but also with a DNA-containing wild type receptor. Such a control would be important because, by using a CD86 transmembrane domain, the authors do not know whether the nanoscale localization of their chimeric receptors is reflecting that of the endogenous Fcg receptor.__

We agree with the reviewer completely. We have repeated experiments shown in Figure 4A with a DNA-CAR containing the Fc𝛾 transmembrane domain instead of CD86 as the reviewer suggests. We also included a DNA-CAR version of the Fc𝛾R1 alpha chain, although this construct was not expressed as well as the others. These data are now included in Figure S5, and referenced in lines 167-168.

__ An important issue that is discussed by the authors but not addressed in this manuscript is whether the different amount and spacing of the ligand is only impacting on signaling or also on the mechanical stress of the cells. Indeed, mechanical stress on the cytoskeleton arrangement could influence the engulfment process. For this, it would be very important to test that the different bead engulfment, for example, those shown in Fig. 4, is strictly dependent on signaling kinases. The authors should repeat the experiment of Fig. 4 a and b in the presence or absence of kinase inhibitors such as the Syk inhibitor R406 or the Src inhibitor PP2 to show whether the different phase of engulfment is dependent on the signaling function of these kinases. This crucial experiment is clearly missing from their study.__

We agree this is an interesting point. We find that ligand spacing affects receptor phosphorylation; however this does not preclude effects on downstream aspects of the signaling pathway. We will clarify this by adding the following comment to the manuscript (line 299-301):

While our data pinpoints a role for ligand spacing in regulating receptor phosphorylation, it is possible that later steps in the phagocytic signaling pathway are also directly affected by ligand spacing.

The DNA-CAR-adhesion in Figure 1 strongly suggests that intracellular signaling is essential for phagocytosis. We have now included additional controls using this construct as detailed in our response to point 3 below. Unfortunately, Src and Syk inhibitors or knockout abrogate Fc𝛾R mediated phagocytosis (for example, PMIDs 11698501, 9632805, 12176909, 15136586) and thus would eliminate phagocytosis in both the 4T and 4S conditions. This precludes analysis of downstream steps in the phagocytic signaling pathway.

__ Another problem of this study is that the authors show in Fig. 1A the control DNA-CAR-adhesion but then hardly use it in their study. For example, the crucial experiments shown in Fig. 4 should be conducted in parallel with DNA-CAR-adhesion expressing macrophage cells. This study could provide another indication whether or not ITAM signaling is important for the engulfment process.__

We have added this control. It is now included in Figure S5 and S7. Figure 3D also shows that the DNA-CAR-adhesion combined with the 4T origami pegboards does not activate phagocytosis and we have amended the text to make this more clear (line 152).

__ Another important aspect is how the concentration of the loaded origami pegboard is influencing the engulfment process. In particular, it would be interesting to show the padlocks with different spacings such as the 4T closed spacing versus 4s large spacing show a different dependency on the concentration of this padlock loading on the beads. This would be another important experiment to add to their study.__

We agree that this is an interesting question. We suspect that at a very high origami density, 4S signaling would improve, and potentially approach the 4T. However, we are currently coating the beads in saturating levels of origami pegboards. Thus we cannot increase origami pegboard density and address this directly.

**Minor comments:**

1. __ The definition of the ITAM is Immunoreceptor Tyrosine-based Activation Motif and not "Immune Tyrosine Activation Motif" as stated by the authors.__ We have corrected this.

__ The authors discuss that it is the segregation of the inhibitory phosphatase CD45 from the clustered Fc receptors is the major mechanism explaining their finding that 4T closed spacing is more effective than 4s large spacing. With the event of the CRISPR/Cas9 technology it is trivial to delete the CD45 gene in the genome of the RAW264.7 macrophage cell line used in this study and I am puzzled why they author are not conducting such a simple but for their study very important experiment (it takes only 1-2 month to get the results).__

This experiment may be informative but we have two concerns about its feasibility. First, CD45 is a phosphatase with many different roles in macrophage biology, including activating Src family kinases by dephosphorylating inhibitory phosphorylation sites (PMID 8175795, 18249142, 12414720). Second, CD45 is not the only bulky phosphatase segregated from receptor nanoclusters. For example, CD148 is also excluded from the phagocytic synapse (PMID 21525931). CD45 and CD148 double knockout macrophages show hyperphosphorylation of the inhibitory tyrosine on Src family kinases, severe inhibition of phagocytosis, and an overall decrease in tyrosine phosphorylation (PMID 18249142). CD45 knockout alone showed mild phenotypes in macrophages. We anticipate that knocking out CD45 alone would have little effect, and knocking out both of these phosphatases would preclude analysis of phagocytosis. Because of our feasibility concerns and the lengthy timeline for this experiment, we believe this is outside of the scope of our study.

In our discussion, we simplistically described our possible models in terms of CD45 exclusion, as the mechanisms of CD45 exclusion have been well characterized. This was an error and we have amended our discussion to read (lines 335-343):

As an alternative model, a denser cluster of ligated receptors may enhance the steric exclusion of the bulky transmembrane proteins like the phosphatases CD45 and CD148 (Bakalar et al., 2018; Goodridge et al., 2012; Zhu, Brdicka, Katsumoto, Lin, & Weiss, 2008).

Reviewer #2 (Significance (Required)):

The innovative part of this study is the combination of SNAP-tag attached, chimeric Fc-receptor with the DNA origami pegboard technology to address important open question on receptor function.

**Referees cross-commenting**

I find most of my three reviewing colleagues reasonable

I also agrée to Reviewer #1 comments 2

Likewise, how much variation was there in the expression of the chimeric receptors? Large variation in receptor numbers per cell could significantly alter the quantitative studies. Aside from the flow sorting for cells expressing two different molecules, how were cells selected for analysis?

But I want to add it is not only the amount of receptors but ils the nanoscale location that is key to receptor function

We have ensured that all receptors are trafficked to the cell surface. We have also measured their intensity at the cell cortex as discussed in response to Reviewer 1.

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

This is a very nicely done synthetic biology/biophysics study on the effect of ligands spacing on phagocytosis. They use a DNA based recognition system that the group has previously use to investigate T cell signaling, but express the SNAP tag linked transmembrane receptor in a macrophage cell line and present the ligands using DNA origami mats to control the number and spacing of complementary ligands that are designed to be in the typical range for low or high affinity FcR, a receptor that can trigger phagocytosis. The study offers some very nice quantitative data sets that will be of immediate interest to groups working in this area and, in the future, for design of synthetic receptors for immunotherapy applications. Other groups are working on similar platform for TCR. I don't feel there is any need for more experiments, but I have some questions and suggestions. Answering and considering these could clarify the new biological knowledge gained.

We thank the reviewer for their support of our manuscript. Given the reviewer’s statement that no new experiments are required, we have answered their questions to the best of our ability given the current data. Should the editor decide that any of these topics require experimental data to enhance the significance of the paper, we are happy to discuss new experiments.

Reviewer #3 (Significance (Required)):

I think the significance would be increased by addressing these questions, that would help understand how the synthesis system described related to other system directed as similar questions and more natural settings.

1. __ The densities of the freely mobile DNA ligands required to trigger phagocytosis is quite high. Was the length of the DNA duplexes optimized? The entire complex for both the intermediate and high affinity duplexes seems quite short, perhaps The extracellular domain of the DNA-CAR (SNAP tag and ssDNA strand) are approximately 10 nm (PMID 28340336). The biotinylated ligand ssDNA is attached to the bilayer via neutravidin, resulting in a predicted 14 nm intermembrane spacing. The endogenous IgG FcR complex is 11.5 nm. Bakalar et al (PMID 29958103) tested the effect of antigen height on phagocytosis and found that the shortest intermembrane distance tested (approximately 15 nm) was the most effective. As the reviewer notes, the optimal distance between macrophage and target may be larger than our DNA-CAR. However we think the intermembrane spacing in our system is within the biologically relevant range.

We saw robust phagocytosis at 300 molecules/micron of ssDNA, which is similar to the IgG density used on supported lipid bilayer-coated beads in other phagocytosis studies (PMID 29958103, 32768386). As the reviewer noticed, this is significantly higher than ligand density necessary to activate T cells (PMID 28340336). We have added a comment on ligand density to lines 96-97.

__ Are the origami mats generally laterally mobile on the bilayers. If so, what is the diffusion coefficient? Can one detect the mats accumulating in the initial interface between the bead and cell, particularly in cased where there is no phagocytosis? Would immobility of the mats make them more efficient at mediating phagocytosis compared to the monodispersed ligands, which I assume are highly mobile and might even be "slippery".__

We have confirmed that our bead protocol generally produces mobile bilayers, where his-tagged proteins can freely diffuse to the cell-bead interface (see accumulation of a his-tagged FRB binding to a transmembrane FKBP receptor at the cell-bead synapse below). We can qualitatively say that the origamis appear mobile on a planar lipid bilayer (see Dong et. al 2021 and images below). Directly measuring the diffusion coefficient on the beads is extremely difficult because the beads themselves are mobile (both diffusing and rotating), and cannot be imaged via TIRF. We do not see much accumulation of the origami at cell-bead synapses. This could reflect lower mobility of the origamis, or could be because the relative enrichment of origamis is difficult to detect over the signal from unligated origamis.

Overall, we expect the origami pegboards (tethered by 12 neutravidins) are less mobile than single strand DNA (tethered by a single neutravidin, supported by qualitative images below). We are uncertain whether this promotes phagocytosis. At least one study suggests that increased IgG mobility promotes phagocytosis (PMID 25771017). However, the zipper model would suggest that tethered ligands may provide a better foothold for the macrophage as it zippers the phagosome closed (PMID 14732161). Hypothetically, ligand mobility could affect signaling in two ways - first by promoting nanocluster formation, and second by serving as a stable platform for signaling as the phagosome closes. Since our system has pre-formed nanoclusters, the effect of ligand mobility may be quite different than in the endogenous setting.

(Image could not be uploaded)

In the above images, a 10xHis-FRB labeled with AlexaFluor647 was conjugated to Ni-chelating lipids in the bead supported lipid bilayer. The macrophages express a synthetic receptor containing an extracellular FKBP and an intracellular GFP. Upon addition of rapamycin, FRB and FKBP form a high affinity dimer, and FRB accumulates at the bead-macrophage contact sites.

(Image could not be uploaded)

In the above images, single molecules were imaged for 3 sec. The tracks of each molecule are depicted by lines, colored to distinguish between individual molecules. The scale bar represents 5 microns in both panels.

__ Breaking down the analysis into initiation and completion is interesting. When using the non-signalling adhesion constructs, would they get to the initiation stage or would that attachment be less extensive than the initiation phase.__

This is an interesting question. While we did not include the DNA-CAR-adhesion in our kinetic experiments, we have now quantified the frequency of cups that would match our ‘initiation’ criteria in 3 representative data sets where macrophages were fixed after 45 minutes of interaction with origami pegboard-coated beads. We found that an average of 16/125 of 4T beads touching DNA-CAR-adhesion macrophages met the ‘initiation’ criteria and an average of 2/125 were eaten (14% total). In comparison, we examined 4T beads touching DNA CAR𝛾 macrophages and found that on average 23/125 met the ‘initiation’ criteria, and 45/125 were already engulfed (54%). This suggests that the DNA-CAR-adhesion alone may induce enough interaction to meet our initiation criteria, but without active signaling from the FcR this extensive interaction is rare. We have added this data in a new Figure S6 and commented on this in lines 213-215.

__ It would be interesting to put these results in perspective of earier work on spacing with planar nanoarrays, although these can't be applied to beads. For integrin mediated adhesion there was a very distinct threshold for RGD ligand spacing that could be related to the size of some integrin-cytoskeletal linkers (PMID: 15067875). On the other hand, T cell activation seemed more continuous with changes in spacing over a wide range with no discrete threshold (PMID: 24117051, 24125583) unless the spacing was increased to allow access to CD45, in which case a more discrete threshold was generated (PMID: 29713075). The results here for phagocytosis with the very small ligands that would likely exclude CD45 seems to be more of a continuum without a discrete threshold, although high densities of ligand are needed. This issue of continuous sensing vs sharp threshold is biologically interesting so would be good assess this by as consistent standards are possible across systems.__

We agree that this is an interesting body of literature worth adding to our discussion. We have added a paragraph that puts our study in the context of prior work on related systems, including these nanolithography studies (Line 364-382):

How does the spacing requirements for Fc𝛾R nanoclusters compare to other signaling systems? Engineered multivalent Fc oligomers revealed that IgE ligand geometry alters Fcε receptor signaling in mast cells (Sil, Lee, Luo, Holowka, & Baird, 2007). DNA origami nanoparticles and planar nanolithography arrays have previously examined optimal inter-ligand distance for the T cell receptor, B cell receptor, NK cell receptor CD16, death receptor Fas, and integrins (Arnold et al., 2004; Berger et al., 2020; Cai et al., 2018; Deeg et al., 2013; Delcassian et al., 2013; Dong et al., 2021; Veneziano et al., 2020). Some systems, like integrin-mediated cell adhesion, appear to have very discrete threshold requirements for ligand spacing while others, like T cell activation, appear to continuously improve with reduced intermolecular spacing (Arnold et al., 2004; Cai et al., 2018). Our system may be more similar to the continuous improvement observed in T cell activation, as our most spaced ligands (36.5 nm) are capable of activating some phagocytosis, albeit not as potently as the 4T. Interestingly, as the intermembrane distance between T cell and target increases, the requirement for tight ligand spacing becomes more stringent (Cai et al., 2018). This suggests that IgG bound to tall antigens may be more dependent on tight nanocluster spacing than short antigens. Planar arrays have also been used to vary inter-cluster spacing, in addition to inter-ligand spacing (Cai et al., 2018; Freeman et al., 2016). Examining the optimal inter-cluster spacing during phagosome closure may be an interesting direction for future studies.

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Additional experiments performed in revision

In addition to these reviewer comments, we have added additional controls validating the DNA-CAR-4x𝛾 used in Figure 6c,d. We compared the DNA-CAR-4x𝛾 to versions of the DNA-CAR-1x𝛾-3x𝛥ITAM construct with the functional ITAM in the second and fourth positions (see the schematics now included Figure S7). We found that four individual receptors with a single ITAM each were able to induce phagocytosis regardless of which position the ITAM was in. However the DNA-CAR-4x𝛾 construct, which also contains 4 ITAMs, was not. This further validates the experiment presented in 6c,d. We also fixed minor errors we discovered in the presentation of data for Figures 1C and S1A.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

__Reviewer #1 __ __1. One key citation missing from the current manuscript is from Hwang et al. 2014 (PMID 25288734). This study has already described that the isp-1 mutant strain survives longer during P. aeruginosa infection. This citation also describes that the gene expression profile of isp-1 mutants animals includes a considerable number of pathogen-responsive genes that are similarly induced during infection. While the current manuscript does go into the mechanism of this resistance with more detail, they should amend the language to more appropriately reflect previous work, notably the above reference.

__

We apologize for the oversight and have added the suggested citation. Hwang et al. show that isp-1 worms have increased resistance to bacterial pathogens that is dependent on HIF-1/HIF1 and AAK-2/AMPK. In future work, it will be interesting to examine whether HIF-1 and AAK-2 act in concert with, or independently of, ATFS-1 and the p38-mediated innate immune signaling pathway to mediate pathogen resistance and longevity in isp-1 worms. We will add these points to our discussion.

__2. The authors suggest that ROS activation of the p38 MAPK pathway is likely not the mechanism that explains the resistance of long-lived mitochondrial mutant animals due to their reduced food intake. However, is ROS production nonetheless involved? Does antioxidant treatment suppress the increased resistance during infection of isp-1 and/or nuo-6 mutant animals?

__

To address this question, we will treat wild-type, isp-1 and nuo-6 worms with antioxidant and then measure resistance to bacterial pathogens using the P. aeruginosa strain PA14 slow kill assay. For the antioxidant treatment, we will use 10 mM Vitamin C as we have previously shown that this concentration is effective at reducing ROS in isp-1 worms to decrease isp-1 lifespan (Van Raamsdonk and Hekimi 2012, PNAS). Although antioxidant treatment can have pleiotropic effects, if this decreases survival of bacterial pathogen exposure, it will suggest that the elevated ROS production in isp-1 and nuo-6 worms may contribute to their enhanced bacterial pathogen resistance.

__3. (line 278-282): the authors should elaborate on how the p38 MAPK pathway plays a permissive role. It is intriguing that ATFS-1 and ATF-7 are both bZIP transcription factors that could theoretically heterodimerize and that they share common immune gene targets. The authors do indicate that the binding sites for ATFS-1 and ATF-7 are very different and are likely acting distinctly but some speculation would nonetheless strengthen this statement.

__

While ATFS-1 and ATF-7 were shown to bind to the promoter regions of the same innate immunity genes, the apparent consensus binding sites are different suggesting that they bind to different regions of the promoter. One way in which the p38 MAPK pathway may be playing a permissive role is that ATF-7 binding and relief from its repressor activity is required for any transcription of p38-mediated innate immunity target genes to occur. This is consistent with our data showing that disruption of nsy-1, sek-1, pmk-1 or atf-7 decreases the expression of innate immunity genes in wild-type worms. In contrast, it may be that the role of ATFS-1 is for enhanced expression of innate immunity genes such that when ATFS-1 is bound to the promoter region, or perhaps enhancer elements, the baseline expression of innate immunity genes that results from the binding of ATF-7 is increased. This idea is supported by our data showing that disruption of atfs-1 does not affect the expression of innate immunity genes in wild-type worms but prevents nuo-6 mutants from having increased expression. We will update our manuscript to include these points.

__4. The authors suggest that reduced food consumption of nuo-6 and isp-1 animals may suppress ROS-induced activation of the p38 innate immune pathway. It is intriguing that dietary restriction was previously shown to increase resistance to infection, presumably through p38-independent mechanisms (PMID 30905669). It would be interesting to measure host survival of nuo-6 and isp-1 mutant animals that are dietary-restricted to see if the enhanced survival rates conferred by mitochondrial stress and DR are additive or not.

__

According to this suggestion, we will compare the bacterial pathogen resistance of wild-type, isp-1 and nuo-6 worms that have undergone dietary restriction to the same strains under ad libitum conditions. This will determine the extent to which their enhancement of pathogen resistance might be additive.

__5. Figure 2: It is intriguing that loss of p38 signaling appears to have different effects in nuo-6 versus isp-1 animals. Specifically, loss of p38 signaling in isp-1 mutants renders them more sensitive to infection than wild-type, whereas it generally suppresses survival rates back to wild-type levels in the nuo-6 mutant background. Even within the nuo-6 mutant group, loss of SEK-1 has more dramatic effects on nuo-6 mutant animals than does loss of NSY-1, PMK-1 or ATF-7(gf). This is despite the fact that the nsy-1, sek-1, and pmk-1 alleles that are used in this study are all reported to be null. Can the authors speculate on these differences?

__

While the isp-1 and nuo-6 mutations both alter mitochondrial function, they affect different components of the electron transport chain. isp-1 mutations affect Complex III (Feng et al. 2001, Dev. Cell), while nuo-6 mutations affect Complex I (Yang and Hekimi 2010, Aging Cell). Although these mutants both have increased lifespan and a similar slowing of physiologic rates, it is not uncommon to observe differences between these mutants. For example, while treatment with the antioxidant NAC completely reverts nuo-6 lifespan to wild-type, it only partially reduces isp-1 lifespan (Yang and Hekimi 2010, PLoS Biology), suggesting that nuo-6 lifespan may be more dependent on ROS than isp-1. We have recently shown that deletion of atfs-1 reduces nuo-6 lifespan, but completely prevents isp-1 worms from developing to adulthood (Wu et al. 2018, BMC Biology), suggesting that isp-1 worms are more dependent on ATFS-1 than nuo-6 worms. Disruption of sek-1 has a greater impact on pathogen resistance than nsy-1 and pmk-1 because SEK-1 is absolutely required for innate immune signaling, while some partial redundancy exists for NSY-1 and PMK-1. We will add these points to our manuscript.

__6. One of the main conclusions from this study is that ATFS-1 likely binds directly to innate immune genes that are in common with ATF-7. Since this is such a pivotal finding, the authors should validate some candidate genes from the referenced ChIP seq datasets using ChIP qPCR. Also, are there predicted ATFS-1 binding sites (PMID 25773600) in these promoters?

__

Our data shows that activation of ATFS-1 increases the expression of innate immunity genes without increasing activation of p38. The simplest explanation for this observation is that ATFS-1 can upregulate the same innate immunity genes as ATF-7. Accordingly, we hypothesized that ATFS-1 and ATF-7 can bind to the same promoter. Fortunately, two previous ChIP-Seq studies, from well-established laboratories who have extensive experience studying ATFS-1 and ATF-7, had already determined which genes are bound by these two transcription factors (Nargund et al. 2015, Molecular Cell; Fletcher et al. 2019, PLoS Genetics). Comparing the results of these two published studies confirmed our hypothesis by demonstrating that the same innate immunity genes are bound by both ATF-7 and ATFS-1 in vivo. In order to provide additional support for the conclusion that ATFS-1 and ATF-7 can bind to the same genes, we will examine the genetic sequence of innate immunity genes that were shown to be bound by both ATFS-1 and ATF-7 in the published ChIP-seq studies to identify predicted binding sites for ATFS-1 and ATF-7, while noting that the ATFS-1-associated sequence is an enriched motif and not an established binding site. If we are able to identify the predicted binding sites for these two transcription factors in the same gene, it will provide further support for the conclusion that these transcription factors can both bind to the same innate immunity genes.

__Reviewer #2:

(1) The authors state that the p38 MAPK PMK-1 is not activated in the long-lived mitochondrial mutants. However, it might be better to state that there is "no enhanced activation" of PMK-1, since they clearly show in nuo-6 and isp-1 mutants the presence of phosphorylated PMK-1 (Fig. 4A), which would indicate an activated form of PMK-1 in these mutants.__

According to this suggestion, we will change the text to indicate that there is no enhanced activation of PMK-1 in nuo-6 and isp-1 worms.

__(2) Are the food-intake behaviors of all mutants in liquid culture (Fig. 4B-F) the same as their food-intake behaviors on solid agar media, the environment where pathogen resistance was measured?

__

We previously compared assays measuring food intake on solid agar media versus the liquid culture approach used in the current study to determine which method is the most robust (Wu et al. 2019, Cell Metabolism). While both assays produced similar results, performing the food intake assay on solid agar plates was much more variable as it is challenging to scrape off all of the uneaten bacteria from solid plates in order to measure it. Since the approach of measuring food intake in liquid media produces more consistent and reliable results, we chose to use this assay for the current study. We will update our manuscript to include this justification.

(__3) Does the p38 pathway single mutant nsy-1 or sek-1 live shorter than wild type on dead E. coli OP50 (Fig. S9) than they do on live OP50 (Fig. 3)? If so, what might that mean? These mutants are also living shorter than wild type on PA14 (Fig. 2), but live as long as wild type on OP50 (Fig. 3). What is in the live OP50 that allows these mutants to live like wild type?

__In a previous publication, we found that sek-1 mutants live shorter than wild-type worms, and nsy-1 live slightly shorter than wild-type worms in a lifespan assay performed in liquid medium with dead OP50 bacteria (Wu et al. 2019, Cell Metabolism). In the current study, we performed lifespan assays on solid NGM plates with live OP50 bacteria and observed a wild-type lifespan in sek-1 and nsy-1 worms. Since there are multiple experimental variables that are different between the previous and current study, most notably liquid versus solid media, the lifespan results cannot be directly compared. In the case of measuring survival of these strains on PA14, the simplest explanation is that they are dying sooner because their innate immune signaling pathway is disrupted, and so they are less able to mount an immune response against the pathogenic bacteria. We will update our manuscript to include these points.

__At the same time, wouldn't it be simpler to call the multiple antibiotic-treated OP50 as "dead bacteria", instead of "non-proliferating bacteria"? Some of the antibiotics used to treat OP50 are bactericidal and not bacteriostatic.

__

We previously monitored the OD600 of the antibiotic-treated, cold-treated OP50 that we used in our experiment, and found that there is only a very small decrease in OD600 after 10 days (Moroz et al. 2014, Aging Cell). Since dead bacteria are rapidly broken down leading to a decrease in OD600, this result is consistent with the bacteria being alive but not proliferating. We will include this point in our manuscript.

__(4) Since nuo-6 and isp-1 do not always behave exactly the same in their dependence on certain genes (e.g., Fig. 2C vs Fig 2D), what happens in isp-1; atfs-1 double mutants? Do these mutants behave in the same manner as nuo-6; atfs-1?

__

This is an interesting question. Unfortunately, isp-1;atfs-1 mutants arrest during development (Wu et al. 2018, BMC Biology), which is why we only examined the effect of atfs-1 deletion in nuo-6 mutants. We will update the manuscript to note this point.

__Regarding nuo-6; atfs-1, why does the double mutant live shorter on PA14 than either single mutant (Fig. 6A)? Is this because atfs-1 is needed to activate the p38 MAPK-dependent and -independent pathways? __

It is possible that the nuo-6 mutation makes worms more sensitive to bacterial pathogens, perhaps due to decreased energy production, and that activation of ATFS-1 is required not only to enhance their resistance to pathogens but also to increase their resistance back to wild-type levels. In a previous study, we showed that loss of ATFS-1 slows down the rate of nuclear localization of DAF-16. Thus, loss of atfs-1 may also be decreasing resistance to bacterial pathogens by diminishing the general stress resistance imparted by the DAF-16-mediated stress response pathway. We will update the manuscript to include these points.

__In Fig. 7B, the atfs-1(gof) appears to have slightly more phosphorylated p38 compared to wild type, although it is not statistically significant?

__

While there is a trend towards a very modest increase in phosphorylated p38 in the constitutively-active atfs-1 mutant compared to wild-type, quantification of four biological replicates indicated that the difference is not significant. This result is consistent with the fact that the levels of phosphorylated p38 are not significantly increased in nuo-6 or isp-1 mutants, both of which show activation of ATSF-1. We have provided raw images of all of these Western blots in our supplementals. In addition, we will repeat these Western blots to determine if this difference becomes significant with additional replicates.

__In Fig. 6B, the atfs-1 loss-of-function single mutant also increases the expression of Y9C9A.8, but suppresses it in a nuo-6 mutant background? What might that mean?

__

It is possible that in wild-type animals disruption of atfs-1 causes a compensatory upregulation of specific stress response genes. We have previously shown that deletion of atfs-1 results in upregulation of chaperone genes involved in the cytoplasmic unfolded protein response (hsp-16.11, hsp-16.2; Wu et al. 2018; BMC Biology). Perhaps Y9C9A.8 is acting in a similar way. In nuo-6, the upregulation of Y9C9A.8 is driven by activation of ATFS-1, and thus is prevented by atfs-1 deletion. We will add these points to the manuscript.

__Reviewer #3:

1) Some studies propose that OP50 offers some toxicity to worms which is not observed in other bacterial strains like HT115. The authors should test the role of the p38-innate immune signaling pathway in nuo-6 and isp-1 lifespan using other non-pathogenic E. coli strains.

__

To determine if the effect of disrupting the p38-mediated innate immune signaling pathway on the lifespan of isp-1 and nuo-6 mutants was simply the result of losing protection against OP50 bacteria, we examined the effect of nsy-1, sek-1 and atf-7(gof) mutations on isp-1 and nuo-6 lifespan using non-proliferating bacteria. We found that even when no proliferating bacteria are present, disruption of the p38-mediated innate immune signaling pathway markedly decreases isp-1 and nuo-6 lifespan. This suggests that the p38-mediated innate immune signaling pathway is required for their long lifespan independently of its ability to protect against bacterial infection. Similarly, we have previously shown that lifespan extension resulting from dietary restriction is dependent on the p38-mediated innate immune signaling pathway even when non-proliferating bacteria are used (Wu et al. 2019, Cell Metabolism). We will clarify this important point in the manuscript.

__ 2) The authors should measure food intake in worms exposed to pathogenic bacteria, given that reduced bacterial intake may be related to reduced mortality.

__

Unfortunately, it is not feasible to perform the food intake assay using the pathogenic bacteria because the bacteria cause death thereby complicating the calculation of food consumed per worm (which requires at least 3 days to assess). As an alternative to measuring food intake, we will attempt to measure intestinal accumulation of P. aeruginosa, which is a balance between food intake and other factors. To do this we will use a P. aeruginosa strain that expresses GFP and quantify the amount of intestinal fluorescence in wild-type, isp-1 and nuo-6 worms that have been grown on the GFP-labelled P. aeruginosa.

__3) The authors should check if ROS is required for the activation of the p38-mediated innate immune signaling pathway and reduction in food intake.

__

To determine if the elevated ROS that is present in isp-1 and nuo-6 worms affects activation of the p38-mediated innate immune signaling pathway, we will treat wild-type, isp-1 and nuo-6 worms with Vitamin C and measure the ratio of phosphorylated p38 to total p38 by Western blotting. Similarly, to examine the effect of ROS on food intake, we will treat wild-type, isp-1 and nuo-6 worms with Vitamin C and then quantify its effect on food intake. For these experiments, we will use 10 mM Vitamin C as we have previously shown that this concentration is effective at reducing ROS in isp-1 worms to decrease isp-1 lifespan (Van Raamsdonk and Hekimi 2012, PNAS).

__4) Since ATFS-1 and the p38 pathway control food intake, how related to dietary restriction the phenotypes the authors are studying are?

__

While the lifespan extension that results from mild impairment of mitochondrial function and the lifespan extension resulting from dietary restriction are both dependent on the p38-mediated innate immune signaling pathway, these interventions modulate innate immunity gene expression in opposite directions. We previously reported that dietary restriction primarily downregulates innate immunity genes (Wu et al. 2019 Cell Metabolism). Here, we show that mutations in isp-1 or nuo-6 primarily result in upregulation of innate immunity genes. To more globally examine gene expression changes between dietary restriction and mild impairment of mitochondrial function, we compared differentially expressed genes. We found that there was very little overlap of either upregulated or downregulated genes between dietary restriction and isp-1/nuo-6 mutants. We will add a supplementary figure to demonstrate this, and add these points to our manuscript.

__ 5) Somewhat related to the previous points, I am not so sure whether the changes in food intake are cause or consequence of the alterations in the innate immunity-related genes. Reduced food intake is depicted in Fig. 8 as the cause of the activation of the p38 pathway, but there is not enough evidence to unequivocally prove that. In fact, food intake might be controlled by the p38 or ATFS-1 pathway or by a common regulator such as ROS.

__

We apologize that we didn’t make this clearer. In our previous work, we showed that dietary restriction results in decreased activation of the p38 pathway (Wu et al. 2019, Cell Metabolism). Here, we show that activation of ATFS-1 results in decreased food intake. Based on our previous study, this decrease in food intake should similarly decrease p38 pathway activation. In Figure 8, we have depicted ATFS-1 inhibiting food intake, and food intake activating the p38-mediated innate immune signaling pathway. Combined, our model suggests that activation of ATFS-1 should act to decrease p38-mediated innate immune signaling. We will clarify this in the figure legend.

__6) I am not so convinced of the role of DAF-16. In fact, in Fig. 5A daf-16 mutation reduces pathogen resistance and that could represent a toxic effect of the mutation. Furthermore, the results in Fig. 4D do not exclude the possibility that daf-16 and isp-1 act in parallel.

__

We agree that the role of DAF-16 could be non-specific. While we show that disruption of daf-16 leads to decreased bacterial pathogen survival in isp-1 worms, it also decreases bacterial pathogen survival in wild-type worms. Since DAF-16 is known to be required for general resistance to stress, the decreased survival when daf-16 is disrupted could be due to a general toxic effect of reducing general stress resistance. This conclusion is consistent with our observation that DAF-16 is not involved in the upregulation of innate immunity genes in isp-1 worms. We will emphasize these points in our manuscript.

__ 7) Loss of innate immunity related genes may result in toxicity and sensitize worms to pathogenic bacteria. This is further supported by an even lower resistance to pathogens in the double mutants mainly in Fig. 2D.

__

We agree. Our data confirms that disruption of the p38-mediated innate immune signaling pathway makes worms more susceptible to bacterial pathogens. We will emphasize this point.

__ 8) The blots are saturated, particularly in Fig. 4A, and this can be masking the differences in p38 phosphorylation. In fact, the fact that p38 phosphorylation is not changed is contradictory to the other results. How is p38 regulated by mitochondrial mutations then? I am concerned that p38 is actually not altered and the changes in gene expression are exclusively due to ATFS-1. The interaction with the p38 pathway demonstrated genetically could be due to the toxicity elicited by the loss of function mutations in this pathway.__

To address this concern, we will repeat the Western blotting experiment to compare the ratio of phosphorylated p38 to total p38 between wild-type, isp-1 and nuo-6 worms. We will take multiple exposures to ensure that the blots are not over-saturated. Having already completed four replicates, we believe that there is not a major change in p38 activation. Our data suggests that the p38-mediated innate immunity pathway is playing a permissive role such that it is required for baseline expression of innate immunity genes, but that activation of ATFS-1 is driving the enhanced expression of innate immunity genes that we observe in the long-lived mitochondrial mutants and constitutively active atfs-1 mutants. We will update our manuscript to clarify this.

__ **Minor concerns**

1) Lines 167 and 174: What are these p values referred to?

__

The p-values indicate the significance of the overlap between the two gene sets. Given the size of the two gene sets, this is the probability that the observed number of overlapping genes would result by picking genes at random. We will clarify this in the manuscript.

__2) Line 258: I partially agree with the conclusions, since the functions may not necessarily be associated with innate immune signaling but rather other functions of p38.

__

Since isp-1 and nuo-6 worms have extended longevity even when grown on non-proliferating bacteria this indicates that their long life is not dependent on their enhanced resistance to bacterial pathogens. Similarly, since disruption of genes in the p38-mediated innate immune signaling pathway decrease isp-1 and nuo-6 lifespan even when the worms are grown on non-proliferating bacteria, this suggests that this pathway enhances longevity independently of its ability to increase innate immunity.

__ 3) Why in figures 4D and E different mutants were used?

__

We only used isp-1 mutants to examine the effect of daf-16 because we were unable to generate nuo-6;daf-16 mutants due to close proximity of the two genes on the same chromosome. We only used nuo-6 mutants to examine the effect of atfs-1 because isp-1;atfs-1 worms arrest during development. We will include this explanation in our manuscript.

__ 4) Line 498: revise writing.

__

We will rewrite this sentence to improve clarity.

__ 5) Show blots in Fig. 7B.

__

We will provide an image of a representative Western blot in Figure 7, and will provide the raw images for all of Western blots in our supplementals.

__ 6) It would be interesting to know where the activation of the immune-related genes by the mitochondrial mutations is happening, whether this is a cell autonomous or cell non-autonomous mechanism.

__While it would be interesting to explore whether specific tissues are important in sensing mitochondrial impairment in order to upregulate genes involved in innate immunity, it is beyond the scope of this manuscript. Previous work has shown that knocking down the expression of the cytochrome c oxidase gene cco-1 in neurons can activate the ATFS-1 target gene hsp-6 in the intestine (Durieux et al., 2011). Based on this, one could hypothesize that a similar cell non-autonomous mechanism might be involved. We will note this possible future direction in our discussion.

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Reply to the reviewers

__Reviewer #1: __ __ **Major concerns:**

1) This manuscript has some overlap with another manuscript from the same group recently submitted to EMBO Reports. Although I believe both manuscripts have sufficient elements to justify publication of two papers, I strongly recommend that these publications are made back-to-back and they should be discussed in context with one-another.

__

We agree that this manuscript is distinct from but highly complementary to our manuscript on innate immunity in the long-lived mitochondrial mutants, which has been invited for revision at EMBO Reports. According to this suggestion, we have arranged for these papers to be considered for publication at the same time in EMBO Reports and Life Science Alliance. We have updated the discussions of both manuscripts to incorporate the findings of the other manuscript.

__ 2) How is ATFS-1 function regulated in long-lived worms or under multiple stress conditions? Is there a common regulator such as oxidative stress or mitochondrial dysfunction? Both manuscripts would benefit from a clear understanding on how ATFS-1 is controlled under conditions where mitochondrial function is altered. Is mitoUPR required for this activation? If so, is mitoUPR upregulated in all interventions where ATFS-1 has been shown to play a role in stress response. __

We have previously used a reporter strain to determine which external stressors activate ATFS-1. The reporter strain has a transgene that links the promoter of the ATFS-1 target gene hsp-6 to GFP (Phsp-6::GFP) such that these worms exhibit increased fluorescence whenever ATFS-1 is activated. After exposing these worms to heat, cold, osmotic stress, anoxia, oxidative stress, starvation, ER stress and bacterial pathogens, we only observed increased fluorescence after exposure to oxidative stress (Dues et al. 2016, Aging). Here, we show that constitutive activation of ATFS-1 results in increased resistance not only to oxidative stress but also ER stress, osmotic stress, anoxia and bacterial pathogens (fast kill assay). Thus, ATFS-1 activation does not just protect against stresses that lead to its activation. Notably, the constitutively active atfs-1 mutants (et15 and et17) exhibit activation of the mitoUPR under unstressed conditions (e.g. upregulation of hsp-6 in Fig. 1A; increased fluorescence of hsp-6 and hsp-60 reporter strains in Rauthan et al. 2013, PNAS; upregulation of many other stress pathway target genes Fig. 2). It is likely that the activation of the mitoUPR and downstream stress response pathways under unstressed conditions results in the increased resistance to stress that we observe. We have included these points in the revised manuscript.

__Is there any intervention that controls longevity and does not trigger ATFS-1 response?

__

When we compared RNA-seq data on a panel of long-lived mutants representing multiple pathways of lifespan extension to ATFS-1 target genes (defined as genes that are upregulated by spg-7 RNAi in an ATFS-1 dependent manner from Nargund et al. 2012, Science), we found that seven of the nine long-lived mutants that we examined showed enrichment of ATFS-1 target genes (clk-1, isp-1, nuo-6, daf-2, glp-1, ife-2) while two did not (eat-2, osm-5) (Fig. 5). Interestingly, in six of these seven strains (all except ife-2), there is an increase in reactive oxygen species (ROS) that contributes to their longevity (treatment with antioxidants decreases their lifespan; Yang and Hekimi 2010, PLoS Biology; Zarse et al. 2012, Cell Metabolism; Wei and Kenyon 2016, PNAS). This observation is consistent with the idea that ROS/oxidative stress is sufficient to activate ATFS-1/mitoUPR. We have previously shown that exposure to a mild heat stress (35°C, 2 hours) or osmotic stress (300 mM, 24 hours) can extend lifespan but does not increase expression of the ATFS-1 target gene hsp-6 (Dues et al. 2016, Aging). Thus, there are multiple examples in which a genetic mutation or intervention increases longevity but does not trigger upregulation of ATFS-1 target genes. We have updated the manuscript to include these points.

__3) In Fig. 3, some of these genes appear to be unspecifically associated with different stressors. Therefore, it is difficult to rule out the participation of ATFS-1 in specific stress responses without looking at specific stress-responsive genes or a wider range of genes. For example, the conclusion that ATFS-1 does not control osmotic stress gene expression response comes from looking at 3 genes: sod-3, gst-4 and Y9C9A.8. gst-4 does not appear to be directly controlled by ATFS-1 regardless of the stressor. sod-3 is also upregulated by oxidative stress and Y9C9A.8 by anoxia. On the other hand, somewhat contradicting the authors' conclusions that ATFS-1 does not participate in osmotic stress response based on these 3 genes, ATFS-1 appears to be required for osmotic stress resistance.

__

In this experiment, we treated wild-type and atfs-1 deletion mutants with six different stressors (oxidative stress, bacterial pathogens, heat stress, osmotic stress, anoxia, and ER stress), isolated mRNA and then examined the expression of 14 different stress response genes. To select these genes, we chose a combination of the most established target genes of the stress response pathways that we examined in Figures 1/2, and genes that we had previously shown to be upregulated by specific stresses using fluorescent reporter strains (Dues et al. 2016, Aging). These genes included hsp-6, hsp-4, hsp-16.2, sod-3, gst-4, nhr-57, Y9C9A.8, trx-2, ckb-2, gcs-1, sod-5, T24B8.5, clec-67 and dod-22. To determine if ATFS-1 is required for gene upregulation in response to any of the six different stressors, we first identified which of these stress genes is significantly upregulated in response to each stressor and then looked to see if this upregulation is reduced or prevented by atfs-1 mutation. We found that there were multiple examples of this for both oxidative stress and bacterial pathogen stress, but not for other stresses. We selected three representative genes to display in Figure 3. Nonetheless, it is possible that there are genes that we didn’t examine that are upregulated by the other four stressors in an ATFS-1-dependent manner. To definitively address this question, one would have to do RNA sequencing on wild-type and atfs-1(gk3094) worms comparing untreated and stressed, but this is beyond the scope of the current manuscript. We have updated the manuscript to include these points, and noted the possibility that there are genes, which we didn’t measure, that are upregulated by the other four stressors in an ATFS-1-dependent manner. We have also included the qPCR data for all 14 genes for each of the six external stressors in Supplemental Figures S3-S8.

__ **Minor concerns:**

1) The paragraph starting in line 107 is confusing. They write that "Constitutive activation of ATFS-1 in atfs-1(et 15) and atfs-1(et17) mutants resulted in upregulation of most of the same genes that are upregulated in nuo-6 mutants, except for gst-4" and later they state that "Activating the mitoUPR through the nuo-6 mutation, or through the constitutively-active ATFS-1 mutants did not significantly increase the expression of target genes from the ER-UPR (hsp-4; Fig. 1B) or the cyto-UPR (hsp-16.2; Fig. 1C)." I understand the upregulation of ER-UPR and cyto-UPR is not statistically significant (isn't it for hsp-16.2?), but the first sentence is not accurate if statistics is considered.

__

To clarify this, we have modified the first sentence to describe which genes are significantly upregulated in atfs-1(et15) mutants, and separately describe the findings for atfs-1(et17) mutants in the second sentence. The results for hsp-16.2 are not significant because this gene shows highly variable expression between replicates and can be induced 60-fold. We have noted this in the text as well.

__ 2) The authors should discuss why they think atfs-1(et15) gain-of-function mutant exhibited decreased resistance to chronic oxidative stress, while it is protected from acute oxidative stress. In fact, the et15 allele differs in many aspects in relation to the et17 and in some cases it behaves similarly to the gk3094 loss-of-function allele.

__

While atfs-1(et15) and atfs-1(et17) mutants generally show similar results, they also exhibit differences. We previously used RNA sequencing to examine gene expression in these two strains. We found that atfs-1(et15) mutants have far more extensive changes in gene expression than atfs-1(et17) mutants (6227 differentially expressed genes versus 958 differentially expressed genes). It is possible that the et15 mutation is more disruptive to the mitochondrial targeting sequence than et17, thereby resulting in increased nuclear localization and more gene expression changes. The additional gene expression changes in the atfs-1(et15) mutant may contribute to their decreased resistance to chronic oxidative stress. We have included these points in the revised manuscript.

__ 3) Fig 4I is very similar to Fig. 6A of the other manuscript which strengthen the notion that ATFS-1 is not required (it is rather detrimental) for bacterial pathogen response when no underlying stress (most likely oxidative) occurs.

__

Yes, our results indicate that ATFS-1 is not required for wild-type survival of bacterial pathogen exposure. This is consistent with our findings in the other manuscript that baseline expression of innate immunity genes does not depend on ATFS-1 (innate immunity gene expression is similar between wild-type and atfs-1(gk3094) mutants). We have updated the manuscript to emphasize these points.

__ 4) In the paragraph starting in line 213, the authors conclude that "ATFS-1 is sufficient to protect against oxidative stress, osmotic stress, anoxia, and bacterial pathogens but not heat stress". The results do not unequivocally support a participation of ATFS-1 in oxidative stress or bacterial pathogen response, given the responses vary depending on the allele or condition.

__

We have modified this sentence by replacing “activation of ATFS-1 is sufficient to protect” with “activation of ATFS-1 can protect” to indicate that we didn’t observe protection in all cases.

__ 5) "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." It actually does, since ATFS-1 gain-of-function decreases lifespan.

__

We have rewritten this sentence to say that constitutive activation of ATFS-1 does not extend lifespan, despite increasing resistance to multiple stresses.

__

__

__ __

__6) Paragraph starting in line 359 needs to be discussed in light of the results of the other manuscript submitted by the authors to EMBO.

__

Combined these two manuscripts indicate that baseline levels of innate immunity are dependent on the p38-mediated innate immune signaling pathway, and not dependent on ATFS-1. This idea is supported by the fact that deletion of atfs-1 does not decrease resistance to bacterial pathogens and does not reduce the expression of innate immunity genes. In contrast, disrupting genes involved in the p38-mediated innate immune signaling pathway does decrease resistance to bacterial pathogens and does decrease the expression of innate immunity genes. We have updated this paragraph to include these points and reference the findings from our manuscript on innate immunity in the long-lived mitochondrial mutants.

__ 7) In Fig. 1C, it appears that atfs-1 loss of function increases hsp-16.2. Is that significant?

__

While there is a strong trend towards increased hsp-16.2 expression in atfs-1(gk3094) mutants, this difference did not reach significance because this gene shows highly variable expression and can be induced 60-fold.

__ 8) In Fig. 2, 5 and S1, it would be interesting to build one single Venn Diagram with all the lists of genes to see if there are common genes associated with multiple pathways and if there are many ATFS-1 target genes not associated with these classical stress or longevity pathways.

__

While we would be very interested in performing this type of visualization, weighted Venn diagrams with more than 3 or 4 groups are challenging to generate and more challenging to interpret. Instead, we have generated an UpSetR plot to demonstrate the number of overlapping genes between each of the stress response pathways, as well as how many ATFS-1 target genes are not involved in stress response. We have included this plot in Figure 2, Panel I. We have also generated simpler figure to show the overlap between pairs of stress response pathways (Figure S1). In addition, we have also added Table S4 with these gene lists.

__ 9) In Fig. 2, 5 and S1: What are the p values referred to?

__

The p-values indicate the significance of the difference between the observed number of overlapping genes between the two gene sets, and the expected number of overlapping genes if the genes were picked at random. We have clarified this in the manuscript.

__ 10) In paragraph starting in line 85, the authors should include references that evidence the genes are bona fide markers of the stress response pathways.

__

We have added references for each of the genes that we examined to link it to the associated stress response pathway.

__ 11) Tables S2 and S3 are missing. __

Tables S2 and S3 were uploaded as Excel spreadsheets, not included with the supplemental figures as the other supplementary Tables were. We apologize that these were difficult to locate. In the revision, Table S1 is in the manuscript file, while Table S2 to S6 will be uploaded as separate files.

__ __

__Reviewer #2:

**Major comments:**

The only major conclusion that I would qualify is "ATFS-1 serves a vital role in organismal survival of acute stresses through its ability to activate multiple stress response pathways"-the data, as presented, does not make clear whether ATFS-1 directly activates these pathways (ie, by binding response elements in genes in those pathways), or indirectly influences them by altering the physiology of the worm).

__

We agree that our data does not determine precisely how ATFS-1 acts to modulate the expression of the different stress response pathways. To determine the extent to which ATFS-1 might be able to bind directly to the target genes of other stress response pathways, we have compared the ChIP-seq results for ATFS-1 to ChIP-seq studies for other stress responsive transcription factors (DAF-16, SKN-1, HSF-1, HIF-1, ATF-7). We found that in each case there are sets of genes that can be bound by both transcription factors. This suggests that ATFS-1 may be direct regulating at least some of the target genes from other stress response pathways. We have updated our manuscript to include these points and included the ChIP-seq data comparisons in Figure S2.

__ **Minor comments:**

In abstract, consider broadening/re-wording "Gene expression changes resulting from the activation of the mitoUPR are mediated by the transcription factor ATFS-1/ATF-5." Because a naïve reader may understand this to suggest that ATFS-1 is activated only by mitochondrial protein misfolding.

__

In this sentence we are describing the role of ATFS-1 in mediating the gene expression changes resulting from the activation of the mitoUPR. We would be happy to modify the sentence if this is unclear.

__Please indicate whether strains were outcrossed, and how often.

__

We have added these details to our materials and methods.

__ How was "young adult" defined? Were worms synchronized, and if so, how?

__

Young adult worms are picked on day 1 of adulthood before egg laying begins. The worms were not synchronized, but picked visually as close to the L4-adult transition as possible. We have added these details to our method section.

__ For the gene expression experiments, do I understand correctly that FUDR was used only for oxidative stress and adult day 2 experiments? Please clarify.__

Yes, that is correct. FUdR was used for these samples because (1) with the 2-day duration of this stress, worms can produce progeny which would complicate the collection of the experimental worms; and (2) 4 mM paraquat often results in internal hatching of progeny when FUdR is absent, which might have affected the results. The control worms for the 48-hour 4 mm paraquat stress were also treated with FUdR. We have clarified this in the manuscript and noted that the presence of FUdR has the potential to alter gene expression.

__ Important: Please make clear how many replicates were performed for each experiment, and where relevant, how many worms were measured per replicate (e.g., stress survival and lifespan). __

We have added a spreadsheet (Table S6) to include the number of replicates and number of worms per replicate for all experiments.__

For 2-way ANOVA analyses, please specify p values of both main factors as well as interaction terms and posthoc analyses where relevant.

__

We have included these additional details from our statistical analyses in Table S6.

__ In the second paragraph of the introduction, I suggest broadening slightly the description of why normal mitochondrial function is required for ATFS-1 important and degradation, because this helps the reader understand that any one of many perturbations to mitochondrial function (decreased bioenergetics, membrane potential, protein degradation, protein import; increased ROS; etc.) could prevent or reduce ATFS-1 import and degradation.

__

We have added these additional factors that might prevent ATFS-1 import and degradation in paragraph one of our introduction and broadened the description in paragraph two.

__ For Figure 1: The authors present their choice of genes to analyze as if, and interpret their results assuming, that each of these gene is ONLY regulated by the indicated stress response pathways. I think this is very unlikely. For example: is it certain that sod-3 and trx-2 are not also skn-1 regulated? How is "antioxidant" distinguished from the skn-1 pathway? Further clouding the water is the likelihood that nuo-6 and atfs-1 manipulations alter physiology in such a way that there are secondary/indirect stress pathways activated (for example: the authors show that ATFS-1 overexpression shortens lifespan. Perhaps this is why it appears that ATFS-1 overexpression also appears to cause a strong, although variable, upregulation of the cytosolic UPR?). The likelihood (in my opinion) that these genes are in fact regulated by more than one type of response element, and that the manipulations used to study these relationships have pleiotropic effects, do not invalidate the general conclusion that these pathways interact-but they do mean that the results should be discussed with more caveats regarding HOW they interact.

__

These are excellent points. The genes that we selected for Figure 1 are the genetic targets that in our reading of the literature have been most often used to represent a particular stress response pathway. We have added references to justify the association of each gene with the indicated stress response pathway. We have also noted that in at least some cases the stress response genes that have been typically used to represent a specific pathway can be activated by multiple pathways. We agree that the selection of genes for Figure 1 is not a comprehensive approach, and that it is possible that if we chose a different gene from each of these pathways, the results might be different. We have updated our manuscript to specifically note these limitations. To avoid these limitations, we examined the overlap between all of the genes significantly upregulated by ATFS-1 activation and all of the genes significantly upregulated by the different stress response pathways in Figure 2. In addition, to gain a better understanding of the overlap between these different stress response pathways globally, we have compared gene expression between each of the stress response pathways studied in Figure S1.

__Figure 1 also illustrates why a more detailed description of sample size and statistical analysis should be provided. What was the "n"? What were the main effects and interaction terms of each 2-way ANOVA? The design is not full factorial and therefore does not permit a simple 2-way ANOVA (i.e., not all condition combinations are performed)-which responses precisely were compared to which? Were 2 2-way ANOVAs performed per mRNA?

__

For Figure 1 we used a one-way ANOVA to compare all of the groups to wild-type with a Bonferroni’s Multiple Comparison post-hoc test. We have updated the manuscript to include the sample size and statistical details in Table S6.

__ The work shown in Figure 2 is a very nice way to leverage previous data to further explore this idea of cross-talk. I would suggest including a bit more meta-data in the supplemental data files related to each dataset. For example, what lifestages were used (were they all young adult?), was FUDR used, etc.

__

We have added these details to Table S3, which includes the lists of target genes from each stress response pathway.

__ However, again, I don't understand how the authors can reach this conclusion: "Combined, this indicates that activation of ATFS-1 is sufficient to upregulate genes in multiple stress response pathways." (lines 152-153 but similar phrasing occurs multiple times) Could it not simply be that one form of cellular stress often eventually triggers broader cellular dysfunction, thus activating other cell stress pathways? Ie-how do we know whether these genes are directly regulated by atfs-1 binding regulatory elements, as implied by this phrasing?

__

This conclusion is derived from our data showing that constitutively active ATFS-1 mutants have significant upregulation of target genes from multiple stress response pathways (Figure 2). As the worms in those experiments were not exposed to stress, we don’t have reason to believe that they are experiencing cellular stress or dysfunction. We think it is more plausible that activation of ATFS-1, which normally occurs in response to stress, leads to the activation of other stress response pathways, either directly or indirectly, and that these pathways are recruited to help regain mitochondrial homeostasis. We don’t mean to imply that activated ATFS-1 binds directly to the target genes of other stress response pathways. We have clarified this in the revised manuscript.

__ The stress response experiments are very nicely done and very interesting. I appreciate that the authors did not shy away from describing counterintuitive results (eg et15 mutants showing increased sensitivity to chronic oxidative stress), and think that these results should also be briefly considered in the Discussion.

__

We have updated our manuscript to discuss the observation that atfs-1(et15) mutants have increased sensitivity to chronic oxidative stress.

__

__

__ __

__Figure 3: please report ANOVA interaction terms-these are what tell whether the inductions are in fact dependent on atfs-1 (not the post-hoc analyses). Again, it also appears that in some cases, there is an upregulation of certain genes with atfs-1 knockdown-please report all p-values (because there will be many, I recommend a supplemental table with all main and interaction and posthoc analyses). Again, the "n" also needs to be specified.

__

We have added Table S6 to include all of these statistical details.

__ Figure 4 A-C appear to be lacking error bars? Please add. Perhaps relatedly-the effect size for 4A looks much larger than for 4B, but this does not come across in the text.

__

We have added error bars to Figure 4A-C. We think the difference in effect size might result from the fact that 4A is an acute assay and 4B is a chronic assay. We speculate that the negative effect of the et15 and et17 mutations on lifespan might be a stronger factor in the chronic assay. We have updated the text to comment on the relative effect sizes.

__ For Figures 4 and 6, please indicate sample size-number of independent experimental replicates, and number of worms per replicate (or range per replicate).

__

We have added the number of replicates and sample size in Table S6.

__ Lines 224-225 re. sod-2 mutants: these may also act by decreasing ROS signaling (less conversion of superoxide anon to hydrogen peroxide); also, why would this strain not be considered another long-lived mitochondrial mutant (like clk-1, isp-1 and nuo-6, to which it is contrasted)?

__

We think the sod-2 mutation extends lifespan by increasing ROS signaling, as treatment with antioxidants decreases their lifespan. The increased superoxide from the loss of sod-2 may be converted to H2O2 by sod-3 or sod-1, which are also present in the mitochondria. We don’t include sod-2 with the mitochondrial mutants because the mutation does not directly impact the mitochondrial electron transport chain, but may do so secondarily due to elevated ROS.

__ The confirmation that atfs-1 overexpressing strains are short-lived is very interesting. However, I think this statement "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." (lines 265-267 and similar in several places throughout the Discussion, eg line 279) should be altered to indicate that this was observed under controlled laboratory conditions. Eg, "...this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background under optimized laboratory conditions..."

__

This is an interesting point. It is possible that constitutive activation of ATFS-1 may be beneficial for lifespan in an environment where worms are exposed to external stressors. We have noted that our lifespan results were obtained under lab conditions, which are believed to be relatively unstressful.

__

__

__ __

__Discussion: consider adding in a consideration of dose-response, both of knockdown of mitochondrial genes (eg, k/d of many mitochondrial genes promotes lifespan at low levels, but decreases lifespan with greater knockdown) and of stressors (chemicals, heat, etc; for chemicals, at the least, dose-response is very important, with low levels not infrequently triggering apparently beneficial stress responses, and higher levels causing toxicity).

__

It is possible that the magnitude of ATFS-1 activation will impact its effect on stress resistance and lifespan. Perhaps, a milder activation of ATFS-1 will be more beneficial with respect to lifespan. The degree of ATFS-1 activation may also account for differences that we observe between atfs-1(et15) and atfs-1(et17) mutants. atfs-1(et15) has more differentially expressed genes than atfs-1(et17) suggesting the possibility that it has more ATFS-1 activation. We have updated our manuscript to include these points.

__ Section beginning on line 384 "ATFS-1 upregulates target genes of multiple stress response pathways"-again, please revise to make clear that this work does not demonstrate direct regulation.

__

We have clarified that our results don’t demonstrate direct regulation. In addition, we have examined published ChIP-seq datasets to determine if there is evidence of direct regulation.

__ It seems to me that our reviews are in pretty good agreement. I agree with Reviewers 1 and 3 where they commented on things that I did not. While I did not consider the manuscripts as overlapping in the sense of being redundant, I very much like Reviewer 1's suggestion that they be published back to back and that the Discussion of each incorporate consideration of the Results of the other.

__According to this suggestion, we have arranged for these papers to be considered for publication at the same time in EMBO Reports and Life Science Alliance. We have updated the discussions of both manuscripts to incorporate the findings of the other manuscript.

__ Reviewer #3:

**Major comments**

1.The authors mention that activation of the UPRmt by nuo-6 mutants or atfs-1(gf) do not activate the ER UPR or cyto-UPR gene expression targets (lines 111-113). However, they also find that atfs-1(gf) animals have 25% overlap with the ER UPR pathway (line 146-147). Is 25% overlap not substantial?

__

The genes that we are referring to in lines 111-113 are the genetic targets that in our reading of the literature have been most often used to represent the ER-UPR or Cyto-UPR. This is not a comprehensive approach, and it is possible that if we chose a different gene from each of these pathways, the result might be different. We have updated our manuscript to include this limitation. To avoid this limitation, we examined the overlap between all of the genes significantly upregulated by ATFS-1 activation and all of the genes significantly upregulated by the ER-UPR or Cyto-UPR in Figure 2. In both cases, we find the overlap is significant, indicating that activation of ATFS-1 leads to activation of ER-UPR and Cyto-UPR target genes.

__

__

__ __

__To determine whether ATFS-1 mediates any protective effect during ER stress, authors should test atfs-1(gf) and atfs-1(lf) animals' resistance to ER stress.

__

To examine the effect of ATFS-1 on resistance to ER stress, we exposed wild-type, atfs-1(gk3094), atfs-1(et15) and atfs-1(et17) worms to 50 µM tunicamycin beginning at young adulthood and monitor survival daily. We found that both constitutively active atfs-1 mutants, et15 and et17, have increased resistance to ER stress compared to wild-type worms, while atfs-1 deletion mutants have a similar survival to wild-type. We have added this new data to Figure 4.

__ Authors should comment on the difference in outcomes with atfs-1(et17) and atfs-1(et15) animals to chronic oxidative stress (line 184-187).

__

We have updated our manuscript to discuss the observation that atfs-1(et15) mutants have increased sensitivity to chronic oxidative stress.

__ Lines 258-260. The authors should make clear in this section that a previous study had already measured lifespans of atfs-1(gf) animals and found that it was reduced (PMID 24662282). Also, an elaboration on why this experiment was repeated would be warranted.

__

We have referenced the lifespan results from this previous study in our introduction (line 53-54, Bennett et al), in our results section (lines 342-343; “which is consistent with a previous study finding shortened lifespan in atfs-1(et17) and atfs-1(et18) worms”) and in our discussion (lines 429-431; “as well as previous results using constitutively active atfs-1 mutants (et17 and et18) show that constitutive activation of ATFS-1 in wild-type worms results in decreased lifespan”). The reasons that we repeated this result are (1) because the lifespan of the atfs-1(et15) mutant had not been measured and this was the allele that we used in our paper; and (2) because the shortened lifespan is a surprising result given the beneficial effect of ATFS-1 on stress resistance, we thought it was important to repeat this experiment under the same conditions that we measured stress resistance.

__ The authors find that atfs-1(gk3094) animals lived longer during infection with PA14 (line 208-211). Another study found that atfs-1(gk3094) animals died faster on PA14 (PMID 28283579), which should be mentioned and commented on.

__

We have added this finding to our discussion. We have also compared the protocols used by Jeong et al. (who observed decreased survival in atfs-1(gk3094) deletion mutants), Pellegrino et al. (who observed wild-type survival in atfs-1(tm4919) deletion mutants and our manuscript (in which we observed slightly increased survival in atfs-1(gk3094) deletion mutants), to see which parameters might account for the observed differences.

__**Minor comments**

Line 38: "Inside the mitochondria, ATFS-1 is degraded by the Lon protease CLPP-1/CLP1". The phrasing suggests that CLPP-1/CLP1 is a Lon protease, when in fact they are independent proteases.

__

We have removed the word “Lon” to clarify this.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Soo et al. investigates the role of the mitochondrial unfolded protein response (UPRmt) during different organismal stresses. Using both loss-of-function and gain-of -function alleles of atfs-1, the gene encoding the transcription factor and main regulator of the UPRmt, the authors discover that ATFS-1 is both required and sufficient for the expression of genes associated with different types of cellular stresses including hypoxia, innate immunity, and antioxidant defense. Consistent with these gene regulations, gain-of-function atfs-1 animals were more resistant to specific cellular stresses, while loss of ATFS-1 animals were generally more sensitive.

Major comments

1.The authors mention that activation of the UPRmt by nuo-6 mutants or atfs-1(gf) do not activate the ER UPR or cyto-UPR gene expression targets (lines 111-113). However, they also find that atfs-1(gf) animals have 25% overlap with the ER UPR pathway (line 146-147). Is 25% overlap not substantial?

To determine whether ATFS-1 mediates any protective effect during ER stress, authors should test atfs-1(gf) and atfs-1(lf) animals' resistance to ER stress.

1. Authors should comment on the difference in outcomes with atfs-1(et17) and atfs-1(et15) animals to chronic oxidative stress (line 184-187).
2. Lines 258-260. The authors should make clear in this section that a previous study had already measured lifespans of atfs-1(gf) animals and found that it was reduced (PMID 24662282). Also, an elaboration on why this experiment was repeated would be warranted.
3. The authors find that atfs-1(gk3094) animals lived longer during infection with PA14 (line 208-211). Another study found that atfs-1(gk3094) animals died faster on PA14 (PMID 28283579), which should be mentioned and commented on.
4. At the request of the Editor, I was asked to comment on potential overlap between this manuscript and a recently submitted article submitted by the current authors (RC-2021-00651). There is only minor overlap in my opinion, with the finding in the current manuscript that the UPRmt is associated with stimulation of a pathogen defense program (innate immunity). Manuscript RC-2021-00651 goes into more detail regarding the mechanism of the UPRmt/innate immunity association and regulation.

Minor comments

Line 38: "Inside the mitochondria, ATFS-1 is degraded by the Lon protease CLPP-1/CLP1". The phrasing suggests that CLPP-1/CLP1 is a Lon protease, when in fact they are independent proteases.

Significance

The finding that the UPRmt regulates other cellular stress response pathways which provides resistance to a variety of stressors is of interest. However, associations of the UPRmt with increased resistance to exogenous stresses such as hypoxia and pathogen infection have been reported before (PMID 26234215, 25274306, 28283579), which might reduce the impact of the current manuscript to some degree.

This work would be interest to those in the fields of mitochondria, stress responses, and longevity.

My expertise is in stress responses, longevity, and host-pathogen interactions.

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors carried out experiments, and mine published datasets, to further characterize the role of the ATFS1 transcription factor in mediating survival and lifespan in laboratory or stressed conditions. The role of ATFS-1 was assessed by using a loss-of-function deletion and two constitutive gain-of function mutants in which the mitochondrial leader sequence is not functional, resulting in continual nuclear translocation. The effect of ATFS1 loss or constitutive activation was assessed in both wild-type and mutant (mitochondrial function and long-lived mutants) strains, and either under standard laboratory conditions or in the context of a variety of physical, chemical, and pathogen stressors. Constitutive ATFS-1 activation upregulated genes from a number of stress-response pathways, and the loss of atfs-1 blocked upregulation of some stress-response genes by a variety of exogenous stressors, with little or no effect on baseline expression of those genes. Loss of atfs-1 also increased sensitivity to many exogenous stressors (not all mitochondria-targeting), and overexpression was generally protective. However, overexpression also decreased lifespan in the absence of exogenous stressor.

Major comments:

• Are the key conclusions convincing? Mostly, assuming sample size was adequate (see below). The only major conclusion that I would qualify is "ATFS-1 serves a vital role in organismal survival of acute stresses through its ability to activate multiple stress response pathways"-the data, as presented, does not make clear whether ATFS-1 directly activates these pathways (ie, by binding response elements in genes in those pathways), or indirectly influences them by altering the physiology of the worm).
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No.
• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. No.
• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. N/A
• Are the data and the methods presented in such a way that they can be reproduced? Mostly; see below.
• Are the experiments adequately replicated and statistical analysis adequate? Unclear; see below.

Minor comments:

• Specific experimental issues that are easily addressable:

In abstract, consider broadening/re-wording "Gene expression changes resulting from the activation of the mitoUPR are mediated by the transcription factor ATFS-1/ATF-5." Because a naïve reader may understand this to suggest that ATFS-1 is activated only by mitochondrial protein misfolding. Please indicate whether strains were outcrossed, and how often.

How was "young adult" defined? Were worms synchronized, and if so, how?

For the gene expression experiments, do I understand correctly that FUDR was used only for oxidative stress and adult day 2 experiments? Please clarify. Important: Please make clear how many replicates were performed for each experiment, and where relevant, how many worms were measured per replicate (e.g., stress survival and lifespan).

For 2-way ANOVA analyses, please specify p values of both main factors as well as interaction terms and posthoc analyses where relevant. - Are prior studies referenced appropriately? Yes. - Are the text and figures clear and accurate? Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Yes:

In the second paragraph of the introduction, I suggest broadening slightly the description of why normal mitochondrial function is required for ATFS-1 important and degradation, because this helps the reader understand that any one of many perturbations to mitochondrial function (decreased bioenergetics, membrane potential, protein degradation, protein import; increased ROS; etc.) could prevent or reduce ATFS-1 import and degradation.

For Figure 1: The authors present their choice of genes to analyze as if, and interpret their results assuming, that each of these gene is ONLY regulated by the indicated stress response pathways. I think this is very unlikely. For example: is it certain that sod-3 and trx-2 are not also skn-1 regulated? How is "antioxidant" distinguished from the skn-1 pathway? Further clouding the water is the likelihood that nuo-6 and atfs-1 manipulations alter physiology in such a way that there are secondary/indirect stress pathways activated (for example: the authors show that ATFS-1 overexpression shortens lifespan. Perhaps this is why it appears that ATFS-1 overexpression also appears to cause a strong, although variable, upregulation of the cytosolic UPR?). The likelihood (in my opinion) that these genes are in fact regulated by more than one type of response element, and that the manipulations used to study these relationships have pleiotropic effects, do not invalidate the general conclusion that these pathways interact-but they do mean that the results should be discussed with more caveats regarding HOW they interact.

Figure 1 also illustrates why a more detailed description of sample size and statistical analysis should be provided. What was the "n"? What were the main effects and interaction terms of each 2-way ANOVA? The design is not full factorial and therefore does not permit a simple 2-way ANOVA (i.e., not all condition combinations are performed)-which responses precisely were compared to which? Were 2 2-way ANOVAs performed per mRNA?

The work shown in Figure 2 is a very nice way to leverage previous data to further explore this idea of cross-talk. I would suggest including a bit more meta-data in the supplemental data files related to each dataset. For example, what lifestages were used (were they all young adult?), was FUDR used, etc.

However, again, I don't understand how the authors can reach this conclusion: "Combined, this indicates that activation of ATFS-1 is sufficient to upregulate genes in multiple stress response pathways." (lines 152-153 but similar phrasing occurs multiple times) Could it not simply be that one form of cellular stress often eventually triggers broader cellular dysfunction, thus activating other cell stress pathways? Ie-how do we know whether these genes are directly regulated by atfs-1 binding regulatory elements, as implied by this phrasing?

The stress response experiments are very nicely done and very interesting. I appreciate that the authors did not shy away from describing counterintuitive results (eg et15 mutants showing increased sensitivity to chronic oxidative stress), and think that these results should also be briefly considered in the Discussion.

Figure 3: please report ANOVA interaction terms-these are what tell whether the inductions are in fact dependent on atfs-1 (not the post-hoc analyses). Again, it also appears that in some cases, there is an upregulation of certain genes with atfs-1 knockdown-please report all p-values (because there will be many, I recommend a supplemental table with all main and interaction and posthoc analyses). Again, the "n" also needs to be specified.

Figure 4 A-C appear to be lacking error bars? Please add. Perhaps relatedly-the effect size for 4A looks much larger than for 4B, but this does not come across in the text.

For Figures 4 and 6, please indicate sample size-number of independent experimental replicates, and number of worms per replicate (or range per replicate).

Lines 224-225 re. sod-2 mutants: these may also act by decreasing ROS signaling (less conversion of superoxide anon to hydrogen peroxide); also, why would this strain not be considered another long-lived mitochondrial mutant (like clk-1, isp-1 and nuo-6, to which it is contrasted)?

The confirmation that atfs-1 overexpressing strains are short-lived is very interesting. However, I think this statement "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." (lines 265-267 and similar in several places throughout the Discussion, eg line 279) should be altered to indicate that this was observed under controlled laboratory conditions. Eg, "...this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background under optimized laboratory conditions..."

Discussion: consider adding in a consideration of dose-response, both of knockdown of mitochondrial genes (eg, k/d of many mitochondrial genes promotes lifespan at low levels, but decreases lifespan with greater knockdown) and of stressors (chemicals, heat, etc; for chemicals, at the least, dose-response is very important, with low levels not infrequently triggering apparently beneficial stress responses, and higher levels causing toxicity).

Section beginning on line 384 "ATFS-1 upregulates target genes of multiple stress response pathways"-again, please revise to make clear that this work does not demonstrate direct regulation.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. The mitoUPR has generally been viewed and tested as an isolated mitochondrial stress-specific response; the authors have built upon previous work to convincingly show that it is integrated with a variety of other stress response pathways. This is an important contribution to the field.
• Place the work in the context of the existing literature (provide references, where appropriate). The authors have done a nice job of this in their discussion.
• State what audience might be interested in and influenced by the reported findings. Researchers interested in stress response in general, and mitochondrial homeostasis and stress response in particular, as well as the relation of these to lifespan.
• 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. Mitochondrial response to exogenous stressors, particularly pollutants.

Referees cross-commenting

It seems to me that our reviews are in pretty good agreement. I agree with Reviewers 1 and 3 where they commented on things that I did not. While I did not consider the manuscripts as overlapping in the sense of being redundant, I very much like Reviewer 1's suggestion that they be published back to back and that the Discussion of each incorporate consideration of the Results of the other.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript, Soo et al explore the role of the mitoUPR-associated transcription factor ATFS-1 as a hub in multiple stress response pathways controlling gene expression changes and resistance to a variety of exogenous and endogenous stressors. They found that ATFS-1 gain-of-function is sufficient to upregulate a number of genes involved in oxidative stress response, DAF-16-related response, hypoxia and innate immunity. Moreover, they found that many ATFS-1-responsive genes are upregulated under multiple stress conditions and by interventions that prolong lifespan. They also showed that ATFS-1 is required for stress response and resistance under different stress conditions. Finally, they demonstrate that ATFS-1 is not necessary for normal lifespan, but ATFS-1 gain-of-function decreases lifespan.

Major concerns:

1) This manuscript has some overlap with another manuscript from the same group recently submitted to EMBO J. Although I believe both manuscripts have sufficient elements to justify publication of two papers, I strongly recommend that these publications are made back-to-back and they should be discussed in context with one-another. While the main focus of the other manuscript is how mitochondrial mutations lead to improved bacterial pathogen response, it concludes ATFS-1 is key to explain how genes involved in this particular stress response are upregulated upon mitochondrial dysfunction. Their model is that mitochondrial mutations lead to upregulation of innate immunity genes via ATFS-1-mediated transcriptional activation. Here they show that ATFS-1 controls many other stress response pathways in addition to the innate immunity response. Somewhat contradicting their model in the other manuscript, here they show that ATFS-1 is not necessarily required for bacterial pathogen response. In contrast, they even found protection against PA in atfs-1 loss-of-function mutants. This could be explained in light of the fact that ATFS-1 appears to have a protective role under oxidative stress conditions (e.g., mitomutants or paraquat) whereas in worms that have no underlying stress, high ATFS-1 levels may be detrimental. This is consistent with the results in Figure 6. These aspects considered, I believe both manuscripts need to be revised back-to-back so that the data can be reconciled and discuss in context.

2) How is ATFS-1 function regulated in long-lived worms or under multiple stress conditions? Is there a common regulator such as oxidative stress or mitochondrial dysfunction? Both manuscripts would benefit from a clear understanding on how ATFS-1 is controlled under conditions where mitochondrial function is altered. Is mitoUPR required for this activation? If so, is mitoUPR upregulated in all interventions where ATFS-1 has been shown to play a role in stress response. Is there any intervention that controls longevity and does not trigger ATFS-1 response?

3) In Fig. 3, some of these genes appear to be unspecifically associated with different stressors. Therefore, it is difficult to rule out the participation of ATFS-1 in specific stress responses without looking at specific stress-responsive genes or a wider range of genes. For example, the conclusion that ATFS-1 does not control osmotic stress gene expression response comes from looking at 3 genes: sod-3, gst-4 and Y9C9A.8. gst-4 does not appear to be directly controlled by ATFS-1 regardless of the stressor. sod-3 is also upregulated by oxidative stress and Y9C9A.8 by anoxia. On the other hand, somewhat contradicting the authors' conclusions that ATFS-1 does not participate in osmotic stress response based on these 3 genes, ATFS-1 appears to be required for osmotic stress resistance.

Minor concerns:

1) The paragraph starting in line 107 is confusing. They write that "Constitutive activation of ATFS-1 in atfs-1(et 15) and atfs-1(et17) mutants resulted in upregulation of most of the same genes that are upregulated in nuo-6 mutants, except for gst-4" and later they state that "Activating the mitoUPR through the nuo-6 mutation, or through the constitutively-active ATFS-1 mutants did not significantly increase the expression of target genes from the ER-UPR (hsp-4; Fig. 1B) or the cyto-UPR (hsp-16.2; Fig. 1C)." I understand the upregulation of ER-UPR and cyto-UPR is not statistically significant (isn't it for hsp-16.2?), but the first sentence is not accurate if statistics is considered.

2) The authors should discuss why they think atfs-1(et15) gain-of-function mutant exhibited decreased resistance to chronic oxidative stress, while it is protected from acute oxidative stress. In fact, the et15 allele differs in many aspects in relation to the et17 and in some cases it behaves similarly to the gk3094 loss-of-function allele.

3) Fig 4I is very similar to Fig. 6A of the other manuscript which strengthen the notion that ATFS-1 is not required (it is rather detrimental) for bacterial pathogen response when no underlying stress (most likely oxidative) occurs.

4) In the paragraph starting in line 213, the authors conclude that "ATFS-1 is sufficient to protect against oxidative stress, osmotic stress, anoxia, and bacterial pathogens but not heat stress". The results do not unequivocally support a participation of ATFS-1 in oxidative stress or bacterial pathogen response, given the responses vary depending on the allele or condition.

5) "Combined, this indicates that ATFS-1 does not play a major role in lifespan determination in a wild-type background despite having an important role in stress resistance." It actually does, since ATFS-1 gain-of-function decreases lifespan.

6) Paragraph starting in line 359 needs to be discussed in light of the results of the other manuscript submitted by the authors to EMBO.

7) In Fig. 1C, it appears that atfs-1 loss of function increases hsp-16.2. Is that significant?

8) In Fig. 2, 4 and S1, it would be interesting to build one single Venn Diagram with all the lists of genes to see if there are common genes associated with multiple pathways and if there are many ATFS-1 target genes not associated with these classical stress or longevity pathways.

9) In Fig. 2, 4 and S1: What are the p values referred to?

10) In paragraph starting in line 85, the authors should include references that evidence the genes are bona fide markers of the stress response pathways.

11) Tables S2 and S3 are missing.

Significance

Nature and significance of the advance:

The study advances our knowledge about the role of ATFS-1 - a transcription factor involved in mitoUPR - in multiple stress response pathways.

Compare to existing published knowledge:

The role of ATFS-1 has been previously studied in the context of mitoUPR, although the present manuscript expands it to a variety of other stress response pathways. It is yet to be defined whether mitoUPR itself is promiscuously activated in response to different kinds of stressors or ATFS-1 may be activated independently of mitoUPR. As mentioned before, the present manuscript has considerable overlap with a manuscript from the same group under review in EMBO J. These manuscripts need to be discussed in light of one-another.

Audience:

The audience interested in this study is expected to be aging biologists, mitochondrial biologists, as well as researchers using C. elegans as a model organism.

Expertise:

I am interested in mechanisms of aging and their association with metabolism.

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Reply to the reviewers

Reviewer comments:

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

In this paper, the authors examine the relationship between the transcription factor Ribbon, its ribosomal protein gene (RPG) targets, and cell growth during the process of salivary gland tubulogenesis in the Drosophila embryo. This study builds upon previous work they published in 2016 (Loganathan et al., 2016). While the previous study identified RPGs as potential targets of Ribbon from ChIP-Seq analysis, they did not delve into the role of these targets in salivary gland morphogenesis. Here, the authors demonstrate that mutation of ribbon results in decreased cell volumes via immunostaining and image analysis. They identify and confirm RPGs as ribbon transcriptional targets using ChIP-SEQ, Microarray data, in situ hybridization, and qRT-PCR. They analyze these targets in an effort to identify a Rib consensus binding sites by MEME and find that Rib binding is not specific using EMSA. They suggest specificity arises from association with transcriptional cofactors. Binding with cofactors was confirmed by CO-IP and in vivo RNAi experiments demonstrated the requirement of these cofactors in mediating changes in cell volume during salivary gland tubulogenesis. They demonstrate that Ribbon regulation of cell growth via transcription of RPGs is not a universal mechanism for Ribbon function, as Ribbon regulates transcription of other genes in the context of tracheal development.

**Major comments:**

Results of all experiments are conclusive, and significant numbers of samples were noted for most figure panels. For a few panels the sample number/number of replicates was not noted, and it is recommended that the authors add this information (Figure 1F; 5B,C; 7B).

Additional experiments are not needed to support the conclusions presented in this work. The data and methods are presented clearly and the statistical analyses performed were appropriate.

In regard to microarray data, Figure 4E shows fold change as log2 values, but it is unclear if this is the case for Table S2. This should be clarified. The authors note in the text on page 7 that few targets show a greater than 1.5-fold change. Based on Figure 4E, this is a log2 value, and should be specified as such.

As the Rib antibody was generated in this study, it would be helpful to include data illustrating a confirmation of antibody specificity. This could include Rib antibody staining on rib mutant embryos, or showing a lack of band for ribbon in ribbon mutants on a Western blot. If the specificity has been published elsewhere, please add a reference.

**Minor Comments:**

As the microarray data was previously published in Loganathan et al 2016, as mentioned in the results section, this citation should also be included in the Methods section describing the Microarray data.

In the discussion section on page 15, a list of factors in the gene network are listed. What is viz.?

Reviewer #1 (Significance (Required)):

•As described in the introduction, the role of cell growth during embryonic tissue morphogenesis is a relatively unexplored topic. The authors point out that most previous studies describing regulation of tissue growth have focused on the role of mitosis and increased polyploidy, as in the gut (https://doi.org/10.1016/S0925-4773(00)00512-8 ), as primary mechanisms. In the case of the salivary gland, only a single endocycle occurs during embryogenesis and cells are post-mitotic, suggesting another mechanism is at play. This study identifies Ribbon as a mediator of cell growth and demonstrates that Ribbon mediates this function through transcriptional regulation of RPGs. In addition, they identify Ribbon cofactors that are important for salivary gland cell growth and tissue morphogenesis. Interestingly, they find that this mechanism for cell growth may be tissue specific, as Ribbon appears to regulate different genes in the trachea.

•This work has implications for the regulation of cell growth in other tissues and organisms and would be of broad interest to those studying organ development.

•In order to contextualize my review, I am a developmental biologist that works with Drosophila.

**Referees cross-commenting**

In regard to the comments by reviewer #2: I agree that point # 2 should be addressed to more thoroughly describe the method, but as the authors have looked at DNA Amplification at a time point following the normal endocycle, which occurs at stage 12, and DNA content is not significantly different, I don't think analysis of earlier stages would influence their conclusions.

Given that the authors do include some RNAi data for RPGs and Trf2, it would enhance the paper further to include M1BP and Dref RNAi data if quality reagents are available as described in point 5. Point 6 can be easily addressed. In regard to point 8, the effects of rib overexpression alone would be interesting to see given the ability of this construct to rescue the phenotype.

While I think points 3 and 7 are excellent ideas for a follow up study, I think they are outside of the scope of this paper. I do not view point 4 as essential to this study, as the study focuses on the regulation of transcription of the RPGs by Rib.

In regard to the comments by reviewer #3, I agree that points 1 and 2 should be addressed. It would be extremely difficult to address point #3 by dissecting out the tissue, but it could be addressed via further explanation in the text, as could point #4. I don't think minor points 4-6 need to be addressed, but the minor points 1-3 should addressed to improve the paper. For minor point #3, I would suggest the number of genes be included in Supplementary Table 1.

As reviewer #1, I think my comments should be addressed to improve the quality and clarity of the paper.

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

This paper reported a role for the BTB/POZ-domain transcription factor rib in mediating early cell growth of embryonic salivary gland (SG) cells. the authors show that during tubulogenesis of the salivary glands, rib binds the transcription start site of almost all SG-expressed ribosomal protein gene (RPG) and promotes their transcription, thus providing a material foundation for cell growth. Interestingly, in embryo trachea cells, rib targets do not include RPGs, which indicates that rib may use different mechanisms to regulate cell growth of different organs. In general, this is a well-written, well designed research article with many conclusions well-supported by experimental evidence. Listed below are a few issues (mostly minor/unessential) for the authors to consider.

**Major comments:**

1.Although in Figure 1G, the nucleus size is indistinct in rib mutant and wt cells at stage 15 and 16, Figure 1C appeared to look like that the rib mutant nuclei at stage 11, 13 and 14 are significantly smaller than those in wild type cells. The authors need to make sure that the rib phenotype has nothing to do with DNA amplification.

2.Please describe the details on calculating DNA volume by DAPI staining in the method session.

3.The authors have demonstrated weak DNA binding ability of Rib, and physical interactions between Rib with the known regulators of RPG transcription (Trf2, M1BP, and Dref), but what is the functional relationships between Rib and the known RPG regulators? e.g., does Rib function to promote DNA binding and transcriptional activity of Trf2, M1BP, and Dref, or vice versa?

4.To confirm the rib function on RPG translation, it is recommended to examine ribosomal proteins by western, and comparing the total protein content would also be helpful.

5.As Trf2, M1BP and Dref are physically interacted with Rib, it would be helpful to determine Whether M1BP and Dref knockdown can phenocopy the cell growth deficit observed in rib mutant SGs.

6.Page12, paragraph 3, "Thus, despite the shared requirement for Rib in embryonic cell growth of both tubular organs, Rib-dependent growth in the trachea is likely through regulation of alternative growth-promoting factors." Please list the potential growth-promoting factors targeted by Rib according to the Chip-seq data, if possible.

7.It would be interesting to determine whether rib mutation differently affect the secretory function of salivary gland at embryo, larva, pupa or adult stage.

8.Does Rib overexpression have any effects to SG development? Considering the authors adopted GAL4-UAS system to rescue Rib under Rib-KO, it would be interesting to see if Rib overexpression could cause an opposite overgrowth phenotype.

Reviewer #2 (Significance (Required)):

This paper discovered a new mechanism underlying organ-specific cell growth regulation during a specific time-window of animal development, which should be of interest to the field of cell and developmental biology.

Drosophila genetics; Developmental biology

**Referees cross-commenting**

I agree with all the other referees that the comments raised by reviewer #1 should be addressed entirely.

In regard to the comments by reviewer #3, all of the 4 major points are excellent and should be addressed, but it is okay to address points #3 and 4 by simple explanation or re-wording. I find the minor point #6 is nice to have but not essential, the rest should be addressed.

In case of my comments (reviewer 2), points #1,2,5,8 should be addressed, others are nice to have.

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

In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. The manuscript is well written while presenting novel and solid data. The data could be strengthened by some further analysis and clarification, but none of the issues raised represent major flaws.

**Key points:**

1.Cell segmentations: The way the cell segmentations / volume quantifications are presented it is impossible to judge their quality. The authors should provide the extracted geometries as Supplementary Data. The methods could be clearer on how the segmentations for cell and DNA volume were done; were the surfaces done manually, were there any image preprocessing steps etc.? In Figure 7C, it is not clear from the images whether cells or nuclei were segmented. Also, it would strengthen the work if the authors analysed the cell shapes (in particular cell height, and apical cell shape bias), considering that they mention it to be different in the Rib mutant. In addition, it would add to the manuscript if the authors could quantify the volume of the luminal space, of the epithelial layer in wt and mutant, and the bias in tube outgrowth.

2.The authors show nicely that the rib mutants have a smaller overall cell size, can this be the reason why the secretory tube in figure is smaller? In addition, if the overall size of the mutant and the WT is the same as suggested in figure 1H then why does the mutant larvae in figure 1f appear so much smaller than the WT in the same panel?

3.In figure 4f the authors see 4 out of 7 RPGs been significantly down-regulated, do they have an explanation for that? Why are not all 7 tested RPGs significantly down-regulated? Can it be that the results will be significantly improved by dissecting the tissue of interest instead of using whole embryos? Finally with what criteria were these 7 genes selected?

4.The authors state in their manuscript the limitations of the chip-seq and the fact that the 11 unbound RPGs are essentially a technical artifact. I suggest that the authors either perform ChIP on some of these RPGs to prove their point or that they ton down their statements about chip-seq limitations and Rib binding all SG-expressed RPGs

**Minor points**

The authors need to clarify in the text what is early and late stage of tubulogenisis.

In figure 1c the Mipp1 staining is of low quality and although the white lines help the reader on where to focus, noise vs signal is almost indistinguishable. Furthermore, the authors claim that they only take under consideration SG cells that show uniform membrane staining but Figure 1c does not show such uniform staining.

Figure 1d needs the addition of statistical analysis WT vs rib mutant st12 look very similar.

In their ChIP-seq data the authors identify 436 peaks that correspond to 413 genes. It is worth to add a pie chart depicting how many of those 413 are RPGs and how may are non-ribosomal.

Throughout the manuscript the authors exhibit nicely the effects of rib mutants. What happens to the tested genes in panel 4f when rib is overexpressed?

RPls are known to be involved in size regulation. If the authors use another driver than fkh to express Rib, Rpl19 etc will they still see similar phenotypes or not?

Figure 7b is hard to follow, the IP panels should be in agreement with the order that they appear in the text e.g., first experiment then controls

Reviewer #3 (Significance (Required)):

In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. As I am only vaguely familiar with the field, I would leave it to someone who is closer to judge the advance and relevance. But with the additional quantifications, the paper should be of interest more generally to developmental biologists who are interested in tubulogenesis, and if the authors make the 3D cell geometries available, the work should also be of interest to computational modellers with an interest in epithelial organization as segmented 3D cell geometries are still rare.

**Referees cross commenting**

Looking at all 3 referee reports, I find all points made by referee 1 either essential and/or easy to fix. As such, I would insist on all points made.

With regard to referee 2, I see points 1,5,8 as essential, and point 2 is too easy to do to not request it. The others I would consider nice-to-have, but not essential.

In case of my own report, I would insist on points 1 & 2. Among the minor points, points 4 & 6 are NOT essential. The others are either important or easy enough to fix.

I look forward to the views of my colleagues.

Our response to reviewer comments

We thank the reviewers for their very positive comments regarding the importance of this paper and for the constructive feedback they have provided. Indeed, we would be delighted to address every suggestion raised, but since we would also like to have this work published in a timely manner, it is quite helpful to have consensus among the three reviewers regarding which changes and experiments are the most important to include. Since all three reviewers felt it important to address all of the comments from Reviewer #1, we will do so. For the comments raised by reviewers #2 and #3, we will follow the consensus opinion and address those comments by changes in the text or by including more experiments. In this revision plan, we also address the comments that were considered to be beyond the scope of the current study.

Points raised by Reviewer #1

Include N values for all the figure panels: We will provide sample number information for those panels currently missing that information: Figures 1F; 5B, C; and 7B.

Microarray fold-change clarification: We will clarify that we are reporting the fold-change values in Table S2. As is standard with Volcano plots for reporting microarray data, Figure 4E is plotted as Log2 data.

Antibody validation: We will provide a supplemental figure with information about the Rib antiserum and its specificity.

Add citation regarding the microarray data: We will add the citation referring to the microarray data to the Methods section.

Uncommon word usage pg 15: We will remove “viz.”—contraction of a Latin phrase “videre licet” to mean “namely” or “specifically”—from the discussion of factors in the gene network, since it was clearly distracting.

Points raised by Reviewer #2

Appearance of Nuclei and Calculation of DNA volume: The rib mutant nuclei shown in Fig. 1C depict CrebA staining and were used only for identification of SG secretory cells – we did not measure nuclear volume in these samples. To eliminate any potential confusion, we have re-labelled the last column “3D cell volume”. All of the calculations of nuclear size (as a measure of DNA amplification) were carried out with DAPI-staining as shown In Fig 1G, which revealed no difference between WT and rib mutant SG secretory cells. Measurement of entire nuclear volume is critical, since, in any single focal plane, how much of the nucleus is captured varies. We will provide information detailing how DNA volume was obtained in the methods section.

SG cell size phenotypes of M1BP and Dref RNAi Knockdowns: We agree with the reviewers that determining if M1BP and Dref SG-specific RNAi also phenocopy the cell growth deficit observed in the rib mutant SGs is a meaningful experiment and could strengthen our conclusions. We will, therefore, perform this experiment. It should be noted, however, that whereas rib and Trf2 do not have significant levels of maternal mRNA or protein, both M1BP and Dref have high levels of both [based on ModEncode data; Flybase]. Thus, it may be challenging to deplete these genes with only SG driven expression of the RNAi constructs.

List of potential Rib-dependent growth promoting factors in the trachea: In the revised version, we provide the list of candidate growth genes bound by Rib from the tracheal Chip-Seq data as requested by reviewer #2 (and agreed upon by reviewer #1 as important) in the supplement.

Effects of Rib overexpression on SG cell growth: All of the reviewers agree that testing for a SG secretory cell over-growth phenotype with Rib overexpression is worthwhile and we will do this experiment. Nonetheless, we recognize that we may not see overgrowth phenotypes based on a few observations. Our ChIP-Seq data indicate that Rib binds neither the promoters of ribosomal RNAs [rRNAs; the other essential component of ribosomes] nor the promoters of known rRNA transcription factors. Based on a study from another group, it seems likely that Myc upregulates rRNA expression (Grewal et al., 2005). Correspondingly, myc is transcriptionally upregulated in the embryonic SG (supplemental panel 7C) and myc expression in the SG is independent of rib (i.e. Rib does not bind the myc gene based on the SG ChIP-Seq and myc levels in the embryonic SG do not change in rib null embryos based on microarray and whole mount in situs). Also based on ChIP-Seq, Rib binds its own promoter and, based on qRT-PCR experiments, represses its own expression (Loganathan et al., 2016). Thus, over-expression of Rib with GAL4:UAS driven expression may reduce rib transcription from the endogenous locus. Nonetheless, this experiment is still worth doing.

Points raised by Reviewer #3

Information on cell segmentations: In the revised manuscript, we will provide sample 3D views of cell volume quantifications as movie files. In the methods section, we will also make it clear that the surfaces were manually segmented and that no image preprocessing steps were performed. We will also provide the excel spread sheets on size calculations in a supplement. We will provide information in the legend for figure 7 that whole secretory cells were segmented for the calculations done for panel C. The information on cell shapes, apical membrane dynamics, and luminal volumes (including the assessment of developmental dynamics of tube elongation based on live-imaging construction of computational elastic and analytical viscoelastic models) has been presented in previous publications from our lab (Cheshire et al., 2008; Loganathan et al., 2016) and from work in other labs (Blake et al., 1998). We will include this information in the revised discussion and will include the appropriate citations.

Panel 1F and comment on the apparent smaller size of the rib mutant shown: rib mutant embryos show characteristic head invagination defects along with amioserosa and dorsal closure defects [Bradley and Andrew, 2001]. The partial embryo image in Panel 1F captures the head invagination defect making the embryo appear smaller. We will include images of whole embryos in the revised version to clarify that whole embryo volumes of rib mutants are comparable to WT for the representations shown in Fig. 1F.

Clarify early vs. late Tubulogenesis: Early SGs are stage 11, 12 – when the SG cells are internalizing. Late SGs are stages 13 – 16, when the glands are fully internalized. We will clarify this in the figure legend.

Statistics on Panel 1D: We will perform statistical analysis of growth profiles shown in Fig 1D as suggested by the reviewer and include the results in the figure or figure legend.

Pie-chart for RPG fraction: Given how crowded the figures currently are, instead of providing pie charts, we simply provide the fraction of the bound genes that are RP genes in the text. Using our set cut-off of 4.0: 12.9% of genes bound by Rib (with both drivers) were RP genes. Using the IDR platform for peak calling, 12.8% of bound genes were RP genes. In Fig 4A, we also include genes above the cut-off with one GAL4 driver, but not the other, as described in the legend.

Effects of Rib Overexpression: As discussed earlier, we will perform this experiment (please also see our response to the last comment by reviewer #2)

Order of presentation of co-IP results in Panel 7B: As requested, we will reorder the IP results in Fig. 7B as suggested by the reviewer to present first the results from the experiments and then the results from controls in accord with how we discuss the data in the results section.

Testing the functional relationships between Rib and known RPG regulators: We will not determine if Rib promotes DNA binding and transcriptional activity of Trf2, M1BP, and Dref, as this experiment was considered to not be critical for this paper by any of the three reviewers.

Panel 4F and tissue-specific RT-qPCR: We agree that it would be ideal to have tissue-specific qRT-PCR, but it is not technically feasible to dissect out enough embryonic SGs for analysis (as acknowledged by Reviewer 1). In future studies, we do plan to get that kind of information from single cell RNA sequencing (scRNA-Seq) of WT and rib mutant embryos, but there are a few hurdles to overcome before those experiments. In selecting the RP genes for qRT-PCR, we chose sample RpL and RpS genes, making sure to include at least one gene (RpS9) that was “not bound” by Rib based on ChIP-Seq criteria.

Determine Rib function on RPG translation: We will not examine levels of RP proteins by Western since this experiment was deemed be unnecessary for the current study by the three reviewers.

Effects of rib on the secretory function of the SG at the embryo, larva, pupa, or adult stage: We agree with the reviewer that these data would be interesting to have; as pointed out by reviewer #1, however, it’s a question for a future follow-up study.

Chip-Seq technical artifact / limitations: We don’t think we are incorrect in suggesting that the failure to detect Rib binding to all RP genes could be a technical artifact because of the following: (1) a direct examination of the binding tracts associated with every RP gene reveals a peak at/near the TSS. The values associated with those peaks do not always reach the cut-off, but when the peak values are lower than the cut-off, the signals in the flanking DNA are often also much lower than average (for details, see Supplemental Figure 1). (2) Among the RP genes whose expression went down significantly by qRT-PCR is RpS9 – an RP gene “not bound” by Rib, based on the cut-offs we followed.

Using another SG driver: We agree with reviewer #1 that the results obtained using the fkh-GAL4 driver for RNAi of RP regulators and RP genes are robust and sufficient to support the conclusion that Rib binds RPGs to regulate SG secretory cell size. Thus, we will not redo these experiments using another SG driver.

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Referee #3

Evidence, reproducibility and clarity

In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. The manuscript is well written while presenting novel and solid data. The data could be strengthened by some further analysis and clarification, but none of the issues raised represent major flaws.

Key points:

1.Cell segmentations: The way the cell segmentations / volume quantifications are presented it is impossible to judge their quality. The authors should provide the extracted geometries as Supplementary Data. The methods could be clearer on how the segmentations for cell and DNA volume were done; were the surfaces done manually, were there any image preprocessing steps etc.? In Figure 7C, it is not clear from the images whether cells or nuclei were segmented. Also, it would strengthen the work if the authors analysed the cell shapes (in particular cell height, and apical cell shape bias), considering that they mention it to be different in the Rib mutant. In addition, it would add to the manuscript if the authors could quantify the volume of the luminal space, of the epithelial layer in wt and mutant, and the bias in tube outgrowth.

2.The authors show nicely that the rib mutants have a smaller overall cell size, can this be the reason why the secretory tube in figure is smaller? In addition, if the overall size of the mutant and the WT is the same as suggested in figure 1H then why does the mutant larvae in figure 1f appear so much smaller than the WT in the same panel?

3.In figure 4f the authors see 4 out of 7 RPGs been significantly down-regulated, do they have an explanation for that? Why are not all 7 tested RPGs significantly down-regulated? Can it be that the results will be significantly improved by dissecting the tissue of interest instead of using whole embryos? Finally with what criteria were these 7 genes selected?

4.The authors state in their manuscript the limitations of the chip-seq and the fact that the 11 unbound RPGs are essentially a technical artifact. I suggest that the authors either perform ChIP on some of these RPGs to prove their point or that they ton down their statements about chip-seq limitations and Rib binding all SG-expressed RPGs

Minor points

The authors need to clarify in the text what is early and late stage of tubulogenisis.

In figure 1c the Mipp1 staining is of low quality and although the white lines help the reader on where to focus, noise vs signal is almost indistinguishable. Furthermore, the authors claim that they only take under consideration SG cells that show uniform membrane staining but Figure 1c does not show such uniform staining.

Figure 1d needs the addition of statistical analysis WT vs rib mutant st12 look very similar.

In their ChIP-seq data the authors identify 436 peaks that correspond to 413 genes. It is worth to add a pie chart depicting how many of those 413 are RPGs and how may are non-ribosomal.

Throughout the manuscript the authors exhibit nicely the effects of rib mutants. What happens to the tested genes in panel 4f when rib is overexpressed?

RPls are known to be involved in size regulation. If the authors use another driver than fkh to express Rib, Rpl19 etc will they still see similar phenotypes or not?

Figure 7b is hard to follow, the IP panels should be in agreement with the order that they appear in the text e.g., first experiment then controls

Significance

In the manuscript "The Ribb-osome: Ribbon boosts ribosomal protein gene expression to coordinate organ form and function" the authors show evidence that Ribbon mediates early cell growth in Drosophila embryonic salivary gland through direct interaction with ribosomal protein genes. As I am only vaguely familiar with the field, I would leave it to someone who is closer to judge the advance and relevance. But with the additional quantifications, the paper should be of interest more generally to developmental biologists who are interested in tubulogenesis, and if the authors make the 3D cell geometries available, the work should also be of interest to computational modellers with an interest in epithelial organization as segmented 3D cell geometries are still rare.

Referees cross commenting

Looking at all 3 referee reports, I find all points made by referee 1 either essential and/or easy to fix. As such, I would insist on all points made.

With regard to referee 2, I see points 1,5,8 as essential, and point 2 is too easy to do to not request it. The others I would consider nice-to-have, but not essential.

In case of my own report, I would insist on points 1 & 2. Among the minor points, points 4 & 6 are NOT essential. The others are either important or easy enough to fix.

I look forward to the views of my colleagues.

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Referee #2

Evidence, reproducibility and clarity

This paper reported a role for the BTB/POZ-domain transcription factor rib in mediating early cell growth of embryonic salivary gland (SG) cells. the authors show that during tubulogenesis of the salivary glands, rib binds the transcription start site of almost all SG-expressed ribosomal protein gene (RPG) and promotes their transcription, thus providing a material foundation for cell growth. Interestingly, in embryo trachea cells, rib targets do not include RPGs, which indicates that rib may use different mechanisms to regulate cell growth of different organs. In general, this is a well-written, well designed research article with many conclusions well-supported by experimental evidence. Listed below are a few issues (mostly minor/unessential) for the authors to consider.

Major comments:

1.Although in Figure 1G, the nucleus size is indistinct in rib mutant and wt cells at stage 15 and 16, Figure 1C appeared to look like that the rib mutant nuclei at stage 11, 13 and 14 are significantly smaller than those in wild type cells. The authors need to make sure that the rib phenotype has nothing to do with DNA amplification.

2.Please describe the details on calculating DNA volume by DAPI staining in the method session.

3.The authors have demonstrated weak DNA binding ability of Rib, and physical interactions between Rib with the known regulators of RPG transcription (Trf2, M1BP, and Dref), but what is the functional relationships between Rib and the known RPG regulators? e.g., does Rib function to promote DNA binding and transcriptional activity of Trf2, M1BP, and Dref, or vice versa?

4.To confirm the rib function on RPG translation, it is recommended to examine ribosomal proteins by western, and comparing the total protein content would also be helpful.

5.As Trf2, M1BP and Dref are physically interacted with Rib, it would be helpful to determine Whether M1BP and Dref knockdown can phenocopy the cell growth deficit observed in rib mutant SGs.

6.Page12, paragraph 3, "Thus, despite the shared requirement for Rib in embryonic cell growth of both tubular organs, Rib-dependent growth in the trachea is likely through regulation of alternative growth-promoting factors." Please list the potential growth-promoting factors targeted by Rib according to the Chip-seq data, if possible.

7.It would be interesting to determine whether rib mutation differently affect the secretory function of salivary gland at embryo, larva, pupa or adult stage.

8.Does Rib overexpression have any effects to SG development? Considering the authors adopted GAL4-UAS system to rescue Rib under Rib-KO, it would be interesting to see if Rib overexpression could cause an opposite overgrowth phenotype.

Significance

This paper discovered a new mechanism underlying organ-specific cell growth regulation during a specific time-window of animal development, which should be of interest to the field of cell and developmental biology.

Drosophila genetics; Developmental biology

Referees cross-commenting

I agree with all the other referees that the comments raised by reviewer #1 should be addressed entirely.

In regard to the comments by reviewer #3, all of the 4 major points are excellent and should be addressed, but it is okay to address points #3 and 4 by simple explanation or re-wording. I find the minor point #6 is nice to have but not essential, the rest should be addressed.

In case of my comments (reviewer 2), points #1,2,5,8 should be addressed, others are nice to have.

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Referee #1

Evidence, reproducibility and clarity

In this paper, the authors examine the relationship between the transcription factor Ribbon, its ribosomal protein gene (RPG) targets, and cell growth during the process of salivary gland tubulogenesis in the Drosophila embryo. This study builds upon previous work they published in 2016 (Loganathan et al., 2016). While the previous study identified RPGs as potential targets of Ribbon from ChIP-Seq analysis, they did not delve into the role of these targets in salivary gland morphogenesis. Here, the authors demonstrate that mutation of ribbon results in decreased cell volumes via immunostaining and image analysis. They identify and confirm RPGs as ribbon transcriptional targets using ChIP-SEQ, Microarray data, in situ hybridization, and qRT-PCR. They analyze these targets in an effort to identify a Rib consensus binding sites by MEME and find that Rib binding is not specific using EMSA. They suggest specificity arises from association with transcriptional cofactors. Binding with cofactors was confirmed by CO-IP and in vivo RNAi experiments demonstrated the requirement of these cofactors in mediating changes in cell volume during salivary gland tubulogenesis. They demonstrate that Ribbon regulation of cell growth via transcription of RPGs is not a universal mechanism for Ribbon function, as Ribbon regulates transcription of other genes in the context of tracheal development.

Major comments:

Results of all experiments are conclusive, and significant numbers of samples were noted for most figure panels. For a few panels the sample number/number of replicates was not noted, and it is recommended that the authors add this information (Figure 1F; 5B,C; 7B).

Additional experiments are not needed to support the conclusions presented in this work. The data and methods are presented clearly and the statistical analyses performed were appropriate.

In regard to microarray data, Figure 4E shows fold change as log2 values, but it is unclear if this is the case for Table S2. This should be clarified. The authors note in the text on page 7 that few targets show a greater than 1.5-fold change. Based on Figure 4E, this is a log2 value, and should be specified as such.

As the Rib antibody was generated in this study, it would be helpful to include data illustrating a confirmation of antibody specificity. This could include Rib antibody staining on rib mutant embryos, or showing a lack of band for ribbon in ribbon mutants on a Western blot. If the specificity has been published elsewhere, please add a reference.

Minor Comments:

As the microarray data was previously published in Loganathan et al 2016, as mentioned in the results section, this citation should also be included in the Methods section describing the Microarray data.

In the discussion section on page 15, a list of factors in the gene network are listed. What is viz.?

Significance

•As described in the introduction, the role of cell growth during embryonic tissue morphogenesis is a relatively unexplored topic. The authors point out that most previous studies describing regulation of tissue growth have focused on the role of mitosis and increased polyploidy, as in the gut (https://doi.org/10.1016/S0925-4773(00)00512-8 ), as primary mechanisms. In the case of the salivary gland, only a single endocycle occurs during embryogenesis and cells are post-mitotic, suggesting another mechanism is at play. This study identifies Ribbon as a mediator of cell growth and demonstrates that Ribbon mediates this function through transcriptional regulation of RPGs. In addition, they identify Ribbon cofactors that are important for salivary gland cell growth and tissue morphogenesis. Interestingly, they find that this mechanism for cell growth may be tissue specific, as Ribbon appears to regulate different genes in the trachea.

•This work has implications for the regulation of cell growth in other tissues and organisms and would be of broad interest to those studying organ development.

•In order to contextualize my review, I am a developmental biologist that works with Drosophila.

Referees cross-commenting

In regard to the comments by reviewer #2: I agree that point # 2 should be addressed to more thoroughly describe the method, but as the authors have looked at DNA Amplification at a time point following the normal endocycle, which occurs at stage 12, and DNA content is not significantly different, I don't think analysis of earlier stages would influence their conclusions.

Given that the authors do include some RNAi data for RPGs and Trf2, it would enhance the paper further to include M1BP and Dref RNAi data if quality reagents are available as described in point 5. Point 6 can be easily addressed. In regard to point 8, the effects of rib overexpression alone would be interesting to see given the ability of this construct to rescue the phenotype.

While I think points 3 and 7 are excellent ideas for a follow up study, I think they are outside of the scope of this paper. I do not view point 4 as essential to this study, as the study focuses on the regulation of transcription of the RPGs by Rib.

In regard to the comments by reviewer #3, I agree that points 1 and 2 should be addressed. It would be extremely difficult to address point #3 by dissecting out the tissue, but it could be addressed via further explanation in the text, as could point #4. I don't think minor points 4-6 need to be addressed, but the minor points 1-3 should addressed to improve the paper. For minor point #3, I would suggest the number of genes be included in Supplementary Table 1.

As reviewer #1, I think my comments should be addressed to improve the quality and clarity of the paper.

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Reply to the reviewers

1. General Statements [optional]

On behalf of the authors, I would like to thank the three reviewers for providing a valuable feedback on our manuscript. We appreciate that they all considered that the first part of our study conveys novelty to the field of neuroblastoma research, while being coherent with recent studies aimed at identifying the NB cells-of-origin through transcriptomics and single-cell RNA-sequencing approaches. We indeed identified a transcriptional signature that distinguishes LR-NBs from HR-NBs, revealing that these two NB subgroups are better discriminated by the core transcriptional signature shared by the distinct SA cell types, rather than by the transcriptional specificities of any of these cell types, as recently debated. Of note, our findings unveil that the sympatho-adrenal transcriptional program facilitates NB formation but concomitantly restricts its malignant potential. We also wish to thank the reviewers for acknowledging that, in contrast to previous studies, we pursued further by testing the functional relevance of this signature through a combination of in vitro and in vivo experiments. We thereby identified NXPH1 and its receptor α-NRXN1 as ones of the very first factors showing an anti-metastatic activity in the context of NB. Uncovering NXPH1/α-NRXN signaling as a possible target to treat metastatic HR-NBs gives our study considerable clinical relevance.

We consider that the critics and recommendations provided by the three reviewers are positive and pertinent. We are thus willing to address nearly all the reviewers’ concerns and suggestions within the scope of a revision, including performing additional in vivo experiments, as explained in details in the following section. We hope that the planned revisions will be sufficient to make our manuscript suitable for publication.

__ __

2. Description of the planned revisions

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

**Summary:**

In the reviewed manuscript, the authors aimed to characterize the sympathoadrenal (SA) transcriptional landscape that defines low- and high-risk neuroblastomas (LR-NBs and HR-NBs respectively). In particular, they analyze previously published Affymetrix U219 expression profiles of 18 low- (n=8) and high-risk (n=10) neuroblastomas, and 2 fetal human adrenal glands. The authors define transcriptional signatures of LR- and HR-NBs, and further unbiasedly classified them in 4 clusters defining groups of patients with different prognosis (as tested using the 498 SEQC cohort). Within these transcriptional signatures, the authors delineated a SA signature using a human fetal adrenal gland transcriptional profile recently published (Kildisiute et al. 2021), that can discriminate low-risk neuroblastomas. From these genes, the authors further select NXPH1 and NRNX1-2 as promising targets for extensive experimental in vitro and in vivo validation, including validation in cell cultures, and xenografts, to determine that NXPH1/alpha-NRXN1-2 signaling is sufficient for NB tumor growth, and that the expression of either stimulates the metastatic potential of NB cells.

**Major comments:**

1- The cohorts and data used by the authors to conduct the main analysis of the paper are already published, and thus the contribution of the analysis is incremental. In particular, the authors analyzed arrays from a limited cohort size, in comparison with others available sequenced with RNA-seq (e.g. 176 HR- and 322 non-HR NB in 498 SEQC; 224 HR- and 342 non-HR NBs included in the Westermann-genecode19 cohort; and 80 HR- and 20 non-HR in the Jagannathan cohort). Furthermore, the 12,000 most-expressed genes (out of ~20,000 available) were analyzed by the authors, as opposed to more than 40,000 (coding and non-coding) included in a normal RNA-seq study. Only the 498 SEQC dataset provides 12,000 genes significantly up-regulated in either high-risk (n~5,500) or non-high-risk (n~7,000). The differences between datasets could influence the results of the study. For example, in the reviewed manuscript, genes with a high expression in LR-NB (compared to fAG) included DNMT3B, SEMA5A, SOX5, and TET1, all of which have a significantly higher expression in HR-NBs of the 498 SEQC cohort. The quality of the manuscript will be enhanced with consistent results obtained by conducting the same reported analysis in larger cohorts.

Reply: To define the transcriptional signatures associated to the aetiology of LR- and HR-NBs and to the malignant behavior of HR-NBs, we first needed to compare the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online databases cited by the reviewer #1 do not contain any healthy samples, thus precluding the possibility to use it for the first step of our analysis. This is why we decided to use as a starting point the cohort of samples from the Hospital Sant Joan de Déu (HSJD cohort), which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. We then used the SEQC database in different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3) to test the relevance and coherence of the results obtained with the HSJD cohort in the context of a larger cohort. As mentioned by the reviewer #1, we indeed focused our analysis on the 12,000 most-expressed genes. We did so to follow the recommendations of the software Phantasus for transriptomic analyses of mammalian datasets.

2- In the reviewed manuscript, PHOX2A and PHOX2B are significantly more expressed in both LR-NB and HR-NB compared to fAG. This is also the case for other adrenergic markers including TH and DBH. Oppositely, the expression of cortex markers (i.e. STAR and CYP11A1) is significantly higher in fAG. Nevertheless PNMT is not significantly up-regulated in fAG in comparison to LR-NB nor HR-NB. Is it possible that the fetal adrenal glands analyzed include a large proportion of cortex that confounds the transcriptional signals? The quality of the manuscript will be enhance if the authors could establish what proportion of the fAG transcriptional signal belongs to cortex, and if they account for its influence in the analysis.

Reply: The samples of human fetal adrenal gland from which RNA was extracted were obtained from donations (samples staged at 22 weeks post-conception or 2 days after birth) and evaluated by a pathologist who confirmed their correct preservation before sample processing. As fetal adrenal glands are very small tissues, successfully separating the cortical and adrenal regions requires micro-dissection, which was not applied. These samples were instead processed entirely, presenting a ratio of medulla/cortex tissue according to their developmental stage.

3- A recent published paper (Bedoya-Reina et al. 2021) study the differences of HR-NB and LR-NB from a single-cell perspective. In the published manuscript, the authors conclude that LR-NBs are enriched in cells that resemble chromaffin and sympathoblast cells, while the high-risk neuroblastomas are enriched in undifferentiated cells that resemble cells with progenitor characteristics in post-natal adrenal gland. This is broadly consistent with the conclusion reached by the authors in the manuscript under review. It will enhance the content of the reviewed manuscript if the authors compare their transcriptional signatures with the recently published transcriptional signatures in this paper to answer the following questions:

-1) to what extent the transcriptional signatures for HR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published undifferentiated cluster (nC3) enriched in HR-NBs, and the progenitor cluster (hC1) in post-natal adrenal gland;

-2) to what extent the transcriptional signatures for LR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published NOR (nC7, nC8, and nC9) enriched in LR-NBs, and that from the chromaffin cells (hC4) enriched in post-natal adrenal gland;

-3) how is the expression of NXPH1 and alpha-NRXN1-2 in the reported LR- and HR-NBs, and adrenal gland.

Reply: As stated by the reviewer #1, our findings are indeed in agreement with the main conclusions recently reported by Bedoya-Reina et al (Bedoya-Reina et al, 2021). We agree with the reviewer #1’s suggestion that a detailed comparison of our transcriptomic signatures with those of Bedoya-Reina et al would be interesting. We will thus perform this comparison and provide these complementary results in the revised version of the manuscript.

4- In the discussion, the authors indicate that they do not aim to identify the transcriptional signature associated to NB origin but rather use the component of the SA lineage that distinguish LR- and HR-NBs. This statement implies that neuroblastoma can originate from any cell in the developing SA lineage (i.e. SCP, bridge, chromaffin and sympathoblast), a controversial assumption that requires further proof. In particular, when discussing about the core sympathoadrenal signatures enriched in LR-NBs and HR-NBs, the authors obtained a SA signature of genes shared by at least 3 of the 4 SA cell signatures. Further justification needs to be provided as for why (in particular) one of these SA cell signatures exclude the sympathoblast/neuroblast contribution.

Reply: We decided to use as a core SA signature the genes shared by at least 3 of the 4 SA cell types to avoid being too restrictive, the SCP signature being particularly distant from the 3 others. As such, the list of genes shared by all 4 cell types consists of 663 genes, of which only 51 are retrieved in our list of 503 LR vs HR DEGs. Conversely, the list of genes shared by Bridge cells, Chromaffin cells and Sympathoblasts (but not shared by SCPs) consists 3,530 genes, of which 199 are retrieved in the list of 503 LRvsHR DEGs. These complementary results, which can be discussed and provided if required, therefore suggest that the core SA signature discriminating LR-NBs from HR-NBs represents mostly the Bridge-Chromaffin-Sympathoblast lineage and excludes the SCP identity. They are therefore in agreement with the recent notion that NB cells-of-origin derive from the sympathoblast-chromaffin lineage.

5- Some of the most interesting results in the paper are limited to proportions in a subset of top-ranked genes. It will be valuable to set the analysis in an hypothesis driven context, add probabilities, test names, and corrected p-values to the results.

Reply: Our study is based on the initial hypothesis that LR- and HR-NBs might differ in the way they exploit the transcriptional program underlying their developmental origin. To get a deeper insight into this notion we performed a sequential differential expression analysis of primary samples of LR-NBs, HR-NBs and human fetal adrenal gland using the web-based Phantasus software. This software identifies differentially expressed genes between groups using the Limma R package, as detailed in the Methods section. As such, basic statistics for significance analysis were performed using a modetared T-test (as specified in the Limma R package) and FDR-adjusted P-values were set to PAdditionally, top-ranked genes of SA clusters were selected as part of a heuristic approach aimed at highlighting the clinical implication of the transcriptional clusters retrieved in our analysis. The relationship between their expression levels and patient survival was further analyzed using the SEQC database (Fig. S1E). Next, and in contrast to previous studies, we tested experimentally the validity of our analytical findings correlating the expression of SA-c1 genes with a better patient prognosis. To this aim, we selected the candidate gene NXPH1, one of the top-ranked genes from the SA-c1 subset, on the basis of several complementary arguments (listed in response to the reviewer #2’ comment #4). We thereby analyzed how modulating the expression of NXPH1 or that of its receptor α-NRXN1 affect the growth and metastatic potential of human NB cells. The results obtained argue for the validity of our model, by proposing that the neural crest-derived sympatho-adrenal developmental program, in particular the SA-c1 signature, plays a complex role in NB tumorigenesis: it facilitates tumor growth but blocks metastasis formation, hence opposing NB malignancy.

**Minor comments.**

6- In comparison with other cohorts that include low- and intermediate-risk NBs as non-HR NBs, the reviewed data specifically includes low- and high- risk NBs. It is important that the authors include a characterization of intermediate-risk neuroblastomas in their analysis.

Reply: The samples forming the HSDJ cohort were all obtained from NB patients diagnosed and treated at the Hospital Sant Joan de Déu. Several clinical and biological parameters were used for classification, among which the age of the patient. If we apply the cut-off point of 1 year (used at the time the samples were obtained), our cohort does not include any intermediate-risk NB. If we apply the cut-off of 18 months which is now more usual, our cohort would contain only one case (#HSJD-NB14 - aged 16 months at the time of diagnosis) that could be classified as intermediate-risk.

7- Further details and figures on what precise criteria was used to remove the sample #LR-08 is required. How including this sample changes the reported results?

Reply: As explained in the Methods section, before comparing the transcriptomic landscapes of LR-NBs and HR-NBs, we first assessed the sample dispersion by performing a principal component analysis. This analysis identified one outlier (#LR-08) that was clearly distant from all the other NB samples (both LR- and HR-NBs). We thus removed this sample to limit the dispersion and variability that would have impacted the subsequent analyses, as recommended by the Phantasus guidelines. We will provide an illustration of the PCA including this outlier. We believe that performing de novo the whole bioinformatical analysis including this outlier would not bring any novel significant conclusion.

8- GO-term distribution was assessed using the 50 most-enriched GO-terms. How would the results change if all the significant GO terms were analyzed?

Reply: We will re-analyse the GO-term distribution by including all the significant terms.

9- Was the SEQC 498 (GSE62564) dataset obtained with microarrays (as indicated in the methods) or with RNA-seq (i.e. Illumina HiSeq 2000)?

Reply: Similar results were obtained using either the SEQC database obtained with microarrays or the one obtained with RNAseq. The data presented in our manuscript correspond to the ones obtained with the RNA-seq SEQC database.

10- In methods, the first quartile (Q1) in SA-c1 has a higher limit in 487 samples and the fourth quartile the lowest limit in 4, how many samples (out of 498 NBs) were excluded and why?

Reply: As explained in the Methods section and in Fig. 2F, the complete SEQC cohort was included in this survival analysis. To subdivide the 498 samples of the SEQC cohort into 4 expression quartiles, we evaluated whether the expression level of each of the 242 SA-c1 genes (corresponding to 573 ref-seq IDs) in a given patient sample was above or below the mean expression of that gene in the complete cohort. Samples were then distributed into quartiles based on the number of genes presenting an expression level above the mean. As detailed in the Methods section, the resulting sample distribution was as follows: 487≤Q1≤360 (124 patients); 359≤Q2≤250 (125 patients); 249≤Q3≤135 (126 patients) and 134≤Q4≤4 (123 patients).

11- In the 503 DEGs between LR-HR NBs, NTRK2 and MYCN are not included, even if the HR samples included MYCN amplified tumors. Can the authors comment on this?

Reply: MYCN did not pass the cut-off when comparing its expression levels in LR-NB and HR-NB samples (showing an adjusted P=0.10226 for a cut-off of adjusted PNTRK2 was present in our initial 12,000 gene dataset, but it was not differentially expressed in any of the comparisons made (LR vs fAG: adj-P=0.4916; HR vs fAG: adj-P=0.63757; HRvsLR: adj-P=0.90431)

12- The authors mention that the top 30 genes found in cluster c1 (and also in c2) are correlated with favorable patient prognosis. Is it the case that *all* the genes in c1 (and also c2, c3 and c4) are significantly associated with a favorable or else unfavorable prognosis?

Reply: As presented in the datasheet “KM analyses” of Table S3:

• 32 out of the 33 genes (97%) of the cluster c2 correlate to an unfavorable prognosis, the 33th gene showing no particular correlation.

• 29 out of the 32 genes (91%) of the cluster c3 correlate to a favorable prognosis, 1 gene correlates to an unfavorable prognosis and the last 2 do not show any particular correlation.

For the clusters c1 and c4, we focused on the top 30 genes because these clusters contain numerous genes (338 and 100, respectively). The results obtained showed:

• All the top 30 genes (100% of the genes tested) of the cluster c1 correlate to a favorable prognosis

• 23 out of the top 30 genes (77% of the genes tested) of the cluster c4 correlate to an unfavorable prognosis, whereas the other 7 genes do not show any particular correlation

We believe that these results are convincing enough. However, if considered mandatory we will assess the prognosis of all the genes found in clusters c1 and c4.

13- The high expression of a (significant?) number of genes in cluster c4 is observed in patients with worst outcome (i.e. lower event-free survival), including ATR, HIF1A, ING2, POLR2L, SRPRB (498 SEQC, analyzed with R2).

Reply: Indeed, 23 out of the top 30 genes (77% of the genes tested) of the cluster c4 correlate to an unfavorable prognosis, which appears to us as a significant number of genes. We will test whether the expression of the remaining genes forming the cluster c4 also correlate to an unfavorable prognosis.

14- Regarding the 242 genes in the core SA signature, although its a smaller number, the expression of several genes in the core SA signature with a higher expression in HR compared to LR belonging to clusters 2, 3, and 4 is observed in worst outcome patients in the 498 SEQC cohort (CHD7, DNMT1, HMGA1, HSD17B12, LBR, LSM7, MCM4, NKAP, POLA1, and others). Is this small fraction significant?

Reply: The genes presenting a higher expression in HR-NBs than in LR-NBs are found either in cluster c2 or cluster c4. The core SA signature retrieved 262 genes of the 503 LR vs HR DEGs, of which 14 belong to cluster c2 (including CHD7, DNMT1, HMGA1, LBR, LSM7, MCM4, and POLA1) and 3 to cluster c4 (UQCRFS1, NKAP and HSD17B12). As shown in the “KM analyses” datasheet of Table S6, these 17 genes all correlated with an unfavorable prognosis.

15- In Kildisiute et al. 2021, NRXN1 is expressed in SCPs, while NXPH1 is expressed in bridge, chromaffin and sympathoblastic cells. How are the microenviroment of these cells regulating the expression of these genes in a developmental context (particularly as sympathoblastic cells are know to have larger proliferative capabilities than SCPs)? how is this cell heterogeneity replicated by a NB cell line? are mesenchymal and adrenergic cells expressing differentially NRXN1 and NXPH1?

Reply: Unfortunately, the literature about NXPH1 remains very limited (less than 40 articles referenced in Pubmed) and nothing is known about the regulation of its expression during development. The data from Kildisiute et al (Kildisiute et al, 2021) indeed identified NXPH1 in the signatures of bridge cells, chromaffin cells and sympathoblasts, while its receptors NRXN1 and NRXN2 were found in the transcriptomic signatures of all 4 SA cell types. Interestingly, the data provided by Kildisiute et al further established that the expression of NXPH1, NRXN1 and NRXN2 is specifically enriched during the pseudo-time transition from bridge cells to sympathoblasts. This suggests that NXPH1/α-NRXN signaling might be particularly important at that stage and could participate in regulating this transition. But this remains purely speculative and it will need further investigation.

We initially used a panel of 10 human NB cell lines harboring distinct characteristics in terms of genetic profile and morphological properties. We did not find any specific correlation between NXPH1 or α-NRXN1/2 expression and the different types of NB cell lines. We will provide an illustration of this observation in the revised version of the manuscript.

NB cells can convert or be reprogrammed from an adrenergic state, which is less chemoresistant in vitro, to a mesenchymal state (van Groningen et al, 2019). As asked by the reviewer #1, we investigated the expression of NXPH1 and α-NRXN1 in relation with the mesenchymal vs adrenergic status of NB cells (using the dataset GSE90803 from (van Groningen et al, 2019). We found that both genes are expressed at higher levels in cells of the adrenergic phenotype, suggesting that NXPH1/α-NRXN signaling might be particularly relevant for the maintenance of this phenotype. If needed, we will provide an illustration of this observation in the revised version of the manuscript.

16- Figure 1B and C, 2B,D: might the information provided be enhanced? otherwise these inserts might be excluded.

Reply: We thought that the panels presented as Fig.1B, C and 2B, D would be helpful to the readers. We could remove them if the editors and reviewers consider it mandatory.

17- Figure 3D: Kildisiute et al. 2021 data and GTEX available at human protein atlas indicate expression of NRXN1 and NXPH1 in developing and adult adrenal gland. Might the results illustrated suggest a confounding effect in the sampled fetal adrenal glands, perhaps from cortex?

Reply: The samples of human fetal adrenal gland from which RNA was extracted were obtained from donations (samples staged at 22 weeks post-conception or 2 days after birth) and evaluated by a pathologist who confirmed their correct preservation before sample processing. As fetal adrenal glands are very small tissues, successfully separating the cortical and adrenal regions requires micro-dissection, which was not applied. These samples were instead processed entirely, presenting a ratio of medulla/cortex tissue according to their developmental stage.

18- The authors conduct extensive experiments in NRXN1, and make conclusions about its role in for instance metastasis, nevertheless the LR-NB/HR-NB SA signal only includes NRXN2. Can the authors comment on the differences between NRXN1s and NRXN2?

Reply: NRXN1 and NRXN2 were both found to be differentially expressed between LR vs fAG and HR vs fAG, and were thus retrieved among the list of 3.096 common DEGs (Table S3). NRXN2 was further found in the list of 503 LR vs HR DEGs (adjusted P=0.037), showing higher expression levels in LR-NBs than in HR-NBs (see Fig.3D). NRNX1 presented an expression profile comparable to that of NRXN2 (Fig.3D) but did not pass the cut-off (adjusted P=0.38) due to an increased variability in HR-NB samples and was thus absent from the list of LR vs HR DEGs. In vitro, the expression levels of NRXN1 and NRXN2 showed comparable patterns. When we initiated the functional experiments there was no fluorescence-conjugated antibody available to detect and sort α-NRXN2, but there was for α-NRXN1. This is the practical reason that led us to focus on α-NRXN1 in the second part of our study.

Reviewer #1 (Significance (Required)):

The significance of the study relies in investigating the role of selected targets in neuroblastomas within a risk group. In particular, HR-NBs have poor outcomes and are generally metastatic at the time of diagnosis.

The results of the manuscript are somehow consistent with a recently published manuscript analyzing LR- and HR-NBs from a single-cell perspective. The manuscript will be enhanced by conducting the suggested comparison between the reviewed and the reported results. The authors further need to comment why HR-NBs markers, particularly MYCN is not recovered in the LR-NB/HR-NB and the LR-NB/HR-NB SA signals. Also they need to comment on possible confounding effects in the fetal adrenal gland.

The paper is directed to a broader audience of cancer and developmental biologists, and computational biologist. Yet further statistical support needs to be provided.

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

**Summary:**

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

In this paper, Fanlo-Escudero et al., determines gene signatures differentiating low-risk (LR) neuroblastoma (NB) from high-risk neuroblastoma (HR-NB), as well as LR- and HR-NB as an entity from human fetal adrenal gland. They identify a transcriptional signature corresponding to a core sympathoadrenal lineage that can discriminate between LR-NB and HR-NB. This signature is composed of genes associated with favorable patient outcome. The authors further choose one gene, NXPH1, for functional analysis and investigates the effects this gene has on NB progression using in vitro assays, chick CAM assay and mouse in vivo models. The authors conclude that this transcriptional signature can distinguish LR-NB from HR-NB and that NXPH1 is involved in NB cell growth.

**Major comments:**

• Are the key conclusions convincing? The key conclusions are 1) a core SA lineage signature can discriminate between LR-NB and HR-NB, and 2) NXPH1 represses NB malignancy (in terms of metastatic capacity) and is a therapeutic target. The first conclusion is indeed convincing, and not contradictive to common beliefs. The second conclusion is poorly supported by data. The authors perform a range of experiments using in vitro and in vivo settings, but lack some fundamental experiments and overstate their findings.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Several claims should be softened, re-phrased and/or clearly marked as preliminary or speculative. No data nor claims need to be removed.

For example, the following statements need to be changed:

1- Page 10, second paragraph: The authors state "Remarkably, NXPH1 and α-NRXN1/2 levels increased in all the NB cell lines harbouring a sphere-forming capacity (Fig. 3E), thereby revealing a strong positive correlation between the expression of NXPH1 and α-NRXN1/2 and the acquisition of a NCC stem cell identity". The authors only show that NXPH1 is expressed in 8 out of 10 NB cell lines. Sphere-forming capacity is displayed in a relative and not absolute scale which makes it difficult to assess which cell lines that do form spheres and to what extent. The capacity to form spheres (from low to high) does not correlate to the levels of NXPH1 in the different cell lines.

Reply: In its current form, Fig.3E presents via a heatmap representation how the expression of selected genes changes after growing cell lines in sphere-forming conditions as compared to basal (normal) ones. We understand from various reviewers’ comments that this representation has been misleading. We will change it, showing more explicitly the expression levels both in basal and sphere-forming conditions and will bring further details on how the sphere-forming ability of each cell line was assessed and characterized.

2- Page 13, paragraph 1. The authors write "...these data revealed that NXPH1/α-NRXN1 signaling is necessary and sufficient for NB tumor growth in vivo". This is an overstatement. Tumors still form, meaning that NXPH1 signaling is not sufficient.

Reply: Indeed, tumors still form after xenografting sh-NXPH1 or sh-NRNX1 cells but they form with a decreased frequency. Specifically, sh-NXPH1 cells formed tumors in 4 out of 6 xenografted mice, and the 4 tumors all showed a markedly reduced volume. Tumors formed from sh-NRNX1 cells were observed in only 2 out of 5 xenografted mice, with 1 of the 2 tumors showing a markedly reduced volume. We consider that these results support the conclusion that inhibiting NXPH1/α-NRXN1 signaling impairs tumor growth, affecting both tumor initiation and tumor growth. We however understand the reviewer #2’s comment and will thus rephrase this part accordingly. In addition, we will provide complementary data showing the mean volume of the tumors generated in the distinct experimental conditions.

3- Throughout the text, the authors convert their statements. One example is page 15, first paragraph. They write "...growth of NB cells but markedly restrict their metastatic potential", but they do not show this. Instead, they only address the opposite situation - Knockdown enhances metastasis. This is not equal to their statement. See other experiments in other sections. The authors need to go through the manuscript and make sure that they explain their conclusions to actually fit their experiments.

Reply: We will follow the reviewer #2’s recommendation and will rephrase the conclusions whenever needed to better fit to the experimental results.

• Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.

**Issues to respond to:**

4- The authors should more clearly explain why they choose to further study NXPH1 (second highest on the list), involved in synaptogenesis and neurotransmission, instead of SOX6, highest on the list, that is highly relevant in neural crest development.

Reply: The combination of several reasons led us to choose NXPH1 for functional studies:

• NXPH1 is a secreted factor, whose activity might be much more easy to target and modulate in future pharmacological/clinical assays than that of a transcription factor like SOX6
• NXPH1 and its receptor NRNX2 both came out in the list of SA-c1 genes, and NRXN1 presented a comparable expression profile (higher levels in LR-NBs than in HR-NBs, although it did not pass the cut-off required to appear in the SA-c1 list), emphasizing the putative importance of this signaling pathway in NB tumor biology
• The functional involvement of NXPH1 or that of its receptors has never been addressed in cancer formation or progression to date

  5- When the authors investigate the expression of NXPH1 and other genes and compare that with sphere-forming capacity (Fig. 3E and page 10) they analyze expression in cells cultured in basal medium while sphere-forming capacity is measured after 5 weeks in restricted medium. How does the expression of the analyzed genes change under these conditions?

Reply: In its current form, our Fig.3E is actually showing how the expression of the analyzed genes changes after growing cell lines in sphere-forming conditions as compared to basal (normal) ones. As explained above (reviewer #1, point #1), we understand that this representation has been misleading and we will modify it.

6- Is the subpopulation of NRXN1+ cells low (1.5%) because these samples are from aggressive ("High-risk") cell lines?

Reply: All the NB cell lines used in this study derive from HR-NB tumors. As mentioned by the reviewer #3, there is no cell line modeling low-risk neuroblastoma to date. We indeed observed that the proportion α-NRXN1+ cells, as detected by FACS, was very low in all three cell lines tested. A similar observation was made using cells dissociated from three different patient-derived xenografts. We believe that a similar observation made in 6 samples of distinct origins highlights the consistency of finding a low proportion of α-NRXN1+ cells in NB samples.

7- The authors state "...the number of cells quantified per tumor section was decreased by ~50% for the α-NRXN1+-deprived cells relative to their control (Fig. 3J, K), thus revealing that α-NRXN1+ cells are required to support NB tumor growth in vivo". This is not a correct conclusion. This experiment shows that a-NRXN1- cells do not grow and expand to the same extent as control cells. They cannot say that a-NRXN1+ cells support NB growth without comparing growth between a pure a-NRXN1+ and control cells.

Reply: Unfortunately we could not assess the growth of purified α-NRXN1+ cells, given the low number of α-NRXN1+ cells that could be sorted as compared to the numbers of cells required to perform a CAM assay. We thus opted for comparing the growth of total SK-N-SH cells with that of SK-N-SH cells in which the α-NRXN1+ subpopulation had been experimentally removed. We believe that the results obtained convincingly argue for the importance of the α-NRXN1+ subpopulation in promoting NB proliferation and growth. Nevertheless, we understand the reviewer #2’s comment and will rephrase the conclusion.

8- The authors use shRNAs to knock down NXPH1. They enrich their cells by two means - puromycin or doxocycline. This results in equal cell populations. The authors however state that they use doxocycline to circumvent the growth arrest they observe with puromycin selection. They need to elaborate on this and show why this would be the case and what difference the two methods do and show.

Reply: We generated two types of knock-downs: a constitutive one and an inducible one. Puromycin was used to select constitutive knock-downs, whereas doxycycline was used to trigger sh-RNA production in an inducible manner, in stable clones previously established through neomycin selection. We apologize if this was not stated clearly enough in the manuscript and we will correct it.

9- The major flaw of this paper is that the authors use one cell line in total, and even more that they use the same cell line for both knockdown and activation. Since they do show that different NB cell lines have different expression levels (ranging from high to absent), they should choose one cell line for KD and one for overexpression. The authors could also do a rescue experiment with knockout and gain-of-function (e.g., construct that will not be targeted by the shRNA) in the same cells.

Reply: Since NXPH1 is a secreted protein, we needed a cell line that expresses both NXPH1 and its receptors to expect noticing effects on NB cell behavior when their expression is reduced. We reasoned that performing a gain-of-function of NXPH1 in a cell line that does not express its receptors would have no interest, and vice versa. We also believe that it is more conclusive to perform gain- and loss-of-function experiments in the same cell line, because of the likely differences in cell behavior and aggressiveness of distinct cell lines. We however agree that our conclusions would be strengthened if similar conclusions were reached using different cell lines. We will thus perform growth and metastasis assays both in vivo and in the CAM using NXPH1 and α-NRXN1 shRNAs in an additional cell line. We will moreover consider performing rescue experiments and will think about the best methodology to do so.

10- They only use one shRNA after trying several (Fig. S4). The efficiency is substantially variable and not convincing. As stated also elsewhere in this review, they need to check protein level. And to ensure that their results are not off-target they should perform at least some crucial experiments with two shRNAs.

Reply: We agree that the decreased mRNA levels caused by NXPH1 and α-NRXN1 shRNAs showed variability. Yet, they were sufficient to significantly reduce cell viability, which was impaired by two distinct shRNA constructs, both for NXPH1 and α-NRXN1. To complete these experiments as recommended by the reviewer #2, we will assess how NXPH1 and NRXN1 expression is altered at the protein level by western-blotting. We will moreover address possible off-target effects by RT-qPCR.

11- Why don't the authors add BrdU post-implantation? This is easily done in the egg considering the accessibility and would better reflect the proliferation in vivo.

Reply: Adding BrdU pre-implantation allowed us to get a read-out of the global proliferative behavior of NB cells over the whole post-implantation duration. Adding it at the end of the post-implantation would have only allowed us to assess the proliferative behavior of cells at the end of the experiment. We believe that this would have been less informative.

12- Why do the authors switch between CAM and mouse xenografts? I understand that the mouse model must be employed for "metastasis", but can it be explained why and when they perform the different "tumor growth" experiments?

Reply: The CAM assay was used for 2 reasons: 1) when cell numbers were limiting (i.e. testing the importance of the α-NRXN1+ subpopulation for tumor growth), and 2) to perform a gain-of-function strategy using a recombinant rNXPH1 protein and testing its effects on tumor growth over a duration of 1 week. Such experiment would not have been possible using mouse xenografts, due to the extended experimental duration (7-8 weeks) of this assay and to the need for repeated rNXPH1 injections. The rNXPH1 gain of function experiment in the CAM and the NXPH1/α-NRXN1 loss-of-function experiment using mouse xenografts were performed concomitantly.

13- Why do the authors do left ventricle injections for metastatic studies and not AG implantations?

Reply: We reasoned that cell injections into the left ventricle were ideal to test the organotropism of metastatic NBs, as this methodology facilitates cell dissemination and colonization of organs targeted by NB metastasis such as the liver and bone marrow. Cell implantation into the adrenal gland is especially useful to study cell growth in one of the primary sites of NB growth, which was not our experimental purpose at that stage of the study.

14- The measurement of bioluminescence is very difficult to interpret. The authors discuss metastatic spread, but the images show only large blobs covering the heart and areas surrounding it, especially for the sh-αNRXN1. To be able to say that the cells have colonized specific organs the authors need to dissect these organs and perform staining. I see it as tumors recur rather than particularly metastasize when they re-appear after 6 weeks.

Reply: The parameters for bioluminescence detection were set equally for all experimental conditions. The differences in bioluminescence intensities observed in mice injected with control cells compared to those injected with shNXPH1 and shNRXN1 cells explain why it is so intense in the case of shNXPH1 and shNRXN1 cells.

As suggested by the reviewer #2, we had further dissected the mice and recovered the organs of interest. We will provide additional data confirming that an intense bioluminescent signal was effectively detected in the liver and hind legs (containing bone marrow) of mice injected with shNXPH1 and shNRXN1 cells, but not in those injected with control cells.

15- How do the authors explain results presented in Fig. S5: There are more HuNu+ cells but tumor size is unchanged?

Reply: In our experimental CAM setup, 5·105 SK-N-SH cells were embedded in 10μl of Matrigel, which served as a matrix facilitating tridimensional NB cell proliferation. The addition of rNXPH1 increased the number of HuNu+ (NB) cells per section and per mm3 (Fig.4G, H and Fig.S5C), without significantly increasing the tumor area (Fig.S5D) nor the tumor/matrigel volume (data not shown in the current version, but it will be included in the revised version). As illustrated in Fig. 4G, the matrigel was not totally filled with NB cells, even at the time of recovery. We thus deduced from these observations that more NB cells progressively filled the matrigel, without reaching the point where they significantly altered the tumor/matrigel volume. Nevertheless, we can provide additional data revealing that the addition of rNXPH1 caused a slight, yet reproducible increase in the tumor/matrigel weight, in agreement with the increased cell density already shown in Fig.S5C.

• Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments.

Some suggested experiments take time (orthotopic implantations, rescue experiments, adding cell lines and # of shRNAs).

However, as long as the authors discuss and address their methods for in vivo growth (in ovo vs in vivo vs their choice of metastasis model), an orthotopic AG model is not necessary. The authors should however consider it for future studies.

Experiments required to support their conclusions: A rescue experiment and use of different cell lines for KD and overexpression is somewhat time-consuming, as well as ensuring KD at protein level and include an additional shRNA in crucial experiment. I expect that this would take 2-4 months.

• Are the data and the methods presented in such a way that they can be reproduced? 16- The authors should elaborate on their methods, but in general they are reproducible.

Reply: As requested, we will bring further details on the experimental setups.

Are the experiments adequately replicated and statistical analysis adequate?

17- In several places the number of replicates is questionable. Especially at the end of page 26, the authors state that they have performed n=1-4 replicates. N=1 replicate is never ok. In several instances they do n=2 replicates. This can be acceptable but the authors could address this.

Reply: When assessing the sphere-forming capacity of the different NB cell lines, some cell lines only produced spheroids in 1 of the 4 replicates tested. This is a result in itself, which helped us classifying cell lines based on their sphere-forming capacity. We understand that this Methods paragraph was elusive. We apologize for it and will clarify this aspect.

**Minor comments:**

• Specific experimental issues that are easily addressable. Mainly text changes can be seen as minor. For experiments and other issues, see other sections.

• Are prior studies referenced appropriately? Yes. 18- The reference list is not coherently styled.

Reply: We understand that using numbered references can be annoying. We will adapt the reference format to stick to the guidelines of the specific journal to which the manuscript will be addressed.

• Are the text and figures clear and accurate?

19- Overall, the paper is written in a complex way and difficult to easily comprehend. For example, the authors need to clarify several issues on the material they use and experiments they perform. I suggest substantial re-writing to better convey their messages. Sentences should be short and clear, data explained in the context of it was derived.

Reply: We will take into consideration the reviewer #2’s suggestion and modify the manuscript to facilitate its comprehension.

The following text edits and clarifications are required:

20- Page 3. The authors write "cell-of-origin" in several places, this should be changed to "cells-of-origin" (i.e., plural). The view that all NBs originate from only one cell is too simplistic, and the authors should definitely edit this considering that they are investigating different subgroups of NB.

Reply: We will correct this mistake.

21- Page 5. The authors MUST define where the material from the 18 NB patients as well as fetal AG derive from. There is no reference, and taken from the material&methods section, the transcriptome data from these data has not been generated by the authors themselves?

Reply: The transcriptomic data used herein have indeed been previously generated by two co-authors of the study, and the corresponding reference is cited (ref #20; (Gomez et al, 2015). All the NB samples included in our transcriptomic analysis were obtained at the time of diagnosis from patients attended at Hospital Sant Joan de Déu (HSJD, Barcelona, Spain). Tumors were evaluated by a pathologist and only the snap-frozen pre-treatment samples showing at least 70% of viable tumor content were included for analysis. Neuroblastoma risk assessment was defined by the International Neuroblastoma Staging System (INSS). Samples of normal fetal adrenal gland (n=2) were used as a non-tumoral reference tissue. Total RNA from frozen samples was extracted by TRIzol® Reagent. High quality RNA (RINe>7.00) was hybridized to Human Genome U219 microarray plates at the Functional Genomic Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS, Barcelona, Spain) according to Affymetrix standard protocols. Microarray data are deposited in NCBI (#GSE54720). We will include this information in the Methods section of the revised version of the manuscript.

22- Why do the authors choose to work with the data set from Jansky et al in particular?

Reply: Actually, we chose to work with the data from Kildisiute et al. (ref #16 (Kildisiute et al, 2021). We selected this study because: 1) they identified the transcriptional signatures of the 4 SA subtypes relevant to our study and additional related signatures of interest (adrenal cortex, mesenchyme…), 2) these data were obtained from human tissues, and 3) these signatures were easily accessible from their supplementary data.

23- Page 7, paragraph 1. The authors write "Remarkably, 67% of the 503 genes found in this signature, which formed the cluster c1, are both associated with a neural identity and a better patient outcome". I do not agree that this is remarkable and the authors should remove this word.

Reply: We will remove the term “Remarkably”.

24- Page 10, second paragraph. The authors should clarify that the expression levels of the displayed genes are derived from qPCR analysis (if I have understood that correctly, I had to find and guess this from the M&M section), as well as explain how they have set the scale for sphere-forming capacity and what this corresponds to. What do the colors actually represent?

Reply: We apologize for this information lacking in the Results section. We will precise that the expression levels were assessed by RT-qPCR, we will add a representation of their expression levels in basal culture conditions and will improve the explanation of the assessment of the sphere-forming capacity of the NB cell lines.

25- Page 11 and onwards. The authors write "deprived of their a-NRXN1+ subpopulation". This is highly confusing and difficult to read. The authors should write "a-NRXN1- subpopulation"

Reply: We will follow the reviewer #2’s recommendation and change the text for "α-NRXN1- subpopulation".

26- Page 11, end of paragraph 2. The authors write "...arguing that NXPH1/α-NRXN signaling could control NB growth and/or aggressiveness". Number of cells do not directly correlate to aggressiveness, and this needs to be re-phrased to only state what the experiment actually shows - proliferation of a-NRXN1- cells.

Reply: We will rephrase this sentence according to the reviewer #2’s recommendation.

27- I am in favor of using the CAM assay as a complementary system. The authors however use this to define "...required to support tumor growth in vivo". The CAM assay using NB (i.e., transformed cells) shows the growth of these cells in response to presence of blood vessels and not a full tumor micro-environment. This should be clarified.

Reply: We will clarify this aspect.

28- As stated in the previous comment, the authors write "...required to support tumor growth in vivo". The next paragraph has the following headline "NXPH1/α-NRXN signaling stimulates NB growth". This is to me the same thing. The authors should elaborate on how these differ, or if they use them to show the same thing, clearly state that.

Reply: We will better explain how these distinct pieces of evidence are complementary and reinforce the conclusion that NXPH1/α-NRXN signaling stimulates NB growth.

29- The authors conclude that NXPH signaling can be used as a therapeutic target. This would however be extremely difficult considering the opposing effects shown on growth vs metastasis. I agree that it is important to find means to inhibit metastasis, but that does not mean we can allow for enhanced growth of the primary tumor. A better reflection would be to use this as a biomarker, but this can only be predictive/speculative since the authors do not perform for example a tissue microarray to show this at IHC protein level, something that is currently the practice in the clinic.

Reply: While we agree with reviewer #2 that we cannot allow for enhanced growth of the primary tumor, we believe that having identified a secreted factor whose activity inhibits NB metastatic potential is a novel and important finding. We did not wish to suggest that our experimental setup could be directly applied to inhibit the metastatic dissemination of HR-NB tumors. However, we believe that our findings can set the basis of a therapeutic design in which NXPH1/α-NRXN signaling would be enhanced locally to prevent/block metastases from HR-NB tumors.

As mentioned in the Discussion section (page 17), one study reported that NXPH1 can be used as a DNA methylation biomarker associated with a good prognosis for NB patients (reference #54, (Decock et al, 2016).

**Material and Methods:**

30- The authors have misspelled the cell line SK-N-BE(2)c.

Reply: Indeed. We will correct this mistake.

31- Why are some cell lines grown in 20% FBS? This is not standard and could impact the results.

Reply: The 3 cell lines of our panel which were grown in 20% FBS correspond to the 3 cell lines of the mixed subtype, including SK-N-SH, SK-N-Be(2)c and IMR-32 cells. These cell lines have been established and originally grown in presence of a high FBS content (Tumilowicz et al, 1970; Biedler et al, 1973; Ciccarone et al, 1989). In our hands and as recommended by the colleagues that provided us with these cell lines, growing the cell lines in presence of 20% FBS was indeed crucial to sustain their morphological heterogeneity. While we agree with the reviewer #2 that culture conditions could affect cell behavior in vitro, we wish to emphasize that our main conclusions are derived from in vivo experiments. We are thus convinced that our main findings were not impacted by in vitro culture conditions.

32- The authors should state what the tumor volume limit in their ethical permit is (page 30).

Reply: The tumor volume limit was set at 1,500 mm3 as specified by our ethical committee.

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

33- Fig. 3E. As discussed elsewhere, the authors should clarify the scale/meaning for sphere-forming capacity

Reply: As explained above, we will modify the representation of the results shown in Fig.3E to improve their clarity and facilitate their comprehension.

34- Fig. 4D. What do the numbers (i - vi) refer to? I cannot find this in the figure, figure legend, text or material&methods.

Reply: These numbers were used to call different tumors and show their GFP content, as appearing in the lower part of this panel. We will precise this information in the corresponding figure legend.

35- The authors do not present the tumor volume in Fig. 4. The authors discuss tumor growth in the text and this data should be included.

Reply: We agree with the reviewer #2’ suggestion and will present tumor volumes in the revised version of the manuscript.

36- Fig. S4. Knockdown efficiency is variable, and efficiency does not correlate to growth capacity. Especially because of this, the authors need to investigate this at protein level.

Reply: As requested by the reviewer #2, we will investigate the knockdown efficiency at the protein level.

Reviewer #2 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This paper is partly within the scope of the ongoing debate of NB origin (see references below). This paper is however not as extensive, provides no new patient material (applies data from Jansky et al), and do not address the actual cell-of-origin (which the authors themselves also clearly states). This paper provides new gene signatures that can be used to define low-risk vs high-risk patients which is highly important to the field, but these signatures are not unexpected and does not add a significant advance to the field. The authors also do not address how these signatures could be applied clinically. The authors do not use any new methodology. With this said, with revision of the paper, it will still add to the current focus on NB biology research.

• Place the work in the context of the existing literature (provide references, where appropriate). 37- There is a recently initiated, important and extensive debate about the cells-of-origin for NB. The authors indeed bring this up in the paper and also state that they do not intend to add to this debate. They use data from one paper from referenced debate above, and I would argue that because of this fact, and that they extract gene signatures from it, they do, at least partly, touch on the NB cells-of-origin debate, and the authors should put their results into context, from a big picture perspective. As of now, they dodge this complex issue.

References: Dong et al., Cancer Cell 2020; Hanemaaijer et al., PNAS 2021; Jansky et al., Nat Genet 2021; Kameneva et al., Nat Genet 2021; Kildisiute et al., Sci Adv 2021; Furlan et al., Science 2017).

Reply: We understand the reviewer #2’s argument. We will thus try to put our results in perspective regarding the NB cells-of-origin.

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

This paper will be interesting for scientists within the neuroblastoma field, in particular those working on defining NB subgroups in correlation to developmental stages.

• 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. Reviewer's expertise: Neuroblastoma, neural crest, trunk neural crest, chick embryos, mouse models, in vitro models

Parts of paper outside expertise of the reviewer: Analysis of the bioinformatics data (i.e., extracting signatures).

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

**Summary: short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).**

Dr. Le Dréau and colleagues provide a transcriptional analysis comparing low versus high-risk neuroblastomas using a cohort of 18 patients. By comparing them to normal transcriptional signatures obtained from human fetal adrenal glands, they conclude that low risk tumors share a specific core sympatho-adrenal developmental program, which is associated with favorable patient prognosis. Among this signature, they specifically assess the role of NXPH1/a-NRXN in vitro and in vivo using different models (cell lines, PDX-derived cell lines, mouse and chick xenografts). They propose that NXPH1/a-NRXN axis promotes neuroblastoma tumor growth via cell proliferation, and inhibits metastases.

**Major comments:**

• Are the key conclusions convincing?

There are major points that need to be addressed before being convinced by the conclusions.

1- The choice for using the SKNSH cell line, among others, to study the role of NXPH1/a-NRXN axis need to be explained. Indeed, whereas there is no cell line modeling low-risk neuroblastoma, authors should properly illustrate the basal expression of NXPH1/a-NRXN in the cell lines and not the ratio provided in Fig3E. If I understood well, according to Fig3F, only 1.5% of SKNSH cells express NRXN1. Could the authors provide FACS plots? And explain how they can sort high and low expressing cells among 1.5%? And then how do they justify using shRNA approach in a cell line in which only 1.5% are concerned cells? Also, the authors should prove the efficiency of the shRNA used on each specific target for both in vitro and in vivo studies by WB.

Reply: We chose the SK-N-SH cell line for experimental assays based on the following facts: 1) the expression levels of NXPH1 and α-NRXN1/2, 2) this cell line showed the highest sphere-forming capacity, 3) this cell line harbored the highest percentage of α-NRXN1+ cells detected by FACS among the different cell lines tested, and 4) this cell line is of a mixed type, which is supposed to encompass more cell heterogeneity than other (N, I and S) types, thus reproducing more faithfully the complex heterogeneity of primary NB tumors.

In a revised version of the manuscript we will provide data on NXPH1 and a-NRNX1/2 expression levels in basal culture conditions, and will improve the representation of Fig.3E to facilitate its comprehension. The method used to sort α-NRXN1+high, α-NRXN1+low and α-NRXN1- cells is explained in the Methods section (page 25). As requested by the reviewer #3, we will provide FACs plots to illustrate the sorting method.

As requested by the reviewers #2 and #3, we will assess the shRNA efficiency by testing the knockdown at the protein level.

2- The conclusion of inhibition of metastatic process is not supported by enough data. To achieve such a conclusion, authors should provide more than one in vivo experiment (which need to be completed already with the proof of protein deregulation). Some in vitro characterization of metastatic properties such as invasion and migration assays and/or transcriptional analyses could be done.

Reply: We agree with the reviewer #3 that that our conclusion on the anti-metastatic ability of NXPH1/α-NRXN signaling would be reinforced by complementary experiments. To this aim, we propose to test how NXPH1/α-NRXN knockdown affects the metastatic potential of the SK-N-SH cell line and of another cell line in the CAM assay. This assay can indeed be used to assess not only NB tumor growth (as we already did), but also NB cell invasion in target organs (such as the liver and the bone marrow), thus mimicking a metastatic dissemination.

3- Why don't the authors use the transcriptome dataset of 498 patients to realize a more powerful comparative study of low versus high risk tumors (Zhang et al, Genome biology, 2015)? The authors should show the expression plot of NXPH1/a-NRXN in low versus high risk patients, in their cohort of 18 patients but also in the cohort of 498 patients. How do the authors reconciliate the idea that NXPH1/a-NRXN could be associated to stem cell identity but low risk tumors?

Reply: As explained in response to the reviewer #1’ (comment #1), our strategy entailed comparing the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online SEQC database cited by reviewers #1 and #3 does not contain healthy samples, and therefore could not be used for this initial step of the analysis. This is why we decided to use as a starting point the cohort of samples from the Hospital Sant Joan de Déu, which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. We then used the SEQC database at different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3) to test the relevance and coherence of the results obtained with the HSJD cohort in the context of a larger cohort.

As requested by the reviewer #3, we will provide a dot-plot representation of the expression levels of NXPH1 and its receptors in both the HSDJ and SEQC cohorts.

At this point we can only speculate on the association between NXPH1/α-NRXN expression and stem cell identity. This correlation might simply reflect the fact that cells from human NB cell lines return to a transcriptional program closer to their neural crest-derived identity when grown in sphere-forming conditions (as suggested by the increased expression of p75/NTR). Alternatively, this correlation might reflect the ability of NXPH1/α-NRXN signaling to retain cells in an immature state. Such ability could explain how NXPH1/α-NRXN signaling participates in promoting primary tumor growth, and the fact that their expression is increased in LR-NBs as compared to normal fetal adrenal gland. On the other hand, NXPH1/α-NRXN expression is higher in LR-NBs than in HR-NBs, and our findings suggest that this is linked to the anti-metastatic ability of NXPH1/α-NRXN. We could further imagine that by favoring stem cell identity NXPH1/α-NRXN signaling might also provide LR-NB cells with an enhanced ability to “re-enter” a normal developmental path or to be eliminated. In this regard, it is worth reminding that LR-NBs are detected earlier during development than HR-NBs, and sometimes show the puzzling ability to regress spontaneously.

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

Details of experiments and supplementary experiments have to be provided (see previous question).

• 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.

4- Additional details of the experiments have to be provided (cf above): levels of expression of NXPH1/a-NRXN in cell lines, FACS analyses to prove enrichment and/or depletion, WB for validation of shRNA knock-down etc...Moreover, the conclusion that NXPH1/a-NRXN axis inhibits metastatic potential of neuroblastoma is supported by only one in vivo experiment using SKNSH cell line with shRNAs anti-NXPH1/a-NRXN. It should be completed with invasion/migration assays in vitro for example, and/or transcriptional signature of tumors obtained +/- shRNAs anti-NXPH1/a-NRXN. Ideally, these results could be validated in an additional model.

Reply: As mentioned above, we will perform additional experiments following the reviewer #3’s recommendations, including assessing NXPH1 and α-NRXN1 knockdown efficiency at the protein level, assessing how knocking down NXPH1 and α-NRXN1 expression alters the in vivo growth and metastatic potential of an additional cell line, and studying how knocking down NXPH1 and α-NRXN1 alters the metastatic potential of NB cells using a second and complementary metastasis assay (CAM assay).

• 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.

Yes

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

Yes, Material and methods section is well described. But, several points are missing:

5- The paragraphs describing how RNAs from 18 patients and fetal adrenal glands were obtained, how good was the quality, and how transcriptomes have been realized and sequenced are missing.

Reply: The transcriptomic data used herein have indeed been previously generated by two co-authors of the study, and the corresponding reference is cited (ref #20; (Gomez et al, 2015). All the NB samples included in our transcriptomic analysis were obtained at the time of diagnosis from patients attended at Hospital Sant Joan de Déu (HSJD, Barcelona, Spain). Tumors were evaluated by a pathologist and only the snap-frozen pre-treatment samples showing at least 70% of viable tumor content were included for analysis. Neuroblastoma risk assessment was defined by the International Neuroblastoma Staging System (INSS). Samples of normal fetal adrenal gland (n=2) were used as a non-tumoral reference tissue. Total RNA from frozen samples was extracted by TRIzol® Reagent. High quality RNA (RINe>7.00) was hybridized to Human Genome U219 microarray plates at the Functional Genomic Unit, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS, Barcelona, Spain) according to Affymetrix standard protocols. Microarray data are deposited in NCBI (#GSE54720). We will include this information in the Methods section of the revised version of the manuscript.

6- The sequences of shRNAs have to be provided.

Reply: We will provide the oligo sequences used to generate shRNAs against NXPH1 and aNRNX1.

-Are the experiments adequately replicated and statistical analysis adequate?

Yes

**Minor comments:**

• Specific experimental issues that are easily addressable.

7- The authors should show the basal expression of their genes of interest in the patients, cell lines and PDX-derived cell lines, to justify the choice to work then with SKNSH cell line. In addition to the ratio they show in Fig3.E.

Reply: As explained above, we will provide more details on the expression levels of NXPH1 and α-NRXN1/2 in the HSJD and SEQC cohorts and in the NB cell lines used in our study.

8- Authors should also illustrate the FACS analyses they realized in this study, in order to appreciate the quantity of positive cells that are either enriched or depleted.

Reply: The methodology used to sort α-NRXN1+high, α-NRXN1+low and α-NRXN1- cells is explained in the Methods section (page 25). As requested by the reviewer #3, we will provide FACs plots to illustrate this methodology.

9- Authors could precise in their schemes that DOX is maintained in vivo.

Reply: We will follow the reviewer #3’s suggestion.

10- The number of mice need to be integrated in each experiment.

Reply: The numbers of mice used to assess growth and metastasis in vivo were included in the corresponding figure legends (Figs. 4D, 4E and 5C) and appear discreetly on the panel 4D (to the right). We will follow the reviewer #3’s recommendation and add these details on the corresponding figure panels.

• Are prior studies referenced appropriately?

11- No some elements are not right in the introduction:

• Mutations of PHOX2B are not associated to poor prognosis.

• Original publications could be provided instead of reviews.

• Genetic alterations in NB are not only 16%, the authors forgot to mention TERT and ATRX.

• Maybe the NXPH1 methylation in ref 54 could be more explicit, if DNA methylation is detected on NXPH1, it would be of poor prognosis because driving low expression ..?

Reply: We will follow the reviewer #3’s critics and correct the mistakes and information lacking in the introduction and discussion sections.

• Are the text and figures clear and accurate?

Text is very clear and well written, as are the figures.

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

Already mentioned before: FACS and WB are needed.

Reviewer #3 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. This work aimed first at better understanding the fundamental transcriptional differences between two risk groups in Neuroblastoma. The authors defend the conceptual idea that these two groups represent two distinct diseases, which is not new in the area but requires attention, and indeed supported here by distinct transcriptional signatures.

Using published signatures of fetal sympatho-adrenal system, they define a core transcriptional program that is more strongly expressed in low-risk tumors, but we already know that tumors of this category are often more differentiated, and by definition expressed strong markers of SA differentiation, whereas high-risk tumors have a more undifferentiated phenotype.

Not surprisingly, several genes as well as a specific gene signature could be associated to better prognosis, a well-known characteristic of low-risk tumors. However, among them, a novel axis (NXPH1/a-NRXN) is proposed to explain the proliferation but absence of metastasis that define the low-risk group.

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

Neuroblastoma is a rare and very heterogeneous disease, in terms of biology and clinical presentation. To try to decipher such a heterogeneity, recent works have allied single cell transcriptome analyses on tumors and on human fetal cells during development. These studies are well cited in the discussion, and I agree that they yielded discrepant conclusions concerning the cell(s) of origin of Neuroblastoma. By comparing the normal developing human adrenal gland cells to cells from series of neuroblastomas, most of the studies converge towards that the tumors resembled differentiating adrenal neuroblasts. In one study, MYCN-amplified neuroblastoma cells (high risk group) were most similar to normal neuroblasts from seven- or eight-week post-conception, while lower-risk neuroblastomas included more cells resembling late neuroblasts (Janksy et al, 2021).

During the submission of this work, another paper using single cell technologies was published and supported the idea of two distinct tumor entities (Bedoya-reina et al, 2021), with also a stronger signature of sympatho-adrenal cells in low risk tumors.

• State what audience might be interested in and influenced by the reported findings. As the current clinical classification based on various criteria already allows clinicians to identify well low-risk tumors, I think this work would mainly attract fundamental researchers on the molecular differences between low-risk and high-risk tumors.

• 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. I'm specialized on pediatric cancers, and especially Neuroblastoma for the past 3 years. I'm interested in tumor cell identity and cell plasticity, particularly in response to treatment. I think that I have sufficient expertise to evaluate all parts of the manuscript.

__ __

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

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

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

1: The reviewers #1 and #3 suggested using a large dataset (the SEQC database containing 498 samples) to realize a more powerful comparative transcriptomic study. However, to define the transcriptional signatures associated to the aetiology of LR- and HR-NBs and to the malignant behavior of HR-NBs, we first needed to compare the transcriptomic signatures of LR-NB and HR-NB samples to the one of a non-tumoral, healthy tissue such as the fetal adrenal gland. The online SEQC does not contain any healthy samples, thus precluding the possibility to use it for the first step of our analysis. This is why we decided to use as a starting point the cohort of samples from HSJD cohort, which further provided the advantage of comparing samples from patients all diagnosed and treated at the same facility. In different steps of our analytic strategy (Fig.S1F, 2F-H, 3A-B, S3), we already used the SEQC database to test the relevance of the results in the context of a larger cohort. In doing so we obtained coherent results. As recommended by the reviewer #1 (comment #3), we will further compare our data with the data from another study (Bedoya-Reina et al, 2021). We thus believe that the request of the reviewers #1 and #3 does not need to be addressed within the scope of a revision.

2: The reviewer #2 suggested that some crucial functional experiments should be repeated with another shRNA construct to ensure that our results are not off-target and because the knock-down efficiency of the shRNAs used was variable and not convincing. We have tested two distinct shRNA constructs for both NXPH1 and α-NRXN1, which all comparably reduced cell viability. Following this reviewer’s suggestion we will assess possible off-target effects of the sh-NXPH1 and sh-aNRXN1 constructs by RT-qPCR. Moreover, we will also test the effects of these sh-NXPH1 and sh-aNRXN1 constructs in another cell line and using a novel and complementary metastasis assay (CAM assay).

3: The reviewer #2 suggested to study the metastatic potential of NB cells by performing orthotopic implantations of NB cells into the mouse adrenal gland instead of performing cell injections into the mouse left cardiac ventricle. We reasoned that cell injections into the left cardiac ventricle were ideal to test the organo-tropism of metastatic NBs, as this methodology facilitates cell dissemination and colonization of organs targeted by NB metastasis such as the liver and bone marrow, as shown in Fig.5B. Cell implantation into the adrenal gland is especially useful to study cell growth in one of the primary sites of NB formation. We thus believe that our current experimental approach is more relevant to the question we wish to address within the scope of a revision.

References:

Bedoya-Reina OC, Li W, Arceo M, Plescher M, Bullova P, Pui H, Kaucka M, Kharchenko P, Martinsson T, Holmberg J, et al (2021) Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors. Nat Commun 12: 1–15

Biedler JL, Helson L & Spengler BA (1973) Morphology and Growth, Tumorigenicity, and Cytogenetics of Human Neuroblastoma Cells in Continuous Culture. Cancer Res 33: 2643–2652

Ciccarone V, Spengler BA, Meyers MB, Biedler JL & Ross RA (1989) Phenotypic Diversification in Human Neuroblastoma Cells: Expression of Distinct Neural Crest Lineages. Cancer Res 49: 219–225

Decock A, Ongenaert M, Cannoodt R, Verniers K, Wilde B De, Laureys G, Van Roy N, Berbegall AP, Bienertova-Vasku J, Bown N, et al (2016) Methyl-CpG-binding domain sequencing reveals a prognostic methylation signature in neuroblastoma. Oncotarget 7: 1960–72

Gomez S, Castellano G, Mayol G, Sunol M, Queiros A, Bibikova M, Nazor KL, Loring JF, Lemos I, Rodriguez E, et al (2015) DNA methylation fingerprint of neuroblastoma reveals new biological and clinical insights. Epigenomics 7: 1137–1153

van Groningen T, Akogul N, Westerhout EM, Chan A, Hasselt NE, Zwijnenburg DA, Broekmans M, Stroeken P, Haneveld F, Hooijer GKJ, et al (2019) A NOTCH feed-forward loop drives reprogramming from adrenergic to mesenchymal state in neuroblastoma. Nat Commun 10: 1–11

Kildisiute G, Kholosy WM, Young MD, Roberts K, Elmentaite R, van Hooff SR, Pacyna CN, Khabirova E, Piapi A, Thevanesan C, et al (2021) Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Sci Adv 7: eabd3311

Tumilowicz JJ, Nichols WW, Cholon JJ & Greene AE (1970) Definition of a continuous human cell line derived from neuroblastoma. Cancer Res 30: 2110–2118

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Referee #3

Evidence, reproducibility and clarity

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

Dr. Le Dréau and colleagues provide a transcriptional analysis comparing low versus high-risk neuroblastomas using a cohort of 18 patients. By comparing them to normal transcriptional signatures obtained from human fetal adrenal glands, they conclude that low risk tumors share a specific core sympatho-adrenal developmental program, which is associated with favorable patient prognosis. Among this signature, they specifically assess the role of NXPH1/a-NRXN in vitro and in vivo using different models (cell lines, PDX-derived cell lines, mouse and chick xenografts). They propose that NXPH1/a-NRXN axis promotes neuroblastoma tumor growth via cell proliferation, and inhibits metastases.

Major comments:

• Are the key conclusions convincing? There are major points that need to be addressed before being convinced by the conclusions. • The choice for using the SKNSH cell line, among others, to study the role of NXPH1/a-NRXN axis need to be explained. Indeed, whereas there is no cell line modeling low-risk neuroblastoma, authors should properly illustrate the basal expression of NXPH1/a-NRXN in the cell lines and not the ratio provided in Fig3E. If I understood well, according to Fig3F, only 1.5% of SKNSH cells express NRXN1. Could the authors provide FACS plots? And explain how they can sort high and low expressing cells among 1.5%? And then how do they justify using shRNA approach in a cell line in which only 1.5% are concerned cells? Also, the authors should prove the efficiency of the shRNA used on each specific target for both in vitro and in vivo studies by WB. • The conclusion of inhibition of metastatic process is not supported by enough data. To achieve such a conclusion, authors should provide more than one in vivo experiment (which need to be completed already with the proof of protein deregulation). Some in vitro characterization of metastatic properties such as invasion and migration assays and/or transcriptional analyses could be done. • Why don't the authors use the transcriptome dataset of 498 patients to realize a more powerful comparative study of low versus high risk tumors (Zhang et al, Genome biology, 2015)? The authors should show the expression plot of NXPH1/a-NRXN in low versus high risk patients, in their cohort of 18 patients but also in the cohort of 498 patients. How do the authors reconciliate the idea that NXPH1/a-NRXN could be associated to stem cell identity but low risk tumors?
• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Details of experiments and supplementary experiments have to be provided (see previous question).
• 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. Additional details of the experiments have to be provided (cf above): levels of expression of NXPH1/a-NRXN in cell lines, FACS analyses to prove enrichment and/or depletion, WB for validation of shRNA knock-down etc...

Moreover, the conclusion that NXPH1/a-NRXN axis inhibits metastatic potential of neuroblastoma is supported by only one in vivo experiment using SKNSH cell line with shRNAs anti-NXPH1/a-NRXN. It should be completed with invasion/migration assays in vitro for example, and/or transcriptional signature of tumors obtained +/- shRNAs anti-NXPH1/a-NRXN.

Ideally, these results could be validated in an additional model.

• 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. Yes
• Are the data and the methods presented in such a way that they can be reproduced? Yes, Material and methods section is well described. But, several points are missing:
• The paragraphs describing how RNAs from 18 patients and fetal adrenal glands were obtained, how good was the quality, and how transcriptomes have been realized and sequenced are missing.
• The sequences of shRNAs have to be provided.
• Are the experiments adequately replicated and statistical analysis adequate? Yes

Minor comments:

• Specific experimental issues that are easily addressable. The authors should show the basal expression of their genes of interest in the patients, cell lines and PDX-derived cell lines, to justify the choice to work then with SKNSH cell line. In addition to the ratio they show in Fig3.E.

Authors should also illustrate the FACS analyses they realized in this study, in order to appreciate the quantity of positive cells that are either enriched or depleted.

Authors could precise in their schemes that DOX is maintained in vivo. The number of mice need to be integrated in each experiment. - Are prior studies referenced appropriately? No some elements are not right in the introduction: - Mutations of PHOX2B are not associated to poor prognosis. - Original publications could be provided instead of reviews. - Genetic alterations in NB are not only 16%, the authors forgot to mention TERT and ATRX. - Maybe the NXPH1 methylation in ref 54 could be more explicit, if DNA methylation is detected on NXPH1, it would be of poor prognosis because driving low expression ..? - Are the text and figures clear and accurate? Text is very clear and well written, as are the figures. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Already mentioned before: FACS and WB are needed.

Significance

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.. This work aimed first at better understanding the fundamental transcriptional differences between two risk groups in Neuroblastoma. The authors defend the conceptual idea that these two groups represent two distinct diseases, which is not new in the area but requires attention, and indeed supported here by distinct transcriptional signatures.

Using published signatures of fetal sympatho-adrenal system, they define a core transcriptional program that is more strongly expressed in low-risk tumors, but we already know that tumors of this category are often more differentiated, and by definition expressed strong markers of SA differentiation, whereas high-risk tumors have a more undifferentiated phenotype.

Not surprisingly, several genes as well as a specific gene signature could be associated to better prognosis, a well-known characteristic of low-risk tumors. However, among them, a novel axis (NXPH1/a-NRXN) is proposed to explain the proliferation but absence of metastasis that define the low-risk group.

• Place the work in the context of the existing literature (provide references, where appropriate). Neuroblastoma is a rare and very heterogeneous disease, in terms of biology and clinical presentation. To try to decipher such a heterogeneity, recent works have allied single cell transcriptome analyses on tumors and on human fetal cells during development. These studies are well cited in the discussion, and I agree that they yielded discrepant conclusions concerning the cell(s) of origin of Neuroblastoma. By comparing the normal developing human adrenal gland cells to cells from series of neuroblastomas, most of the studies converge towards that the tumors resembled differentiating adrenal neuroblasts. In one study, MYCN-amplified neuroblastoma cells (high risk group) were most similar to normal neuroblasts from seven- or eight-week post-conception, while lower-risk neuroblastomas included more cells resembling late neuroblasts (Janksy et al, 2021).

During the submission of this work, another paper using single cell technologies was published and supported the idea of two distinct tumor entities (Bedoya-reina et al, 2021), with also a stronger signature of sympatho-adrenal cells in low risk tumors.

• State what audience might be interested in and influenced by the reported findings. As the current clinical classification based on various criteria already allows clinicians to identify well low-risk tumors, I think this work would mainly attract fundamental researchers on the molecular differences between low-risk and high-risk tumors.
• 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. I'm specialized on pediatric cancers, and especially Neuroblastoma for the past 3 years. I'm interested in tumor cell identity and cell plasticity, particularly in response to treatment. I think that I have sufficient expertise to evaluate all parts of the manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

In this paper, Fanlo-Escudero et al., determines gene signatures differentiating low-risk (LR) neuroblastoma (NB) from high-risk neuroblastoma (HR-NB), as well as LR- and HR-NB as an entity from human fetal adrenal gland. They identify a transcriptional signature corresponding to a core sympathoadrenal lineage that can discriminate between LR-NB and HR-NB. This signature is composed of genes associated with favorable patient outcome. The authors further choose one gene, NXPH1, for functional analysis and investigates the effects this gene has on NB progression using in vitro assays, chick CAM assay and mouse in vivo models. The authors conclude that this transcriptional signature can distinguish LR-NB from HR-NB and that NXPH1 is involved in NB cell growth.

Major comments:

• Are the key conclusions convincing?

The key conclusions are 1) a core SA lineage signature can discriminate between LR-NB and HR-NB, and 2) NXPH1 represses NB malignancy (in terms of metastatic capacity) and is a therapeutic target. The first conclusion is indeed convincing, and not contradictive to common beliefs. The second conclusion is poorly supported by data. The authors perform a range of experiments using in vitro and in vivo settings, but lack some fundamental experiments and overstate their findings.

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

Several claims should be softened, re-phrased and/or clearly marked as preliminary or speculative. No data nor claims need to be removed.

For example, the following statements need to be changed:

• Page 10, second paragraph: The authors state "Remarkably, NXPH1 and α-NRXN1/2 levels increased in all the NB cell lines harbouring a sphere-forming capacity (Fig. 3E), thereby revealing a strong positive correlation between the expression of NXPH1 and α-NRXN1/2 and the acquisition of a NCC stem cell identity". The authors only show that NXPH1 is expressed in 8 out of 10 NB cell lines. Sphere-forming capacity is displayed in a relative and not absolute scale which makes it difficult to assess which cell lines that do form spheres and to what extent. The capacity to form spheres (from low to high) does not correlate to the levels of NXPH1 in the different cell lines.
• Page 13, paragraph 1. The authors write "...these data revealed that NXPH1/α-NRXN1 signaling is necessary and sufficient for NB tumor growth in vivo". This is an overstatement. Tumors still form, meaning that NXPH1 signaling is not sufficient.
• Throughout the text, the authors convert their statements. One example is page 15, first paragraph. They write "...growth of NB cells but markedly restrict their metastatic potential", but they do not show this. Instead, they only address the opposite situation - Knockdown enhances metastasis. This is not equal to their statement. See other experiments in other sections. The authors need to go through the manuscript and make sure that they explain their conclusions to actually fit their experiments. • Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary to evaluate the paper as it is, and do not ask authors to open new lines of experimentation.

Issues to respond to:

• The authors should more clearly explain why they choose to further study NXPH1 (second highest on the list), involved in synaptogenesis and neurotransmission, instead of SOX6, highest on the list, that is highly relevant in neural crest development.
• When the authors investigate the expression of NXPH1 and other genes and compare that with sphere-forming capacity (Fig. 3E and page 10) they analyze expression in cells cultured in basal medium while sphere-forming capacity is measured after 5 weeks in restricted medium. How does the expression of the analyzed genes change under these conditions?
• Is the subpopulation of NRXN1+ cells low (1.5%) because these samples are from aggressive ("High-risk") cell lines?
• The authors state "...the number of cells quantified per tumor section was decreased by ~50% for the α-NRXN1+-deprived cells relative to their control (Fig. 3J, K), thus revealing that α-NRXN1+ cells are required to support NB tumor growth in vivo". This is not a correct conclusion. This experiment shows that a-NRXN1- cells do not grow and expand to the same extent as control cells. They cannot say that a-NRXN1+ cells support NB growth without comparing growth between a pure a-NRXN1+ and control cells.
• The authors use shRNAs to knock down NXPH1. They enrich their cells by two means - puromycin or doxocycline. This results in equal cell populations. The authors however state that they use doxocycline to circumvent the growth arrest they observe with puromycin selection. They need to elaborate on this and show why this would be the case and what difference the two methods do and show.
• The major flaw of this paper is that the authors use one cell line in total, and even more that they use the same cell line for both knockdown and activation. Since they do show that different NB cell lines have different expression levels (ranging from high to absent), they should choose one cell line for KD and one for overexpression. The authors could also do a rescue experiment with knockout and gain-of-function (e.g., construct that will not be targeted by the shRNA) in the same cells.
• They only use one shRNA after trying several (Fig. S4). The efficiency is substantially variable and not convincing. As stated also elsewhere in this review, they need to check protein level. And to ensure that their results are not off-target they should perform at least some crucial experiments with two shRNAs.
• Why don't the authors add BrdU post-implantation? This is easily done in the egg considering the accessibility and would better reflect the proliferation in vivo.
• Why do the authors switch between CAM and mouse xenografts? I understand that the mouse model must be employed for "metastasis", but can it be explained why and when they perform the different "tumor growth" experiments?
• Why do the authors do left ventricle injections for metastatic studies and not AG implantations?
• The measurement of bioluminescence is very difficult to interpret. The authors discuss metastatic spread, but the images show only large blobs covering the heart and areas surrounding it, especially for the sh-aNRXN1. To be able to say that the cells have colonized specific organs the authors need to dissect these organs and perform staining. I see it as tumors recur rather than particularly metastasize when they re-appear after 6 weeks.
• How do the authors explain results presented in Fig. S5: There are more HuNu+ cells but tumor size is unchanged? • Are the suggested experiments realistic for the authors? It would help if you could add an estimated cost and time investment for substantial experiments.

Some suggested experiments take time (orthotopic implantations, rescue experiments, adding cell lines and # of shRNAs).

However, as long as the authors discuss and address their methods for in vivo growth (in ovo vs in vivo vs their choice of metastasis model), an orthotopic AG model is not necessary. The authors should however consider it for future studies.

Experiments required to support their conclusions: A rescue experiment and use of different cell lines for KD and overexpression is somewhat time-consuming, as well as ensuring KD at protein level and include an additional shRNA in crucial experiment. I expect that this would take 2-4 months.

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

The authors should elaborate on their methods, but in general they are reproducible. • Are the experiments adequately replicated and statistical analysis adequate?

In several places the number of replicates is questionable. Especially at the end of page 26, the authors state that they have performed n=1-4 replicates. N=1 replicate is never ok. In several instances they do n=2 replicates. This can be acceptable but the authors could address this.

Minor comments:

• Specific experimental issues that are easily addressable.

Mainly text changes can be seen as minor. For experiments and other issues, see other sections. • Are prior studies referenced appropriately? Yes. The reference list is not coherently styled. • Are the text and figures clear and accurate?

Overall, the paper is written in a complex way and difficult to easily comprehend. For example, the authors need to clarify several issues on the material they use and experiments they perform. I suggest substantial re-writing to better convey their messages. Sentences should be short and clear, data explained in the context of it was derived.

The following text edits and clarifications are required: * Page 3. The authors write "cell-of-origin" in several places, this should be changed to "cells-of-origin" (i.e., plural). The view that all NBs originate from only one cell is too simplistic, and the authors should definitely edit this considering that they are investigating different subgroups of NB. * Page 5. The authors MUST define where the material from the 18 NB patients as well as fetal AG derive from. There is no reference, and taken from the material&methods section, the transcriptome data from these data has not been generated by the authors themselves? * Why do the authors choose to work with the data set from Jansky et al in particular? * Page 7, paragraph 1. The authors write "Remarkably, 67% of the 503 genes found in this signature, which formed the cluster c1, are both associated with a neural identity and a better patient outcome". I do not agree that this is remarkable and the authors should remove this word. * Page 10, second paragraph. The authors should clarify that the expression levels of the displayed genes are derived from qPCR analysis (if I have understood that correctly, I had to find and guess this from the M&M section), as well as explain how they have set the scale for sphere-forming capacity and what this corresponds to. What do the colors actually represent? * Page 11 and onwards. The authors write "deprived of their a-NRXN1+ subpopulation". This is highly confusing and difficult to read. The authors should write "a-NRXN1- subpopulation" * Page 11, end of paragraph 2. The authors write "...arguing that NXPH1/α-NRXN signaling could control NB growth and/or aggressiveness". Number of cells do not directly correlate to aggressiveness, and this needs to be re-phrased to only state what the experiment actually shows - proliferation of a-NRXN1- cells. * I am in favor of using the CAM assay as a complementary system. The authors however use this to define "...required to support tumor growth in vivo". The CAM assay using NB (i.e., transformed cells) shows the growth of these cells in response to presence of blood vessels and not a full tumor micro-environment. This should be clarified. * As stated in the previous comment, the authors write "...required to support tumor growth in vivo". The next paragraph has the following headline "NXPH1/α-NRXN signaling stimulates NB growth". This is to me the same thing. The authors should elaborate on how these differ, or if they use them to show the same thing, clearly state that. * The authors conclude that NXPH signaling can be used as a therapeutic target. This would however be extremely difficult considering the opposing effects shown on growth vs metastasis. I agree that it is important to find means to inhibit metastasis, but that does not mean we can allow for enhanced growth of the primary tumor. A better reflection would be to use this as a biomarker, but this can only be predictive/speculative since the authors do not perform for example a tissue microarray to show this at IHC protein level, something that is currently the practice in the clinic.

Material and Methods:

The authors have misspelled the cell line SK-N-BE(2)c. Why are some cell lines grown in 20% FBS? This is not standard and could impact the results. The authors should state what the tumor volume limit in their ethical permit is (page 30).

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? * Fig. 3E. As discussed elsewhere, the authors should clarify the scale/meaning for sphere-forming capacity * Fig. 4D. What do the numbers (i - vi) refer to? I cannot find this in the figure, figure legend, text or material&methods. * The authors do not present the tumor volume in Fig. 4. The authors discuss tumor growth in the text and this data should be included. * Fig. S4. Knockdown efficiency is variable, and efficiency does not correlate to growth capacity. Especially because of this, the authors need to investigate this at protein level.

Significance

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

This paper is partly within the scope of the ongoing debate of NB origin (see references below). This paper is however not as extensive, provides no new patient material (applies data from Jansky et al), and do not address the actual cell-of-origin (which the authors themselves also clearly states). This paper provides new gene signatures that can be used to define low-risk vs high-risk patients which is highly important to the field, but these signatures are not unexpected and does not add a significant advance to the field. The authors also do not address how these signatures could be applied clinically. The authors do not use any new methodology. With this said, with revision of the paper, it will still add to the current focus on NB biology research.

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

There is a recently initiated, important and extensive debate about the cells-of-origin for NB. The authors indeed bring this up in the paper and also state that they do not intend to add to this debate. They use data from one paper from referenced debate above, and I would argue that because of this fact, and that they extract gene signatures from it, they do, at least partly, touch on the NB cells-of-origin debate, and the authors should put their results into context, from a big picture perspective. As of now, they dodge this complex issue. References: Dong et al., Cancer Cell 2020; Hanemaaijer et al., PNAS 2021; Jansky et al., Nat Genet 2021; Kameneva et al., Nat Genet 2021; Kildisiute et al., Sci Adv 2021; Furlan et al., Science 2017)

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

This paper will be interesting for scientists within the neuroblastoma field, in particular those working on defining NB subgroups in correlation to developmental stages.

• 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.

Reviewer's expertise: Neuroblastoma, neural crest, trunk neural crest, chick embryos, mouse models, in vitro models

Parts of paper outside expertise of the reviewer: Analysis of the bioinformatics data (i.e., extracting signatures).

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Referee #1

Evidence, reproducibility and clarity

Summary:

In the reviewed manuscript, the authors aimed to characterize the sympathoadrenal (SA) transcriptional landscape that defines low- and high-risk neuroblastomas (LR-NBs and HR-NBs respectively). In particular, they analyze previously published Affymetrix U219 expression profiles of 18 low- (n=8) and high-risk (n=10) neuroblastomas, and 2 fetal human adrenal glands. The authors define transcriptional signatures of LR- and HR-NBs, and further unbiasedly classified them in 4 clusters defining groups of patients with different prognosis (as tested using the 498 SEQC cohort). Within these transcriptional signatures, the authors delineated a SA signature using a human fetal adrenal gland transcriptional profile recently published (Kildisiute et al. 2021), that can discriminate low-risk neuroblastomas. From these genes, the authors further select NXPH1 and NRNX1-2 as promising targets for extensive experimental in vitro and in vivo validation, including validation in cell cultures, and xenografts, to determine that NXPH1/alpha-NRXN1-2 signaling is sufficient for NB tumor growth, and that the expression of either stimulates the metastatic potential of NB cells.

Major comments:

The cohorts and data used by the authors to conduct the main analysis of the paper are already published, and thus the contribution of the analysis is incremental. In particular, the authors analyzed arrays from a limited cohort size, in comparison with others available sequenced with RNA-seq (e.g. 176 HR- and 322 non-HR NB in 498 SEQC; 224 HR- and 342 non-HR NBs included in the Westermann-genecode19 cohort; and 80 HR- and 20 non-HR in the Jagannathan cohort). Furthermore, the 12,000 most-expressed genes (out of ~20,000 available) were analyzed by the authors, as opposed to more than 40,000 (coding and non-coding) included in a normal RNA-seq study. Only the 498 SEQC dataset provides 12,000 genes significantly up-regulated in either high-risk (n~5,500) or non-high-risk (n~7,000). The differences between datasets could influence the results of the study. For example, in the reviewed manuscript, genes with a high expression in LR-NB (compared to fAG) included DNMT3B, SEMA5A, SOX5, and TET1, all of which have a significantly higher expression in HR-NBs of the 498 SEQC cohort. The quality of the manuscript will be enhanced with consistent results obtained by conducting the same reported analysis in larger cohorts.

In the reviewed manuscript, PHOX2A and PHOX2B are significantly more expressed in both LR-NB and HR-NB compared to fAG. This is also the case for other adrenergic markers including TH and DBH. Oppositely, the expression of cortex markers (i.e. STAR and CYP11A1) is significantly higher in fAG. Nevertheless PNMT is not significantly up-regulated in fAG in comparison to LR-NB nor HR-NB. Is it possible that the fetal adrenal glands analyzed include a large proportion of cortex that confounds the transcriptional signals? The quality of the manuscript will be enhance if the authors could establish what proportion of the fAG transcriptional signal belongs to cortex, and if they account for its influence in the analysis.

A recent published paper (Bedoya-Reina et al. 2021) study the differences of HR-NB and LR-NB from a single-cell perspective. In the published manuscript, the authors conclude that LR-NBs are enriched in cells that resemble chromaffin and sympathoblast cells, while the high-risk neuroblastomas are enriched in undifferentiated cells that resemble cells with progenitor characteristics in post-natal adrenal gland. This is broadly consistent with the conclusion reached by the authors in the manuscript under review. It will enhance the content of the reviewed manuscript if the authors compare their transcriptional signatures with the recently published transcriptional signatures in this paper to answer the following questions: 1) to what extent the transcriptional signatures for HR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published undifferentiated cluster (nC3) enriched in HR-NBs, and the progenitor cluster (hC1) in post-natal adrenal gland; 2) to what extent the transcriptional signatures for LR-NBs (4 hierarchical clusters incl.) in the manuscript under review resembles that from the published NOR (nC7, nC8, and nC9) enriched in LR-NBs, and that from the chromaffin cells (hC4) enriched in post-natal adrenal gland; and 3) how is the expression of NXPH1 and alpha-NRXN1-2 in the reported LR- and HR-NBs, and adrenal gland.

In the discussion, the authors indicate that they do not aim to identify the transcriptional signature associated to NB origin but rather use the component of the SA lineage that distinguish LR- and HR-NBs. This statement implies that neuroblastoma can originate from any cell in the developing SA lineage (i.e. SCP, bridge, chromaffin and sympathoblast), a controversial assumption that requires further proof. In particular, when discussing about the core sympathoadrenal signatures enriched in LR-NBs and HR-NBs, the authors obtained a SA signature of genes shared by at least 3 of the 4 SA cell signatures. Further justification needs to be provided as for why (in particular) one of these SA cell signatures exclude the sympathoblast/neuroblast contribution.

Some of the most interesting results in the paper are limited to proportions in a subset of top-ranked genes. It will be valuable to set the analysis in an hypothesis driven context, add probabilities, test names, and corrected p-values to the results.

Minor comments.

1) In comparison with other cohorts that include low- and intermediate-risk NBs as non-HR NBs, the reviewed data specifically includes low- and high- risk NBs. It is important that the authors include a characterization of intermediate-risk neuroblastomas in their analysis.

3) Further details and figures on what precise criteria was used to remove the sample #LR-08 is required. How including this sample changes the reported results?

4) GO-term distribution was assessed using the 50 most-enriched GO-terms. How would the results change if all the significant GO terms were analyzed?

5) Was the SEQC 498 (GSE62564) dataset obtained with microarrays (as indicated in the methods) or with RNA-seq (i.e. Illumina HiSeq 2000)?

6) In methods, the first quartile (Q1) in SA-c1 has a higher limit in 487 samples and the fourth quartile the lowest limit in 4, how many samples (out of 498 NBs) were excluded and why?

7) In the 503 DEGs between LR-HR NBs, NTRK2 and MYCN are not included, even if the HR samples included MYCN amplified tumors. Can the authors comment on this?

8) The authors mention that the top 30 genes found in cluster c1 (and also in c2) are correlated with favorable patient prognosis. Is it the case that all the genes in c1 (and also c2, c3 and c4) are significantly associated with a favorable or else unfavorable prognosis?

9) The high expression of a (significant?) number of genes in cluster c4 is observed in patients with worst outcome (i.e. lower event-free survival), including ATR, HIF1A, ING2, POLR2L, SRPRB (498 SEQC, analyzed with R2).

10) Regarding the 242 genes in the core SA signature, although its a smaller number, the expression of several genes in the core SA signature with a higher expression in HR compared to LR belonging to clusters 2, 3, and 4 is observed in worst outcome patients in the 498 SEQC cohort (CHD7, DNMT1, HMGA1, HSD17B12, LBR, LSM7, MCM4, NKAP, POLA1, and others). Is this small fraction significant?

11) In Kildisiute et al. 2021, NRXN1 is expressed in SCPs, while NXPH1 is expressed in bridge, chromaffin and sympathoblastic cells. How are the microenviroment of these cells regulating the expression of these genes in a developmental context (particularly as sympathoblastic cells are know to have larger proliferative capabilities than SCPs)? how is this cell heterogeneity replicated by a NB cell line? are mesenchymal and adrenergic cells expressing differentially NRXN1 and NXPH1?

12) Figure 1B and C, 2B,D: might the information provided be enhanced? otherwise these inserts might be excluded.

13) Figure 3D: Kildisiute et al. 2021 data and GTEX available at human protein atlas indicate expression of NRXN1 and NXPH1 in developing and adult adrenal gland. Might the results illustrated suggest a confounding effect in the sampled fetal adrenal glands, perhaps from cortex?

14) The authors conduct extensive experiments in NRXN1, and make conclusions about its role in for instance metastasis, nevertheless the LR-NB/HR-NB SA signal only includes NRXN2. Can the authors comment on the differences between NRXN1s and NRXN2?

Significance

The significance of the study relies in investigating the role of selected targets in neuroblastomas within a risk group. In particular, HR-NBs have poor outcomes and are generally metastatic at the time of diagnosis.

The results of the manuscript are somehow consistent with a recently published manuscript analyzing LR- and HR-NBs from a single-cell perspective. The manuscript will be enhanced by conducting the suggested comparison between the reviewed and the reported results. The authors further need to comment why HR-NBs markers, particularly MYCN is not recovered in the LR-NB/HR-NB and the LR-NB/HR-NB SA signals. Also they need to comment on possible confounding effects in the fetal adrenal gland.

The paper is directed to a broader audience of cancer and developmental biologists, and computational biologist. Yet further statistical support needs to be provided.

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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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Reply to the reviewers

A detailed point wise response has been uploaded along with revised manuscript files

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Reply to the reviewers

Manuscript number: RC-2023-02157

Corresponding author(s): Satish, Mishra

1. General Statements [optional]

We thank the editor and reviewers for their helpful comments. We have successfully addressed most of the comments. We are performing some additional experiments as suggested by the reviewers and will be included if considered further. We attempted to pulldown the S14 interacting partner using anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. So, accordingly, we have toned down the conclusion.

The point-by-point response to the reviewer’s comments is given as follows.

2. Description of the planned revisions

Reviewer #1:

Figure 1F You have not formally shown that this signal corresponds to palmitoylated S14. Could be heavy chain. Response: The possibility of a heavy chain is negligible because we have used sporozoite samples and probed it with an anti-rabbit antibody conjugated to HRP. Also, the size of the S14 bands does not correspond to heavy chain. However, we have toned down the conclusion. Currently, we are performing the depalmitoylation experiment, and data will be included in the next round of revision.

Reviewer #2

Line 149: To definitively state S14 is a membrane protein, biochemical assays proving such should be performed. (or perhaps genetic mutation of the predicted palmitoylation site?) Otherwise, this should be rephrased. Response: We are performing the depalmitoylation assay, and the data will be included during the second round of revision. However, we have rephrased the sentence in the current version of the manuscript.

Lines 257-258: for yeast 2-hybrid, the controls of expressing S14, GAP45 and MTIP together with control proteins where no interaction would be predicted are absent. Response: We are performing experiments with additional controls, and data will be included in the next round of revision.

Reviewer #3

Conclusions that S14 knockout does not impact the expression and organization of two surface proteins, CSP and TRAP, and two IMC rely on a qualitative analysis only. However, quantitative analysis to support their observations is missing. Response: We are quantifying the IFA images and data will be included in the next round of revision.

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

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

Summary: The authors have identified a sporozoite gliding motility protein through bioinformatic analysis. From the main text I do not know how, or what bioinformatic analysis was performed, in order to focus on this protein which is called S14. The authors then go on to tag the protein, produce a KO and show its involvement in gliding motility. The KO shows that parasites lacking S14 fail to invade the mosquito salivary glands. This is due to a motility defect. Y2H and docking studies are used to define an interaction with MTIP and GAP45, two known components of the glideosome. Response: We identified this gene from the Kaiser et al., 2004 paper (DOI: 10.1046/j.1365-2958.2003.03909.x). The S14 was found to be highly upregulated in salivary gland sporozoites but lacked signal sequence and transmembrane domain. Next, we looked into other sporozoite proteins lacking signal sequence and transmembrane domain and found several gliding-associated proteins with similar properties. By using the guilt-by-association principle (DOI: 10.1186/gb-2009-10-4-104), we studied the following properties of existing glideosome components along with S14: (1) Classical pathway secretion using the signal peptide (SignalP, https://services.healthtech.dtu.dk/services/SignalP-5.0) (http://dx.doi.org/10.1016/j.jmb.2004.05.028). (2) Nonclassical pathway secretion (SecretomeP , https://services.healthtech.dtu.dk/services/SecretomeP-1.0/) (10.1093/protein/gzh037). (3) Presence of transmembrane domains (TMHMM , https://services.healthtech.dtu.dk/services/TMHMM-2.0/) (10.1006/jmbi.2000.4315). (4) Presence of a potential palmitoylation site (CSS-Palm, http://bioinformatics.lcd-ustc.org/css_palm) (Ren et al, 2008). This is a similar association prediction method as employed by the STRING database. However, mentioning that we identified a gliding motility protein by bioinformatic analysis was wrong, and we modified the sentence.

Major comments: The paper is sometimes hard to follow and lacks clarity. The reason: important information is omitted, or explained at the end of a section rather than at first mention; experimental details that are of essence need to be mentioned or explained in the main text; there is ample use of the word 'bioinformatic' without explaining what kind of analysis was performed in the main text. I cite from the abstract: 'In silico analysis of a novel protein, S14, which is uniquely upregulated in salivary gland sporozoites, suggested its association with glideosome-associated proteins.' I cite from the introduction: 'A study comparing transcriptome differences between sporozoites and merozoites using suppressive subtraction hybridization found several genes highly upregulated in sporozoites and named them 'S' genes (Kaiser et al, 2004). We narrowed it down to a candidate named S14, which lacked signal peptide and transmembrane domains.' From reading the main text, I do not know why Plasmodium berghei S14 was chosen in this manuscript. S14 is one of 25 transcripts identified by Kappe et al in Plasmodium yoelii (https://doi.org/10.1046/j.1365-2958.2003.03909.x) to be upregulated in sporozoites. The material and methods section does not explain either why S14 was chosen. Perhaps the authors could update Figure 2 from Kappe et al with the most recent annotations from plasmodb. Response: We have edited the manuscript for clarity and mentioned the name of the bioinformatic analysis performed. We chose S14 from Kaiser et al., 2004 (https://doi.org/10.1046/j.1365-2958.2003.03909.x) that identified transcripts in P. yoelii. We work on the rodent malaria parasite P .berghei and validated S14 transcripts by qPCR which showed its upregulation in sporozoites.

Rodent malaria parasites P. berghei and P. yoelii have been used extensively as models of human malaria. Both species have been widely used in studies on the biology of Plasmodium sporozoites and liver stages due to the availability of efficient reverse genetics technologies, and the ability to analyze these parasites throughout the life cycle stages have made these two species the preferred models for the analysis of Plasmodium gene function. A genetic screen and phenotype analysis were performed in P. berghei (DOI: 10.1016/j.cell.2017.06.030 and DOI: 10.1016/j.cell.2019.10.030) that makes in-depth characterization easier due to the availability of reagents and preliminary gene-phenotype like its dispensability in the blood. As suggested by this reviewer, we have updated the most recent annotations from PlasmoDB.

Reproducibility: None of the main Figures or Figure legends define ' N = '. For example I cite: 'The S14 KO clonal lines were first analyzed for asexual blood-stage propagation, and for this, 200 µl of iRBCs with 0.2% parasitemia was intravenously injected into a group of mice.' There are 2 mentions of 'N=' in the supplementary figures. I have not found any others.

I'm not sure what the convention is. Should unpublished data for this gene (PBANKA_0605900) found in pberghei.eu (a database for mutant berghei parasites) be cited? After all it confirms their findings.

The authors need to use more recent references for some of their statements; see some comments below. __Response: __We have mentioned N in the figures legends of the revised manuscript and also mentioned the unpublished data of RMGM. We have also added recent references in the revised manuscript.

Minor comments:

line 1-2 Add the Plasmodium species of this study.

Response: Added.

abstract Which species do you work with?

Response: We have mentioned P. berghei in the abstract of the revised manuscript.

29 mosquito salivary glands and human host hepatocytes

Response: Corrected.

30 to the glideosome, a protein complex containing [...]

Response: Corrected.

32-33 What kind of in silico analysis suggested S14 is part of the glideosome? S14 is not uniquely upregulated; there are other S-type genes identified by Kappe and Matuschewski. 25 I believe.

Response: Mentioning that in silico analysis suggested S14 is part of the glideosome was a wrong statement, and we have modified the sentence for clarity in the revised manuscript.

32 Please point out he species were S genes were identified. SGS of which species?

Response: The S genes were identified in the transcriptomic study of Plasmodium yoelii.

34 expression: change to transcription

Response: Changed.

39 What kind of in silico analysis was used here? and therefore malaria transmission

__Response: __In silico, protein-protein docking interaction analysis was used.

55 A single zygote transforms into a single ookinete, which establishes a single oocyst, which in turn can produce thousands of midgut sporozoites. Please correct the life cycle passage.

Response: Corrected. located or anchored in the IMC? And located between the IMC and plasma membrane?

Response: Glideosome is located between the plasma membrane and IMC, and the same has been corrected in the revised manuscript.

61-63 Refer to Table S1 and its contents here 64 Name the known GAPs. Response: Done.

65-67 Which transmembrane domain proteins? Please add more recent references than King 1988.

Response: We have added TRAP as a transmembrane domain protein and updated the reference.

71-72 TRAP was the first protein found to be ...

Response: Corrected.

74-76 Add additional, more recent references: for example search Frischknecht and TRAP

Response: Added.

76 S6 (TREP) is also [...]

Response: Done.

88 Some of these proteins are also expressed in ookinetes.

Response: Corrected.

89-91 The sentence needs a verb.

Response: Added.

88-96 Please add some more recent glideosome papers. After 2013.

Response: Added.

91 Why do you call it a peripheral protein?

Response: Because the GAP45 was detected at the periphery of the merozoite in P. falciparum. As there are no such reports in sporozoites hence we have removed peripheral in the revised manuscript.

91-93 There are more recent citations for GAP45 and GAP50. Response: We have added recent citations.

96 Insert a reference here.

Response: Added.

99 Please define the gliding-associated proteins. What are they? Aren't there papers on GAP40, 45 and 50? DOI: 10.1016/j.chom.2010.09.002

Response: Done.

99 .... What prompted you to identify a novel GAP? And why is S14 classified as a GAP?

Response: This was a wrong statement, which we removed in the revised manuscript.

99-102 What kind of bioinformatic study? Why was S14 chosen? Please outline how you ended up with S14. Any other proteins that came out of the bioinformatic screen from the list of S genes?

Response: We identified S14 from the Kaiser et al., 2004 paper and analyzed its properties using the “guilt-by-association” principle. The analysis showed that S14 had properties similar to GAP45 and MTIP. The S14 upregulation in sporozoites and its properties similar to known GAPs, we were prompted to study this gene's function.

How many proteins were identified in the screen for sporozoite upregulated proteins by Kappe and Matuschewski?

Response: 25 genes were identified in that paper, including the two characterized genes CSP and TRAP during that study.

102-103 Define the nonclassical secretion pathway. Please reference GAP45 and GAP50 data for the nonclassical pathway.

Response: When proteins are secreted out of the cytosol without predictable or known signal sequences or secretory motifs are classified as non-classically secreted proteins, and the pathway is called a non-classical protein secretory pathway. References: https://doi.org/10.1371/journal.pone.0125191; https://doi.org/10.1016/S0171-9335(99)80097-1; doi: 10.3389/fmicb.2016.00194

105 Please add P. berghei to the title, the abstract, the introduction.

Response: Added.

111 The results section does not outline what bioinformatic analysis was used

Response: The guilt-by-association principle was used, and it is outlined in the revised manuscript.

112-114 Please specify the exact number of upregulated in sporozoites genes. I think it was 25. And add the species the study was performed in. Why did you choose the Kappe study but not the uis genes from berghei?

Response: It was 25, and the species was P. yoellli. The domains of all 25 proteins are shown in Figure 2 of Kappe study. It intrigued us after having a glance at it. Later, we confirmed the upregulation of S14 transcripts in P. berghei sporozoites and chose to study the function of this gene.

114-115 How did you narrow it down to S14? The Kappe paper lists 25 S-type genes from P. yoelii.

Response: The domains of all 25 proteins are shown in the Kappe study. Two proteins, S14 and S15, lack signal sequence and transmembrane domain, which intrigued us after glancing at them. We chose S14 because its microarray induction is higher compared to S15.

118 Plasmodia is not the plural for a group of different Plasmodium species. Use: [...] conserved among Plasmodium spp.

Response: Corrected.

118-119 Which proteins did you analyze? And how did you analyze them? Where is the data for this analysis? Outline the amino acids that predict palmitoylation? The nonclassical pathway?

Response: The proteins we analyzed are given in Table S1. We analyzed them by the guilt-by-association principle. The data for this analysis is shown in Table S1. The amino acids predicted to be palmitoylated are C59 and C228 (S14), C5 (GAP45), C8 and C5 (MTIP). Non-classical pathway secretion was predicted by SecretomeP ( 10.1093/protein/gzh037).

119-122 Here: do you mean S14 has similar properties as GAP 45 and GAP50? Define the nonclassical pathway? How do you know S14 is in the IMC?

Response: The similar properties of S14 and GAP45 are Signal Peptide Prediction, Prediction of Non-classical pathway secretion, number of predicted transmembrane domains and prediction of Palmitoylation signal. GAP50 was wrongly mentioned here and has been removed from the revised manuscript.

When proteins are secreted out of the cytosol without predictable or known signal sequences or secretory motifs are classified as non-classically secreted proteins. The pathway is called a non-classical protein secretory pathway.

Our colocalization data of S14 with GAP45 and MTIP indicated that S14 is in the IMC.

122-123 Please reference the bioinformatic analysis plus URL that allows targeting to the IMC to be analyzed.

Response: All the URLs with references are given in the method section, lines 348-358 in the revised manuscript.

123-124 Please reference the URLs for TM, palmitoylation, and interactions analyses.

Response: All URLs with references are given in the method section, lines 348-358 in the revised manuscript.

125-127 How did you predict that S14 is secreted via the nonclassical pathway?

Response: We predicted non-classical pathway secretion of S14 using - SecretomeP (https://services.healthtech.dtu.dk/services/SecretomeP-1.0/) (10.1093/protein/gzh037).

128-130 Define the nonclassical pathway when it first appears in your manuscript.

The citation Moskes 2004 is not in the reference list

Response: The nonclassical pathway is defined in lines 105-107. The citation Moskes 2004 has been included in the revised manuscript.

132 Which membrane?

Response: Live S14-mCherry localization on the membrane does not differentiate between the outer membrane or IMC. Hence, only membrane is mentioned. Next, in Figure 4A, we confirmed S14 localization on IMC by treating sporozoites with Triton X-100 and colocalizing with IMC proteins GAP45 and MTIP.

134-135 In which species?

Response: We have mentioned P. berghei in the text in the revised manuscript.

141-142 Please include images of blood stage and liver stage parasites.

Response: Blood and liver stage images are included in the revised manuscript as Figure S2.

142-143 Which membrane?

Response: Live S14-mCherry localization on the membrane does not differentiate between the outer membrane or IMC. Hence, only membrane is mentioned. Next, in Figure 4A, we confirmed S14 localization on IMC by treating sporozoites with Triton X-100 and colocalizing with IMC proteins GAP45 and MTIP.

148-149 I cannot find the specific figure you refer to; I checked the online version of the Frenal 2010 paper.

Response: Electromobility shifts of GAP45 due to the palmitoylation have been reported in (Rees-Channer et al, 2006; DOI: 10.1016/j.molbiopara.2006.04.008). Frenal 2010 paper has stated about two bands but experimentally, it was shown in Rees-Channer et al, 2006 in Figures 1 and 2B.

175 gland, we counted [...]

Response: Corrected.

177 Compared to the

Response: Corrected.

177-179 Failed to invade (absolutely)? Or invaded in highly reduced numbers?

Response: Corrected.

182-186 Please be precise: I think you mean you let all types of mosquitoes take a blood meal; s14 knockout-infected mosquitoes did not infect mice.

Response: Corrected.

181-202 Perhaps use paragraphs to indicate the different types of experiments performed here.

Response: Done.

204 Please introduce paragraphs to identify the different experiments in this section

Response: Done.

208 Outer or inner membrane of what? IMC, the plasma membrane?

Response: We treated sporozoites with Triton X-100 to analyze whether S14 is present on the outer membrane (plasma membrane) or IMC.

228 onwards Structural models were obtained from whom? Which species did you use for the docking study? Could you use in one approach 3 berghei proteins, and confirm your docking studies with the falciparum proteins? That would strengthen your model. Should you include a negative control protein in the approach? Response: The structural models were obtained using the trROSETTA server. We used P. berghei for the docking study. In the old annotation and RMGM, the ortholog of P. berghei (PBANKA_0605900) in P.falciparum (PF3D7_1207400) was indicated. However, the updated PlasmodDB does not show PBANKA_0605900 ortholog in P. falciparum. We did try to generate structure models of P. falciparum MTIP, GAP45 and S14 using the trROSETTA server. We successfully reproduced the structure of MTIP, and GAP45 but the quality of S14 structure was unsuitable for the interaction studies. The negative control cannot be included in this kind of study because it gives a false interface, and none of the previous studies have used negative control.

250-251 Was all of the gene cloned? Please define amino acid range. discussion

Response: Full-length gene of S14, MTIP and GAP45 was cloned and the same has been mentioned in materials and methods in the revised manuscript.

Please discuss data from https://elifesciences.org/articles/77447 in relation to your protein Response: Discussed.

298-300 More recent glideosome papers exist. For example https://doi.org/10.1038/s42003-020-01283-8

Response: Included.

340 List the proteins you analysed. Add URL (websites) to the analyses tools.

Response: They are listed in Table S1. The method section gives all the URLs with references, lines 348-358 in the revised manuscript.

343 Known association from the literature: how was this done?

Response: The interactions demonstrated by different groups have been summarized in the review by Boucher & Bosch, 2015 (doi: 10.1016/j.jsb.2015.02.008).

346-349 A few glideosome components? On what basis were they selected and which are they? Response: The analysis showed that S14 had properties similar to GAP45 and MTIP. Additionally, S14 localized with GAP45 and MTIP, hence selected for interaction studies.

471 Can AlphaFold Structure Predictions be used in the docking studies?

Response: Even the Alphafold AI is trained on existing sequence/structure information despite being advertised as a de novo prediction system. That's why it can't produce good quality structures of evolutionarily unique proteins such as S14. We initially started our protein model generation by alphafold2, but the quality of the structure was very low; then we further used the trRosetta server (https://yanglab.nankai.edu.cn/trRosetta/), which shows the quality of all three protein structures above 95 after validation by using UCLA-DOE LAB-SAVES V6.0 (https://saves.mbi.ucla.edu/).

tr-Rosetta includes inter-residue distance, orientation distribution by a deep-neural network, and homologous template to improve the accuracy of models (DOI: 10.1038/s41596-021-00628-9).

We have given the model structure generated using alphafold2 for your reference.

Model generated by using AlphaFold2.ipynb (https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb#scrollTo=kOblAo-xetgx).

Structure quality assessment by __http://saves.mbi.ucla.edu/.__

GAP 45

__S14 __

MTIP

487 What parts of theses genes was cloned? Define the amino acid range.

Response: The full-length protein-encoding gene was cloned.

714 Please split the table into A Mosquito bite and B haemolymph Sporozoites Response: Done.

Figure 1 For clarity, maybe write S14::mCherry

Response: Done.

Figure 1 It would be useful to show blood stage parasite images.

Response: Blood stage parasite image is included in the revised manuscript as Figure S2.

Figure 2G Haemolymph sporozoites ?

Response: Done.

Figure 8 You argued that S14 is a membrane-bound protein through palmitoylation. Here the protein is shown to be cytoplasmic. Please update our model with more recent ones. Response: We have shown that S14 colocalizes with GAP45 and MTIP, suggesting its IMC localization. We have updated our model in Figure 8.

Figure S2B It would be good to include a positive control for these PCRs.

Response: We have replaced the figure's new gel with a positive control.

Figure S3 It would be good to include a positive control for these PCRs. Response: We already have positive controls in Figure S3C and S3F for all the primer pairs used.

Tabel S1 Table S1 is only mentioned twice in the text: lines 124 and 128. There is no mention that the table contains all (??) known gliding motility proteins.

Response: The table does not contain all the gliding proteins; however, most of the proteins mentioned in the Boucher & Bosch, 2015 paper (doi: 10.1016/j.jsb.2015.02.008) were included.

Table S1 The algorithms / websites used for bioinformatic prediction need to be listed here.

Response: Included.

Table S2 Add the plasmodb gene identifiers here. The table does not show all Plasmodium spp. but a selection. Response: All the orthologs mentioned in Figure S1 and Table S2 are not shown in the updated PlasmoDB. Accordingly, we have removed the Figure S1 and Table S2 in the revised manuscript__.__

Reviewer #1 (Significance (Required)):

General assessment: The authors provide an in-depth analyses of the Plasmodium berghei protein S14 and its involvement in gliding motility. Response: Thank you.

Advance: This paper is the first analysis of the S14 protein. The authors suggest a bridging function for the protein between MTIP and GAP45. Response: Thank you.

Audience: Gliding motility is of interest to the apicomplexan field. I think this particular proteins is specific to Plasmodium spp. Response: Thank you.

Reviewer #2

Summary:

The authors tag the sporozoite protein S14 in P. berghei and show localization near the sporozoite plasma membrane. They also convincingly show, through the generation of S14 knockout lines, that S14 is required for sporozoite motility and thereby also salivary gland and hepatocyte invasion. Their bioinformatic results support possible interactions between S14 and the inner membrane complex proteins MTIP and GAP45. These analyses were performed with these specific candidate proteins rather than being unbiased searches for potential interaction partners. The yeast 2-hybrid data to support these possible protein interactions need further controls.

Lines 143-144: Unless the sporozoites were not permeablized prior to staining, it is not clear if the protein is "on" the plasma membrane or just under the plasma membrane. Furthermore, this statement anyway seems contradictory to the authors' interpretation of Figure 4A. Response: Live S14-mCherry localization on the membrane does not differentiate between the outer membrane or IMC. Next, in Figure 4A, we confirmed S14 localization on IMC by treating sporozoites with Triton X-100 and colocalizing with IMC proteins GAP45 and MTIP. Further, we ensured that mCherrey signals were bleached post-fixation and performed IFA with and without permeabilization. We revealed the mCherry and CSP signals using Alexa 488 and Alexa 594, respectively. We observed the mCherrey signal with permeablized sporozoites, not without permeabilization.

Line 218: "This result indicates that S14 is present within the inner membrane of sporozoites." While this data shows that S14 is not in the plasma membrane of the parasite, how can the authors be sure it is at the IMC? Response: S14 colocalization with MTIP and GAP45 suggested its localization on IMC.

Line 225-226: This sentence overreaches in its conclusion. There is no indication that this protein provides the power or force behind the sporozoites forward movement. Several proteins are known to be required for gliding motility, but they are not all force-providing factors. Response: We have modified the sentence, and now it states, ‘These data demonstrate that S14 is an IMC protein, essential for the sporozoite's gliding motility.

Minor comments:

Line 99: "the role of gliding-associated proteins is unexplored" There are several publications on GAP40, GAP45 and GAP50 (some of which are referenced in the previous paragraph). Response: We have included the reference for studied proteins and modified the sentence for clarity.

Line 114: "We narrowed it down to a candidate" Narrowed down how? Or rephrase. Response: We identified the S14 gene from the Kaiser et al., 2004 paper (DOI: 10.1046/j.1365-2958.2003.03909.x) and rephrased the sentence in the revised manuscript.

Lines 120-123 are strangely written, and I don't follow the logic. What "similar properties" do GAP45 and GAP50 have with S14 and are they really indicative of function? Also if palmitoylation and myristylation and nonclassical secretion are present in most eukaryotes, why would they necessarily be evidence of IMC targeting? Response: It was wrongly written, we have modified the sentence for clarity.

Line 148-149. I did not see examples of this electromobility shift of GAP45 in this publication (although I may have overlooked it).

Response: Electromobility shifts of GAP45 due to the palmitoylation have been reported in (Rees-Channer et al, 2006; DOI: 10.1016/j.molbiopara.2006.04.008). Frenal 2010 paper has stated about two bands, but experimentally it was shown in Rees-Channer et al, 2006 in Figure 1 and 2B.

Table 1 legend should preferably specify that hemolymph sporozoites were used for IV infections. Response: Done.

Line 228: Should be rephrased for accuracy. "revealed the" should be replaced with "suggests" Response: Replaced.

Lines 305-307: I don't entirely understand the logic laid out here.

Response: This was written about GAP45 and MTIP coordination; however, it has been removed in the revised manuscript.

Lines 320-322: "We hypothesize that S14 possibly plays a structural role and maintains the stability of IMC required for the activity of motors during gliding and invasion." The data about the IMC structure shown is fluorescence microscopy - and there no change is observed in the IMC in the knockout line. I suggest removing or rephrasing this point if no extra data is provided to show this. Response: We have removed this sentence in the revised manuscript.

Reviewer #2 (Significance (Required)):

The work gives insights into an unstudied, conserved Plasmodium protein, S14, which the authors show is critical for Plasmodium transmission from mosquitoes. The parasite genetics and phenotyping demonstrating this are strong. The conclusions about interactions with glideosome/inner membrane complex components need further experimental support. The work is of interest to the Plasmodium field and may be also of interest to people interested in other protozoan parasites or in cellular motility.

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

The manuscript by Gosh and colleagues demonstrates that S14 is a glideosome-associated protein in sporozoites. S14 knockout sporozoites fail to infect mosquito salivary glands and liver cells in the mammalian host. These sporozoites are also defective in gliding motility as S14 localizes to the inner membrane. S14 was shown to interact with the glideosome-associated proteins GAP45 and MTIP using the yeast two-hybrid system. The authors also provide an in-silico prediction on the S14, GAP45 and MTIP interaction.

Major issues:

Overall, there is information lacking in the manuscript, including on the figure legends, regarding experiments replication and n analyzed.

For complementation, the authors engineered an independent S14 knockout line. For this line is clear that parasites failed to infect salivary glands contrarily to the knockout line. Despite not showing it, did the authors confirm that this knockout line has no defects in infecting mosquito midguts and producing sporozoites? Response: We analyzed the midgut for sporozoite formation, which was comparable to the original KO line, and included the data (Figure 2D) in the revised manuscript.

Did the authors conduct IV injections in mice with a higher number of sporozoites? Hemolymph sporozoites are less infectious than sporozoites collected from the salivary glands and I was wondering whether patent infections with S14 ko sporozoites can be obtained by injecting a higher inoculum. The same applies to the infectivity experiments with HepG2cells. Response: We increased the sporozoites dose and infected mice with 10,000 hemolymph sporozoites, but no infection was observed (Table 1). No EEFs were observed in HepG2 cells infected with 10,000 S14 KO hemolymph sporozoites.

Please provide information on the number of sporozoites that were analyzed in the trails experiment. Response: We analyzed 210, 225, and 212 sporozoites for WT GFP, S14 KO c1, and S14 KO c2, respectively.

Minor issues:

In Figure 1. F) WB on S14-3xHA-mCherry tagged sporozoites showing two bands on the WB. The Palm-band is only inferred thus I suggest correcting the figure to S14-3xHA-mcherry. On 1D all the mcherry signal is detected on the membrane but then on WB, a smaller fraction is palm? What is the explanation for the ratio between the two bands? Why so distinct CSP intensity bands between wt and tagged line? Were very distinct amounts of protein loaded?

Response: We have corrected the Palm-S14-3xHA-mcherry to S14-3xHA-mcherry.

This reviewer raises a valid point regarding the discrepancy between IFA and Western blot. The non-palmitoylated S14-mCherrey signal was possibly corrected after deconvolution in image 1D and mainly the membrane signal was prominent. In Figure 1C, many sporozoites show some cytosolic signal, perhaps representing non-palmitoylated S14. Western blot concentrates the protein of interest as a single band, allowing more accurate visualization of protein.

The distinct CSP intensity bands between wt and the tagged line are due to the loading of a higher amount of parasite lysate in WT lane. To ensure that the western blot signal is specific to S14, we loaded a higher amount of protein in WT.

Figure 1. A) Statistical analysis is missing. Not clear if the bars represent mean values +/- standard deviation. No information on the material and methods of how the relative expression was calculated. Response: No error bars are shown in Figure 1 because it was performed once.

In the introduction lines 54 and 58 I suggest replacing humans with mammalian host. Response: Replaced.

Line 58. Not clear why the ref Ripp et al., 2021 is used for a general sentence to introduce the Plasmodium life cycle. Response: Removed.

Line 72: I suggest replacing "TRAP mutant" with "TRAP knockouts" (Sultan et al., 1997). More recently there are TRAP mutants with impaired motility and normal invasion of mosquito salivary glands (Klug et al., 2020) Response: Replaced.

Lines 78 to 86: In this paragraph, authors refer to several proteins involved in sporozoite gliding motility and host cell invasion, however for most of the studies this conclusion comes from the characterization of knockouts defective phenotype and actually a direct role for some of these molecules in the process awaits clear demonstration. Response: We have replaced involved with implicated.

Line 78: Authors do not consider that maebl knockout sporozoites display reduced adhesion, including to cultured hepatocytes, which could contribute to the defects in multiple biological processes, such as in gliding motility, hepatocyte wounding, and invasion. Response: We have corrected maebl role in the revised manuscript.

Line 80: I suggest authors reconcile the contradictory reports in the literature on the role of TRSP in sporozoites invasion. Response: We have removed this reference in the revised manuscript.

Line 82-83: Please revise it. Response: Revised.

Table 1. Correct table as when sporozoites were transmitted by mosquito bite the term "number of sporozoites injected" does not apply. Please give more details on the bite experiments. Is this the number of mosquitoes for all four animals? For how long the mosquitoes were allowed to bite? Response: For clarity, we have split the table into A Mosquito bite and B haemolymph Sporozoites. We used ten mosquitoes/mice in the bite experiment. Mosquitos were allowed to probe for blood meal for 20 minutes, and the feeding was ensured by observing mosquitoes post-blood meal; approximately 70% of mosquitoes received the blood meal in all the cages.

Line 288 and 289. There are several publications showing that maebl knockout sporozoites are impaired at invading the mosquito salivary glands and at infecting the vertebrate host contradicting Kariu et al., 2002 findings in the vertebrate host. Response: We have removed maebl from this line.

Line 290. I suggest "was most likely due to" instead of " due to" as sporozoite adhesion to cells was not evaluated. Response: Corrected.

Line 291: "Cellular transmigration and host cell invasion are prerequisites for gliding motility" please revise. Response: Revised.

Line 437: indicate which clone was used.

Response: Indicated (3D11).

Line: 463: indicate the % of the gel in the SDS-PAGE Response: We have used 10% SDS-PAGE gel and it is indicated in the revised manuscript.

Line 499: indicate the version of the GraphPad Prism software. Response: GraphPad Prism version 9.

Figure S3 legend needs to be corrected. Panels in the figure are from A to F while in legend G and H are included. Response: Corrected.

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

Reviewer #2

Line 39-41: "Using in silico and the yeast two-hybrid system, we showed the interaction of S14 with the glideosome-associated proteins GAP45 and MTIP. Together, our data show that S14 is a glideosome-associated protein" Although these interactions can be speculated based on the results shown, these interactions were not confirmed in this study. Response: We attempted to pulldown the S14 interacting partner using anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.

In order to claim interaction between S14 and IMC proteins, interaction needs to be shown experimentally. Well-controlled yeast 2-hybrid would be a start - then interaction would be more than just speculative. But immunoprecipitation from sporozoites or other biochemical interactions would give more support to this idea. Response: We attempted to pulldown the S14 interacting partner using an anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.

Reviewer #3

The authors provide convincing data on the S14 localization in the inner membrane of sporozoites and interaction with GAP45 and MTIP using the yeast model. Did the authors consider conducting co-IP followed by MS analysis to pull down S14 in the complex with GAP45 and MTIP? Response: We attempted to pulldown the S14 interacting partner using an anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify the interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins, MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.

__Reviewer #3 (Significance (Required)):____ __ Sporozoite gliding motility is a critical feature of parasite infectivity. Impairment of this important feature has been described for several mutant/knockout parasite lines. This study goes beyond the phenotypic analysis of mutant parasites to infer the role of S14 by providing more mechanistic evidence to show S14 interaction with other glideosome-associated proteins. However, this interaction was investigated using the two-hybrid system in yeast. Still, in sporozoites, no experiments were conducted to evaluate the interaction between these proteins.

Response: We attempted to pulldown the S14 interacting partner using an anti-mCherry antibody from S14-3XHA-mCherry transgenic sporozoites and then further tried to identify interactome using mass spectrometry but failed. Hence, we selected two known IMC localized gliding proteins, MTIP and GAP45. Performing pull-down from sporozoites is challenging, so we checked this interaction using yeast 2-hybrid assay and bioinformatic analysis for protein-protein interaction.

Please consider I'm not an expert on the in-silico interaction 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 #3

Evidence, reproducibility and clarity

The manuscript by Gosh and colleagues demonstrates that S14 is a glideosome-associated protein in sporozoites. S14 knockout sporozoites fail to infect mosquito salivary glands and liver cells in the mammalian host. These sporozoites are also defective in gliding motility as S14 localizes to the inner membrane. S14 was shown to interact with the glideosome-associated proteins GAP45 and MTIP using the yeast two-hybrid system. The authors also provide an in-silico prediction on the S14, GAP45 and MTIP interaction.

Major issues:

Overall, there is information lacking in the manuscript, including on the figure legends, regarding experiments replication and n analyzed.

For complementation, the authors engineered an independent S14 knockout line. For this line is clear that parasites failed to infect salivary glands contrarily to the knockout line. Despite not showing it, did the authors confirm that this knockout line has no defects in infecting mosquito midguts and producing sporozoites?

Did the authors conduct IV injections in mice with a higher number of sporozoites? Hemolymph sporozoites are less infectious than sporozoites collected from the salivary glands and I was wondering whether patent infections with S14 ko sporozoites can be obtained by injecting a higher inoculum. The same applies to the infectivity experiments with HepG2cells.

The authors provide convincing data on the S14 localization in the inner membrane of sporozoites and interaction with GAP45 and MTIP using the yeast model. Did the authors consider conducting co-IP followed by MS analysis to pull down S14 in the complex with GAP45 and MTIP?

To characterize the gliding defective phenotype authors based their assessment on trails quantifications. This analysis is very limited as the percentage of attached, waving, floating sporozoites cannot be determined. Due to their conclusion on the role of S14 for sporozoite gliding motility, I recommend a more detailed investigation using live microscopy imaging. Please provide information on the number of sporozoites that were analyzed in the trails experiment.

Conclusions that S14 knockout does not impact the expression and organization of two surface proteins, CSP and TRAP, and two IMC rely on a qualitative analysis only. However, quantitative analysis to support their observations is missing.

Minor issues:

In Figure 1. F) WB on S14-3xHA-mCherry tagged sporozoites showing two bands on the WB. The Palm-band is only inferred thus I suggest correcting the figure to S14-3xHA-mcherry. On 1D all the mcherry signal is detected on the membrane but then on WB, a smaller fraction is palm? What is the explanation for the ratio between the two bands? Why so distinct CSP intensity bands between wt and tagged line? Were very distinct amounts of protein loaded?

Figure 1. A) Statistical analysis is missing. Not clear if the bars represent mean values +/- standard deviation. No information on the material and methods of how the relative expression was calculated.

In the introduction lines 54 and 58 I suggest replacing humans with mammalian host.

Line 58. Not clear why the ref Ripp et al., 2021 is used for a general sentence to introduce the Plasmodium life cycle.

Line 72: I suggest replacing "TRAP mutant" with "TRAP knockouts" (Sultan et al., 1997). More recently there are TRAP mutants with impaired motility and normal invasion of mosquito salivary glands (Klug et al., 2020)

Lines 78 to 86: In this paragraph, authors refer to several proteins involved in sporozoite gliding motility and host cell invasion, however for most of the studies this conclusion comes from the characterization of knockouts defective phenotype and actually a direct role for some of these molecules in the process awaits clear demonstration.

Line 78: Authors do not consider that maebl knockout sporozoites display reduced adhesion, including to cultured hepatocytes, which could contribute to the defects in multiple biological processes, such as in gliding motility, hepatocyte wounding, and invasion.

Line 80: I suggest authors reconcile the contradictory reports in the literature on the role of TRSP in sporozoites invasion.

Line 82-83: Please revise it.

Table 1. Correct table as when sporozoites were transmitted by mosquito bite the term "number of sporozoites injected" does not apply. Please give more details on the bite experiments. Is this the number of mosquitoes for all four animals? For how long the mosquitoes were allowed to bite?

Line 288 and 289. There are several publications showing that maebl knockout sporozoites are impaired at invading the mosquito salivary glands and at infecting the vertebrate host contradicting Kariu et al., 2002 findings in the vertebrate host.

Line 290. I suggest "was most likely due to" instead of " due to" as sporozoite adhesion to cells was not evaluated.

Line 291: "Cellular transmigration and host cell invasion are prerequisites for gliding motility" please revise.

Line 437: indicate which clone was used.

Line: 463: indicate the % of the gel in the SDS-PAGE

Line 499: indicate the version of the GraphPad Prism software.

Figure S3 legend needs to be corrected. Panels in the figure are from A to F while in legend G and H are included.

Significance

Sporozoite gliding motility is a critical feature of parasite infectivity. Impairment of this important feature has been described for several mutant/knockout parasite lines. This study goes beyond the phenotypic analysis of mutant parasites to infer the role of S14 by providing more mechanistic evidence to show S14 interaction with other glideosome-associated proteins. However, this interaction was investigated using the two-hybrid system in yeast. Still, in sporozoites, no experiments were conducted to evaluate the interaction between these proteins.

Please consider I'm not an expert on the in-silico interaction studies.

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

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

Summary:

The authors tag the sporozoite protein S14 in P. berghei and show localization near the sporozoite plasma membrane. They also convincingly show, through the generation of S14 knockout lines, that S14 is required for sporozoite motility and thereby also salivary gland and hepatocyte invasion. Their bioinformatic results support possible interactions between S14 and the inner membrane complex proteins MTIP and GAP45. These analyses were performed with these specific candidate proteins rather than being unbiased searches for potential interaction partners. The yeast 2-hybrid data to support these possible protein interactions need further controls.

Major comments:

Line 39-41: "Using in silico and the yeast two-hybrid system, we showed the interaction of S14 with the glideosome-associated proteins GAP45 and MTIP. Together, our data show that S14 is a glideosome-associated protein" Although these interactions can be speculated based on the results shown, these interactions were not confirmed in this study.

Lines 143-144: Unless the sporozoites were not permeablized prior to staining, it is not clear if the protein is "on" the plasma membrane or just under the plasma membrane. Furthermore, this statement anyway seems contradictory to the authors' interpretation of Figure 4A.

Line 218: "This result indicates that S14 is present within the inner membrane of sporozoites." While this data shows that S14 is not in the plasma membrane of the parasite, how can the authors be sure it is at the IMC?

Line 149: To definitively state S14 is a membrane protein, biochemical assays proving such should be performed. (or perhaps genetic mutation of the predicted palmitoylation site?) Otherwise, this should be rephrased.

Line 225-226: This sentence overreaches in its conclusion. There is no indication that this protein provides the power or force behind the sporozoites forward movement. Several proteins are known to be required for gliding motility, but they are not all force-providing factors.

Lines 257-258: for yeast 2-hybrid, the controls of expressing S14, GAP45 and MTIP together with control proteins where no interaction would be predicted are absent.

In order to claim interaction between S14 and IMC proteins, interaction needs to be shown experimentally. Well-controlled yeast 2-hybrid would be a start - then interaction would be more than just speculative. But immunoprecipitation from sporozoites or other biochemical interactions would give more support to this idea.

Minor comments:

Line 99: "the role of gliding-associated proteins is unexplored" There are several publications on GAP40, GAP45 and GAP50 (some of which are referenced in the previous paragraph).

Line 114: "We narrowed it down to a candidate" Narrowed down how? Or rephrase.

Lines 120-123 are strangely written, and I don't follow the logic. What "similar properties" do GAP45 and GAP50 have with S14 and are they really indicative of function? Also if palmitoylation and myristylation and nonclassical secretion are present in most eukaryotes, why would they necessarily be evidence of IMC targeting?

Line 148-149. I did not see examples of this electromobility shift of GAP45 in this publication (although I may have overlooked it).

Table 1 legend should preferably specify that hemolymph sporozoites were used for IV infections.

Line 228: Should be rephrased for accuracy. "revealed the" should be replaced with "suggests"

Lines 305-307: I don't entirely understand the logic laid out here.

Lines 320-322: "We hypothesize that S14 possibly plays a structural role and maintains the stability of IMC required for the activity of motors during gliding and invasion." The data about the IMC structure shown is fluorescence microscopy - and there no change is observed in the IMC in the knockout line. I suggest removing or rephrasing this point if no extra data is provided to show this.

Significance

The work gives insights into an unstudied, conserved Plasmodium protein, S14, which the authors show is critical for Plasmodium transmission from mosquitoes. The parasite genetics and phenotyping demonstrating this are strong. The conclusions about interactions with glideosome/inner membrane complex components need further experimental support. The work is of interest to the Plasmodium field and may be also of interest to people interested in other protozoan parasites or in cellular motility.

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

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Referee #1

Evidence, reproducibility and clarity

Summary: The authors have identified a sporozoite gliding motility protein through bioinformatic analysis. From the main text I do not know how, or what bioinformatic analysis was performed, in order to focus on this protein which is called S14. The authors then go on to tag the protein, produce a KO and show its involvement in gliding motility. The KO shows that parasites lacking S14 fail to invade the mosquito salivary glands. This is due to a motility defect. Y2H and docking studies are used to define an interaction with MTIP and GAP45, two known components of the glideosome.

Major comments: The paper is sometimes hard to follow and lacks clarity. The reason: important information is omitted, or explained at the end of a section rather than at first mention; experimental details that are of essence need to be mentioned or explained in the main text; there is ample use of the word 'bioinformatic' without explaining what kind of analysis was performed in the main text. I cite from the abstract: 'In silico analysis of a novel protein, S14, which is uniquely upregulated in salivary gland sporozoites, suggested its association with glideosome-associated proteins.' I cite from the introduction: 'A study comparing transcriptome differences between sporozoites and merozoites using suppressive subtraction hybridization found several genes highly upregulated in sporozoites and named them 'S' genes (Kaiser et al, 2004). We narrowed it down to a candidate named S14, which lacked signal peptide and transmembrane domains.' From reading the main text, I do not know why Plasmodium berghei S14 was chosen in this manuscript. S14 is one of 25 transcripts identified by Kappe et al in Plasmodium yoelii (https://doi.org/10.1046/j.1365-2958.2003.03909.x) to be upregulated in sporozoites. The material and methods section does not explain either why S14 was chosen. Perhaps the authors could update Figure 2 from Kappe et al with the most recent annotations from plasmodb.

Reproducibility: None of the main Figures or Figure legends define ' N = '. For example I cite: 'The S14 KO clonal lines were first analyzed for asexual blood-stage propagation, and for this, 200 µl of iRBCs with 0.2% parasitemia was intravenously injected into a group of mice.' There are 2 mentions of 'N=' in the supplementary figures. I have not found any others.

I'm not sure what the convention is. Should unpublished data for this gene (PBANKA_0605900) found in pberghei.eu (a database for mutant berghei parasites) be cited? After all it confirms their findings.

The authors need to use more recent references for some of their statements; see some comments below.

Minor comments:

line

1-2 Add the Plasmodium species of this study. abstract Which species do you work with? 29 mosquito salivary glands and human host hepatocytes 30 to the glideosome, a protein complex containing [...] 32-33 What kind of in silico analysis suggested S14 is part of the glideosome? S14 is not uniquely upregulated; there are other S-type genes identified by Kappe and Matuschewski. 25 I believe. 32 Please point out he species were S genes were identified. SGS of which species? 34 expression: change to transcription 39 What kind of in silico analysis was used here? and therefore malaria transmission 55 A single zygote transforms into a single ookinete, which establishes a single oocyst, which in turn can produce thousands of midgut sporozoites. Please correct the life cycle passage. located or anchored in the IMC? And located between the IMC and plasma membrane? 61-63 Refer to Table S1 and its contents here 64 Name the known GAPs.

65-67 Which transmembrane domain proteins? Please add more recent references than King 1988. 71-72 TRAP was the first protein found to be ... 74-76 Add additional, more recent references: for example search Frischknecht and TRAP 76 S6 (TREP) is also [...] 88 Some of these proteins are also expressed in ookinetes. 89-91 The sentence needs a verb. 88-96 Please add some more recent glideosome papers. After 2013. 91 Why do you call it a peripheral protein? 91-93 There are more recent citations for GAP45 andGAP50. 96 Insert a reference here. 99 Please define the gliding-associated proteins. What are they? Aren't there papers on GAP40, 45 and 50? DOI: 10.1016/j.chom.2010.09.002 99 .... What prompted you to identify a novel GAP? And why is S14 classified as a GAP? 99-102 What kind of bioinformatic study? Why was S14 chosen? Please outline how you ended up with S14. Any other proteins that came out of the bioinformatic screen from the list of S genes? How many proteins were identified in the screen for sporozoite upregulated proteins by Kappe and Matuschewski? 102-103 Define the nonclassical secretion pathway. Please reference GAP45 and GAP50 data for the nonclassical pathway. 105 Please add P. berghei to the title, the abstract, the introduction. 111 The results section does not outline what bioinformatic analysis was used 112-114 Please specify the exact number of upregulated in sporozoites genes. I think it was 25. And add the species the study was performed in. Why did you choose the Kappe study but not the uis genes from berghei? 114-115 How did you narrow it down to S14? The Kappe paper lists 25 S-type genes from P. yoelii. 118 Plasmodia is not the plural for a group of different Plasmodium species. Use: [...] conserved among Plasmodium spp. 118-119 Which proteins did you analyze? And how did you analyze them? Where is the data for this analysis? Outline the amino acids that predict palmitoylation? The nonclassical pathway? 119-122 Here: do you mean S14 has similar properties as GAP 45 and GAP50? Define the nonclassical pathway? How do you know S14 is in the IMC? 122-123 Please reference the bioinformatic analysis plus URL that allows targeting to the IMC to be analyzed. 123-124 Please reference the URLs for TM, palmitoylation, and interactions analyses. 125-127 How did you predict that S14 is secreted via the nonclassical pathway? 128-130 Define the nonclassical pathway when it first appears in your manuscript. The citation Moskes 2004 is not in the reference list 132 Which membrane? 134-135 In which species? 141-142 Please include images of blood stage and liver stage parasites. 142-143 Which membrane? 148-149 I cannot find the specific figure you refer to; I checked the online version of the Frenal 2010 paper. 175 gland, we counted [...] 177 Compared to the 177-179 Failed to invade (absolutely)? Or invaded in highly reduced numbers? 182-186 Please be precise: I think you mean you let all types of mosquitoes take a blood meal; s14 knockout-infected mosquitoes did not infect mice. 181-202 Perhaps use paragraphs to indicate the different types of experiments performed here. 204 Please introduce paragraphs to identify the different experiments in this section 208 Outer or inner membrane of what? IMC, the plasma membrane? 228 onwards Structural models were obtained from whom? Which species did you use for the docking study? Could you use in one approach 3 berghei proteins, and confirm your docking studies with the falciparum proteins? That would strengthen your model. Should you include a negative control protein in the approach? 250-251 Was all of the gene cloned? Please define amino acid range. discussion Please discuss data from https://elifesciences.org/articles/77447 in relation to your protein

298-300 More recent glideosome papers exist. For example https://doi.org/10.1038/s42003-020-01283-8 340 List the proteins you analysed. Add URL (websites) to the analyses tools. 343 Known association from the literature: how was this done? 346-349 A few glideosome components? On what basis were they selected and which are they?

471 Can AlphaFold Structure Predictions be used in the docking studies? 487 What parts of theses genes was cloned? Define the amino acid range. 714 Please split the table into A Mosquito bite and B haemolymph Sporozoites Figure 1 For clarity, maybe write S14::mCherry Figure 1 It would be useful to show blood stage parasite images. Figure 1F You have not formally shown that this signal corresponds to palmitoylated S14. Could be heavy chain. Figure 2G Haemolymph sporozoites ? Figure 8 You argued that S14 is a membrane-bound protein through palmitoylation. Here the protein is shown to be cytoplasmic. Please update our model with more recent ones.

Figure S2B It would be good to include a positive control for these PCRs. Figure S3 It would be good to include a positive control for these PCRs.

Tabel S1 Table S1 is only mentioned twice in the text: lines 124 and 128. There is no mention that the table contains all (??) known gliding motility proteins. Table S1 The algorithms / websites used for bioinformatic prediction need to be listed here. Table S2 Add the plasmodb gene identifiers here. The table does not show all Plasmodium spp. but a selection.

Significance

General assessment: The authors provide an in-depth analyses of the Plasmodium berghei protein S14 and its involvement in gliding motility.

Advance: This paper is the first analysis of the S14 protein. The authors suggest a bridging function for the protein between MTIP and GAP45.

Audience: Gliding motility is of interest to the apicomplexan field. I think this particular proteins is specific to Plasmodium spp.

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Reply to the reviewers

1. General Statements

This manuscript aimed at:

1. a) producing the evidence that supports the need for performing RNA hydrolysis and applying the appropriate nucleoside MACs for the determination of nucleoside-modified mRNA concentrations using UV spectroscopy.
2. b) Providing the m1Y MAC value and a new resource to the mRNA field community to perform the above-mentioned procedure. This piece is therefore a “resource” manuscript, rather than a biotechnological innovation or basic research manuscript.

2. Point-by-point description of the revisions

Considering that the reviewers coincided in some of their comments, we compiled them in topic and provided our response to the reviewers.

Topics:

• On the novelty in this manuscript
• On the impact of nucleoside modifications on the DFBHI-Broccoli complex.
• On the role of modified nucleosides on mRNA folding and the independent verification on a distinct mRNA.
• On the cap used for IVT in the webserver.
• On the average of Epsilon (e or MAC) values.
• Other minor comments. On the novelty in this manuscript:

Comments:

Reviewer #1

“Major Comments:

• In the introduction, the authors should discuss the novelty more by describing which techniques are currently available for quantification of modified RNA and how this study is novel.” __Reviewer #2 __

“(Significance (Required)):

The study develops an accurate method to measure RNA concentrations which can improve dosing accuracy. The methods developed here will be beneficial for a broad range of fields employing mRNA-therapies.”

__Reviewer #3 __

“(Significance (Required)):

Since 50 years, scientists that works in the field of modified nucleic acids have determined the concentration of the nucleic acids in the same way, which means by determining the epsilon values of the modified nucleosides, using the epsilon values of the natural nucleosides at same wavelength, and then calculating the concentration after measuring absorption at (for example) 260 nm (wavelength could change dependent on modified nucleoside that is incorporated). This manuscript is not really innovative.”

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Response:

We thank the reviewers for bringing up this topic. We want to reassure to the reviewers, editors and readers that, throughout the manuscript, we have carefully selected the wording to avoid claiming any novelty on the principle of RNA hydrolysis or the use of nucleotide Molar absorption coefficients (MAC) and UV spectroscopy for the determination of RNA concentrations. We have “revised”, “assessed” and “examined” these experimental procedures, we “determined” the M1Y and we “developed” the mRNAcalc webserver.

This “resource” manuscript therefore mainly aims at introducing the mRNAcalc webserver to the community and providing the underlying biochemical principles of the methods suggested in the webserver. These articles are often published in webserver issues or as “resource” articles in certain journals, including some of the journals in Review Commons.

The authors understand that the data in our manuscript are not often provided for this type of “resource” manuscripts, and it might have led to a misunderstanding. For instance, the OligoCalc webserver was published in Nucleic Acid Research, it has become a valuable tool for the oligonucleotide research community (1621 citations in 15 years), and no experimental evidence supporting its underlying calculations is provided in the manuscript.

For our manuscript, we have cited the corresponding source of the principle of the experimental methods, and we additionally performed some experiments to reproduce the findings using nucleoside-modified mRNAs with the intention of highlighting the importance of performing RNA hydrolysis (Fig.2b) and implementing the MAC of modified nucleotides (Fig 1e and 1f) for the determination of modified-nucleoside mRNA concentration using the Beer-Lambert law. We have felt compelled to do so, despite the fact that they represent well-established science and methods, as correctly pointed out by one of the reviewers.

We have taken into account that a few dozen of non-RNA biochemistry focused laboratories around the world are currently embracing for the first time the nucleoside-modified mRNA technologies and, to our knowledge, not a single article in the nucleoside-modified mRNA field has mentioned the need of implementing a different MAC for the determination of nucleoside-modified mRNA concentration using UV spectroscopy in either its main text or Materials & Methods section. We want to reassure the reviewers that the authors, before starting the experimental investigation, performed an extensive literature search and failed to find the m1Y MAC at 260 nm. Our search included a few hundreds of research articles, several doctoral thesis (including Sister Miriam Michael Stimson’s work), classic books such as Hall, Ross “The modified nucleosides in nucleic acids” and nucleotide manufacturers’ datasheets. However, the authors cannot rule out that other investigators in the mRNA field have previously determined the m1Y MAC at 260 nm in aqueous buffered solution and this knowledge has remained hidden under the frequently used statement of “The mRNA concentrations were determined spectroscopically” or any alike statement.

Following the suggestion of Reviewer #1, we have also included a brief comment in the introduction on the fluorescence-based techniques for the determination of nucleic acid concentration (lines 87-91), as follows:

“Other non-UV-spectroscopic methods relying on the unspecific RNA binding of certain fluorophores (such as RiboGreen, Thermo Fisher Scientific) for the determination of RNA concentration may help to overcome any change in the MAC of modified nucleoside mRNA. However, the impact of RNA modifications on the binding affinity of these fluorophores also remains unknown.”

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On the impact of nucleoside modifications on the DFBHI-Broccoli complex:

Comments:

Reviewer #1

“3) The broccoli aptamer has U in it which when mutated to pseudouridine (Ψ) or N1-methylpseudouridine may change the structure minutely affecting the cis-trans transition in aptamer- DFHBI-1 complex and hence in fluorophore properties. A control which shows the effect (or lack thereof) of aptamer modifications on fluorophore properties should be carried out. The ratio of A260/F507 can get affected by the denominator although it may/may not be insignificant.”

Reviewer #2

“Specific comments:

…

In Fig. 1D, the authors normalize the absorbance on mRNA to fluorescence of DFHBI-1T when bound to dBroccoli aptamer. The aptamer will contain uridines and therefore modified uridines. Will modified uridines affect binding affinity of the substrate to the aptamer? Could the differences in fluorescence be because of stronger/weaker binding of the substrate with modified uridines?”

Response:

We thank reviewers for enquiring about the effect of U-to-Y and U-to-M1Y substitutions on the DFHBI-1T-dBroccoli interaction, RNA folding or fluorophore properties. We have indeed investigated thoroughly and observed that there was no significant difference in the binding affinity, melting point, or relative brightness across the three DFHBI-1T-Broccoli complexes. These results go in line with the previously published photophysical and biochemical properties of the Broccoli−DFHBI-1T (reference 15 in manuscript). These data are provided as supplementary Table 1 in the revised manuscript.

Supplementary Table 1: photophysical and biochemical properties of mutated Broccoli−DFHBI-1T complexes.

Complex

Max em (nm)

Relative brightness*

KD (nM)+

Tm (°C)+

U-Broc−DFHBI-1T

(ref. 15)

507

---

360

48

U-Broc−DFHBI-1T

507

1.000 ± 0.002

379.6 ± 13.89

49.13 ± 0.13

Y-Broc-DFHBI-1T

507

1.005 ± 0.004

378.7 ± 8.11

49.46 ± 0.09

m1Y-Broc-DFHBI-1T

507

1.004 ± 0.003

375.6 ± 8.17

49.23 ± 0.07

*Relative to the U-Broc-DFHBI-1T complex. Data are shown as mean ± SD.

• Data are shown as KD ± Error of the fit or Tm ± Error of the fit.

On the role of modified nucleosides on mRNA folding and the independent verification on a distinct mRNA:

Comments:

Reviewer #1

“2) Fig 1d- In this experiment, the RNA is not hydrolyzed prior to concentration measurement. The authors should discuss how nucleoside modifications in the RNA may affect structure of the RNA, how significant that effect is on the ____e ____(MAC) and how justified it is to attribute the reduction in ____e ____(MAC) entirely to the mutations.

…

4) The reduction in A260 in modified nucleosides should be accurately measured and independent of the RNA. Hence, the values determined here should be shown to be independent of at least another RNA sequence.”

Response:

We want to express our gratitude to Reviewer #1 for enquiring about the potential impact of the modified nucleosides on the mRNA folding. We have further discussed this aspect on our interpretation of the data in Fig 1d. No doubt, this reviewer’s comment has substantially enriched the discussion in our manuscript.

For the revised version of the manuscript, we have also performed the same measurements using a different mRNA. We have used an mRNA with a higher m1Y composition. We have observed a stronger reduction in mRNA UV absorption (A260) in the m1Y-modified mRNA, confirming that the MAC of the nucleobase composition is the main determinant of mRNA UV absorption. We have appended these data to the manuscript as supplementary Figure 2 and the associated text can be found in lines 141-155 of the manuscript and in the following lines:

“By normalizing the UV absorbance (A260) of each mRNA by its corresponding fluorescence (F507), it was observed that in practice the relative UV absorbance of the nucleoside-modified mRNA was significantly reduced as compared to the standard mRNA (DA260 = -10.6%, Fig. 1d and 1e). The hypochromicity was more pronounced in a second m1Y-mRNA with higher m1Y composition (DA260 = -11.8%, Supplementary Figure 1). In principle, the modified nucleosides can also promote mRNA folding and reduce its UV absorption. This is particularly relevant for the pseudouridine modification. Its N1-hydrogen can engage in additional hydrogen bonds, promoting and stabilizing RNA folding. For instance, the U-to-Y substitution in tRNA stabilizes the folded structure that is essential for translation (reviewed in ref. 16). However, the m1Y nucleobase lacks this additional hydrogen bonding capability, and it is expected to have little or no effect on the RNA folding of low CG-content (1Y-mRNAs followed the anticipated hypochromicity associated to the nucleobase hypochromicity at 260 nm wavelength and not their expected contribution to RNA folding, these data suggest that the observed reduction in nucleoside-modified mRNA UV absorption is mainly determined by the nucleobase composition and the intrinsic MAC of the nucleosides in these mRNA.”

On the cap used for IVT in the webserver:

Comment:

__Reviewer #2 __

“(Evidence, reproducibility and clarity (Required)):

Specific comments:

1. The authors should expand discussion on the effect of different caps used for IVT as the choice of the cap appears to be an important selection on the server.” Response:

We thank Reviewer #2 for spotting that we had forgotten to comment on this feature of mRNAcalc web server in the manuscript. We have included a short paragraph on the 5’ mRNA cap nucleotides and their implementation in the mRNACalc webserver (line 160-164), as follow:

*“In the mRNACalc webserver, the MACs of distinct modified nucleosides that form the capping nucleotide in the 5’ mRNA cap were also implemented for the sake of completeness. The capping nucleotide only represents one nucleotide out of thousands of nucleotides in a mRNA molecule and its contribution to the mRNA molar absorption is rather negligible.” *

On the averaged Epsilon (____e ____or____ MAC) values:

Comment:

__Reviewer #3 __

(Evidence, reproducibility and clarity (Required)):

“I find the way the authors use the epsilon value of pseudoU (and analogues), as a mean value of literature data to be incorrect. The epsilon value is absolute and can not vary from one measurement to another. In fact it is a good parameter to define concentration. When different values are obtained, it means that compound is not pure , or measured at different pH or solvent, or the compound is not weighted exactly. When publishing a methodology to determine concentration of nucleic acids, it might be good to determine the exact epsilon value you want to use, yourself.”

Response:

We thank Reviewer #3 for bringing up this topic. We had experimentally determined the MAC values when we observed that it was completely absent in the literature and it was essential to provide a valuable tool to the nucleoside-modified mRNA community, as it is the case of the m1Y nucleoside. For the Y and m5C nucleosides, we have now determined their MAC for this revised version of the manuscript and recalculated the average, this time including our own determination and the previously determined values (in aqueous buffered solution) from multiple sources in the literature or manufacturer’s product datasheets and we implemented it in the webserver. We agree that the different values may relate to distinct amount and nature of the impurities in the manufacturer’s preparation. We believe that the average of these values may reflect a better approximation to their absolute epsilon value. Detailed information on these calculations and methods are now provided in the supplementary notes (Lines 109 to 142).

Other minor comments:

Reviewer #1

“5) Fig 2c- The figure should be remade using larger symbols as it is difficult to see how different the concentration measurements are depending on the method of hydrolysis.”

Response:

Figure 2c is intended to show a schematic representation of the experimental workflow and use of the mRNAcalc webserver. We assume that Reviewer #1 referred to Fig 2b. We have enlarged the symbols to ease the visibility of the data.

“6) Lines 27, 45 and 33 have an incorrect symbol for ____e____(MAC) and for the word 'coefficient”

Response:

We thank Reviewer #1 for the interest of improving our manuscript in detail. The typos have been corrected in the revised manuscript.

We thank Reviewers for all the values comments to our manuscript; they have enriched and substantially improved its quality and readability.

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

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Referee #3

Evidence, reproducibility and clarity

I find the way the authors use the epsilon value of pseudoU (and analogues), as a mean value of literature data to be incorrect. The epsilon value is absolute and can not vary from one measurement to another. In fact it is a good parameter to define concentration. When different values are obtained, it means that compound is not pure , or measured at different pH or solvent, or the compound is not weighted exactly. When publishing a methodology to determine concentration of nucleic acids, it might be good to determine the exact epsilon value you want to use, yourself.

Significance

Since 50 years, scientists that works in the field of modified nucleic acids have determined the concentration of the nucleic acids in the same way, which means by determining the epsilon values of the modified nucleosides, using the epsilon values of the natural nucleosides at same wavelength, and then calculating the concentration after measuring absorption at (for example) 260 nm (wavelength could change dependent on modified nucleoside that is incorporated). This manuscript is not really innovative.

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

General Comments:

Finol and colleagues argue that uridine modifications absorb less UV light than uridine, leading to underestimation of RNA concentration for modified RNAs. Based on this observation, they created a web server for accurate calculation of RNA concentrations. This is an interesting manuscript and would benefit the field. Specific comments are listed below:

Specific comments:

1. The authors should expand discussion on the effect of different caps used for IVT as the choice of the cap appears to be an important selection on the server.
2. In Fig. 1D, the authors normalize the absorbance on mRNA to fluorescence of DFHBI-1T when bound to dBroccoli aptamer. The aptamer will contain uridines and therefore modified uridines. Will modified uridines affect binding affinity of the substrate to the aptamer? Could the differences in fluorescence be because of stronger/weaker binding of the substrate with modified uridines?

Significance

The study develops an accurate method to measure RNA concentrations which can improve dosing accuracy. The methods developed here will be beneficial for a broad range of fields employing mRNA-therapies. Reviewer expertise: Lipid nanoparticles, mRNA, self-amplifying RNA, immunoengineering

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

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Referee #1

Evidence, reproducibility and clarity

Summary: The study aims at developing a method of accurate quantification of concentration of self- amplifying RNA and modified RNA by determining molar absorption coefficient of modified nucleosides with higher accuracy.

Major Comments:

1. In the introduction, the authors should discuss the novelty more by describing which techniques are currently available for quantification of modified RNA and how this study is novel.
2. Fig 1d- In this experiment, the RNA is not hydrolyzed prior to concentration measurement. The authors should discuss how nucleoside modifications in the RNA may affect structure of the RNA, how significant that effect is on the (MAC) and how justified it is to attribute the reduction in (MAC) entirely to the mutations.
3. The broccoli aptamer has U in it which when mutated to pseudouridine (Ψ) or N1-methylpseudouridine may change the structure minutely affecting the cis-trans transition in aptamer- DFHBI-1 complex and hence in fluorophore properties. A control which shows the effect (or lack thereof) of aptamer modifications on fluorophore properties should be carried out. The ratio of A260/F507 can get affected by the denominator although it may/may not be insignificant.
4. The reduction in A260 in modified nucleosides should be accurately measured and independent of the RNA. Hence, the values determined here should be shown to be independent of at least another RNA sequence.

Minor comments:

1. Fig 2c- The figure should be remade using larger symbols as it is difficult to see how different the concentration measurements are depending on the method of hydrolysis.
2. Lines 27, 45 and 33 have an incorrect symbol for (MAC) and for the word 'coefficient'

Significance

Accurate measurement of RNA concentrations can be key where precise quantification of RNA is required and any error gets amplified such as RNA-based therapeutics dependent on RNA amplification or RNA modification. This study is also important for research on the effect of RNA modifications on RNA structure in vitro or in vivo.

Transparent Peer Review

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Reply to the reviewers

Manuscript number: RC-2023-02123

Corresponding author(s): Holger Sültmann

[The “revision plan” should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the preprint and help them to obtain a balanced view of the paper.

If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We would like to thank the editorial team of Review Commons for sending our manuscript for peer review and all Reviewers for carefully reading our manuscript. The reviewer’s detailed and constructive feedback and comments were instrumental to improve the quality and rigor of our manuscript. We highly appreciate the thoroughness of the review and have carefully considered all suggestions and concerns. Below, we have made point-by-point responses to the reviewer’s comments, and outlined revisions we plan to make, or have made. Textual changes in the revised manuscript are marked in Red.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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

Daum AK et al. indicated that CAFs promote TKI resistance via lipid biosynthesis in ALK-driven lung adenocarcinoma cells. This work provides novel CAF-induced drug resistant mechanisms in ALK-driven lung cancer cells, however, there are several issues to be resolved by the following.

Major critiques:

1. The authors claimed that CAF-produced HGF and NRG1 boosts AKT signaling and de novo lipogenesis to promote ALK-TKI resistance in lung cancer cells. They showed that CAF-secreted HGF and NRG1 inhibition attenuates tumor cell viability in the presence of FB2-CM, however, whether stromal HGF and NRG1 inhibition suppresses de novo lipogenesis in carcinoma cells has not yet been investigated. The authors should inhibit HGF and NRG1 expression in CAFs by shRNA prior to addition of CAF-CM onto cancer cells to evaluate AKT signaling and de novo lipogenesis.

Author response:

The necessity of demonstrating the direct impact of stromal HGF and NRG1 inhibition on de novo lipogenesis in cancer cells is a crucial aspect of our study, and appreciate the reviewer’s valuable suggestion to inhibit HGF and NRG1 expression in CAFs before assessing the effect on AKT signaling and de novo lipogenesis. To address this concern, we will conduct additional experiments to evaluate the impact of HGF and NRG1 expression knock-downs in CAFs by shRNA.

This work lacks human correlation. Are HGF and NRG1 expressions in CAFs related with de novo lipogenesis, drug resistance and poor outcomes in ALK-driven lung cancer patients?

__Author response: __

We agree with the reviewer’s point that human correlation would underline the significance of the given findings. However, conducting such analyses presents certain challenges.

1. The availability of ALK-mutated samples among TCGA samples is limited and the sample size is quite small (n = 5), making survival analyses less statistically meaningful due to low statistical power.
2. Using bulk RNA-seq data for this analysis necessitates deconvolution methods to differentiate between tumor and stromal cell compartments. While deconvolution methods are valuable, they have limitations, including potential inaccuracies in estimating cell-specific gene expression due to the inherent heterogeneity of cell populations (1). This may lead to imprecise conclusions about the specific contributions of stromal factors, such as CAF-secreted HGF and NRG1, in the tumor microenvironment. Nonetheless, we are considering leveraging the available dataset of Maynard et al.(2), to address the raised concerns by the reviewer. Here, the authors performed single-cell RNA-seq on clinical biopsies, including a number of ALK+ samples from both treatment-naive and progressive-disease patients. The analysis of this dataset could allow us to investigate whether the effects observed in our study hold true in the in vivo human tissue environment, providing a more direct and clinically relevant assessment.

The most experiments lack the appropriate control for CAFs. They would use primary isolated counterpart fibroblasts as the patient-specific control for lung cancer CAFs by extracting from non-cancerous regions of the same individual in their experiments.

__Author response: __

This point is well taken. Therefore, we will take this feedback into account and incorporate the suggested controls into our experimental design to enhance the robustness and validity of our results.

Minor issues:

In Fig 1B, coculture with TGF-b-treated MRC-5 attenuated cancer cell death with the lorlatinib treatment. However, HGF and NRG1 production is comparable between MRC-5 cells treated with or without TGF-b in Fig 5A. These data indicate that any fibroblasts but not CAFs could suppress cancer cell death with the lorlatinib treatment.

__Author response: __

We recognize the need for further clarification. To address this, we plan to include additional data to demonstrate the differences in tumor therapy response when cultured with CM derived from native fibroblasts versus TGF-β1-activated fibroblasts (CAFs). This will help elucidate the specific role of CAFs in suppressing cancer cell death with lorlatinib treatment and provide a more comprehensive understanding of the observed effects.

The pAKT induction of H3122 treated with lorlatinib in the presence of CAM-CM or HGF or NRG in Fig 7A, B is barely observed and lacks the significance.

Author response:

We acknowledge the need for more robust data to demonstrate the significance of the observed pAKT induction in H3122 cells treated with lorlatinib in the presence of CAF-CM, HGF or NRG. We will work to provide additional data that strengthens the significance of this effect.

Reviewer #1 (Significance (Required)):

The concept of this work is interesting, however, molecular mechanisms underlying CAF-medicated ALK-TKI resistance remain poorly elucidated. Characterization of human primary fibroblasts (FB1, FB2) is not clearly described, and the most experiments lack proper control. Immunoblot in Fig 6 and 7 looks snap-shot and the reviewer has concerns about the reproducibility.

Author response:

In response to the comments, we will revise our manuscript to provide a more detailed characterization of the human primary fibroblasts to ensure transparency. To address the concern regarding controls, we will implement further controls in our experimental procedures. Regarding the concern about the reproducibility of the immunoblots, we appreciate the feedback, and we will provide additional data in the manuscript to ensure the reproducibility of our results.

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

In this review, the authors describe a possible mechanism of resistance in lung tumor cells that is induced by CAFs in response to ALK-specific inhibitors. The manuscript is well written and conclusions are in general well supported by experimental data, however additional experiments are needed to fully support the conclusions stated by the authors.

1. Brigatinib and lorlatinib are used to target ALK-driven lung tumor cells. In many of the experiments described heterotypic 3D co-cultures are used. Although both inhibitors act preferentially over the cancer cells, and no effect is expected in the CAFs, it would be desirable to confirm it.

Author response:

We thank the reviewer for this endorsement of our study, and we are pleased to learn that our conclusions are generally supported by the experimental data. Regarding the use of brigatinib and lorlatinib to target ALK-driven lung tumor cells in our experiments, we acknowledge the importance of confirming the specific action of these inhibitors. While these inhibitors act preferentially on the cancer cells, we agree that it is desirable to confirm their limited effect on CAFs. Therefore, we will address the reviewer’s suggestion by performing further cell viability analyses of TKI-treated fibroblast spheriods to specifically assess the impact of brigatinib and lorlatinib on CAFs.

The authors demonstrated initial proliferation advantage and apoptosis protection of H2228 and H3122 cells in response to conditioned media (CM) of three independent CAF clones. However, once they identify lipid enzymes altered and specific ligand-receptor interaction, then they focus only in FB2. This imply that the mechanism described by authors is relevant for that particular clone, but it does not validate a general resistance mechanism induced by CAFs. In order to claim that this is a more general mechanism, other CAF clones should be tested.

Author response:

We appreciate the reviewer’s comment regarding the focus on a specific CAF clone (FB2) in our study. This point is well taken, and we understand the importance of demonstrating the generalizability of the resistance mechanism induced by CAFs.

Since our results indicated consistent therapy response across all CAF clones tested (Figure 1), with the most pronounced effect observed with FB2-CAFs, we chose to focus our efforts on conducting sc-RNAseq experiments with FB2-CAFs and subsequently performed downstream validation experiments to corroborate our findings. This approach allowed us to prioritize the CAF clone with the most robust response while acknowledging the broader therapy response observed in all tested clones. Nevertheless, as requested by the reviewer, we will perform additional experiments using other independent CAF clones to assess whether the identified mechanism is broadly applicable.

Authors show that the identified ligands secreted by CAFs (HGF, NRG1β1, etc) are found in conditioned media from CAFs. It would be good to determine if the amount of these ligands somehow is dependent on the presence of tumor cells and/or ALK-TKi. Additionally, both HGF, NRG1β1, are able to partially restore the expression of the lipogenic enzymes identified, or AKT activation pathway, but they are not able to completely restore it. Since CAF-derived CM would have both factors, maybe combination of both ligands may induce stronger rescue of the expression of these proteins.

Author response:

To provide a comprehensive understanding, we will investigate whether the levels of identified CAF-derived ligands are influenced by tumor cells and/or ALK-TKI treatment by performing additional assays on CAF-supernatants. Furthermore, we will explore the potential synergy between HGF and NRG1β1 in rescuing the expression of lipogenic enzymes and the AKT signal transduction pathway on protein level.

Does PI3K/mTOR inhibitors revert the proliferation advantage of 3D heterotypic cultures? And expression of lipid biosynthesis genes?

Author response:

To address the impact of PI3K/mTOR inhibitors on the proliferation advantage of CAFs on ALK+ lung tumor spheroids and the expression of lipid metabolic genes, we will conduct treatment experiments using agents such as alpelisib (PI3Kα inhibitor), ipatasertib (panAKT inhibitor), or everolimus (mTORC1/2 inhibitor). Cell viability will be assessed using the 3D CellTiterGlo assay, and we will investigate changes in the expression of lipid biosynthesis genes to comprehensively evaluate the effects of these inhibitors on the resistance mechanism induced by CAFs.

Reviewer #2 (Significance (Required)):

This study underscores the multifaceted nature of resistance mechanisms in ALK-rearranged lung adenocarcinomas, highlighting the pivotal role of CAFs and lipid metabolic reprogramming. Lung adenocarcinoma remains a challenge in oncology, and while targeted therapy with tyrosine kinase inhibitors (TKIs) has shown promise in treating ALK-rearranged lung adenocarcinomas, the development of resistance to these therapies is nearly inevitable. This study delves into a critical aspect of this resistance by describing an important aspect of the intricate interplay between cancer-associated fibroblasts (CAFs) and tumor cells within the tumor microenvironment.

One of the primary findings of this research is the impact of CAFs on the therapeutic response of ALK-driven lung adenocarcinoma cells. While intrinsic mechanisms within cancer cells are well-studied drivers of resistance, this study underscores the emerging importance of stromal components, particularly CAFs, in shaping therapeutic vulnerabilities. The observation that CAFs promote therapy resistance by hampering apoptotic cell death and fueling cell proliferation highlights the complexity of tumor-stroma interactions.

The study utilizes three-dimensional (3D) spheroid co-culture models, providing a more physiologically relevant platform to investigate these interactions. This approach bridges the gap between conventional monolayer cultures and in vivo models, allowing for a deeper understanding of the role of the tumor microenvironment.

Perhaps one of the most notable findings is the identification of lipogenesis-related genes as major players in TKI-treated lung tumor spheroids. This finding not only sheds light on a previously underexplored facet of cancer biology but also suggests that lipid metabolism may be a central determinant of therapeutic susceptibility in this context. Although data provided here suggests that it might not be the only mechanisms taking place in the development of resistance to ALK inhibitors, it clearly shows that it plays an important role in it.

The study proposes a potential solution to overcome CAF-driven resistance by targeting vulnerabilities downstream of oncogenic signaling. The simultaneous targeting of ALK and SREBP-1, a key regulator of lipogenesis, emerges as a promising strategy to thwart the established lipid metabolic-supportive niche within TKI-resistant lung tumor spheroids.

Author response:

We thank the reviewer for this endorsement of our study and are gratified that the reviewer recognizes the critical implications of our research in the context of ALK-rearranged lung adenocarcinomas and their treatment resistance.

One of the stronger limitations of this study is that it rely on a limited number of cell lines or patient-derived models, which may not fully capture the heterogeneity of ALK-rearranged lung adenocarcinomas. Furthermore, for most of the validatory assays performed, only a CAF cell line is used, higly limiting the significance of their conclusions to a more general resistance mechanism. Furthermore, the study provides valuable insights into the involvement of lipogenesis-related genes and AKT signaling but do not delve deeply into the precise molecular mechanisms underlying these processes. Further mechanistic studies are needed to understand the exact interactions and signaling pathways involved. In conclusion, while this study provides valuable insights into the role of CAFs and lipid metabolism in ALK-TKI resistance, its limitations underscore the need for further research, including more comprehensive in vivo models and clinical studies, to confirm and expand upon these findings.

Author response:

We appreciate the reviewer’s thoughtful assessment of our study and acknowledge the limitations highlighted in your comment. The limited number of cell lines and patient-derived models used in our study is indeed a limitation, and we agree that this may not fully capture the heterogeneity of ALK-rearranged lung adenocarcinomas. However, the number of ALK+ lung adenocarcinoma cell lines is limited (3), as is the availability of patient-derived tissue material. To address this, we are actively working on expanding our research to include a more comprehensive range of models (e.g. primary ALK+ lung cancer cells, patient-derived organoids (PDOs)) for future studies.

We also acknowledge the importance of the limitations related to the use of a single CAF cell line in many of our validation assays. We are committed to broadening our experimental scope to involve multiple CAF cell lines to strengthen the significance of our conclusions.

Regarding the need for deeper mechanistic studies to understand the precise molecular interactions and signaling pathways, we agree that this is a crucial point. To this end, we are planning additional mechanistic studies to uncover the exact molecular mechanisms underlying the described resistance processes in future studies.

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

Summary: In this manuscript the authors use a spheroid co-culture model of human EML4-ALK non-small cell lung cancer (NSCLC) cell lines and fibroblasts to investigate the mechanism by which tumor-CAF crosstalk mediates non-genetic resistance to ALK inhibition. Spheroid culture of tumor cell lines with CAFs or CAF conditioned media was sufficient to reduce apoptosis and boost proliferation in the context of ALK inhibition. Using single-cell RNA-seq (scRNA-seq) of co-cultures treated with lorlatinib, the authors show that lipogenesis-associated genes are enriched in drug-treated tumor cells co-cultured with CAFs. Specifically, the authors propose that CAF-derived HGF and NRG1 derepress lorlatinib-induced changes in oncogenic Akt and mTOR signaling to boost expression of SREBP and FASN and restore lipid composition in NSCLC cancer cells.

Major comments:

The authors' conclusion that the effect of CAF CM on lorlatinib sensitivity is mediated by HGF and NRG is somewhat weak. Nrg1B1 was sufficient to rescue cell viability in the context of lorlatinib treatment (Figure 5B) only at a concentration significantly higher than that which was produced by fibroblast lines in culture (Figure 5A). Although the authors note that the real level of NRG1 could be higher than detected, this is speculative. Neither HGF nor NRG1 blocking antibodies appear to have rescued the elevated cell viability driven by CAF CM in the context of lorlatinib treatment (Figure 5C). These results, though statistically significant, do not appear biologically relevant. To strengthen their conclusions, the authors should consider ablating HGF or NRG1 in CAFs via shRNA or CRISPRi and then testing if CAF CM is no longer sufficient to rescue the viability of lorlatinib treated cancer spheroids.

Author response:

We refer the reviewer to our response to comment #1 of reviewer 1.

In addition to the western blots used to demonstrate and effect of CAF CM or HGF/NRG1 on Akt and mTOR signaling, the authors could strengthen their conclusions by testing the effect of Akt and mTOR inhibitors on the rescue effect of CAF CM.

Author response:

We refer the reviewer to our response to comment #6 of reviewer 2.

Minor comments:

1. In addition to the cell death and proliferation assays shown in Figure 1B-E, it would be helpful to show and quantify images of spheroid co-cultures treated +/- lorlatinib (as in Figure 1A, Supplemental Figure S9). Although the effects on percent cell death and proliferation are significant, images of spheroid size and morphology would make these results more convincing.

__Author response: __

We can certainly incorporate representative images in the revised manuscript. However, we'd like to clarify that comparing spheroid mono- and direct co-cultures can be challenging due to differences in initial cell seeding numbers, variations in the growth rates of fibroblasts and tumor cells, and subsequently, differences in spheroid sizes at the initiation of treatment. These factors can confound direct comparisons between the two culture conditions upon treatment.

In Figure 5B, the effect of CAF secreted factors on cell viability should be tested in comparison to CAF CM as a biological control. This would allow the reader to understand how the effect of each factor alone compares to the effect of CM.

__Author response: __

We will incorporate a comparison to CAF-CM as a biological control to provide a clearer understanding of the individual effects of CAF-secreted factors.

**Referees cross-commenting**

I am in agreement with all of the points made by Reviewer #1. I also suggested that the authors should use shRNA to inhibit HGF and NRG1 expression in CAFs, and am similarly concerned about both human and in vivo relevance of the authors' findings. The experiments suggested by Reviewer #1 to further characterize the fibroblast subtypes and to use non-CAF control cells are also reasonable.

Author response:

The reviewers alignment on the points raised is duly noted, and we understand the importance of addressing the concerns regarding the relevance of our findings. The use of shRNA to inhibit HGF and NRG1 expression in CAFs is a valuable suggestion, and we are actively considering this approach to enhance the specificity of our findings. Furthermore, we acknowledge the need for a deeper characterization of fibroblast subtypes and the inclusion of non-CAF control cells to strengthen the robustness of our research.

I am also in agreement with the critiques presented by Reviewer #2 and find them reasonable; they would strengthen the manuscripts and better support the authors' findings. The work is indeed limited by the models used here, and mechanistic findings would be better supported by further metabolic analysis such as Seahorse or assessment of lipid synthesis.

Author response:

We greatly appreciate your alignment with Reviewer #2's critiques and your recognition of their reasonableness. Expanding our research to include additional models and conducting further metabolic analyses are valuable suggestions that we are actively considering to bolster the mechanistic underpinnings of our work.

Reviewer #3 (Significance (Required)):

As a reviewer, I have expertise in fibroblast biology and the contributions of the tumor microenvironment to pancreatic tumor development. Although my research has not focused on lung cancer specifically, I also have experience in lipid metabolism, therapy resistance, and tumor heterogeneity. In this manuscript the authors use a co-culture system to show that soluble CAF factors drive tyrosine kinase inhibitor (TKI) resistance in vitro in Alk-fusion driven NSCLC in line with prior work (Reviewed by Wong et al. 2021, Domen et al. 2021, Li et al. 2022). Mechanistically, the authors propose that CAF-secreted HGF and NRG1 restore Akt and mTor signaling pathways suppressed by lorlatinib, thus rescuing SREBP expression and the phospholipidome in TKI-treated NSCLC cells. Prior work has specifically demonstrated the ability of CAFs to rescue the effect of lorlatinib on NSCLC cell lines with ALK fusions via HGF/Met signaling (Hu et al. 2021), and the general effect of CAF-secreted HGF on therapy resistance through Akt/mTor signaling has been well characterized (CITE). The regulation of SREBP and lipid metabolism by Akt/mTOR signaling in cancer and TKI resistance have been similarly described (CITE). Thus, the authors largely connect these well-known pathways, demonstrating that CAF co-culture restores lipid-associated transcriptional programs and lipidomic profile in lorlatinib-treated cells via HGF/NRG1 activation of Akt, mTOR, and SREBP. A few points presented in the manuscript that could represent potential scientific advances include scRNA-seq analysis of CAF/NSCLC co-cultures and the implication of CAFs in TKI-resistance through the modulation of lipid metabolism. However, the scientific and clinical significance of these findings are limited by the biological systems used and by their incremental contribution in context of the current literature.

The scRNA-seq analysis of NSCLC cells co-cultured with CAFs generated here could represent a potential advance and resource for future study; however, the application of this analysis to 2D cell lines in vitro may have limited utility as the heterogeneity of these long-culture lines is likely quite narrow (CITE OTHER scRNAseq in vivo or PDOs). The authors themselves did not leverage the scRNA-seq data for a deep analysis of cancer cell heterogeneity but rather uncovered lipid-associated transcriptional programs using bulk analysis across all tumor cells in their dataset. The significance of the authors' finding that CAF conditioned media (CM) mediates lorlatinib-sensitivity through the regulation of lipid metabolism is also somewhat limited by prior work directly implicating SREBP and phospholipid remodeling in TKI resistance (Xu et al. 2021, OTHERS). Although focusing on EGFR-mutant NSCLC, Xu et al. 2021 showed that SREBP upregulation and increased lipogenesis and decreased oxidative stress was associated with resistance to gefitinib and could be reversed by treatment with the SREBP inhibitor fatostatin in vivo. TKI-resistance is often driven by the activation of convergent signaling pathways (Akt, mTOR) in both in EGFR-mutant ALK-fusion NSCLC, so it is perhaps not particularly surprising that the authors find that lipid programs are similarly important in lorlatinib-resistance. The novelty in this manuscript is limited to the connection between CAFs and these critical lipid metabolism pathways, and the implication that SREBP inhibition similarly blocks non-genetic CAF-mediated TKI resistance. The significance of this finding might be greater if the authors explored whether fatostatin could improve therapy response to lorlatinib in vivo.

Author response:

We highly appreciate the reviewer’s detailed review and expertise in the fields of fibroblast biology, tumor microenvironment, lipid metabolism, and therapy resistance.

The reviewer’s perspective on the utility of scRNA-seq analysis in our study is justified. We acknowledge that applying this analysis to 2D cell lines in vitro may have limitations due to the narrow heterogeneity of long-culture lines. We therefore attempted to enhance the relevance of our findings by applying 3D cell culture models, which are known to resemble the in vivo situation more closely than conventional monolayer cultures (4, 5). Nevertheless, we agree that incorporation of additional models (e.g. patient-derived organoids) would better capture the heterogeneity of the tumor and its surrounding microenvironment. We concur that a deeper analysis would enhance our understanding of the interactions between CAFs and tumor cell (sub)populations.

The insights into the significance of our findings in the context of prior research on SREBP-dependent phospholipid remodeling in TKI resistance are well taken. We agree that the novelty of our study lies in the connection between CAFs and lipid metabolism pathways as a non-genetic CAF-mediated TKI resistance mechanism. However, it is also important to note that no prior studies have investigated stroma-driven lipid metabolic reprogramming in EML4-ALK-positive NSCLC. This unique aspect of our research adds to its originality and potential significance in advancing the understanding of ALK-positive NSCLC and therapy resistance.

We agree with the reviewer’s point that an in vivo study would be important in exploring whether fatostatin could improve therapy response to lorlatinib. However, due to technical and timing limitations, the establishment of corresponding mouse models is beyond the scope of our present study.

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

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer #1

Minor issues:

"----stronger in in case of-------" needs to be improved to "----stronger in case of-------", line 128, page 5.

__Author response: __

This was changed accordingly.

Reviewer #2

1. Based on sc-RNA-seq data authors then explore molecular processes facilitating survival of ALK-driven lung cancer cells under the influence of CAFs and mention that "among the enriched biological processes and pathways related to metabolic activities, the most striking terms were linked to lipid metabolism" (lines 182-184). Attending to the graph depicted in Fig. 3A, that is not true, finding other processes more significant. In fact, linked to metabolism, glycolysis is more significant than lipid metabolism. This sentence should be changed accordingly and a different rationale should be done to focus on lipid metabolism.

__Author response: __

The phrasing was changed accordingly.

Reviewer #3

Major comments:

1. Although the authors note that the scRNA-seq data generated here may be an important resource in the field, it could also be explored in greater depth to further support the conclusions of this manuscript. As is, the scRNA-seq data is primarily analyzed at a bulk level to identify lipid-associated genes and gene sets as down-regulated by lorlatinib. In this case, it would be more useful and perhaps a better resource to conduct bulk RNA-seq in triplicate to generate a stronger dataset and generate a set of genes significantly regulated by lorlatinib and CAF co-culture or CAF CM. The scRNA-seq data could be leveraged to support the conclusions of the manuscript by plotting lipid-associated genes identified in Figure XB-C by U-map. This analysis would identify which clusters are enriched for lipid-associated genes and demonstrate whether these particular clusters are depleted by lorlatinib or rescued by CAF co-culture.

__Author response: __

We opted for scRNA-seq as it allowed us to simultaneously sequence co-cultivated tumor cells and fibroblasts, without the need for sorting experiments typically required for bulk RNA-sequencing experiments. With this we intended to avoid potential biases introduced by sorting procedures, which can be challenging, particularly in the case of identifying appropriate markers for fibroblasts.

In response to the reviewer’s suggestions we have now refined our analysis to depict lipid-associated genes in a cluster-dependent manner (Supplementary Figure S9). This analysis, however, did not showcase a cluster-specific enrichment of lipid-associated genes and a demonstrated a TKI-induced depletion of these genes across all tumor cell cluster.

The authors' conclusion that CAF co-culture restores the lipid profile of lorlatinib-treated tumor cells is somewhat weak due to the representation of lipidomic data. Although the Figure legends note that lipidomic analyses were conducted at n=3 replicates, the data as represented in Figure 8A-B do not allow the reader to assess variability across samples or the significance of the fold change differences in lipid species. Although it can be useful to view the data this way, the authors should also show variability across samples in some way via PCA plot or by including a heatmap of lipid abundance across all treatment groups and replicates. Especially as some differences appear subtle, it is also difficult to understand to what extent CAF CM rescues lorlatinib-induced effects on lipid species as values are shown as fold change relative to control for the independent groups. In this way, the reader cannot assess, for example, how lipid species abundance compares in lorlatinib-treated tumor cells +/- CAF CM. Again, a heatmap across treatment groups might be helpful in addition to an analysis for statistically significant differences in lipid abundance across treatment groups. The issues outlined here make it difficult to assess whether "addition of CAF-CM to H3122 lung tumor spheroids was able to partly abrogate this shift towards higher levels of poly-unsaturated lipid". As is, the statements describing the results in Figure 8A-B are vague and don't appear to totally align with the data. To my eyes, there is no apparent general trend in SFA or MUFA reduction in lorlatinib-treated cells as implied by the authors, though particular species may be down-regulated. The authors should also calculate saturation indices across lipid species to support their conclusion that lipid saturation is modulated by lorlatinib and rescued by CAF CM.

__Author response: __

Given the reviewer’s suggestions, we have made significant improvements to the presentation of our lipidomic data in the revised manuscript. We now provide a more comprehensive view of the data to allow for a better assessment of variability across samples and the significance of saturation index differences across lipid species. Specifically, we have included a PCA plot (Figure 8A) and a heatmap of lipid abundances across all treatment groups and replicates to address the issue of variability (Supplementary Figure S11). Furthermore, we have performed additional analyses to calculate saturation indices across lipid species (Supplementary Figure S12A), which support our conclusion that lipid saturation, i.e. de novo lipogenesis, is modulated by lorlatinib and rescued by CAF-CM. These additions provide a clearer visualization of the data and enhance the robustness of our findings.

Minor comments:

The ablation of specific cell clusters upon lorlatinib treatment in Figure 2 is compelling and visually striking. To make it easier for the reader to interpret this data, it might be useful to denote the general functional annotations of each cluster in the legend (for example, "cluster 3: proliferative"). This would allow the reader to visualize which populations are preferentially depleted by the inhibitor and rescued by CAF co-culture. Further, some quantification showing the number of cells in each cluster by treatment would group (or fold-reduction per cluster upon inhibitor treatment) would more clearly show how each cluster is impacted by the inhibitor and CAF co-culture.

__Author response: __

To facilitate a clearer understanding of which populations are preferentially affected by the ALK-inhibitor and rescued by CAF co-culture, we provided the cluster-specific annotations in the legend of Figure 2.

Furthermore, we included quantifications showing the number of cells in each cluster by culture condition and treatment group (Supplementary Table S3), to provide a more comprehensive view of how each cluster is impacted by the inhibitor and CAF co-culture.

In Supplemental Figure S3A please specify which gate is being used to quantify the percentage of dead cells shown in subsequent plots. It would also be useful to show the gating strategy used to separate labelled tumor cells and CAFs in heterotypic co-cultures by FACS so it is clear that CAF cells are not included in the cell death/proliferation analysis.

__Author response: __

Gates for quantification of dead cells are now specified, while the gating strategy used for analyzing cell death rates of separated tumor cells is given as requested in Supplementary Figure S3A. This gating strategy was likewise used to separate labelled tumor cells from CAFs to analyze cell cycle distributions.

In Supplemental Figure S1B and C, please briefly clarify in the legend how fibroblast lines were cultured for collection of RNA and protein. It would be useful to know if the cells were assessed in spheroid culture and thus representative of their cell state when used for the following heterotypic co-culture experiments.

__Author response: __

Culture conditions were added in the figure legend as requested. Prior to generation of heterotypic tumor spheroids, fibroblasts were cultured as monolayers. Nevertheless, we also verified that the activation status is maintained following TGF-β1 removal and subsequent cultivation as homotypic fibroblast spheroid via WB analysis. We added the results as shown in Supplementary Figure S1D.

In Figure 8C, the authors should plot all individual values in the bar graph as done in all other panels.

__Author response: __

This was changed accordingly

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

*Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. *

Reviewer #1

Major critiques:

The concept of this work is largely based on findings in vitro culture. The authors should perform animal experiments to convince their findings in vivo. CAFs and tumor cells would be implanted into recipient mice to determine whether AKT signaling, de novo lipogenesis and ALK-TKI resistance are increased in tumor cells by the presence of CAFs.

__Author response: __

We agree with the reviewer’s point that an in vivo study would be important in interrogating the full impact of CAFs on therapy response, AKT signaling, and de novo lipogenesis of ALK-driven lung adenocarcinoma cells. However, due to technical and timing limitations, establishing and performing co-injection and treatment experiments of corresponding mouse models is beyond the scope of our present study.

The author described that human primary fibroblasts (FB1, FB2) were derived from NSCLC adenocarcinoma patients---. Have FB1 and FB2 been isolated from tumor tissues or no-tumor tissues? If these fibroblasts were isolated from tumor tissues as CAFs, why the authors added TGF-b onto the cells? The TGF-b treatment generates myofibroblastic CAFs, which is one of CAF subtypes, but fails to have inflammatory CAFs, which is another CAF subtypes.

__Author response: __

The fibroblasts FB1 and FB2 were indeed isolated from tumor tissue obtained from lung adenocarcinoma patients. In our study, the addition of TGF-β1 was employed as a strategy to maintain the CAF phenotype. It's important to note that CAFs exhibit considerable plasticity and can potentially lose their distinctive CAF characteristics during in vitro cultivation (6). The introduction of TGF-β1 was aimed at mimicking the tumor microenvironment and assisting in the preservation of the CAF phenotype, which was partially reflected in the increased expression of CAF markers such as αSMA and FAP (Supplementary Figure S1B and C).

We acknowledge the existence of various CAF subtypes, including myofibroblastic and inflammatory CAFs, which can be induced by different stimuli. While TGF-β1 treatment tends to push fibroblasts more toward a myofibroblastic phenotype, other factors like IL-1 can induce an inflammatory phenotype (7). In our study, we chose to focus on the myofibroblastic CAF subtype. This decision was based on the prevalence of myofibroblastic CAFs in lung tumors and their established roles in tumor progression, poor prognosis across different cancer types, and resistance to immunotherapy in non-small cell lung cancer (NSCLC) (8, 9).

Reviewer #2

H2228 and H3122 cells are used indistinctively through the paper as ALK-driven lung tumor cells and, although in the discussion some reference is made regarding the worst outcomes observed for v3-driven ALK+ H2228 cells, results are considered similar for both cell lines, including sc-RNA-seq data. During analysis of sc-RNA-seq data numbers of specific genes identified at the different analysis are different, similar to the clusters identified (0-6). In order to determine the degree of overlap in the identified genes on the analysis and within clusters, it would be convenient to show tables with identified genes for each of the cell lines, together with the cluster classification of those genes.

Author response:

Regarding the comparison of v1- vs. v3-driven ALK+ tumors, we would like to clarify that the primary focus of our study is on the interactions CAFs and ALK-driven lung tumor cells, particularly in the context of therapy resistance. While the different ALK fusion variants are certainly of interest, our intention is not to delve into the comparative analysis of these variants in this paper. Instead, we aim to emphasize the broader impact of CAFs on ALK-driven lung tumors.

The comparison of v1- vs. v3-driven tumors, as well as a detailed analysis of the differences between H2228 and H3122 cells, goes beyond the main focus of this paper. Incorporating a comparative analysis of specific genes for each cell line and their cluster classification would require a significantly expanded scope and could lead to a more complex and detailed study.

In order to fully validate the effect of CAF/CAF CM in lipid biosynthesis in tumor cells, seahorse analysis would be highly beneficial, providing simultaneous measurement of multiple metabolic parameters, including glycolysis, oxidative phosphorylation, and fatty acid oxidation in homo (with and without CAF's CM or secreted ligands) and heterotypic conditions. Furthermore, it should be combined with specific substrates and inhibitors (i.e. glucose to measure acetyl-CoA production, labelled fatty acids, etc), to dissect various aspects of lipid biosynthesis and lipid metabolism and assess de novo lipogenesis, fatty acid uptake, or triglyceride.

Author response:

This point is well taken and we acknowledge the potential value of such comprehensive metabolic assessments. However, we would like to clarify that the Seahorse XF Analyzer can primarily measure oxygen consumption rate (OCR) and extracellular acidification rate (ECAR), in response to different substrates to interrogate key metabolic functions such as mitochondrial respiration and glycolysis. At least to our knowledge, the Seahorse analyzer does not specifically measure de novo lipogenesis and fatty acid uptake. Therefore, incorporating these assays in the revised manuscript may not directly address the central question of CAF-driven enhanced lipid biosynthesis.

Nonetheless, we do agree with the reviewer that a more in-depth investigation of various metabolic alterations could be of interest in future studies. Given the GSEA data derived from our scRNA-seq analysis, which hints at alterations in glycolysis (Figure 3A), exploring these aspects of metabolic alterations in the context of CAF-mediated resistance effects could indeed provide valuable insights in the broader mechanisms underlying ALK-TKI resistance.

This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

References

1. Nadel BB, Oliva M, Shou BL, Mitchell K, Ma F, Montoya DJ, et al. Systematic evaluation of transcriptomics-based deconvolution methods and references using thousands of clinical samples. Brief Bioinform. 2021;22(6).
2. Maynard A, McCoach CE, Rotow JK, Harris L, Haderk F, Kerr DL, et al. Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing. Cell. 2020;182(5):1232-51 e22.
3. Bairoch A. The Cellosaurus, a Cell-Line Knowledge Resource. J Biomol Tech. 2018;29(2):25-38.
4. Friedrich J, Seidel C, Ebner R, Kunz-Schughart LA. Spheroid-based drug screen: considerations and practical approach. Nat Protoc. 2009;4(3):309-24.
5. Pampaloni F, Reynaud EG, Stelzer EH. The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol. 2007;8(10):839-45.
6. Sahai E, Astsaturov I, Cukierman E, DeNardo DG, Egeblad M, Evans RM, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174-86.
7. Biffi G, Oni TE, Spielman B, Hao Y, Elyada E, Park Y, et al. IL1-Induced JAK/STAT Signaling Is Antagonized by TGFbeta to Shape CAF Heterogeneity in Pancreatic Ductal Adenocarcinoma. Cancer Discov. 2019;9(2):282-301.
8. Mhaidly R, Mechta-Grigoriou F. Fibroblast heterogeneity in tumor micro-environment: Role in immunosuppression and new therapies. Semin Immunol. 2020;48:101417.
9. Hanley CJ, Waise S, Ellis MJ, Lopez MA, Pun WY, Taylor J, et al. Single-cell analysis reveals prognostic fibroblast subpopulations linked to molecular and immunological subtypes of lung cancer. Nat Commun. 2023;14(1):387.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this manuscript the authors use a spheroid co-culture model of human EML4-ALK non-small cell lung cancer (NSCLC) cell lines and fibroblasts to investigate the mechanism by which tumor-CAF crosstalk mediates non-genetic resistance to ALK inhibition. Spheroid culture of tumor cell lines with CAFs or CAF conditioned media was sufficient to reduce apoptosis and boost proliferation in the context of ALK inhibition. Using single-cell RNA-seq (scRNA-seq) of co-cultures treated with lorlatinib, the authors show that lipogenesis-associated genes are enriched in drug-treated tumor cells co-cultured with CAFs. Specifically, the authors propose that CAF-derived HGF and NRG1 derepress lorlatinib-induced changes in oncogenic Akt and mTOR signaling to boost expression of SREBP and FASN and restore lipid composition in NSCLC cancer cells.

Major comments:

1. Although the authors note that the scRNA-seq data generated here may be an important resource in the field, it could also be explored in greater depth to further support the conclusions of this manuscript. As is, the scRNA-seq data is primarily analyzed at a bulk level to identify lipid-associated genes and gene sets as down-regulated by lorlatinib. In this case, it would be more useful and perhaps a better resource to conduct bulk RNA-seq in triplicate to generate a stronger dataset and generate a set of genes significantly regulated by lorlatinib and CAF co-culture or CAF CM. The scRNA-seq data could be leveraged to support the conclusions of the manuscript by plotting lipid-associated genes identified in Figure XB-C by U-map. This analysis would identify which clusters are enriched for lipid-associated genes and demonstrate whether these particular clusters are depleted by lorlatinib or rescued by CAF co-culture.
2. The authors' conclusion that the effect of CAF CM on lorlatinib sensitivity is mediated by HGF and NRG is somewhat weak. Nrg1B1 was sufficient to rescue cell viability in the context of lorlatinib treatment (Figure 5B) only at a concentration significantly higher than that which was produced by fibroblast lines in culture (Figure 5A). Although the authors note that the real level of NRG1 could be higher than detected, this is speculative. Neither HGF nor NRG1 blocking antibodies appear to have rescued the elevated cell viability driven by CAF CM in the context of lorlatinib treatment (Figure 5C). These results, though statistically significant, do not appear biologically relevant. To strengthen their conclusions, the authors should consider ablating HGF or NRG1 in CAFs via shRNA or CRISPRi and then testing if CAF CM is no longer sufficient to rescue the viability of lorlatinib treated cancer spheroids.
3. The authors' conclusion that CAF co-culture restores the lipid profile of lorlatinib-treated tumor cells is somewhat weak due to the representation of lipidomic data. Although the Figure legends note that lipidomic analyses were conducted at n=3 replicates, the data as represented in Figure 8A-B do not allow the reader to assess variability across samples or the significance of the fold change differences in lipid species. Although it can be useful to view the data this way, the authors should also show variability across samples in some way via PCA plot or by including a heatmap of lipid abundance across all treatment groups and replicates. Especially as some differences appear subtle, it is also difficult to understand to what extent CAF CM rescues lorlatinib-induced effects on lipid species as values are shown as fold change relative to control for the independent groups. In this way, the reader cannot assess, for example, how lipid species abundance compares in lorlatinib-treated tumor cells +/- CAF CM. Again, a heatmap across treatment groups might be helpful in addition to an analysis for statistically significant differences in lipid abundance across treatment groups. The issues outlined here make it difficult to assess whether "addition of CAF-CM to H3122 lung tumor spheroids was able to partly abrogate this shift towards higher levels of poly-unsaturated lipid". As is, the statements describing the results in Figure 8A-B are vague and don't appear to totally align with the data. To my eyes, there is no apparent general trend in SFA or MUFA reduction in lorlatinib-treated cells as implied by the authors, though particular species may be down-regulated. The authors should also calculate saturation indices across lipid species to support their conclusion that lipid saturation is modulated by lorlatinib and rescued by CAF CM.
4. In addition to the western blots used to demonstrate and effect of CAF CM or HGF/NRG1 on Akt and mTOR signaling, the authors could strengthen their conclusions by testing the effect of Akt and mTOR inhibitors on the rescue effect of CAF CM.

Minor comments:

1. In addition to the cell death and proliferation assays shown in Figure 1B-E, it would be helpful to show and quantify images of spheroid co-cultures treated +/- lorlatinib (as in Figure 1A, Supplemental Figure S9). Although the effects on percent cell death and proliferation are significant, images of spheroid size and morphology would make these results more convincing.
2. The ablation of specific cell clusters upon lorlatinib treatment in Figure 2 is compelling and visually striking. To make it easier for the reader to interpret this data, it might be useful to denote the general functional annotations of each cluster in the legend (for example, "cluster 3: proliferative"). This would allow the reader to visualize which populations are preferentially depleted by the inhibitor and rescued by CAF co-culture. Further, some quantification showing the number of cells in each cluster by treatment would group (or fold-reduction per cluster upon inhibitor treatment) would more clearly show how each cluster is impacted by the inhibitor and CAF co-culture.
3. In Supplemental Figure S3A please specify which gate is being used to quantify the percentage of dead cells shown in subsequent plots. It would also be useful to show the gating strategy used to separate labelled tumor cells and CAFs in heterotypic co-cultures by FACS so it is clear that CAF cells are not included in the cell death/proliferation analysis.
4. In Supplemental Figure S1B and C, please briefly clarify in the legend how fibroblast lines were cultured for collection of RNA and protein. It would be useful to know if the cells were assessed in spheroid culture and thus representative of their cell state when used for the following heterotypic co-culture experiments.
5. In Figure 5B, the effect of CAF secreted factors on cell viability should be tested in comparison to CAF CM as a biological control. This would allow the reader to understand how the effect of each factor alone compares to the effect of CM.
6. In Figure 8C, the authors should plot all individual values in the bar graph as done in all other panels.

Referees cross-commenting

I am in agreement with all of the points made by Reviewer #1. I also suggested that the authors should use shRNA to inhibit HGF and NRG1 expression in CAFs, and am similarly concerned about both human and in vivo relevance of the authors' findings. The experiments suggested by Reviewer #1 to further characterize the fibroblast subtypes and to use non-CAF control cells are also reasonable.

I am also in agreement with the critiques presented by Reviewer #2 and find them reasonable; they would strengthen the manuscripts and better support the authors' findings. The work is indeed limited by the models used here, and mechanistic findings would be better supported by further metabolic analysis such as Seahorse or assessment of lipid synthesis.

Significance

As a reviewer, I have expertise in fibroblast biology and the contributions of the tumor microenvironment to pancreatic tumor development. Although my research has not focused on lung cancer specifically, I also have experience in lipid metabolism, therapy resistance, and tumor heterogeneity. In this manuscript the authors use a co-culture system to show that soluble CAF factors drive tyrosine kinase inhibitor (TKI) resistance in vitro in Alk-fusion driven NSCLC in line with prior work (Reviewed by Wong et al. 2021, Domen et al. 2021, Li et al. 2022). Mechanistically, the authors propose that CAF-secreted HGF and NRG1 restore Akt and mTor signaling pathways suppressed by lorlatinib, thus rescuing SREBP expression and the phospholipidome in TKI-treated NSCLC cells. Prior work has specifically demonstrated the ability of CAFs to rescue the effect of lorlatinib on NSCLC cell lines with ALK fusions via HGF/Met signaling (Hu et al. 2021), and the general effect of CAF-secreted HGF on therapy resistance through Akt/mTor signaling has been well characterized (CITE). The regulation of SREBP and lipid metabolism by Akt/mTOR signaling in cancer and TKI resistance have been similarly described (CITE). Thus, the authors largely connect these well-known pathways, demonstrating that CAF co-culture restores lipid-associated transcriptional programs and lipidomic profile in lorlatinib-treated cells via HGF/NRG1 activation of Akt, mTOR, and SREBP. A few points presented in the manuscript that could represent potential scientific advances include scRNA-seq analysis of CAF/NSCLC co-cultures and the implication of CAFs in TKI-resistance through the modulation of lipid metabolism. However, the scientific and clinical significance of these findings are limited by the biological systems used and by their incremental contribution in context of the current literature.

The scRNA-seq analysis of NSCLC cells co-cultured with CAFs generated here could represent a potential advance and resource for future study; however, the application of this analysis to 2D cell lines in vitro may have limited utility as the heterogeneity of these long-culture lines is likely quite narrow (CITE OTHER scRNAseq in vivo or PDOs). The authors themselves did not leverage the scRNA-seq data for a deep analysis of cancer cell heterogeneity but rather uncovered lipid-associated transcriptional programs using bulk analysis across all tumor cells in their dataset. The significance of the authors' finding that CAF conditioned media (CM) mediates lorlatinib-sensitivity through the regulation of lipid metabolism is also somewhat limited by prior work directly implicating SREBP and phospholipid remodeling in TKI resistance (Xu et al. 2021, OTHERS). Although focusing on EGFR-mutant NSCLC, Xu et al. 2021 showed that SREBP upregulation and increased lipogenesis and decreased oxidative stress was associated with resistance to gefitinib and could be reversed by treatment with the SREBP inhibitor fatostatin in vivo. TKI-resistance is often driven by the activation of convergent signaling pathways (Akt, mTOR) in both in EGFR-mutant ALK-fusion NSCLC, so it is perhaps not particularly surprising that the authors find that lipid programs are similarly important in lorlatinib-resistance. The novelty in this manuscript is limited to the connection between CAFs and these critical lipid metabolism pathways, and the implication that SREBP inhibition similarly blocks non-genetic CAF-mediated TKI resistance. The significance of this finding might be greater if the authors explored whether fatostatin could improve therapy response to lorlatinib in vivo.

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Referee #2

Evidence, reproducibility and clarity

In this review, the authors describe a possible mechanism of resistance in lung tumor cells that is induced by CAFs in response to ALK-specific inhibitors. The manuscript is well written and conclusions are in general well supported by experimental data, however additional experiments are needed to fully support the conclusions stated by the authors.

• Brigatinib and lorlatinib are used to target ALK-driven lung tumor cells. In many of the experiments described heterotypic 3D co-cultures are used. Although both inhibitors act preferentially over the cancer cells, and no effect is expected in the CAFs, it would be desirable to confirm it.
• H2228 and H3122 cells are used indistinctively through the paper as ALK-driven lung tumor cells and, although in the discussion some reference is made regarding the worst outcomes observed for v3-driven ALK+ H2228 cells, results are considered similar for both cell lines, including sc-RNA-seq data. During analysis of sc-RNA-seq data numbers of specific genes identified at the different analysis are different, similar to the clusters identified (0-6). In order to determine the degree of overlap in the identified genes on the analysis and within clusters, it would be convenient to show tables with identified genes for each of the cell lines, together with the cluster classification of those genes.
• Based on sc-RNA-seq data authors then explore molecular processes facilitating survival of ALK-driven lung cancer cells under the influence of CAFs and mention that "among the enriched biological processes and pathways related to metabolic activities, the most striking terms were linked to lipid metabolism" (lines 182-184). Attending to the graph depicted in Fig. 3A, that is not true, finding other processes more significant. In fact, linked to metabolism, glycolysis is more significant than lipid metabolism. This sentence should be changed accordingly and a different rationale should be done to focus on lipid metabolism.
• The authors demonstrated initial proliferation advantage and apoptosis protection of H2228 and H3122 cells in response to conditioned media (CM) of three independent CAF clones. However, once they identify lipid enzymes altered and specific ligand-receptor interaction, then they focus only in FB2. This imply that the mechanism described by authors is relevant for that particular clone, but it does not validate a general resistance mechanism induced by CAFs. In order to claim that this is a more general mechanism, other CAF clones should be tested.
• Authors show that the identified ligands secreted by CAFs (HGF, NRG1β1, etc) are found in conditioned media from CAFs. It would be good to determine if the amount of these ligands somehow is dependent on the presence of tumor cells and/or ALK-TKi. Additionally, both HGF, NRG1β1, are able to partially restore the expression of the lipogenic enzymes identified, or AKT activation pathway, but they are not able to completely restore it. Since CAF-derived CM would have both factors, maybe combination of both ligands may induce stronger rescue of the expression of these proteins.
• Does PI3K/mTOR inhibitors revert the proliferation advantage of 3D heterotypic cultures? And expression of lipid biosynthesis genes?
• In order to fully validate the effect of CAF/CAF CM in lipid biosynthesis in tumor cells, seahorse analysis would be highly beneficial, providing simultaneous measurement of multiple metabolic parameters, including glycolysis, oxidative phosphorylation, and fatty acid oxidation in homo (with and without CAF's CM or secreted ligands) and heterotypic conditions. Furthermore, it should be combined with specific substrates and inhibitors (i.e. glucose to measure acetyl-CoA production, labelled fatty acids, etc), to dissect various aspects of lipid biosynthesis and lipid metabolism and assess de novo lipogenesis, fatty acid uptake, or triglyceride.

Significance

This study underscores the multifaceted nature of resistance mechanisms in ALK-rearranged lung adenocarcinomas, highlighting the pivotal role of CAFs and lipid metabolic reprogramming. Lung adenocarcinoma remains a challenge in oncology, and while targeted therapy with tyrosine kinase inhibitors (TKIs) has shown promise in treating ALK-rearranged lung adenocarcinomas, the development of resistance to these therapies is nearly inevitable. This study delves into a critical aspect of this resistance by describing an important aspect of the intricate interplay between cancer-associated fibroblasts (CAFs) and tumor cells within the tumor microenvironment.

One of the primary findings of this research is the impact of CAFs on the therapeutic response of ALK-driven lung adenocarcinoma cells. While intrinsic mechanisms within cancer cells are well-studied drivers of resistance, this study underscores the emerging importance of stromal components, particularly CAFs, in shaping therapeutic vulnerabilities. The observation that CAFs promote therapy resistance by hampering apoptotic cell death and fueling cell proliferation highlights the complexity of tumor-stroma interactions. The study utilizes three-dimensional (3D) spheroid co-culture models, providing a more physiologically relevant platform to investigate these interactions. This approach bridges the gap between conventional monolayer cultures and in vivo models, allowing for a deeper understanding of the role of the tumor microenvironment.

Perhaps one of the most notable findings is the identification of lipogenesis-related genes as major players in TKI-treated lung tumor spheroids. This finding not only sheds light on a previously underexplored facet of cancer biology but also suggests that lipid metabolism may be a central determinant of therapeutic susceptibility in this context. Although data provided here suggests that it might not be the only mechanisms taking place in the development of resistance to ALK inhibitors, it clearly shows that it plays an important role in it.

The study proposes a potential solution to overcome CAF-driven resistance by targeting vulnerabilities downstream of oncogenic signaling. The simultaneous targeting of ALK and SREBP-1, a key regulator of lipogenesis, emerges as a promising strategy to thwart the established lipid metabolic-supportive niche within TKI-resistant lung tumor spheroids.

One of the stronger limitations of this study is that it rely on a limited number of cell lines or patient-derived models, which may not fully capture the heterogeneity of ALK-rearranged lung adenocarcinomas. Furthermore, for most of the validatory assays performed, only a CAF cell line is used, higly limiting the significance of their conclusions to a more general resistance mechanism. Furthermore, the study provides valuable insights into the involvement of lipogenesis-related genes and AKT signaling but do not delve deeply into the precise molecular mechanisms underlying these processes. Further mechanistic studies are needed to understand the exact interactions and signaling pathways involved.

In conclusion, while this study provides valuable insights into the role of CAFs and lipid metabolism in ALK-TKI resistance, its limitations underscore the need for further research, including more comprehensive in vivo models and clinical studies, to confirm and expand upon these findings.

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Referee #1

Evidence, reproducibility and clarity

Daum AK et al. indicated that CAFs promote TKI resistance via lipid biosynthesis in ALK-driven lung adenocarcinoma cells. This work provides novel CAF-induced drug resistant mechanisms in ALK-driven lung cancer cells, however, there are several issues to be resolved by the following.

Major critiques

1. The authors claimed that CAF-produced HGF and NRG1 boosts AKT signaling and de novo lipogenesis to promote ALK-TKI resistance in lung cancer cells. They showed that CAF-secreted HGF and NRG1 inhibition attenuates tumor cell viability in the presence of FB2-CM, however, whether stromal HGF and NRG1 inhibition suppresses de novo lipogenesis in carcinoma cells has not yet been investigated. The authors should inhibit HGF and NRG1 expression in CAFs by shRNA prior to addition of CAF-CM onto cancer cells to evaluate AKT signaling and de novo lipogenesis.
2. The concept of this work is largely based on findings in vitro culture. The authors should perform animal experiments to convince their findings in vivo. CAFs and tumor cells would be implanted into recipient mice to determine whether AKT signaling, de novo lipogenesis and ALK-TKI resistance are increased in tumor cells by the presence of CAFs.
3. This work lacks human correlation. Are HGF and NRG1 expressions in CAFs related with de novo lipogenesis, drug resistance and poor outcomes in ALK-driven lung cancer patients?
4. The author described that human primary fibroblasts (FB1, FB2) were derived from NSCLC adenocarcinoma patients---. Have FB1 and FB2 been isolated from tumor tissues or no-tumor tissues? If these fibroblasts were isolated from tumor tissues as CAFs, why the authors added TGF-b onto the cells? The TGF-b treatment generates myofibroblastic CAFs, which is one of CAF subtypes, but fails to have inflammatory CAFs, which is another CAF subtypes.
5. The most experiments lack the appropriate control for CAFs. They would use primary isolated counterpart fibroblasts as the patient-specific control for lung cancer CAFs by extracting from non-cancerous regions of the same individual in their experiments.

Minor issues

1. In Fig 1B, coculture with TGF-b-treated MRC-5 attenuated cancer cell death with the lorlatinib treatment. However, HGF and NRG1 production is comparable between MRC-5 cells treated with or without TGF-b in Fig 5A. These data indicate that any fibroblasts but not CAFs could suppress cancer cell death with the lorlatinib treatment.
2. The pAKT induction of H3122 treated with lorlatinib in the presence of CAM-CM or HGF or NRG in Fig 7A, B is barely observed and lacks the significance.
3. "----stronger in in case of-------" needs to be improved to "----stronger in case of-------", line 128, page 5.

Significance

The concept of this work is interesting, however, molecular mechanisms underlying CAF-medicated ALK-TKI resistance remain poorly elucidated. Characterization of human primary fibroblasts (FB1, FB2) is not clearly described, and the most experiments lack proper control. Immunoblot in Fig 6 and 7 looks snap-shot and the reviewer has concerns about the reproducibility.

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Reply to the reviewers

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

1. EVIDENCE, REPRODUCIBILITY AND CLARITY

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

This work examines the active compensation of TDH3 by its paralogs TDH1 and TDH2 as a mechanism of robustness against genetic perturbations in yeast. The authors demonstrate that the paralogs compensate in a dose-dependent manner in response to TDH3's absence, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Gcr1p and Rap1p show similar changes in expression, indicating that active compensation of TDH3 is part of a greater homeostatic feedback mechanism. Additionally, the authors suggest that the ability of paralogs to actively compensate for each other and contribute to genetic robustness is actively selected for or is simply a side effect of their ancestrally shared regulators with sensitivity to feedback mechanisms.

Major comments:

• Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The authors present robust evidence in this paper to substantiate their claims and conclusions. The comprehensive data provided effectively establishes a clear and compelling case for the role of active compensation among the TDH paralogs. I think that the authors' conclusions are well-supported with the data. Further experiments are not warranted at this time.

• Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

No need for further experiments to support the manuscript's conclusions at this time.

• If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".

Dear authors, I have a couple of experiments to open further lines of investigation:

Considering the modest expression level increase resulting from gene duplication of TDH3 (~35%), it may be worthwhile to further explore this phenomenon and its potential relationship with the limited availability of GRC1 and RAP1 transcription factors. It is conceivable that an attenuation mechanism could be involved in regulating TDH3 expression, and an examination of this possibility would provide valuable insights. An experimental approach utilizing a titratable promoter and assessment of mRNA and protein levels would offer a compelling means to probe this inquiry. (OPTIONAL).

The strain expressing TDH3 at 135% of the wild-type expression level carries two copies of TDH3, but both copies have mutations in their promoter that reduce their individual expression relative to the wild-type alleles. We have clarified the text by adding “reducing expression levels from each promoter” on page 6, line 17.

The authors' discussion raises the question of whether the active compensation observed between the TDH paralogs is a result of selection or simply a consequence of their shared regulators. To address this question, one potential avenue for future research would be to test the ability of TDH1-2 gene products to compensate for the loss of TDH3 by expressing them under the TDH3 promoter, a stronger or an inducible promoter, and then, measuring the fitness of the resulting strains with a tdh3𝚫 background. This additional line of experimentation has the potential to improve our understanding of the regulatory networks involved and shed light on the selective pressures that contribute to the maintenance of these paralogs over evolutionary time. (OPTIONAL)

We agree that this question - how selection has acted on the catalytic activity of the three paralogous proteins in concert with their expression levels - is very interesting. In fact, experiments including those described by the reviewer are currently underway in the Wittkopp lab and will be the focus of a future manuscript.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.

Not applicable.

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

Yes.

• Are the experiments adequately replicated and statistical analysis adequate?

Yes.

Minor comments:

• Specific experimental issues that are easily addressable.

In the introduction of the manuscript (pp. 4 para. 1), it would be useful to provide a more comprehensive overview of the gene expression patterns and protein abundances of the three TDH paralogs. Including such information would better enable readers to understand the functional roles of these paralogs.

We have added a new figure (Figure S1) showing differences in expression levels and patterns across the growth curve for the three paralogs. In addition, we have added some discussion of the differences in the effects of trans-regulatory mutations on protein abundance of each paralog that was recently published by another group and further indicates some level of regulatory divergence, particularly for TDH1 (pp.4, lines 12-19).

It would be helpful to report the phenotype of the tdh1𝚫/tdh2𝚫 double mutant to provide a clearer understanding of the functional overlap of these paralogs.

The revised manuscript includes additional information about divergence in expression patterns and differences in the effects of trans-regulatory mutations between TDH1 and the other two paralogs. Specifically, TDH1 is expressed under different conditions, and it is likely involved in different processes, than TDH2 and TDH3 (pp.4, lines 12-19, Figure S1). We have also added a sentence to the introduction stating that the double mutant deleting TDH1 and TDH3 has the same growth rate as TDH3 mutants alone, suggesting that TDH1 does not compensate for loss of TDH3 in the same way that TDH2 does. Because of these observations and because of the stronger overlap in expression profiles of TDH2 and TDH3, we have chosen to focus primarily on the compensation for TDH3 by TDH2 in the revised manuscript. We believe that these changes make the TDH1/TDH2 double mutant phenotype (which has not been studied as closely as the double mutants of TDH1 or TDH2 with TDH3) unnecessary for this study.

In the results section (pp. 5, para. 2), while it is understandable that the authors have focused on the transcriptional regulation of these paralogs, it would also be insightful to provide data on their respective protein abundances, as posttranslational regulation is often a crucial component of gene expression. This data may already be available in other high-throughput studies.

We have added new experimental data using fluorescent fusion proteins that shows that the protein abundance of TDH2 increases in response to deletion of TDH3 (Figure 1B). The results of our fluorescence measurements correspond well with transcriptional levels indicated by RNA-seq, indicating that the upregulation of TDH2 expression we saw in TDH3 mutants was controlled primarily at the transcriptional level.

It would be valuable to include more detailed information on the shared cis-regulatory elements between these genes, as this could provide further insight into their regulation and potential functional divergence.

According to experimental data compiled in http://www.yeastract.com/ , ChIP-exo data indicates that promoters for TDH1, TDH2, and TDH3 are all directly bound by Gcr1p (and the complex partner Gcr2p), although the evidence for Gcr1p binding is weaker at TDH1 than the other two paralogs, and this study does not identify Gcr1p TFBS motifs in the promoters of either TDH1 or TDH2 (Holland et al. 2019). However, we were able to locate Gcr1p TFBS motifs (CTTCC, Baker 1991) in the TDH2 promoter by manually searching regions annotated as bound by Gcr1’s complex partner Gcr2p in another publicly available ChIP-chip dataset (MacIsaac et al. 2006). We mutated these four motifs in a copy of the TDH2 promoter driving YFP expression to test for their role in upregulation using flow cytometry. We found that mutation or deletion of these putative TFBS reduced the overall activity of the promoter, indicating that these sequences are functional, and also observed that upon mutation or deletion of these putative TFBSs reduced the upregulation of TDH2 when TDH3 was deleted (Figure 3E). A schematic of the TDH2 promoter has been added to Figure 3 describing these experiments.

• Are prior studies referenced appropriately?

Yes.

• Are the text and figures clear and accurate?

The language used in this manuscript is clear and concise, making the material easily comprehensible to readers of various levels of expertise. The figures have a good quality for the most part and effectively complement the text to aid in the understanding of their findings.

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

I have a few minor suggestions regarding your manuscript's figures:

In figures 1-3, it would be helpful to indicate the number of biological or technical replicates used for the statistical analyses displayed in the plots.

We have added the number of biological replicates for each genotype to our figure legends.

Please consider adding a sentence to the figure legends indicating that the raw data was generated in a previous study.

We have added a sentence indicating that the raw data was generated in a previous study to all relevant figure legends.

Figure 4E may benefit from alternative visualization methods, such as using lines or a different type of plot, to make it easier to distinguish each dataset.

In response to this and other reviewer comments, we have re-formatted Figure 4 to reduce the number of genes displayed in Figure 4E. We believe this greatly increases the readability of the figure and thank the reviewer for their suggestion.

Reviewer #1 (Significance (Required)):

SIGNIFICANCE

===============

• General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

The study is noteworthy for its comprehensive analysis of previously reported data, offering a new understanding of the mechanisms behind the observed robustness of eukaryotic organisms, in particular the active compensation of TDH3 expression. The evidence presented in support of their conclusions is compelling. However, further research is required to investigate the role of active compensation at different regulation levels, in other paralogs, and under different environmental conditions.

• Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

This study provides new insights into the mechanisms of active compensation for the loss of gene expression in yeast. The authors demonstrate that the paralogs TDH1 and TDH2 upregulate in a dose-dependent manner in response to reductions in TDH3, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Rap1p and Gcr1p show similar changes in expression, indicating that active compensation of TDH3 by its paralogs is part of a larger homeostatic response. This study provides a mechanistic understanding of active compensation for the loss of gene expression in yeast and has potential implications for other organisms.

• Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

This study may attract a broad audience, as it provides insight into the mechanisms of active paralogous compensation. Their findings have potential implications beyond the yeast's specific field, as they may provide insight into the mechanisms of robustness in other genes and organisms. This research may be of interest in the fields of molecular biology and evolution in particular gene regulation.

• Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

My field of expertise is molecular biology and evolution, specifically in the areas of gene duplication, gene expression and regulation, protein evolution, and interaction networks. I am familiar with some of the topics discussed in the paper, such as gene expression and regulation, and have a good understanding of the research related to these topics.

We thank the reviewer for their insightful comments and thorough reading of the manuscript. We believe that the revisions, as described in more detail below, improve the manuscript and we greatly appreciate the suggestions.

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

The ms uses RNAseq data on S cerevisiae with TDH3 perturbations (cis and trans) from prior publication to look into RNA expression of TDH3 paralogues and genes within the same pathway. Analysis of both cis and trans TDH3 perturbation data suggests that the compensatory mechanisms (via either the paralogues or the upstream/downstream enzymes of the glycolytic pathway) are dependent on GCR1 and RAP1 transcription factors.

Major comment but OPTIONAL: The RNAseq data presented here convincingly convey the authors claims. Nevertheless, if any of the following data becomes available in the meantime, they will add a lot to the current ms: 1. Protein expression data can independently validate the findings and help support/clarify potential issues emerging from the data on the glycolytic pathway - see 2nd minor comment.

The revised manuscript includes new data showing increased expression of TDH2 upon deletion of TDH3 at the protein level using a TDH2:CFP fusion protein under the control of the native TDH2 promoter and at the native locus (Figure 1B). These protein-level data do indeed independently validate our RNA-seq findings for TDH2. We have also re-arranged Figure 4 and clarified the section of the manuscript describing changes in expression in the rest of the glycolytic pathway to better communicate that these changes in gene expression may or may not be part of an active compensation mechanism (see further discussion below).

1. Any data that show expression of TDH3 as a result of TDH1/TDH2 expression changes occurring independently of Gcr1/Rap1 can support the claims on robustness as a consequence of multiple paralogues being around.

We have RNA-sequencing data for strains in which TDH1 or TDH2 was deleted individually (GSE175398, data from Vande Zande et al., 2022). We saw that in these strains TDH3 expression was not significantly increased. We believe that this finding is most likely due to the difference in basal expression levels between paralogs. TDH3 is expressed at approximately 6x the level of TDH2, and TDH1 is expressed in stationary phase rather than exponential growth as TDH2 and TDH3 are (See new supplementary Figure S1). Deletion or reduction of TDH3 expression represents a much larger change in total GAPDH levels in the cell, and therefore might elicit a much stronger compensation response than deletion of TDH2 or TDH1. We are interested in how the different expression levels, patterns, and enzymatic activity levels have diverged between paralogs and contribute to their relative function in the cell, and, as mentioned above, another member of the Wittkopp lab is currently working on a manuscript addressing these questions in greater detail. For these reasons, we have chosen not to include these data in the current manuscript.

Minor comments

1) Introduction and analysis framing: there seems to be two aspects for robustness and compensation that the manuscript focuses on. The one is through paralogues and the other via alteration in the expression of genes in the same pathway. The study shows both, yet there is particular weight on the paralogues.

The introduction should also mention both in a coherent and organized way. As an example, the second paragraph in the intro refers to 'upregulation of a paralog' in the 1st sentence, then it refers to an example that fits better to compensation through changes in expression of enzymes in the same pathway.

We have adjusted the language in the second paragraph of the introduction to clarify that the other enzymes that are actively compensating for CLV1 or SlCLV3 loss in arabidopsis and tomato are paralogs (pg.2 line 21- pg.3 line 8). In addition, we have adjusted our wording of the final introduction paragraph (pg. 5, lines 11-18), and the final results section (pg. 13, lines 17-23) to better communicate that the other genes are changing as part of a homeostatic response programmed into the regulatory network and may or may not contribute to fitness gains in a TDH3 mutant.

2)Figure 4 results/Discussion: Not unexpectedly, PFK1 and PFK2 (in panels 4D and 4B) have very similar expression profiles with respect to TDH3 expression. (Considering that they are part of the same complex, one would expect that their expression levels should correlate at the very least). Yet, PFK1 did not make the significance cutoff. That can be misleading, so it warrants a comment, either on the respective results section or discussion. For that reason and to make easier comparisons expression data should be shown on the same panel (consolidate 4B and 4D) with significance annotations. It would also be nice to see some commentary in terms of pathway output upon changes in TDH3 expression. It seems as if there is a 'diffused signal' through the whole pathway that compensates for TDH3 perturbations, meaning all enzymes may be compensating to different degrees.

We appreciate these suggestions and have used them to re-arrange Figure 4 to more clearly show the response of genes that function at each step in the glycolytic pathway. As noted in the reviewer’s comment, PFK1 and 2 are nearly identical in their expression profiles. The reason one is not significantly upregulated is that it has a higher variance among replicates than the other, which we now point out explicitly in the figure legend. By grouping these genes together in Figure 4, the similarity of their expression changes is much more obvious.

Reviewer #2 (Significance (Required)):

The study demonstrates an example of paralog-dependent changes in gene expression that contribute to phenotypic robustness. The active paralog compensation is transcription factor-dependent, and the same transcription factors are also responsible for compensatory changes in expression levels of genes in the same pathway. I believe that this is an interesting case showing how a negative feedback mechanism in place to maintain pathway output and contribute to phenotypic robustness, receives and integrates signaling from different components of a pathway, including paralogues. The study relies solely on RNAseq data. Although convincing, protein expression data not only could validate the RNAseq data, but also could give a more accurate view of the respective expression profiles. The study describes a molecular mechanism in pathway regulation with broad interest in basic research. It also has particular interest with respect to paralog evolution and brings up questions on the forces that drive paralog divergence.

We appreciate this reviewer’s comments and suggestions and have added several new figures that use fluorescent fusion proteins to provide a quantitative readout of protein expression levels. Specifically, we have added panels showing increased protein expression of TDH2 fused with CFP upon deletion of TDH3 (Figure 1B). We have also added expression of fluorescent reporter genes driven by the TDH1, TDH2, and TDH3 promoters showing the differences in their expression profiles across population growth stages (Supplementary Figure 1). Finally, we have analyzed fluorescent reporter genes with promoters containing mutated Gcr1p TFBS, which also suggest a dependence of the compensatory upregulation on GCR1p (Figures 2D, 3E).

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

In their preprint titled "Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae" Zande and Wittkopp attempt to delineate the molecular mechanism behind compensation. They have chosen 3 paralogs, TDH1, 2 and 3 in Saccharomyces cerevisiae as their system of choice, building on an earlier study where they have used RNA-seq transcriptomics to characterize how global gene expression is affected by strains harbouring regulatory mutations at the TDH3 locus or a TDH3 gene knockout. The major claims made by the authors in this study are as follows:

1. The TDH1/2/3 system demonstrates compensation, such that changes in levels of expression of TDH3 result in altered expression levels of TDH1 and TDH2

2. The mechanism of this compensation is "active", that is it involves modulating the transcription of paralogs in response to altered TDH3 in the cell. iii. The transcription factors Gcr1 and Rap1 are likely candidates mediating this compensation. The effects of these regulators on TDH1 and TDH2 differ and produces different profiles of compensatory expression for these two genes.

3. Since Gcr1 and Rap1 regulate other genes coding for glycolytic enzymes, compensation is related to altered expression of a larger cohort of genes. Major comments:

4. The authors' claims regarding the roles of Rap1 and Gcr1 as mechanisms of compensation are supported by correlative evidence from RNA seq data. To establish the causal relationships that the authors intend, more directed experiments like the ones listed below are required: i. Monitoring the activity of TDH1 and TDH2 promoters (as YFP-fused reporters) in the various strains

We have added new experimental data to the manuscript monitoring the activity of the TDH1 and TDH2 promoters driving YFP in different phases of the growth curve, demonstrating the divergence in their gene expression patterns (Supplementary Figure 1). Because of the divergence in expression patterns, we chose to focus additional efforts on TDH2 and have added new data showing an increase in expression of a CFP::TDH2 fusion protein upon deletion of TDH3. These new experiments provide strong evidence of the causal relationship between the deletion of TDH3 and an increase in TDH2 expression.

ii. Generating mutant promoter reporters for TDH1, 2 and 3 that are unable to bind to Gcr1 and Rap1 and testing their activity in the various mutant strains

We have added new experimental data to the manuscript demonstrating that increases in the activity of the TDH3 and TDH2 promoters upon deletion of TDH3 are dependent upon Gcr1p transcription factor binding sites, as originally hypothesized. Specifically, the new figure panel 3E consists of flow cytometry data showing that the TDH2 promoter driving YFP expression increases in fluorescence upon deletion of TDH3, but that a comparable increase does not occur when Gcr1p TFBSs in the TDH2 promoter are mutated. In addition, the new figure panel 2D shows that the TDH3 promoter driving YFP no longer increases in activity when a Gcr1 TFBSs is mutated. These new experiments provide strong evidence for the dependence of active compensation by upregulation upon shared transcription factor GCR1.

1. The authors claim that Gcr1 and Rap1 have similar impact on other glycolytic enzymes. However, these conclusions are also based on RNA -seq data and hence remain correlative. Based on the presented results alone, and lack of a molecular mechanism for why the levels of Rap1 and Gcr1 change in TDH3 mutant strains, it may just as easily be argued that the change in expression of other glycolytic enzymes (and therefore glycolytic flux) may be the cause for altered Rap1/Gcr1 activity and not the consequence. To test which of these possibilities are true, I would recommend the following approaches:

i. Promoter reporters for glycolytic enzymes of interest, and mutant versions that don't respond to Rap1/Gcr1

ii. Change glycolytic flux by altering growth conditions (e.g. fermentable/non-fermentable carbon source) and check to see if compensation is altered

While it is possible that mutations in the TDH3 promoter that change TDH3 expression alter the expression of other glycolytic genes, and this in turn alters Rap1/Gcr1 activity, resulting in the upregulation of the TDH paralogs, we believe it is more likely that changes in the activity of Rap1/Gcr1 are a cause rather than a consequence of altered expression of glycolytic genes because it has previously been shown that these genes are under the control of Rap1 and Gcr1. We have adjusted the wording of the final results section, and throughout the paper, to clarify that we believe the similar expression patterns observed for other glycolytic genes suggest that the increase in paralog expression that results in active compensation is part of a larger regulon, which indeed may be responsive to changes in glycolytic flux. We cannot say, however, whether the upregulation of other glycolytic genes is part of the compensatory response per se. We believe this clarification, in addition to the new experiments showing the dependence of TDH3 and TDH2 upregulation on transcription factor binding sites for GCR1, addresses the issues raised above.

Reviewer #3 (Significance (Required)):

This study attempts to address the mechanistic basis for an important homeostatic mechanism, i.e. compensation. Compensation is an almost universal mechanism seen in pathways with genetic redundancy. As pointed out by the authors, compensation ensures that gene regulatory networks produce robust outcomes and are resistant to perturbation. Though compensation is often observed, the mechanistic basis is usually unclear. This study throws light on possible transcriptional mechanisms that orchestrate compensation by altering expression levels of paralogous enzymes. In this regard the study is novel, important, and fills a lacuna in the area. However, in its current form, the study lacks the necessary causal evidence needed to substantiate the claims made by the authors. Further, the mechanism linking transcriptional regulation and metabolic flux is still lacking. As a result, though interesting, the study doesn't provide a complete picture and fails to make an impact.

We thank the reviewer for their comments and believe that the additional experiments and data added to the revised manuscript, including using fluorescent reporter genes and mutant alleles to measure the activity of promoters and show their dependence on RAP1/GCR1 binding sites, provide the causal evidence necessary to make this an impactful study.

Since I am not a yeast geneticist, it is possible that several of the concerns raised by me are due to my lack of knowledge of the system and some of the links that I find missing may have been demonstrated by others. If this is the case, I would suggest that the authors provide adequate background to address these concerns in the manuscript itself. It is my opinion, that this study, once shored up, will be of interest to a wide-readership and could also provide important experimental data that could be used for mathematical modeling.

We appreciate the reviewer’s comments and believe that the changes we’ve made to the manuscript, including the addition of critical new data that complements and supports the RNA-seq data originally presented in the manuscript, does indeed make this a study that will be of interest to a wide readership.

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Referee #3

Evidence, reproducibility and clarity

In their preprint titled "Active compensation for changes in TDH3 expression mediated by direct regulators of TDH3 in Saccharomyces cerevisiae" Zande and Wittkopp attempt to delineate the molecular mechanism behind compensation. They have chosen 3 paralogs, TDH1, 2 and 3 in Saccharomyces cerevisiae as their system of choice, building on an earlier study where they have used RNA-seq transcriptomics to characterize how global gene expression is affected by strains harbouring regulatory mutations at the TDH3 locus or a TDH3 gene knockout. The major claims made by the authors in this study are as follows:

• i. The TDH1/2/3 system demonstrates compensation, such that changes in levels of expression of TDH3 result in altered expression levels of TDH1 and TDH2
• ii. The mechanism of this compensation in "active", that is it involves modulating the transcription of paralogs in response to altered TDH3 in the cell.
• iii. The transcription factors Gcr1 and Rap1 are likely candidates mediating this compensation. The effects of these regulators on TDH1 and TDH2 differ and produces different profiles of compensatory expression for these two genes.
• iv. Since Gcr1 and Rap1 regulate other genes coding for glycolytic enzymes, compensation is related to altered expression of a larger cohort of genes.

Major comments:

1. The authors' claims regarding the roles of Rap1 and Gcr1 as mechanisms of compensation are supported by correlative evidence from RNA seq data. To establish the causal relationships that the authors intend, more directed experiments like the ones listed below are required:

   • i. Monitoring the activity of TDH1 and TDH2 promoters (as YFP-fused reporters) in the various strains
   • ii. Generating mutant promoter reporters for TDH1, 2 and 3 that are unable to bind to Gcr1 and Rap1 and testing their activity in the various mutant strains
   • The authors claim that Gcr1 and Rap1 have similar impact on other glycolytic enzymes. However, these conclusions are also based on RNA -seq data and hence remain correlative. Based on the presented results alone, and lack of a molecular mechanism for why the levels of Rap1 and Gcr1 change in TDH3 mutant strains, it may just as easily be argued that the change in expression of other glycolytic enzymes (and therefore glycolytic flux) may be the cause for altered Rap1/Gcr1 activity and not the consequence. To test which of these possibilities are true, I would recommend the following approaches:
   • i. Promoter reporters for glycolytic enzymes of interest, and mutant versions that don't respond to Rap1/Gcr1
   • ii. Change glycolytic flux by altering growth conditions (e.g. fermentable/non-fermentable carbon source) and check to see if compensation is altered

Significance

This study attempts to address the mechanistic basis for an important homeostatic mechanism, i.e. compensation. Compensation is an almost universal mechanism seen in pathways with genetic redundancy. As pointed out by the authors, compensation ensures that gene regulatory networks produce robust outcomes and are resistant to perturbation. Though compensation is often observed, the mechanistic basis is usually unclear. This study throws light on possible transcriptional mechanisms that orchestrate compensation by altering expression levels of paralogous enzymes. In this regard the study is novel, important, and fills a lacuna in the area. However, in its current form, the study lacks the necessary causal evidence needed to substantiate the claims made by the authors. Further, the mechanism linking transcriptional regulation and metabolic flux is still lacking. As a result, though interesting, the study doesn't provide a complete picture and fails to make an impact.

Since I am not a yeast geneticist, it is possible that several of the concerns raised by me are due to my lack of knowledge of the system and some of the links that I find missing may have been demonstrated by others. If this is the case, I would suggest that the authors provide adequate background to address these concerns in the manuscript itself. It is my opinion, that this study, once shored up, will be of interest to a wide-readership and could also provide important experimental data that could be used for mathematical modelling.

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Referee #2

Evidence, reproducibility and clarity

The ms uses RNAseq data on S cerevisiae with TDH3 perturbations (cis and trans) from prior publication to look into RNA expression of TDH3 paralogues and genes within the same pathway. Analysis of both cis and trans TDH3 perturbation data suggests that the compensatory mechanisms (via either the paralogues or the upstream/downstream enzymes of the glycolytic pathway) are dependent on GCR1 and RAP1 transcription factors.

Major comment but OPTIONAL: The RNAseq data presented here convincingly convey the authors claims. Nevertheless, if any of the following data becomes available in the meantime, they will add a lot to the current ms: 1. Protein expression data can independently validate the findings and help support/clarify potential issues emerging from the data on the glycolytic pathway - see 2nd minor comment. 2. Any data that show expression of TDH3 as a result of TDH1/TDH2 expression changes occurring independently of Gcr1/Rap1 can support the claims on robustness as a consequence of multiple paralogues being around.

Minor comments

1. Introduction and analysis framing: there seems to be two aspects for robustness and compensation that the manuscript focuses on. The one is through paralogues and the other via alteration in the expression of genes in the same pathway. The study shows both, yet there is particular weight on the paralogues. The introduction should also mention both in a coherent and organized way. As an example, the second paragraph in the intro refers to 'upregulation of a paralog' in the 1st sentence, then it refers to an example that fits better to compensation through changes in expression of enzymes in the same pathway.
2. Figure 4 results/Discussion: Not unexpectedly, PFK1 and PFK2 (in panels 4D and 4B) have very similar expression profiles with respect to TDH3 expression. (Considering that they are part of the same complex, one would expect that their expression levels should correlate at the very least). Yet, PFK1 did not make the significance cutoff. That can be misleading, so it warrants a comment, either on the respective results section or discussion. For that reason and to make easier comparisons expression data should be shown on the same panel (consolidate 4B and 4D) with significance annotations. It would also be nice to see some commentary in terms of pathway output upon changes in TDH3 expression. It seems as if there is a 'diffused signal' through the whole pathway that compensates for TDH3 perturbations, meaning all enzymes may be compensating to different degrees.

Significance

The study demonstrates an example of paralog-dependent changes in gene expression that contribute to phenotypic robustness. The active paralog compensation is transcription factor-dependent, and the same transcription factors are also responsible for compensatory changes in expression levels of genes in the same pathway. I believe that this is an interesting case showing how a negative feedback mechanism in place to maintain pathway output and contribute to phenotypic robustness, receives and integrates signaling from different components of a pathway, including paralogues. The study relies solely on RNAseq data. Although convincing, protein expression data not only could validate the RNAseq data, but also could give a more accurate view of the respective expression profiles. The study describes a molecular mechanism in pathway regulation with broad interest in basic research. It also has particular interest with respect to paralog evolution and brings up questions on the forces that drive paralog divergence.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.

This work examines the active compensation of TDH3 by its paralogs TDH1 and TDH2 as a mechanism of robustness against genetic perturbations in yeast. The authors demonstrate that the paralogs compensate in a dose-dependent manner in response to TDH3's absence, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Gcr1p and Rap1p show similar changes in expression, indicating that active compensation of TDH3 is part of a greater homeostatic feedback mechanism. Additionally, the authors suggest that the ability of paralogs to actively compensate for each other and contribute to genetic robustness is actively selected for or is simply a side effect of their ancestrally shared regulators with sensitivity to feedback mechanisms.

Major comments:

• Are the claims and the conclusions supported by the data or do they require additional experiments or analyses to support them?

The authors present robust evidence in this paper to substantiate their claims and conclusions. The comprehensive data provided effectively establishes a clear and compelling case for the role of active compensation among the TDH paralogs. I think that the authors' conclusions are well-supported with the data. Further experiments are not warranted at this time. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether.

No need for further experiments to support the manuscript's conclusions at this time. - If you have constructive further reaching suggestions that could significantly improve the study but would open new lines of investigations, please label them as "OPTIONAL".

Dear authors, I have a couple of experiments to open further lines of investigation: Considering the modest expression level increase resulting from gene duplication of TDH3 (~35%), it may be worthwhile to further explore this phenomenon and its potential relationship with the limited availability of GRC1 and RAP1 transcription factors. It is conceivable that an attenuation mechanism could be involved in regulating TDH3 expression, and an examination of this possibility would provide valuable insights. An experimental approach utilizing a titratable promoter and assessment of mRNA and protein levels would offer a compelling means to probe this inquiry. (OPTIONAL). The authors' discussion raises the question of whether the active compensation observed between the TDH paralogs is a result of selection or simply a consequence of their shared regulators. To address this question, one potential avenue for future research would be to test the ability of TDH1-2 gene products to compensate for the loss of TDH3 by expressing them under the TDH3 promoter, a stronger or an inducible promoter, and then, measuring the fitness of the resulting strains with a tdh3𝚫 background. This additional line of experimentation has the potential to improve our understanding of the regulatory networks involved and shed light on the selective pressures that contribute to the maintenance of these paralogs over evolutionary time. (OPTIONAL) - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated time investment for substantial experiments.

Not applicable. - Are the data and the methods presented in such a way that they can be reproduced?

Yes. - Are the experiments adequately replicated and statistical analysis adequate?

Yes.

Minor comments:

• Specific experimental issues that are easily addressable.

In the introduction of the manuscript (pp. 4 para. 1), it would be useful to provide a more comprehensive overview of the gene expression patterns and protein abundances of the three TDH paralogs. Including such information would better enable readers to understand the functional roles of these paralogs. It would be helpful to report the phenotype of the tdh1𝚫/tdh2𝚫 double mutant to provide a clearer understanding of the functional overlap of these paralogs. In the results section (pp. 5, para. 2), while it is understandable that the authors have focused on the transcriptional regulation of these paralogs, it would also be insightful to provide data on their respective protein abundances, as posttranslational regulation is often a crucial component of gene expression. This data may already be available in other high-thrroughput studies. It would be valuable to include more detailed information on the shared cis-regulatory elements between these genes, as this could provide further insight into their regulation and potential functional divergence. - Are prior studies referenced appropriately?

Yes. - Are the text and figures clear and accurate?

The language used in this manuscript is clear and concise, making the material easily comprehensible to readers of various levels of expertise. The figures have a good quality for the most part and effectively complement the text to aid in the understanding of their findings.

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

I have a few minor suggestions regarding your manuscript's figures: In figures 1-3, it would be helpful to indicate the number of biological or technical replicates used for the statistical analyses displayed in the plots. Please consider adding a sentence to the figure legends indicating that the raw data was generated in a previous study. Figure 4E may benefit from alternative visualization methods, such as using lines or a different type of plot, to make it easier to distinguish each dataset.

Significance

• General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

The study is noteworthy for its comprehensive analysis of previously reported data, offering a new understanding of the mechanisms behind the observed robustness of eukaryotic organisms, in particular the active compensation of TDH3 expression. The evidence presented in support of their conclusions is compelling. However, further research is required to investigate the role of active compensation at different regulation levels, in other paralogs, and under different environmental conditions. - Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

This study provides new insights into the mechanisms of active compensation for the loss of gene expression in yeast. The authors demonstrate that the paralogs TDH1 and TDH2 upregulate in a dose-dependent manner in response to reductions in TDH3, mediated by shared transcriptional regulators Gcr1p and Rap1p. Furthermore, other glycolytic genes regulated by Rap1p and Gcr1p show similar changes in expression, indicating that active compensation of TDH3 by its paralogs is part of a larger homeostatic response. This study provides a mechanistic understanding of active compensation for the loss of gene expression in yeast and has potential implications for other organisms. - Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

This study may attract a broad audience, as it provides insight into the mechanisms of active paralogous compensation. Their findings have potential implications beyond the yeast's specific field, as they may provide insight into the mechanisms of robustness in other genes and organisms. This research may be of interest in the fields of molecular biology and evolution in particular gene regulation. - Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

My field of expertise is molecular biology and evolution, specifically in the areas of gene duplication, gene expression and regulation, protein evolution, and interaction networks. I am familiar with some of the topics discussed in the paper, such as gene expression and regulation, and have a good understanding of the research related to these topics.

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Reply to the reviewers

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

*In their study, Yamano et al. dissect the mechanism of TBK1 activation and downstream effects, especially in its relation to mitophagy adaptor OPTN. The authors find that OPTN's interaction with ubiquitin and the autophagy machinery, forming contact sites between mitochondria and autophagic membranes, results in TBK1 accumulation and subsequent autophosphorylation. Based on these findings, the authors propose a self-propagating feedback loop wherein OPTN phosphorylation by TBK1 promotes recruitment and accumulation of OPTN to damaged mitochondria and specifically the autophagosome formation site. This formation site is then involved in TBK1 autophosphorylation, and the activated TBK1 can then further phosphorylate other pairs of OPTN and TBK1. A OPTN monobody investigation strengthens their findings. *

*Critique: *

• It would be helpful if the authors could more clearly highlight the previous findings in OPTN-TBK1 relationship and which gaps in the understanding their study addresses.* We thank the reviewer for this comment. As suggested, we have highlighted previous findings and detailed in the Discussion how the study advances our understanding of TBK1 activation.

• It is not always clear whether experiments have been replicated sufficiently; this should be indicated in the figure descriptions.* In the original manuscript, most of the data shown was derived from duplicated experiments. For the revised version, we repeated experiments as needed to generate the replication necessary (i.e, N = 3) for determining statistical significance. Error bars and statistical significance have been added to the graphs and figure legends accordingly.

• During the discussion, references to the figures that indicate conclusions should be added where appropriate.* We thank the reviewer for the suggestion. References to figures have been added were appropriate to the Discussion.

*Figure 1 / Result "OPTN is required for TBK1 phosphorylation and subsequent autophagic Degradation": *

*o In a) the TBK1 and TOMM20 blots feature an image artefact that makes it appear like the blots are stitched together or there was a problem with the digital imager. The quantification in b) seems to be missing replications. *

We found that the artifact came from an automatic pixel interpolation process in Adobe Photoshop when the image was rotated by a small angle. We have provided the original immunoblotting data below as evidence that the data were not stitched from separate images. More accurate representations of the images without the artifact are now shown in Fig1 A of the revised manuscript.

For Fig 1b, the experiment was independently replicated three times with error bars added to each plot on the graph.

*o g) should feature the wt cell line on the same blot for better comparability as well as quantification and replication like done in f) *

As suggested, we have included the WT cell line in the immunoblot (See Fig 1g). In addition, Reviewer 2 asked that we provide data for Penta KO cells without exogenous expression of the autophagy adaptors and expressed concern regarding the lower expression of NDP52 relative to OPTN. To address these issues, we repeated the mitophagy experiments and detected phosphorylated TBK1 in six different cell lines: WT, Penta KO, Penta KO stably expressing OPTN at both low and high expression levels, and Penta KO stably expressing NDP52 at low and high expression levels. Immunoblots of phos-TBK1(pS172), TBK1, OPTN, NDP52, TOMM20, and actin were generated under four different conditions (DMSO, valinomycin for 1 hr, valinomycin for 3 hrs, and valinomycin in the presence of bafilomycin for 3 hrs). In addition, phos-TBK1 abundance in the six cell lines was determined in response to val and baf for 3 hrs and the expression levels of NDP52 and OPTN were similarly determined in response to DMSO. Error bars based on three independent experiments have been incorporated into the data, which are shown in Figure 1g and 1h of the revised manuscript.

*o h) is missing the blots for controls actin and TOMM20 *

Immunoblots for actin and TOMM20 have been added, please see Fig 1i in the revised manuscript.

*o In the text to e/f), the authors write that NDP52 KO effect on pS172 are comparable to controls, though the quantitation in f) indicates that pS172 signal is indeed significantly reduced compared to wt *

The reviewer is correct, the phos-TBK1 (pS172) signal in NDP52 KO cells is reduced compared to that in WT cells, but is only moderately lower in NDP52 KO cells relative to OPTN KO. We regret the error, which has been corrected in the revised manuscript.

*o In the text to h/i), the authors write "there was a significant increase in the TBK1 pS172 signal in cells overexpressing OPTN", though the quantification in i) does not indicate significance levels *

We performed statistical analyses on the phos-TBK1 (pS172) levels between cells with or without OPTN overexpression and have added the degree of significance to Fig 1j. As indicated in the original manuscript, there was a significant increase in phos-TBK1 (pS172) levels when OPTN was overexpressed.

*Figure 2 / Result "OPTN association with the autophagy machinery is required for TBK1 activation": ** o In b), pTBK1 at val 1 hr only features one dot/experiment per cell line *

Three independent replicates of the experiment (val 1 hr) were performed. The levels of phos-TBK1 (pS172), total TBK1, and actin were quantified, and the graph was remade with error bars and statistical significance incorporated. Please see Fig 2b in the revised manuscript.

*o In the text to c), the authors claim that the mutants reduce/abolish the recruitment of OPTN to the autophagosome site. A costain for LC3, as done for SupFig 1b, would be necessary to support that specific claim. *

To address the reviewer’s concern regarding the recruitment of OPTN mutants to the autophagosomal formation site, we performed two different experiments. First, when OPTN WT is recruited to the contact site between the autophagosomal formation site and damaged mitochondria, it should be heterogeneously distributed across mitochondria. In contrast, OPTN mutants that are unable to associate with the autophagosome formation sites should be largely localized to damaged mitochondria since the mutants are still capable of binding ubiquitin. When we examined the mitochondrial distribution of OPTN WT following valinomycin treatment for 1 hr, more than 80% of the Penta KO cells exhibited a heterogeneous distribution, whereas only 10% of the cells showed a similar distribution for OPTN 4LA or OPTN 4LA/F178A (please see Fig 2g in the revised manuscript). Although the OPTN F178A mutant exhibited 50% heterogeneous distribution (Fig 2g), this may be because OPTN F178A retains the ability to interact with ATG9A vesicles. In fact, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB), which are strongly correlated with OPTN mitochondrial distribution, showed that the OPTN F178A mutant moderately (~ 60%) induced mitochondrial degradation. This degradation effect was slightly higher (80%) with OPTN WT but significantly lower (9%) with the 4LA/F178A mutant. In the second experiment, Penta KO cells expressing either OPTN WT or the OPTN mutants were immunostained for exogenous FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (a mitochondrial marker) after valinomycin treatment for 1 hr (see Fig 2e and 2f in the revised manuscript). Because LC3B is assembled on the autophagosomal formation site as well as completed autophagosomes, we detected endogenous WIPI2 because WIPI2 is only recruited to autophagosomal formation sites (Dooley et al. 2014 Mol Cell). Confocal microscopy images and their associated quantification data indicate that WIPI2 foci formation during mitophagy was reduced in Penta KO cells expressing the OPTN mutants (4LA, F178A and 4LA/F178A) as compared to Penta KO cells expressing OPTN WT.

*o d) and g) as simple confirmations of KO/KD efficiency might be better suited for the supplemental part, or blots for FIP/ATG be included with the blots in e) and h) *

Based on the reviewer comments, we performed additional experiments related to Figure 2 and have incorporated the new data into the revised figure. The original Figure 2d, e, f, g, h, and I have been moved to supplemental Figure 5.

*o In the text to e), the authors claim that the levels of pS172 in the KO cell lines did not increase during mitophagy, though the blot and quantification in f) seem to indicate an increase. The results therefore don't seem to align completely with the claims that pS172 generation in response to mitophagy requires the autophagy machinery, or that FIP200 and ATG9A rather than ATG5 are critical for TBK1 phosphorylation. *

Although newly generated pS172 TBK1 was reduced in FIP200 KO and ATG9A KO cells relative to WT cells, the signals gradually increased. In the autophagy KO cell lines (FIP200 KO and ATG9A KO), phos-TBK1 accumulates prior to mitophagy stimulation. Although suggesting it is mitophagy-independent, phos-TBK1 accumulation prior to mitophagy stimulation in autophagy KO cell lines complicated interpretation of the results. To avoid this issue, we used siRNA to transiently knock down FIP200 and ATG9A. As shown in the original manuscript (Fig 2g, h, I in the original manuscript, supplementary Fig 5d, e, f in the revised manuscript), knockdown of FIP200 and ATG9A prior to mitophagy induction allowed us to observe mitophagy-dependent phosphorylation of TBK1. This result strongly suggests that the autophagy machinery does induce TBK1 phosphorylation in response to Parkin-mediated mitophagy. However, TBK1 phosphorylation still increases, albeit very slightly, in the FIP200 and ATG9A knock down cells. Thus, it may be reasonable to assume that OPTN-dependent phosphorylation of TBK1 can occur to a certain degree even in the absence of autophagy components. We have noted this in the Discussion.

While conducting experiments for the revised manuscript, we determined that TAX1BP1 is responsible for the accumulation of phos-TBK1 in the autophagy KO cell lines under basal conditions. When TAX1BP1 is knocked down in FIP200 KO or ATG9A KO cells, the basal accumulation of phos-TBK1 was eliminated and then we could observe mitophagy-specific TBK1 phosphorylation (please see Fig 2h, i, j, k in the revised manuscript). These results showed that mitophagy-dependent phos-TBK1 is largely attenuated in FIP200KO and was almost completely eliminated in ATG9A KO cells (Fig 2k in the revised manuscript).

*o f) is missing significance indications. Its description has a typo: "bad" instead of "baf" *

Newly synthesized pTBK1 (pS172) during mitophagy was quantified and statistical significance incorporated into the figure (please see supplementary Fig 5c). The identified typo has been corrected.

*Figure 3 / Result "TBK1 activation does not require OPTN under basal autophagy conditions": *

*o In the text to SupFig2, the authors claim that pS172 levels are significantly elevated, but no significance levels are indicated *

Statistical significance was determined for all proteins shown in original supplementary Fig 2 and the results have been incorporated into the relevant figure. The original supplementary Fig 2 is now supplementary Fig 6.

*o In the text to a), NBR1 is claimed to colocalize with Ub, but no costaining with Ub is shown. The claimed lacking colocalization of OPTN with Ub is not obvious from the images; a quantification might be appropriate. *

Since the anti-NBR1 antibody used in the original manuscript is derived from mouse, we were unable to use it in conjunction with the mouse ubiquitin antibody. Because ubiquitin-positive foci and NBR1-positive foci contain p62 (original Fig 3a) and NBR1 and p62 are known to tightly interact each other (Kirkin et al. 2009 Mol Cell and Sanchez-Martin et al. 2020 EMBO Rep), we stated that "NBR1 colocalizes with Ub". However, the reviewer is correct. To remedy this confusion, we obtained a rabbit anti-NBR1 antibody (a gift from the Masaaki Komatsu group) and used it to co-immunostain with anti-Ub antibodies (please see supplementary Fig 7a of the revised manuscript). NBR1 foci colocalize with both ubiquitin and p62 in FIP200 KO and ATG9A KO cells. Further, based on comments from Reviewer 2, we purchased several anti-TBK1 antibodies and identified one that was able to detect endogenous TBK1 by immunostaining (see Figure 1 for reviewers in our response to Reviewer 2 below). Using this anti-TBK1 antibody, we showed that a part of TBK1 also associates with ubiquitin and p62-positive aggregates.

*o In the text to b), the authors make reference to significant changes, but replication/ quantification/ significance testing is missing. *

We independently performed the same experiments three times. The levels of TBK1, phos-TBK1 (pS172), all five autophagy adaptors, and TOMM20 in both the supernatants and pellets have been quantified with error bars and statistical significance indicated. These results have been incorporated into Figure 3c in the revised manuscript.

*Figure 4b) is missing the pTBK1 data that is referenced in the text. In the text to figure 5 c/d), the authors claim that certain mutants have no significant effect on mitophagy, though d) is missing significance testing *

*Figure 6 c/d/i) appear to be missing replication. *

For Figure 4b, phos-TBK1 was immunoblotted (See Fig 4b of the revised manuscript). For Figure 5b and d, statistical significance was determined for the effect of TBK1 mutations on autophosphorylation and OPTN phosphorylation and the effect of the TBK1 mutants on Parkin-mediated mitophagy. For Figure 6 c/d/I, the experiment was repeated; error bars and statistical significance have been added to the associated graphs.

*Reviewer #1 (Significance (Required)): Removal of damaged mitochondria by the mitophagy pathway provides an important safeguarding mechanism for cells. The Pink1/Parkin mechanism linked to numerous modulators and adaptor proteins ensures an efficient targeting of damaged mitochondria to the phagophore. The Ser/Thr kinase TBK1, in addition of multiple roles in innate immunity, is a major mitophagy regulator as has been revealed by the Dikic and Youle groups in 2016 (Richter et al., PNAS). The mechanistic insights provided by this manuscript add to a growing body of studies of how the autophagy machinery interconnects with cellular signalling networks. Although parts of the results need to be further validated, the data shown is of high quality, revealing an important conceptual advance. The paper is interesting and of general relevance beyond the signalling and autophagy community. *

We would like to thank Reviewer 1 for the comments and suggestions, many of which improved our manuscript. We hope that the reviewer’s comments have been adequately addressed in the revised manuscript.

*Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary In this manuscript, Yamano and colleagues show that as for Sting-mediated TBK1 activation, Optn provides a platform for TBK1 activation by autophosphorylation and that TBK1 is activated after the interaction of Optn with the autophagy machinery and ubiquitin and not before. They show that TBK1 phosphorylation is blocked by bafilomycine A1, an inhibitor of vacuolar ATPases that blocks the late phase of autophagy. Furthermore, they demonstrate that Optn is require for TBK1 phosphorylation since variation of Optn expression regulates TBK1 phosphorylation in response to PINK/Parkin-mediated autophagy. Interestingly, using immunofluorescence microscopy, they show that Optn forms sphere like structures at the surface of damage mitochondria which are more dispersed in the absence of TBK1. In addition, TBK1 is also recruited at the surface of damage mitochondria and as Optn and NDP52 (but not p62) colocalize with LC3B in response to PINK/Parkin-mediated mitophagy. Next, it is demonstrated that the Leucin zipper and LIR domains of Optn (which modulate Optn interaction with autophagosome) play an important role for TBK1 activation. Additionally, the autophagy core is shown to be required for TBK1 activation. Under basal conditions, depletion of the autophagosome machinery leads to an increase in autophagy receptors (except Optn) and TBK1 phosphorylation which colocalize with ubiquitin in insoluble moieties. In contrast, Optn remains cytosolic and is dispensable for TBK1 activation in these conditions. Then, using the fluoppi technic, the authors demonstrate that the generation of Optn-Ubiquitin condensates recruits and activates TBK1. They express in HCT116 TBK1-deficient cells engineered or pathological ALS mutations of TBK1 that affect ubiquitin interaction, structure, dimerization and kinase activity of TBK1. The expression level of TBK1 was only affected by the dimerization-deficient mutations. None of the mutations impaired Optn and TBK1 ubiquitination. Interestingly, some ALS-associated mutations affect TBK1 activity and it is said in the text that the dimerization-deficient mutations of TBK1 affect its activity proportionally to their level of expression, which is not really correct (the expression level of the mutants is very heterogenous and not always correlate to their activity). Regarding their effect on mitophagy, the authors claim that the phosphorylation of TBK1 correlate with mitophagy which is not really the case. By using TBK1 inhibitor or TBK1-depleted cells, the authors conclude that TBK1 is the only kinase phosphorylating Optn. However, BX-795 is not completely specific to TBK1. Finally, the authors use monobodies against Optn effective in inhibiting mitophagy in NDP52 KO cells. Some of the monobodies have been shown to form a ternary complex with Optn and TBK1, while others compete for the interaction between Optn and TBK1 which involves the amino-terminal region of Optn and the C-terminal region of TBK1. Monobodies that compete for the interaction of Optn with TBK1 could alter the cellular distribution of Optn and inactivate TBK1, but they do not alter the ubiquitination of Optn. Finally, these monobodies inhibit 50% of mitophagy. *

*Major and minor points: Introduction The first paragraph of the Introduction section is confused and difficult to read. First and second paragraphs (page 3 and top of page 4) are dedicated to macroautophagy processes but ended with one sentence on Parkin-mediated autophagy without further introduction, while all processes regarding mitophagy are detailed in the next paragraph. Links between ideas developed are also somewhat missing. For example, in page 6, the three last sequences detailed the phosphorylation of autophagosome component, the fact that Optn and TBK1 genes are involved in neurodegenerative diseases and autophosphorylation of TBK1 as a pre-requirement for TBK1 activation without evident links between them, except "interestingly". *

In response to the reviewer’s suggestion, we have rewritten the Introduction. The first paragraph focused on introducing the molecular mechanism underlying macroautophagy and the second paragraph focused on Parkin-mediated mitophagy. As the reviewer indicated, the ALS mutations and TBK1 phosphorylation during Parkin-mediated mitophagy are not well related, so we moved the background material on the relationship between OPTN and TBK1 in neurodegenerative diseases to the beginning of the section describing Figure 5. We believe these changes have made the Introduction easier to read and understand.

*Results *

*Major points: *

*1- Results are often over-interpreted regarding data obtained leading to inadequate conclusions (see below for details); *

We regret the reviewer’s concerns regarding over-interpretation. To address this issue, we have carefully considered the data, performed additional experiments where necessary, and rewritten the results accordingly. Please see our point-by-point responses below.

*2- Quantification of protein levels detected by western blot are provided as "relative intensities" without referring to specific loading control or to total protein when -phosphorylated forms are quantified (Fig. 1b, 1d, 1f, 1i, 2b, 2f, 2i, 5b, 7b, supplemental figures 2b). *

For the immunoblots, we loaded the same amount of total cell lysate and the phosphorylated forms were quantified relative to the total protein input. This has been mentioned in the Materials and Methods.

*3- In western blotting experiments, authors described slower migrating bands as "ubiquitinated" forms of detected proteins, but never provided experimental evidences that it could be the case. Use of non-specific deubiquitinase incubation of extracts prior to western blot could help to correctly identified ubiquitination versus other post-translational modifications such as phosphorylation, glycosylation, acetylation etc... *

We appreciate the reviewer’s suggestion. The cell lysates after mitophagy induction were incubated in vitro with a recombinant USP2 core domain (non-specific DUB), and then immunoblotted. As shown in supplemental Fig 1 of the revised manuscript, the slower migrating OPTN bands disappeared in a USP2-dependent manner. The slower migrating NDP52 and TOMM20 bands likewise disappeared. These results confirm that the slower migrating OPTN, NDP52, and TOMM20 bands are ubiquitinated.

*4- Conclusions from data obtained by immunofluorescent imaging are often drawn from only one image presented without further statistical analysis. *

Statistical significance was determined for the immunofluorescent data (original figures 1j, 2c and 3a). Please see Fig 1l, 2f, 2g, and 3a in the revised manuscript.

*Page 7: - authors referred to TBK1 phosphorylation induced by mitophagy induction as "TBK1 phosphorylation induced by Parkin-mediated ubiquitination" while mitophagy can be induced independently of Parkin (ex: via mitochondrial receptors) and without any evidence (according to referee's knowledge) of a link between ubiquitination by Parkin and TBK1 phosphorylation. *

As the reviewer indicated, Parkin-independent and ubiquitination-independent mitophagy pathways are also known (i.e. receptor-mediated mitophagy driven by NIX, BNIP3, BCL2L13, FKBP8, FUNDC1, or Atg32). Therefore, references to "mitophagy" in our manuscript were reworded as "Parkin-mediated mitophagy". Since TBK1 phosphorylation is observed before mitochondria are degraded and is dependent on Parkin-mediated ubiquitin (for example, see Fig 1c), we use the phrase "TBK1 phosphorylation triggered by Parkin-mediated OMM ubiquitination".

*Fig 1g: Western blots performed in Penta KO cells without exogene expression of any autophagy receptors should be provided as control. Furthermore, lower expression of NDP52 relative to that of Optn (using flag antibodies) should be discussed as it can explained the differential levels in TBK1 phosphorylation observed. *

As suggested, we repeated the experiment using Penta KO cells in the absence of exogeneous autophagy adaptor expression. Furthermore, we expressed different amounts of NDP52 and OPTN (indicated as low and high in the figure) in Penta KO cells to rule out the possibility that higher TBK1 phosphorylation is induced by simple overexpression of autophagy adaptor (please see Fig 1g and h in the revised manuscript). At high NDP52 expression (2.5-3.0-fold higher than endogenous NDP52), phosphorylated TBK1 was reduced to ~30% the level of that observed in WT cells after 3 hrs with val and baf. In contrast, Penta KO cells with higher OPTN expression (3.0-fold higher than endogenous OPTN) had phosphorylated TBK1 signals that were 2-fold higher than those in WT cells. Based on these results, we concluded that OPTN is an important adaptor for TBK1 activation during Parkin-mediated mitophagy.

*Page 8: Supplemental Fig 1a: - The inability of authors to observe TBK1 endogenous signal in HeLa cells using commercially available antibodies is surprising as many publications reported successful staining (see Figure 1 of Suzuki et al. 2013 Cell type-specific subcellular localization of phospho-TBK1 in response to cytoplasmic viral DNA. PLoS One. 8:e83639 among others) as well as commercial promotion (see Anti-NAK/TBK1 antibody from Abcam reference: ab235253). *

For the original manuscript, anti-TBK1 antibodies purchased from abcam (ab235253), CST (#3013S), Proteintech (28397-1-AP), and GeneTex (GTX12116) for immunostaining were unable to yield TBK1-positive signals (please see Fig 1 for reviewers below). WT and TBK1-/- HCT116 cells stably expressing Parkin were treated with valinomycin for 1 hr and immunostained with the indicated antibodies. Anti-phos-TBK1 antibody (CST, #5483) was used as a positive control. Based on these results, we stated in the original manuscript that the "endogenous TBK1 signal could not be observed using commercially available antibodies". At the reviewer’s suggestion, we purchased anti-TBK1 antibodies from abcam (ab40676) and CST (#38066). As shown in the figure below, the immunofluorescent signals generated by these antibodies were detected in WT, but not in TBK1-/- cells. The CST (#38066) antibody yielded a stronger signal, most of which was on damaged mitochondria. Thanks to this suggestion, we repeated the experiment using the new anti-TBK1 antibody. Furthermore, based on a suggestion from Reviewer 3, we detected mitochondrial recruitment of TBK1 during mitophagy stimulation (valinomycin for 30 min or 2 hrs in the presence and absence of bafilomycin; supplemental Fig 2 in the revised manuscript). We also detected association of endogenous TBK1 with ubiquitin-positive condensates in WT, FIP200KO, and ATG9A KO cells (Fig 3a and supplementary Fig 7a in the revised manuscript).

*- Conclusions of the localization of signal on mitochondria (dispersed, in the periphery or at contact sites) are clearly over-interpreted in the absence of other membrane or autophagosome specific labeling and statistical colocalization analyses of multiple images. It is particularly difficult to assess any difference between Tax1BP1, p62 and NBR1 localization on mitochondria subdomains. *

We previously expressed each FLAG-tagged autophagy adaptor in Penta KO cells and observed their localization during Parkin-mediated mitophagy and found that exogenous FLAG-tagged OPTN and NDP52, but not p62, colocalized with LC3B (Yamano et al 2020 JCB). No one has assessed and compared the localization of all five endogenous autophagy adaptors. Although we still believe that the results (supplemental Fig1 in the original manuscript) are informative for researchers in the autophagy field, we decided to remove that data from the revised manuscript since they are not the main focus of the study. We will consider publishing those data elsewhere in the future after co-staining with autophagosome markers and assessing the statistical significance of colocalization as the reviewer suggested.

*Page 9: *

*- First part of results ended without any conclusions. *

As detailed in the previous response, we have removed results for mitophagic recruitment of autophagy adaptors (supplementary Figure 1 in the original manuscript).

*- The observation that "TBK1 phosphorylation was not apparent in the Optn mutant cell lines, even after 3 hrs of valinomycin, ..." is inconsistent with detection of bands with anti-pS172-TBK1 antibodies in Fig 2a detected at 1hr (with F178A) and 3 hrs (4LA, F178A, and 4LA/F178A mutants) of treatment. *

We apologize for the confusion. This statement was clearly our mistake. We had intended to state when "all autophagy adaptors are deleted" no phosphorylated TBK1 was observed. We have rewritten this part as "TBK1 phosphorylation was not apparent in the Penta KO cells even after 3 hrs with valinomycin".

*- Similarly, decreased levels of phosphorylated TBK1 stated for F178A mutant was only observed at 1 but not 3hrs or at 3hrs in the presence of bafilomycin. *

Based on the mitophagy assay previously reported (Yamano et al 2020 JCB), the F178A mutant only moderately inhibited mitophagy (60% mitophagy with the F178A mutant vs 80% mitophagy with OPTN WT). Conversely, the 4LA mutant and 4LA/F178A double mutant had stronger inhibitory effects on mitophagy (35% for 4LA and 9% mitophagy for 4LA/F178A). Therefore, the levels of phos-TBK1 after 1 hr with valinomycin or 3 hrs with valinomycin in the presence of bafilomycin are consistent with mitophagy progression. When mitophagy proceeds efficiently, the amount of phos-TBK1 in the 1 hr val samples is reduced relative to the 3 hr val samples due to autophagic degradation.

To more clearly observe and compare the levels of mitophagy-dependent phos-TBK1 among Penta KO cells expressing OPTN WT and the mutants, we treated cells with valinomycin in the presence of bafilomycin for 0, 0.5, 1, and 2 hrs and quantified phos-TBK1. The results are shown in Fig 2c and d in the revised manuscript. The phos-TBK1 signal increased over time with val and baf treatment in all OPTN expressing cells. Cells with OPTN WT generated the most phos-TBK1, whereas the signal generated by the F178A mutant was 75% that of the OPTN WT-expressing cells and the 4LA and 4LA/F178A mutants were about 40%. The experiments were independently replicated three times and error bars and statistical significance were incorporated into the associated graph. These results indicate that OPTN association with the autophagy machinery, in particular ATG9A vesicles, is important for TBK1 activation.

*Page 10: *

*The results and their repartition between figure 2 d, e, f, g, h, I and figure 3 is a bit confusing. In these experiments, it is shown Figure 2 that the absence or depletion of the autophagy machinery increase the phosphorylation of TBK1 and in Figure 3 it is shown that not only the phosphorylation of TBK1 accumulate but also the expression of NDP52, Tax1BP1 and p62. Is it because their degradation by autophagy is blocked (like for phosphoTBK1)? *

The reviewer is correct that autophagy adaptors other than OPTN (especially TAX1BP1, p62 and NBR1) are constantly degraded by macro/micro autophagy (Mejlvang et al. 2018 J Cell Biol and Yamano et al. 2021 BBA Gen Subj). Therefore, these adaptors accumulate in autophagy deficient cell lines (original Fig 3). In this study, we found that in the absence of mitophagy stimulation phos-TBK1 accumulates in autophagy deficient cell lines. This suggests that the accumulated autophagy adaptors induce TBK1 phosphorylation under basal conditions. In the original manuscript, we claimed that TBK1 phosphorylation under basal conditions does not require OPTN since in FIP200 KO and ATG9A KO cells it did not accumulate and did not primarily colocalize with ubiquitin- and TBK1-positive foci (original Fig 3). To gain more direct evidence for the revised manuscript, we performed additional experiments and discovered that TAX1BP1 is the adaptor responsible for TBK1 autophosphorylation under basal autophagy. We treated FIP200KO and ATG9A KO cells with siRNAs against OPTN, NDP52, TAX1BP, p62, and NBR1, and immunoblotted total cell lysates with an anti-phos-TBK antibody. As shown in Fig 3f in the revised manuscript, TAX1BP1 siRNA treatment decreased phos-TBK1 levels without affecting total TBK1. This result indicates that the accumulation of TAX1BP1 in the FIP200 KO and ATG9A KO cells induced TBK1 autophosphorylation under basal conditions. Considering this result, we treated WT, FIP200 KO, and ATG9A KO cells with TAX1BP1 siRNA, and then induced Parkin-mediated mitophagy with valinomycin in the presence of bafilomycin. This strategy eliminated the basal accumulation of phos-TBK1 and allowed us to focus on mitophagy-dependent TBK1 phosphorylation. Please see revised Fig 2h, I, j, and k. The results showed that mitophagy-dependent phos-TBK1 is predominantly attenuated in FIP200 KO and ATG9A KO cells. In Figs 2 and 3, we would like to emphasize that OPTN is required for TBK1 phosphorylation in response to Parkin-mediated mitophagy, whereas TAX1BP1 is required for TBK1 phosphorylation in basal autophagy. Since Reviewer 3 commented that interpretation of the data in original Figs 2d, e, and f was challenging, we elected to move those results to the supplemental figures. We have incorporated the newly acquired data (mitophagy using FIP200 KO or ATG9A KO with TAX1BP1 siRNA cells) into the main figure. We believe that this makes the text easier for readers to understand.

*- Fig 2c: conclusions on *

*the reduction of recruitment of Optn mutants on autophagosome formation seem over-interpreted as: *

*1- no labeling with LC3 has been used to identified autophagsome, *

*2- immunofluorescent signals observed with mutants are dispersed throughout the entire mitochondria network (see the merged images) rendering impossible to distinguish between autophagosome-associated mitochondria and others. *

*The following conclusive sentence stating that association of Optn to damaged mitochondria is not sufficient for TBK1 activation based solely on IF of figure 2c seems therefore unrelated to the obtained data. *

To address concerns about the recruitment of OPTN mutants to the autophagosome formation site, we performed additional experiments. Penta KO cells and those expressing OPTN WT and mutants were treated with valinomycin for 1 hr, and FLAG-tagged OPTN, endogenous WIPI2, and HAP60 (mitochondrial marker) were detected by immunostaining. We detected endogenous WIPI2 because WIPI2 is recruited only to autophagosome formation sites (Dooley et al. 2014 Mol Cell), whereas LC3B assembles on autophagosome formation sites and is also associated with completed autophagosomes. Confocal microscopy images showed that cup-shaped OPTN WT that had been recruited to damaged mitochondria colocalized with WIPI2. Quantification further showed that during mitophagy the number of WIPI2 foci seen in cells expressing OPTN WT decreased in Penta KO cells and cells expressing OPTN mutants (4LA, F178A and 4LA/F178A). These data are shown in Fig 2e and f in the revised manuscript. In addition, we quantified the number of cells that either exhibited heterogeneous or homogeneous recruitment of OPTN to damaged mitochondria after treatment with valinomycin for 1 hr. More than 80% of Penta KO cells with OPTN WT had heterogeneous OPTN recruitment, whereas this distribution was only present in 10% of cells expressing either OPTN 4LA or OPTN 4LA/F178A. Although cells expressing the OPTN F178A mutant exhibited 50% heterogeneous recruitment, this may be because the mutant can interact with ATG9A. As mentioned above, our previous mitophagy analyses (Keima-based FACS analysis, Yamano et al 2020 JCB) showed that the OPTN F178A mutant induced ~60% mitochondrial degradation (which is correlated strongly with OPTN distribution), whereas it was 80% with OPTN WT and 9% with 4LA/F178A.

*- Fig 2d: authors should explain why ATG KO cells displayed lipidated LC3B in the absence of efficient autophagy processes. *

We thank the reviewer for the suggestion. We added the following sentence to explain the function of ATG5 in LC3B lipidation. "Since LC3B lipidation is catalyzed by ATG5, but not FIP200 and ATG9A, the lipidated form disappears only in ATG5 KO cells (Hanada et al 2007 J Biol Chem). "

*- Fig 2e: despite authors statement that TBK1 phosphorylation did not increase during mitophagy in ATG KO cells, increased pS172-TBK1 is visible in FIP200 and ATG5 KO cells especially between 1 and 3 hrs of stimulation, leading to inaccurate conclusions that TBK1 phosphorylation requires the autophagy machinery. Therefore, overall assumption that both ubiquitination and autophagy subunits are required for TBK1 autophosphorylation appears erroneous. *

As the reviewer indicated, phos-TBK1 levels gradually increased in ATG KO cells. The main text was rewritten to more accurately reflect this increase. Based on experiments using the monobodies and those conducted during the revision process, it is apparent that although the autophagy machinery may not be completely essential for TBK1 phosphorylation, it clearly facilitates TBK1 phosphorylation in response to Parkin-mediated mitophagy.

*Page 12: *

*- Fig 3a: conclusion that Optn signal is more cytosolic and did not localize with Ub condensates seems speculative as based on: *

*1- only one immunofluorescence image without statistical analysis *

*2- Optn and Ub signals are lower in images with Optn is analyzed compared to other images in which NDP52, TAX1BP1 and NBR1 are detected. *

To address these concerns, we compared and quantified the signal intensities of all endogenous autophagy adaptors in FIP200 KO and ATG9A KO cells. The quantification data are shown in Fig 3a and the immunofluorescence images are shown in supplementary Fig 6a of the revised manuscript.

*- Fig 3b: interpretation of western blot data is uncertain due to lack of appropriate loading control, especially with pellets (P) extracts. In addition, it is not clear how to conclude from the experiments in Fig 3b that autophagy adaptors other than Optn mediate TBK1 phosphorylation. *

When autophagy is inhibited, p62 accumulates in the cytosol as aggregates (Komatsu et al. 2007 Cell). Therefore, p62 should be a positive control. Indeed, Fig 3b in the original manuscript (Fig 3b and c in the revised manuscript) showed that the amount of p62 in the pellet fraction was elevated in FIP200 KO and ATG9A KO cells. Furthermore, these aggregates were also observed in the imaging data (Fig 3a and supplementary Fig 7 in the revised manuscript). As the reviewer indicated, the original manuscript did not clarify whether autophagy adaptors other than OPTN mediated TBK1 phosphorylation; however, our revised results clearly demonstrate that TAX1BP1 is the adaptor responsible inducing TBK1 autophosphorylation when basal autophagy is impaired (please see Fig 3f in the revised manuscript).

*Minor point: reference is missing in the last sentence of the paragraph stating that K48-linked chains dominate when autophagy pathways are impaired. *

While several autophagy adaptors preferentially interact with K48-linked ubiquitin chains (Donaldson et al. 2003 PNAS etc), TRAF6 is recruited to ubiquitin-condensates via p62-mediated K63-linked ubiquitination (Linares et al. 2013 Mol Cell). Furthermore, K33-linked ubiquitin chains are also present in p62-positive condensates (Nibe et al. 2018 Autophagy). Because it’s not clear which ubiquitin-linkage is dominant in the condensates, we decided to delete the sentence. We regret the confusion.

*Page 13: *

*Conversely to Optn, they find that the other autophagic receptors localize in insoluble fractions (what does it mean?) independently of TBK1 expression (experiments with DKO cells) and also independently of Optn (where is this shown?). Altogether, these experiments are far from the message of the manuscript. The title of the paragraph "TBK1 activation does not require Optn under basal autophagy conditions" is not correct because even if the level of expression of autophagic receptors and TBK1 phosphorylation are increase in response to the depletion of the autophagy machinery, it does not increase autophagy. *

According to the suggestion, we changed the title of the paragraph to "TAX1BP1, but not OPTN, mediates TBK1 phosphorylation when basal autophagy is impaired." In addition, we rewrote this section.

*- Fig 3d: authors should mention the nature of the upper band observed in Optn western blot and show the same experiment in since solely TBK1 depleted cells since they stated that "electrophoretic migration of Optn was not affected by TBK1 deletion". In addition, suggesting from these sole experiments that "NP52, TAX1BP1, p62, NBR1 and AZI2 form Ub-positive condensates where TBK1 is activated" seems over-interpretated. *

Reviewer 3 suggested we characterize the upper band in the OPTN blot (Fig 3d in the original manuscript). To determine if the band is genuine OPTN, we used phostag-PAGE to analyze cell lysates from cells treated with control siRNA or OPTN siRNA and found that both the lower and upper bands were OPTN species (please see "Figure 2 for reviewers" in our response to Reviewer 3). The same pattern was reported by the Wade Harper group (Heo et al. 2015 Mol Cell). They showed that the OPTN double band pattern on phos-tag PAGE was not affected by TBK1 deletion. We have cited this literature where appropriate in the revised manuscript. In WT cells, it is difficult to detect phosphorylation of autophagy adaptors by TBK1 because basal autophagy constantly degrades them. That’s why we used autophagy KO cell lines.

*Page 14: *

*- Fig 4: TBK1 phosphorylation was analyzed in Fig4d and not in Fig4b as stated. In addition, it is rather difficult to conclude from artificial multimerization experiments, as the authors have done, that interaction between Optn and autophagy components contributes to Optn multimerization in genuine conditions. *

Detection of phos-TBK1 has been corrected to Fig 4b. Although artificial, the fluoppi assay provides insights into how OPTN activates TBK1 and how the autophagy machinery contributes to TBK1 activation via OPTN. To determine if artificial OPTN multimerization could bypass the autophagy machinery requirement, we used the fluoppi assay. This assay was important for us to conclude that the autophagy machinery and Parkin-mediated ubiquitination allow OPTN to be assembled in close proximity to where TBK1 is activated. The main text was rewritten to better convey the benefits of the fluoppi assay.

*Page 15: *

*This work could have therapeutic consequences but the pathological mutants of TBK1 used affect ALS (Figure 5) while in the discussion it is proposed that monobodies could have a therapeutic interest in familial forms of glaucoma due to the E50K mutation of Optn. It should be better to target only one pathology. *

Both TBK1 and OPTN are causative genes for ALS and many pathogenic mutations are known to impact their function. In this study, we focused on ALS mutations in TBK1 that affect self-dimerization and investigated their impact in response to Parkin-mediated mitophagy. We created the monobodies as a tool to physically inhibit OPTN assembly at the contact site. Although our monobodies inhibit Parkin-mediated mitophagy, they would not be a useful therapeutic strategy for ALS due to the loss of function with the TBK1 mutations. However, because TBK1 E50K is a glaucomatous mutation that causes OPTN-TBK1 to bind more tightly, our monobodies might be applicable to glaucomatous pathology since they could disrupt this interaction. We thus feel that it is appropriate to mention the potential of the monobodies and their future utility in the Discussion.

*- Fig 5c, d: Authors stated that degree of TBK1 autophosphorylation correlated with OPTN phosphorylation at S177 whereas phosphorylated TBK1 is unaffected by L693Q and V700Q mutants that display decreased phosphorylated Optn In addition, authors interpretation of Figure 5 data is clearly problematic as they stated that: *

*1- neither 693Q and V700Q mutants had "significant effect on mitophagy", while decreasing efficiency from 78% to 37-51% *

*2- but conclude that 49.7% mitophagy levels of R357Q mutant is significant mitochondrial degradation. *

*Overall conclusion that mitophagy efficiency is correlated with phosphorylated TBK1 levels is therefore inaccurate. *

We regret that this section did not sufficiently describe the data. Reviewer 3 also noted that the text referencing Fig 5 was difficult to interpret. One of the reasons for the complicated data interpretation is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit Parkin-mediated mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutations only moderately affected Parkin-mediated mitophagy. Furthermore, both the L693Q and V700Q mutations were engineered based on the X-ray structure, rather than being authentic pathogenic ALS mutations. To avoid any potential confusion, we decided to remove the L693Q and V700A data. We have re-evaluated the other data and have rewritten this section accordingly. Please see the revised main text.

*Discussion *

*Minor points: *

*page 20: - reference is missing in the sentence "Optn cannot oligomerize on its own on ubiquitin-decorated mitochondria". *

We have provided the appropriate reference.

*Major points: *

*Authors stated that they showed that Optn recruitment to damaged mitochondria, itself, is insufficient for TBK1 autophosphorylation, but did not show experiment of Optn recruitment to mitochondria and its consequences on TBK1 phosphorylation in the absence of mitophagy induction signal. Authors could for example target HA-Ash-6Ub to mitochondria in order to artificially recruit hAG-Optn to "ubiquitinated" mitochondria in the absence of mitophagy signal. *

We showed that the efficiency of TBK1 autophosphorylation was reduced in cells expressing the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (Fig 2c and d in the revised manuscript). Cells with FIP200 or ATG9A knockdown also have reduced phos-TBK1 (pS172) as shown in supplementary Fig 5e and f. The rate of phos-TBK1 (pS172) generation in ATG9AKO cells during Parkin-mediated mitophagy is reduced relative to that in WT cells (Fig 2j and k). Since a small amount of phos-TBK1 was generated in both ATG9A knockdown and KO cells (supplementary Fig 5e, f, Fig 2j and k), we concur that it would be premature to conclude that phosphorylation of TBK1 does not occur at all when autophagy core components are absent. A small amount of phos-TBK1 may be generated by OPTN that is freely distributed on the outer mitochondrial membrane. In the revised manuscript, we mention the possibility that TBK1 might be phosphorylated by OPTN independent of the autophagy machinery and were careful to avoid over-interpretation.

As shown in Fig 4, fusing OPTN with an Azami-Green tag can induce artificial multimerization and trigger the generation of phos-TBK1 (pS172). Therefore, we expect that mitochondria-targeted HA-Ash-6Ub would induce TBK1 phosphorylation in a hAG-OPTN-dependent manner as was observed with cytosolic HA-Ash-6Ub (Fig 4). The accumulation of OPTN at the contact site in Parkin-mediated mitophagy is important for TBK1 phosphorylation. Even if OPTN is forced to anchor to the mitochondria, this would induce isolation membrane formation and subsequent autophosphorylation of TBK1. Therefore, the utility of forcing OPTN to anchor to mitochondria is questionable.

*Similarly, experimental approaches used by authors lack dynamics parameters to conclude on formation and elongation of isolation membranes and contacts sites that could be probably obtained through video microscopy. *

Based on the reviewer’s comment, we performed time-lapse microscopy to observe OPTN recruitment to the contact site and followed its movement along with the elongation of isolation membranes during Parkin-mediated mitophagy. HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin (please see Fig 2l in the revised manuscript). Initial recruitment of GFP-OPTN near mitochondria was evident as small dot-like structures that then elongated over time to become cup-shaped structures and culminated in the formation of spherical structures. Considering the colocalization of OPTN with WIPI1/WIPI2 (markers of autophagosome formation site) in Fig 2e and supplementary Fig 2a, the time-lapse images strongly suggest that OPTN assembles at contact sites followed by elongation in tandem with isolation membranes during Parkin-mediated mitophagy.

*Finally, the model proposed by the authors does not take into account data showing that Optn basally interacts with ubiquitinated mitochondria and LC3 family members (see Wild et al., Phosphorylation of the autophagy receptor optineurin restricts Salmonella growth. Science. 2011 333:228-33), although at lower levels compared to induced conditions, relativizing the impact of the proposed model. *

According to the Reviewer 2 comment, we again read the Science paper (Wild et al. 2011) but could not find data showing that OPTN basally interacts with ubiquitinated mitochondria. At least, we think that under steady state conditions without mitophagy induction, mitochondrial ubiquitination and mitochondrial localization of OPTN are undetectable as shown in supplementary Figure 2 in our revised manuscript.

*In conclusion, this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. *

***Referees cross-commenting** *

*In my opinion, what emerges from these 3 reviews is that the results lack controls or have not been repeated enough to support the message that the interaction of Optn with ubiquitin and the ubiquitination machinery is sufficient to activate TBK1. In particular, as reviewer 1 says, the phosphorylation kinetics shown in Figure 1a are not consistent with TBK1 phosphorylation following the interaction of Optn with the ubiquitination machinery and ubiquitin. In Figure 1e, there is a decrease in TBK1 phosphorylation in contrast to WTcells as mentioned by Reviewer 1. In agreement with Reviewer 1, we believe that the WT cells are missing in Figure 1g. *

*With regard to Figure 2c, we agree with reviewer 1 that an LC3 label is missing in order to be able to interpret the data. In Figure 2e and f, we agree with reviewer 1 that it is difficult to understand why TBK1 phosphorylation increases in the absence of the autophagy machinery (FIP200 KO and ATG5KO). In Figure 3, loading controls are missing for 3b and c. The TBK1 KO cells alone are missing in Fig 2d. In Figure 2b, pTBK1 is missing. In agreement with reviewer 3, we believe that the data with fluoppi contradict the message of the manuscript since they show that TBK1 can be phosphorylated by ubiquitin in the absence of the ubiquitination machinery. In agreement with reviewer 3, we believe that the experiments in Figure 5 are very difficult to interpret. The first reviewer is right to ask the question of the replicates for figures 6c and d. *

We appreciate the summary of the reviewers’ comments. To address their concerns, we have included the appropriate controls and included the results of three independent experiments in the graphs, which now include appropriate error bars and statistical significance. Thus, we believe we have answered the most critical comments concerning the lack of controls.

In Fig 1a, phos-TBK1 was maximal following 30 min of valinomycin treatment. We confirmed using microscopy-based observations that recruitment of endogenous TBK1 and OPTN and the generation of phos-TBK1 and phos-OPTN at contact sites (marked by WIPI1) near damaged mitochondria was also maximal after 30 min of valinomycin treatment (supplementary Fig 2 and 3). Therefore, the kinetics of phos-TBK1 and phos-OPTN generation are consistent with the recruitment of OPTN-TBK1 to the contact site.

The data presented in Fig 2 clearly indicate that the autophagy components are involved in phos-TBK1 generation during Parkin-mediated mitophagy. Therefore, the claim that ubiquitination machinery is sufficient for TBK1 activation is incorrect. Although we agree that the autophagy gene deletions cannot completely inhibit TBK1 autophosphorylation, mitophagy-dependent generation of phos-TBK1 is largely impaired by ATG9A KO (Fig 2j and k). Thus, there is no doubt that isolation membrane formation is important for TBK1 activation following Parkin-mediated mitophagy.

Fig 1e - The reviewer is correct that phos-TBK1 is reduced in the NDP52 knockout. We have rewritten the main text. It is also true that NDP52 has a smaller effect on TBK1 autophosphorylation as compared to OPTN.

Fig 1g - Immunoblots using total cell lysates prepared from six different cell lines (WT, Penta KO alone, Penta KO stably expressing low or high OPTN or NDP52) under four different conditions (DMSO, valinomycin 1 hr, valinomycin 3 hrs, valinomycin + bafilomycin 3 hrs) showed that OPTN is a rate-limiting factor for TBK1 phosphorylation. Please see Fig 1g and h in the revised manuscript

Fig 2c - The recruitment of OPTN WT and associated mutants to the contact site was re-examined by immunostaining with WIPI2 labeling. We found that OPTN WT was both efficiently recruited to and formed the contact site. In contrast, the OPTN 4LA/F178A mutant was unable to interact with FIP200/LC3/ATG9A and was uniformly (i.e. homogenously) distributed on damaged mitochondria with the rate of autophagosome site formation reduced. Please see Fig 2e, f, g in the revised manuscript.

Fig 2e and f - KO of the autophagy core components FIP200 and ATG9A increased phos-TBK1 under basal, non-mitophagy-associated conditions (see Fig 3). The levels of autophagy adaptors other than OPTN also increased in FIP200 KO and ATG9A KO cells. Furthermore, as shown in Fig 3a and supplementary Fig 7, both phos-TBK1 and the autophagy adaptors accumulated in Ub-positive condensates. Based on previous reports (Mejlvang 2018 J Cell Biol), TAX1BP1, p62, and NBR1 have short half-lives and are quicky degraded by macro/micro autophagy. The accumulation of phos-TBK1 in the absence of autophagy occurs because autophagy-dependent degradation of TAX1BP1 (and other adaptors) is inhibited. This allows for the formation of Ub-positive condensates, which brings TBK1 into sufficient proximity for activation. This has been noted in the revised manuscript.

Fig 3b and 3c - We wonder if the "loading controls are missing for Fig 3b and 3c" statement might be a misinterpretation by the reviewer as TOMM20 was used as the loading control in the original Fig 3b. It was recovered in the supernatant fractions of WT, FIP200 KO, and ATG9A KO cells, indicating that the accumulation of autophagy adaptors in the pellet fractions depends on autophagy gene deletion. Similarly, actin and TOMM20 were used as loading controls in the original manuscript Fig 3c.

Fig 2d (perhaps meant to be Fig 3d) – A previous study reported that phos-tag PAGE blot of OPTN in TBK1 KO cells alone revealed no differences between WT and TBK1 KO cells (Heo et al 2015 Mol Cell). We cited this reference in the revised manuscript.

Fig 2b (perhaps meant to be Fig 4b) - Immunoblots of phos-TBK1 have been incorporated into the results of Fig 4b in the revised manuscript.

Fig 4 - We show in Fig 2 that induction of Parkin-mediated mitophagy promotes OPTN accumulation at contact sites formed by isolation membranes and ubiquitinated mitochondria, and that autophagy core subunits are required for efficient generation of phos-TBK1. Fig 3 shows that phos-TBK1 accumulates in Ub-positive condensates with TAX1BP1, rather than OPTN, and that it is responsible for phos-TBK1 accumulation. Together, these results suggest a model in which TBK1 is activated when OPTN and TBK1 are positioned near each other. We hypothesized that if we could force OPTNs into close proximity the autophagy machinery requirement for TBK1 activation might be bypassed. To assess this model, we designed the fluoppi assay shown in Fig 4. This assay was critical in that it provided an important clue for the molecular mechanism that OPTN and the autophagy machinery use to cooperatively induce TBK1 trans-autophosphorylation. Because the original manuscript may not have sufficiently conveyed our reasoning for the fluoppi analysis, we have rewritten this section. The main point of the fluoppi assay is that engineered OPTN multimerization was able to bypass the autophagy requirement for TBK1 activation.

Fig 5 - For easier interpretation, the L693Q and V700Q data, which are not related to ALS pathology, have been removed.

Fig 5d – Statistical significance has been determined for the mitophagy results and the main text has been rewritten for better clarity.

Fig 6c, d, and I – The experiments were independently replicated more than three times with statistical support and error bars incorporated into the associated graphs.

*Reviewer #2 (Significance (Required)): *

*this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. The manuscript is for a broad audience. *

For the revised manuscript, additional experiments were carefully performed with appropriate controls and the manuscript was rewritten to address concerns regarding over-interpretation. We hope that we have adequately addressed the reviewer’s comments.

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

*The authors investigated the mechanisms by which TBK1 is phosphorylated and thus activated in PINK1/Parkin-mediated mitophagy. They show data indicating that OPTN, by interacting both with ubiquitin-coated mitochondria and with the autophagy machinery, provides a platform where OPTN-bound TBK1 can be hetero-autophosphorylated by adjacent TBK1. *

*According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *

*This is an important topic. Overall, the experimental data are of high scientific quality. For the most part, the manuscript is clearly written. The figures have been made with great care. The novel insights are relevant. However, a number of issues need to be addressed or clarified. *

*Major comments: *

• Fig. 1a-b shows that pTBK1 (pS172) formation already peaks after 30 min of valinomycin. Even when bafilomycin is added, pTBK1 level already reaches a near maximum after 30 min of valinomycin. If the model proposed by the authors is correct and pTBK1 (pS172) formation requires extensive interaction of OPTN with both ubiquitin and autophagic isolation membranes, they should be able to show (by immunostaining) that OPTN already extensively forms peri-mitochondrial cup/sphere-shaped structures that colocalize with isolation membrane markers after only 30 min of valinomycin. In the present manuscript, they only show formation of such structures after 1-3 h of valinomycin.* We thank the reviewer for the critical comments. Based on the suggestion, we performed immunostaining to observe the recruitment of TBK1 and OPTN to damaged mitochondria as well as the generation of phos-TBK1 (pS172) and phos-OPTN (pS177). HeLa cells stably expressing Parkin and 3HA-WIPI1 were treated with valinomycin for 30 min, and then TBK1, OPTN, phos-TBK1, and phos-OPTN were immunostained along with 3HA-WIPI1 (a marker of the autophagosome formation site) and TOMM20 (a mitochondria marker). Please see supplementary Fig 2a and 3a in the revised manuscript. The TBK1, OPTN, phos-TBK1, and phos-OPTN signals formed dot-like, cup-shaped, and/or spherical structures, most of which were peri-mitochondrial and colocalized with 3HA-WIPI1. In separate experiments, HeLa cells stably expressing Parkin were treated with valinomycin in the presence or absence of bafilomycin for 30 min or 2 hrs and then immunostained. Please see supplementary Fig 2b in the revised manuscript. After 30 min valinomycin in the absence of bafilomycin, many TBK1 and OPTN signals were observed on damaged mitochondria. These signals were quantified from more than 160 cells for each of the four conditions. Each microscopic image generated contained 18-36 cells and corresponds to one dot in supplementary Fig 2c. Based on these results, the abundance of TBK1 and OPTN on mitochondria after 30 min of valinomycin was much higher than that after 2 hrs with valinomycin (supplementary Fig 2c). Similar results were obtained for phos-TBK1 and phos-OPTN (supplementary Fig 3b and c). These results are consistent with the immunoblot data (Fig1a and b).

Furthermore, we show that Parkin expression levels affect the amount of phos-TBK1 generated during mitophagy. Please see supplementary Fig 4 in the revised manuscript. When PARKIN was integrated into HeLa cells under a CMV promoter via an AAVS1 (Adeno-associated virus integration site 1)-locus, the resultant cell line (referred to as high-Parkin) had higher Parkin levels than HeLa cells in which PARKIN was introduced by retrovirus infection (referred to as low-Parkin). In high-Parkin HeLa cells, phos-TBK1 levels reached a maximum after 30 min with valinomycin, while in low-Parkin HeLa cells, phos-TBK1 levels were comparable after 30 min and 1 hr. High-Parkin HeLa was used for Fig 1a, b, c, and d as well as supplementary Fig 1, 2, 3 and 4. For all other Figs, PARKIN genes were introduced by retrovirus infection. This is one of the reasons why val was used for 30 min in Fig1, but 1-3 hrs for the other Figs. Because 3 hrs valinomycin treatment may be unsuitable for evaluating OPTN recruitment to mitochondria/isolation membrane contact sites, we deleted the original Fig 2c and replaced it with the val 1 hr data (Please see Fig 2e in the revised manuscript).

• The authors propose that OPTN needs to interact both with ubiquitin on mitochondria and with isolation membrane proteins such as ATG9A to allow TBK1 phosphorylation. However, their fluoppi experiments in Fig. 4 seem to contradict this. In the fluoppi experiments, the authors generate multimeric OPTN-Ub foci and this is apparently sufficient to induced TBK1 phosphorylation at S172 (shown in 4d,f). In this experiment, there is no induction of autophagy or formation of isolation membranes, and TBK1 nevertheless gets activated.*

Figure 2 demonstrates that both ubiquitin on mitochondria and formation of the isolation membranes are needed to provide a platform for OPTN to assemble in close proximity to each other and subsequently induce TBK1 autophosphorylation. To determine if OPTN proximity is sufficient for TBK1 autophosphorylation (i.e., if engineered OPTN multimerization can bypass the autophagy machinery requirement for TBK1 autophosphorylation), we used the fluoppi assay. The results clearly showed that engineered OPTN multimerization induced TBK1 autophosphorylation without the need for the autophagy machinery. Although this is not a mitophagy experiment, the fluoppi assay provided crucial insights into the molecular mechanism underlying OPTN-mediated TBK1 autophosphorylation. The main text was rewritten to provide more clarity regarding the purpose of the fluoppi experiments.

• Can the authors be more concrete/specific in the discussion about the molecular mechanisms that explain why this 'platform' that is created by OPTN-autophagy machinery interactions is so crucial for TBK1 activation? If I understand the model in Fig. 7D correctly, the OPTN-autophagy machinery interactions are mainly important because they reduce the distance between OPTN-bound TBK1 molecules so that they can trans-phosphorylate each other. But if TBK1 autophosphorylation was just a matter of proximity between OPTN-bound TBK1 molecules, interaction of OPTN with densely ubiquitinated mitochondria should already be sufficient for TBK1 phosphorylation. When multiple OPTN molecule bind to one ubiquitin chain or to closely adjacent ubiquitin chains (similar to the fluoppi experiments), TBK1 molecules binding to OPTN would be expected to be already closely enough to one another for trans-autophosphorylation.*

The amount of phos-TBK1 during Parkin-mediated mitophagy was reduced in cells with the OPTN 4LA/F178A mutant, which cannot interact with the autophagy machinery (e.g. FIP200, ATG9A, and LC3) but can be targeted to mitochondria (see Fig 2c, d). ATG9AKO cells also had reduced amounts of phos-TBK1 relative to WT cells (See Fig 2j, k). Therefore, rather than OPTN-ubiquitin freely diffusing laterally on the outer membrane, we suggest that the contact site OPTN forms with ubiquitin and the autophagy machinery provides a more suitable platform for TBK1 autophosphorylation because it maintains TBK1 in a proximal position for a longer period of time.

The OPTN UBAN domain binds a ubiquitin-chain composed of two ubiquitin molecules (Oikawa et al. 2016 Nat Comm), and during Parkin-mediated mitophagy only shorter length poly-ubiquitin chains are generated on the mitochondrial surface (Swatek et al. 2019 Nature). Based on those findings, it is unlikely that multiple OPTN bind to one ubiquitin chain. Of course, we cannot rule out the possibility that TBK1 autophosphorylation does not occur on mitochondria in the absence of autophagy components. While full activation of TBK1 requires OPTN to associate with the isolation membrane, initial TBK autophosphorylation at the onset of mitophagy may occur based only on the OPTN-ubiquitin interaction. These explanations have been added to the Discussion in the revised manuscript.

Furthermore, based on comments from Reviewer 2, we performed time-lapse microscopy to observe OPTN dynamics during Parkin-mediated mitophagy (please see Fig 2l). HeLa cells stably expressing GFP-OPTN and pSu9-mCherry (a mitochondrial marker) were treated with valinomycin. GFP-OPTN was initially a peri- mitochondrial dot-like structure that elongated over time to a cup-shaped structure and which eventually ended up forming a spherical structure. The time-laps imaging showed that, at least in WT cells, OPTN is directly recruited to the contact sites and elongates along with the isolation membranes. We thus concluded that TBK1 is activated (autophosphorylated) at the contact site rather than on the outer membrane where OPTN-TBK can move freely.

• Fig. 5c,d and P. 16: the mitophagy experiments in TBK1-/- cells expressing the different mutant forms of TBK1 are hard to interpret because it is not clear which mitophagy differences are statistically significant. The main text about this part (p. 16) is also confusing.*

We regret the confusion. Reviewer 2 also noted that the main text for Fig 5 was difficult to interpret. One of the reasons that complicated interpretation of the data is the number of TBK1 mutants used. The L693Q and V700Q mutations used by Li et al. (2016 Nat Commun) were expected to inhibit mitophagy since those authors reported that the mutations prevented interactions with OPTN. However, our in-cell assay showed that the two mutants only moderately affected Parkin-mediated mitophagy. Furthermore, both L693Q and V700Q were engineered based on the X-ray structure and are not ALS pathogenic mutations. To simplify the data and to make data interpretation easier, we decided to delete the L693Q and V700A data. We also determined statistical significance and rewrote this section.

• Many graphs lack statistics: Fig. 2b (pTBK1), Fig. 2f, Fig. 5b, Fig. 5d, Fig. 6c.*

We apologize for the lack of statistical analyses. We repeated experiments (if the experiments had not been independently performed more than three times) with statistical significance and error bars incorporated into the relevant figures.

*Other comments: *

• Fig. 1a: how do they know that the upper OPTN band is ubiquitinated OPTN? Reviewer 2 raised the same question. To demonstrate that the upper OPTN band is ubiquitinated, cell lysates after mitophagy induction were incubated in vitro* with a recombinant USP2 core domain, and the samples immunoblotted. As shown in supplementary Fig 1 in the revised manuscript, the upper OPTN band disappeared in a USP2-dependent manner. The upper NDP52 and TOMM20 bands similarly disappeared. Therefore, the upper OPTN, NDP52 and TOMM20 bands observed after mitophagy induction are ubiquitinated.

• Fig. 1a,b: the bafilomycin stabilization of pTBK1, OPTN and pOPTN indicates that these proteins are substantially degraded by autophagy within 30-60 minutes. This seems extremely fast for mitophagy completion. Please discuss.*

According to Kulak et al. (2014 Nat Methods), autophagy adaptor abundance (OPTN: 2.32E+4 and NDP52: 3.34E+4 in HeLa cell line) is low compared to that of mitochondria (TOMM20: 1.45E+6 in HeLa cell line). This is one of the reasons why autophagic degradation of adaptors is easier to see. Degradation of phos-TBK1 was likewise easy to detect, whereas total TBK1 was not. This discrepancy is likely based on differences in the abundance of phos-TBK1 and total TBK1. In addition, because autophagy adaptors are localized outside of the mitochondrial membrane they may be easier targets for lysosomal degradation than matrix proteins, which are localized inside the outer and inner membranes.

• Fig. 1a and rest of the manuscript: is there a reason why the authors only looked at S177 phosphorylation of OPTN and not also at OPTN S473, which is also phosphorylated by TBK1?*

Both mass spectrometry and mutational analyses indicated that OPTN S473 is phosphorylated during Parkin-mediated mitophagy and that OPTN phosphorylated at S473 strongly binds ubiquitin chains (Richter et al. 2016 PNAS and Heo et al. 2015 Mol Cell). However, because a phos-S473 OPTN antibody is, to the best of our knowledge, currently not commercially available, we did not focus on S473 phosphorylation.

• Fig. 1e-f: the main text states that "NDP52 KO effects on the pS172 signal were comparable to controls", but the blot in 1e and the graph in 1f indicate a difference between NDP52KO and WT (significant difference shown in 1f). This is confusing.*

We regret the over-interpretation. As the reviewer indicated, the amount of phos-TBK generated in response to mitophagy was reduced in NDP52 KO cells relative to that in WT cells. This has been corrected. We would like to emphasize that, unlike OPTNdeletion, NDP52 deletion has relatively minor effects on TBK1 phosphorylation.

• P. 9: "TBK1 phosphorylation however was not apparent in the OPTN mutant lines, even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation (Fig. 2a)". However, the pTBK1 blot in Fig. 1a does show pTBK1 formation in the OPTN mutant (4LA etc.) lines. This is confusing.*

We apologize for this error. We intended to state “TBK1 phosphorylation was not apparent in the Penta KO cells without OPTN expression even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation”. This sentence has been corrected in the revised manuscript.

• P. 10: "we subtracted the basal phosphorylation signal from that generated post-valinomycin (1 hr) and bafilomycin (3 hr)". Do they mean "from that generated post-valinomycin (3 hr) and bafilomycin (3 hr)?*

The reviewer is correct, we have corrected the error.

• P. 10, same paragraph: "the phosphorylation signal was ~90 but was less than 30 in ATG9A KO cells." Unclear what they mean by 90 and 30. 90% and 30%? 90-fold and 30-fold?*

The newly generated pTBK1 levels following Parkin-mediated mitophagy were calculated as pTBK1 [val & baf 3 hrs] minus pTBK1 [DMSO]. Since pTBK1 [val & baf 3 hrs] in WT cells is set to 100%, the newly generated pTBK1 in WT cells was 100% - 5% = 95%. The calculated values for pTBK1 [DMSO] and pTBK1 [val & baf 3 hrs] in ATG9A KO cells were ~55% and ~85%, respectively. Consequently, newly generated pTBK1 in the ATG9A KO cells is ~85% - ~55% = 30%. For clarity, we modified the figure to make the meaning of the numbers more apparent.

• Fig. 3a: Do they have an idea what kind of ubiquitinated substrates are contained in the ubiquitin-positive condensates that accumulate in FIP200 KO and ATG9A KO cells (i.e. without valinomycin treatment)?*

According to Kishi-Itakura et al. (2014 J Cell Sci), ferritin accumulates in the p62 condensates in FIP200 KO and ATG9A KO cells. However, it is unknown if the ferritin in the condensates is ubiquitinated. In the original manuscript, we confirmed by immunostaining that the p62-NBR1 condensates contain ferritin (Fig 3a in the original manuscript and supplementary Fig 7b in the revised manuscript).

• P. 12 and Fig. 3a: please explain why they look at ferritin, to improve readability.*

We thank the reviewer for the suggestion. As mentioned, ferritin is a known substrate that accumulates in p62 condensates, we thus sought to confirm its presence. We have included this explanation in the revised manuscript.

• Fig. 3a: please also include Ub stain for NBR1.*

We thank the reviewer for the suggestion. We obtained a rabbit anti-NBR1 antibody that allowed us to co-immunostain with the mouse anti-ubiquitin antibody (please see supplementary Fig 7b in the revised manuscript).

• Fig. 3d: the OPTN blot shows 2 OPTN bands. What does the upper OPTN band represent here?*

To determine if the two bands are genuine OPTN, total cell lysates prepared from HeLa cells treated with control siRNA or OPTN siRNA were subjected to phos-tag PAGE followed by immunoblotting with an anti-OPTN antibody. As shown below (Figure 2 for reviewers), the two bands (indicated as blue arrowheads) were absent in the OPTN knock down cells, indicating that both are derived from OPTN. Since phosphorylated species migrate slower in phos-tag PAGE, the upper band might be a phosphorylated form. The specific Ser/Thr phosphorylated in OPTN, however, remains to be determined. Heo et al. (2015 Mol Cell) also reported the two OPTN bands on phos-tag PAGE and that both were unchanged in TBK1 KO cells, suggesting that at least the upper band is not affected by TBK1.

• P. 14 and Fig. 4b: "Here, we found that phosphorylation of ... TBK1 (S172) was induced by the OPTN-ub fluoppi (Fig. 4b)." However, Fig 4b does not show a pTBK1 blot.*

We immunoblotted phos-TBK1. Please see Fig 4b in the revised manuscript.

*Reviewer #3 (Significance (Required)): *

*The novel insights are relevant. *

*According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated. *

Based on our time-lapse microscopy observations (Fig 2l), OPTN recruited to the vicinity of mitochondria was visible as a small dot-like structures that likely correspond to contact sites between mitochondria and the isolation membrane since OPTN colocalizes with WIPI1 (please see supplementary Fig 2). These results support our proposed model that OPTN interacts with both isolation membranes and ubiquitin at the onset of mitophagy. Without TBK1 activation, OPTN can interact with ATG9A vesicles, a seed for isolation membrane formation (Yamano et al 2020 JCB), and TBK1 can interact with the PI3K complex (Nguyen et al 2023 Mol Cell). Therefore, OPTN-TBK1 can be recruited to the contact site from the very beginning of mitophagy induction prior to TBK1 being fully activated. Furthermore, the proposed model also includes an OPTN-TBK1 positive feedback loop; however, the earliest reactions in the positive feedback loop are too difficult to observe. For example, it’s widely known that PINK1 and Parkin form a positive feedback loop to generate ubiquitin-chains on damaged mitochondria, but the initial reaction has yet to be observed. It remains unclear if PINK1 is the first to phosphorylate mitochondrial ubiquitin (if this is the case, it remains unknown how ubiquitin comes to mitochondria) or if cytosolic Parkin first adds ubiquitin to the outer membrane albeit with very weak activity. Similarly, in our proposed model, we cannot determine the earliest OPTN-TBK1 reaction. As described in the Discussion in the revised manuscript, it remains possible that in the absence of autophagy machinery OPTN distributed freely on the outer membrane can induce trans-autophosphorylation, albeit weakly, as the earliest reaction.

We would like to thank Reviewer 3 for the critical comments and suggestions. We have performed several of the suggested experiments, added new data, and rewritten the text. We hope that these changes have sufficiently addressed the reviewer’s concerns.

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Referee #3

Evidence, reproducibility and clarity

The authors investigated the mechanisms by which TBK1 is phosphorylated and thus activated in PINK1/Parkin-mediated mitophagy. They show data indicating that OPTN, by interacting both with ubiquitin-coated mitochondria and with the autophagy machinery, provides a platform where OPTN-bound TBK1 can be hetero-autophosphorylated by adjacent TBK1.

According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated.

This is an important topic. Overall, the experimental data are of high scientific quality. For the most part, the manuscript is clearly written. The figures have been made with great care. The novel insights are relevant. However, a number of issues need to be addressed or clarified.

Major comments:

1. Fig. 1a-b shows that pTBK1 (pS172) formation already peaks after 30 min of valinomycin. Even when bafilomycin is added, pTBK1 level already reaches a near maximum after 30 min of valinomycin. If the model proposed by the authors is correct and pTBK1 (pS172) formation requires extensive interaction of OPTN with both ubiquitin and autophagic isolation membranes, they should be able to show (by immunostaining) that OPTN already extensively forms peri-mitochondrial cup/sphere-shaped structures that colocalize with isolation membrane markers after only 30 min of valinomycin. In the present manuscript, they only show formation of such structures after 1-3 h of valinomycin.
2. The authors propose that OPTN needs to interact both with ubiquitin on mitochondria and with isolation membrane proteins such as ATG9A to allow TBK1 phosphorylation. However, their fluoppi experiments in Fig. 4 seem to contradict this. In the fluoppi experiments, the authors generate multimeric OPTN-Ub foci and this is apparently sufficient to induced TBK1 phosphorylation at S172 (shown in 4d,f). In this experiment, there is no induction of autophagy or formation of isolation membranes, and TBK1 nevertheless gets activated.
3. Can the authors be more concrete/specific in the discussion about the molecular mechanisms that explain why this 'platform' that is created by OPTN-autophagy machinery interactions is so crucial for TBK1 activation? If I understand the model in Fig. 7D correctly, the OPTN-autophagy machinery interactions are mainly important because they reduce the distance between OPTN-bound TBK1 molecules so that they can trans-phosphorylate each other. But if TBK1 autophosphorylation was just a matter of proximity between OPTN-bound TBK1 molecules, interaction of OPTN with densely ubiquitinated mitochondria should already be sufficient for TBK1 phosphorylation. When multiple OPTN molecule bind to one ubiquitin chain or to closely adjacent ubiquitin chains (similar to the fluoppi experiments), TBK1 molecules binding to OPTN would be expected to be already closely enough to one another for trans-autophosphorylation.
4. Fig. 5c,d and P. 16: the mitophagy experiments in TBK1-/- cells expressing the different mutant forms of TBK1 are hard to interpret because it is not clear which mitophagy differences are statistically significant. The main text about this part (p. 16) is also confusing.
5. Many graphs lack statistics: Fig. 2b (pTBK1), Fig. 2f, Fig. 5b, Fig. 5d, Fig. 6c.

Other comments:

1. Fig. 1a: how do they know that the upper OPTN band is ubiquitinated OPTN?
2. Fig. 1a,b: the bafilomycin stabilization of pTBK1, OPTN and pOPTN indicates that these proteins are substantially degraded by autophagy within 30-60 minutes. This seems extremely fast for mitophagy completion. Please discuss.
3. Fig. 1a and rest of the manuscript: is there a reason why the authors only looked at S177 phosphorylation of OPTN and not also at OPTN S473, which is also phosphorylated by TBK1?
4. Fig. 1e-f: the main text states that "NDP52 KO effects on the pS172 signal were comparable to controls", but the blot in 1e and the graph in 1f indicate a difference between NDP52KO and WT (significant difference shown in 1f). This is confusing.
5. P. 9: "TBK1 phosphorylation however was not apparent in the OPTN mutant lines, even after 3 hrs with valinomycin, indicating that autophagy adaptors are essential for TBK1 activation (Fig. 2a)". However, the pTBK1 blot in Fig. 1a does show pTBK1 formation in the OPTN mutant (4LA etc.) lines. This is confusing.
6. P. 10: "we subtracted the basal phosphorylation signal from that generated post-valinomycin (1 hr) and bafilomycin (3 hr)". Do they mean "from that generated post-valinomycin (3 hr) and bafilomycin (3 hr)?
7. P. 10, same paragraph: "the phosphorylation signal was ~90 but was less than 30 in ATG9A KO cells." Unclear what they mean by 90 and 30. 90% and 30%? 90-fold and 30-fold?
8. Fig. 3a: Do they have an idea what kind of ubiquitinated substrates are contained in the ubiquitin-positive condensates that accumulate in FIP200 KO and ATG9A KO cells (i.e. without valinomycin treatment)?
9. P. 12 and Fig. 3a: please explain why they look at ferritin, to improve readability.
10. Fig. 3a: please also include Ub stain for NBR1.
11. Fig. 3d: the OPTN blot shows 2 OPTN bands. What does the upper OPTN band represent here?
12. P. 14 and Fig. 4b: "Here, we found that phosphorylation of ... TBK1 (S172) was induced by the OPTN-ub fluoppi (Fig. 4b)." However, Fig 4b does not show a pTBK1 blot.

Significance

The novel insights are relevant.

According to the prevailing model (prior to this manuscript), TBK1 activation via autophosphorylation leads to TBK1-mediated phosphorylation of OPTN S177 and subsequent pOPTN-mediated recruitment of autophagic isolation membranes to the mitochondria. However, based on the model provided in this manuscript, OPTN needs to interact first with both autophagic membranes and ubiquitin before TBK1 can become activated.

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Referee #2

Evidence, reproducibility and clarity

Summary

In this manuscript, Yamano and colleagues show that as for Sting-mediated TBK1 activation, Optn provides a platform for TBK1 activation by autophosphorylation and that TBK1 is activated after the interaction of Optn with the autophagy machinery and ubiquitin and not before. They show that TBK1 phosphorylation is blocked by bafilomycine A1, an inhibitor of vacuolar ATPases that blocks the late phase of autophagy. Furthermore, they demonstrate that Optn is require for TBK1 phosphorylation since variation of Optn expression regulates TBK1 phosphorylation in response to PINK/Parkin-mediated autophagy. Interestingly, using immunofluorescence microscopy, they show that Optn forms sphere like structures at the surface of damage mitochondria which are more dispersed in the absence of TBK1. In addition, TBK1 is also recruited at the surface of damage mitochondria and as Optn and NDP52 (but not p62) colocalize with LC3B in response to PINK/Parkin-mediated mitophagy. Next, it is demonstrated that the Leucin zipper and LIR domains of Optn (which modulate Optn interaction with autophagosome) play an important role for TBK1 activation. Additionally, the autophagy core is shown to be required for TBK1 activation. Under basal conditions, depletion of the autophagosome machinery leads to an increase in autophagy receptors (except Optn) and TBK1 phosphorylation which colocalize with ubiquitin in insoluble moieties. In contrast, Optn remains cytosolic and is dispensable for TBK1 activation in these conditions. Then, using the fluoppi technic, the authors demonstrate that the generation of Optn-Ubiquitin condensates recruits and activates TBK1. They express in HCT116 TBK1-deficient cells engineered or pathological ALS mutations of TBK1 that affect ubiquitin interaction, structure, dimerization and kinase activity of TBK1. The expression level of TBK1 was only affected by the dimerization-deficient mutations. None of the mutations impaired Optn and TBK1 ubiquitination. Interestingly, some ALS-associated mutations affect TBK1 activity and it is said in the text that the dimerization-deficient mutations of TBK1 affect its activity proportionally to their level of expression, which is not really correct (the expression level of the mutants is very heterogenous and not always correlate to their activity). Regarding their effect on mitophagy, the authors claim that the phosphorylation of TBK1 correlate with mitophagy which is not really the case. By using TBK1 inhibitor or TBK1-depleted cells, the authors conclude that TBK1 is the only kinase phosphorylating Optn. However, BX-795 is not completely specific to TBK1. Finally, the authors use monobodies against Optn effective in inhibiting mitophagy in NDP52 KO cells. Some of the monobodies have been shown to form a ternary complex with Optn and TBK1, while others compete for the interaction between Optn and TBK1 which involves the amino-terminal region of Optn and the C-terminal region of TBK1. Monobodies that compete for the interaction of Optn with TBK1 could alter the cellular distribution of Optn and inactivate TBK1, but they do not alter the ubiquitination of Optn. Finally, these monobodies inhibit 50% of mitophagy.

Major and minor points:

Introduction

The first paragraph of the Introduction section is confused and difficult to read. First and second paragraphs (page 3 and top of page 4) are dedicated to macroautophagy processes but ended with one sentence on Parkin-mediated autophagy without further introduction, while all processes regarding mitophagy are detailed in the next paragraph.

Links between ideas developed are also somewhat missing. For example, in page 6, the three last sequences detailed the phosphorylation of autophagosome component, the fact that Optn and TBK1 genes are involved in neurodegenerative diseases and autophosphorylation of TBK1 as a pre-requirement for TBK1 activation without evident links between them, except "interestingly".

Results

Major points:

1. Results are often over-interpreted regarding data obtained leading to inadequate conclusions (see below for details);
2. Quantification of protein levels detected by western blot are provided as "relative intensities" without referring to specific loading control or to total protein when -phosphorylated forms are quantified (Fig. 1b, 1d, 1f, 1i, 2b, 2f, 2i, 5b, 7b, supplemental figures 2b).
3. In western blotting experiments, authors described slower migrating bands as "ubiquitinated" forms of detected proteins, but never provided experimental evidences that it could be the case. Use of non-specific deubiquitinase incubation of extracts prior to western blot could help to correctly identified ubiquitination versus other post-translational modifications such as phosphorylation, glycosylation, acetylation etc...
4. Conclusions from data obtained by immunofluorescent imaging are often drawn from only one image presented without further statistical analysis.

Page 7:

• authors referred to TBK1 phosphorylation induced by mitophagy induction as "TBK1 phosphorylation induced by Parkin-mediated ubiquitination" while mitophagy can be induced independently of Parkin (ex: via mitochondrial receptors) and without any evidence (according to referee's knowledge) of a link between ubiquitination by Parkin and TBK1 phosphorylation.

Fig 1g: Western blots performed in Penta KO cells without exogene expression of any autophagy receptors should be provided as control. Furthermore, lower expression of NDP52 relative to that of Optn (using flag antibodies) should be discussed as it can explained the differential levels in TBK1 phosphorylation observed.

Page 8:

Supplemental Fig 1a:

• The inability of authors to observe TBK1 endogenous signal in HeLa cells using commercially available antibodies is surprising as many publications reported successful staining (see Figure 1 of Suzuki et al. 2013 Cell type-specific subcellular localization of phospho-TBK1 in response to cytoplasmic viral DNA. PLoS One. 8:e83639 among others) as well as commercial promotion (see Anti-NAK/TBK1 antibody from Abcam reference: ab235253).
• Conclusions of the localization of signal on mitochondria (dispersed, in the periphery or at contact sites) are clearly over-interpreted in the absence of other membrane or autophagosome specific labeling and statistical colocalization analyses of multiple images. It is particularly difficult to assess any difference between Tax1BP1, p62 and NBR1 localization on mitochondria subdomains.

Page 9:

• First part of results ended without any conclusions.
• The observation that "TBK1 phosphorylation was not apparent in the Optn mutant cell lines, even after 3 hrs of valinomycin, ..." is inconsistent with detection of bands with anti-pS172-TBK1 antibodies in Fig 2a detected at 1hr (with F178A) and 3 hrs (4LA, F178A, and 4LA/F178A mutants) of treatment.
• Similarly, decreased levels of phosphorylated TBK1 stated for F178A mutant was only observed at 1 but not 3hrs or at 3hrs in the presence of bafilomycin.

Page 10:

The results and their repartition between figure 2 d, e, f, g, h, I and figure 3 is a bit confusing. In these experiments, it is shown Figure 2 that the absence or depletion of the autophagy machinery increase the phosphorylation of TBK1 and in Figure 3 it is shown that not only the phosphorylation of TBK1 accumulate but also the expression of NDP52, Tax1BP1 and p62. Is it because their degradation by autophagy is blocked (like for phosphoTBK1)?

Fig 2c: conclusions on the reduction of recruitment of Optn mutants on autophagosome formation seem over-interpreted as:

1- no labeling with LC3 has been used to identified autophagsome,

2- immunofluorescent signals observed with mutants are dispersed throughout the entire mitochondria network (see the merged images) rendering impossible to distinguish between autophagosome-associated mitochondria and others.

The following conclusive sentence stating that association of Optn to damaged mitochondria is not sufficient for TBK1 activation based solely on IF of figure 2c seems therefore unrelated to the obtained data.

Fig 2d: authors should explain why ATG KO cells displayed lipidated LC3B in the absence of efficient autophagy processes.

Fig 2e: despite authors statement that TBK1 phosphorylation did not increase during mitophagy in ATG KO cells, increased pS172-TBK1 is visible in FIP200 and ATG5 KO cells especially between 1 and 3 hrs of stimulation, leading to inaccurate conclusions that TBK1 phosphorylation requires the autophagy machinery. Therefore, overall assumption that both ubiquitination and autophagy subunits are required for TBK1 autophosphorylation appears erroneous.

Page 12:

• Fig 3a: conclusion that Optn signal is more cytosolic and did not localize with Ub condensates seems speculative as based on:

1- only one immunofluorescence image without statistical analysis

2- Optn and Ub signals are lower in images with Optn is analyzed compared to other images in which NDP52, TAX1BP1 and NBR1 are detected.

Fig 3b: interpretation of western blot data is uncertain due to lack of appropriate loading control, especially with pellets (P) extracts. In addition, it is not clear how to conclude from the experiments in Fig 3b that autophagy adaptors other than Optn mediate TBK1 phosphorylation. Minor point: reference is missing in the last sentence of the paragraph stating that K48-linked chains dominate when autophagy pathways are impaired.

Page 13:

Conversaly to Optn, they find that the other autophagic receptors localize in insoluble fractions (what does it mean?) independently of TBK1 expression (experiments with DKO cells) and also independently of Optn (where is this shown?). Altogether, these experiments are far from the message of the manuscript. The title of the paragraph "TBK1 activation does not require Optn under basal autophagy conditions" is not correct because even if the level of expression of autophagic receptors and TBK1 phosphorylation are increase in response to the depletion of the autophagy machinery, it does not increase autophagy.

Fig 3d: authors should mention the nature of the upper band observed in Optn western blot and show the same experiment in since solely TBK1 depleted cells since they stated that "electrophoretic migration of Optn was not affected by TBK1 deletion". In addition, suggesting from these sole experiments that "NP52, TAX1BP1, p62, NBR1 and AZI2 form Ub-positive condensates where TBK1 is activated" seems over-interpretated.

Page 14:

• Fig 4: TBK1 phosphorylation was analyzed in Fig4d and not in Fig4b as stated. In addition, it is rather difficult to conclude from artificial multimerization experiments, as the authors have done, that interaction between Optn and autophagy components contributes to Optn multimerization in genuine conditions.

Page 15:

This work could have therapeutic consequences but the pathological mutants of TBK1 used affect ALS (Figure 5) while in the discussion it is proposed that monobodies could have a therapeutic interest in familial forms of glaucoma due to the E50K mutation of Optn. It should be better to target only one pathology.

Fig 5c, d: Authors stated that degree of TBK1 autophosphorylation correlated with OPTN phosphorylation at S177 whereas phosphorylated TBK1 is unaffected by L693Q and V700Q mutants that display decreased phosphorylated Optn In addition, authors interpretation of Figure 5 data is clearly problematic as they stated that:

1- neither 693Q and V700Q mutants had "significant effect on mitophagy", while decreasing efficiency from 78% to 37-51%

2- but conclude that 49.7% mitophagy levels of R357Q mutant is significant mitochondrial degradation. Overall conclusion that mitophagy efficiency is correlated with phosphorylated TBK1 levels is therefore inaccurate.

Discussion

Minor points:

page 20: - reference is missing in the sentence "Optn cannot oligomerize on its own on ubiquitin-decorated mitochondria".

Major points:

Authors stated that they showed that Optn recruitment to damaged mitochondria, itself, is insufficient for TBK1 autophosphorylation, but did not show experiment of Optn recruitment to mitochondria and its consequences on TBK1 phosphorylation in the absence of mitophagy induction signal. Authors could for example target HA-Ash-6Ub to mitochondria in order to artificially recruit hAG-Optn to "ubiquitinated" mitochondria in the absence of mitophagy signal.

Similarly, experimental approaches used by authors lack dynamics parameters to conclude on formation and elongation of isolation membranes and contacts sites that could be probably obtained through video microscopy.

Finally, the model proposed by the authors does not take into account data showing that Optn basally interacts with ubiquitinated mitochondria and LC3 family members (see Wild et al., Phosphorylation of the autophagy receptor optineurin restricts Salmonella growth. Science. 2011 333:228-33), although at lower levels compared to induced conditions, relativizing the impact of the proposed model.

In conclusion, this manuscript represents a lot of work but the experiments often lack controls and are over-interpretated.

Referees cross-commenting

In my opinion, what emerges from these 3 reviews is that the results lack controls or have not been repeated enough to support the message that the interaction of Optn with ubiquitin and the ubiquitination machinery is sufficient to activate TBK1. In particular, as reviewer 1 says, the phosphorylation kinetics shown in Figure 1a are not consistent with TBK1 phosphorylation following the interaction of Optn with the ubiquitination machinery and ubiquitin. In Figure 1e, there is a decrease in TBK1 phosphorylation in contrast to WTcells as mentioned by Reviewer 1. In agreement with Reviewer 1, we believe that the WT cells are missing in Figure 1g. With regard to Figure 2c, we agree with reviewer 1 that an LC3 label is missing in order to be able to interpret the data. In Figure 2e and f, we agree with reviewer 1 that it is difficult to understand why TBK1 phosphorylation increases in the absence of the autophagy machinery (FIP200 KO and ATG5KO). In Figure 3, loading controls are missing for 3b and c. The TBK1 KO cells alone are missing in Fig 2d. In Figure 2b, pTBK1 is missing. In agreement with reviewer 3, we believe that the data with fluoppi contradict the message of the manuscript since they show that TBK1 can be phosphorylated by ubiquitin in the absence of the ubiquitination machinery. In agreement with reviewer 3, we believe that the experiments in Figure 5 are very difficult to interpret. The first reviewer is right to ask the question of the replicates for figures 6c and d.

Significance

This manuscript represents a lot of work but the experiments often lack controls and are over-interpretated. The manuscript is for a broad audience.

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Referee #1

Evidence, reproducibility and clarity

In their study, Yamanato et al. dissect the mechanism of TBK1 activation and downstream effects, especially in its relation to mitophagy adaptor OPTN. The authors find that OPTN's interaction with ubiquitin and the autophagy machinery, forming contact sites between mitochondria and autophagic membranes, results in TBK1 accumulation and subsequent autophosphorylation. Based on these findings, the authors propose a self-propagating feedback loop wherein OPTN phosphorylation by TBK1 promotes recruitment and accumulation of OPTN to damaged mitochondria and specifically the autophagosome formation site. This formation site is then involved in TBK1 autophosphorylation, and the activated TBK1 can then further phosphorylate other pairs of OPTN and TBK1. A OPTN monobody investigation strengthens their findings.

Critique:

• It would be helpful if the authors could more clearly highlight the previous findings in OPTN-TBK1 relationship and which gaps in the understanding their study addresses.
• It is not always clear whether experiments have been replicated sufficiently; this should be indicated in the figure descriptions.
• During the discussion, references to the figures that indicate conclusions should be added where appropriate.

Figure 1 / Result "OPTN is required for TBK1 phosphorylation and subsequent autophagic Degradation":

• In a) the TBK1 and TOMM20 blots feature an image artefact that makes it appear like the blots are stitched together or there was a problem with the digital imager. The quantification in b) seems to be missing replications.
• g) should feature the wt cell line on the same blot for better comparability as well as quantification and replication like done in f)
• h) is missing the blots for controls actin and TOMM20
• In the text to e/f), the authors write that NDP52 KO effect on pS172 are comparable to controls, though the quantitation in f) indicates that pS172 signal is indeed significantly reduced compared to wt
• In the text to h/i), the authors write "there was a significant increase in the TBK1 pS172 signal in cells overexpressing OPTN", though the quantification in i) does not indicate significance levels

Figure 2 / Result "OPTN association with the autophagy machinery is required for TBK1 activation":

• In b), pTBK1 at val 1 hr only features one dot/experiment per cell line
• In the text to c), the authors claim that the mutants reduce/abolish the recruitment of OPTN to the autophagosome site. A costain for LC3, as done for SupFig 1b, would be necessary to support that specific claim.
• d) and g) as simple confirmations of KO/KD efficiency might be better suited for the supplemental part, or blots for FIP/ATG be included with the blots in e) and h)
• In the text to e), the authors claim that the levels of pS172 in the KO cell lines did not increase during mitophagy, though the blot and quantification in f) seem to indicate an increase. The results therefore don't seem to align completely with the claims that pS172 generation in response to mitophagy requires the autophagy machinery, or that FIP200 and ATG9A rather than ATG5 are critical for TBK1 phosphorylation.
• f) is missing significance indications. Its description has a typo: "bad" instead of "baf"

Figure 3 / Result "TBK1 activation does not require OPTN under basal autophagy conditions":

• In the text to SupFig2, the authors claim that pS172 levels are significantly elevated, but no significance levels are indicated
• In the text to a), NBR1 is claimed to colocalize with Ub, but no costaining with Ub is shown. The claimed lacking colocalization of OPTN with Ub is not obvious from the images; a quantification might be appropriate.
• In the text to b), the authors make reference to significant changes, but replication/quantification/significance testing is missing.

Figure 4b) is missing the pTBK1 data that is referenced in the text.

In the text to figure 5 c/d), the authors claim that certain mutants have no significant effect on mitophagy, though d) is missing significance testing

Figure 6 c/d/i) appear to be missing replication.

Significance

Removal of damaged mitochondria by the mitophagy pathway provides an important safeguarding mechanism for cells. The Pink1/Parkin mechanism linked to numerous modulators and adaptor proteins ensures an efficient targeting of damaged mitochondria to the phagophore. The Ser/Thr kinase TBK1, in addition of multiple roles in innate immunity, is a major mitophagy regulator as has been revealed by the Dikic and Youle groups in 2016 (Richter et al., PNAS). The mechanistic insights provided by this manuscript add to a growing body of studies of how the autophagy machinery interconnects with cellular signalling networks. Although parts of the results need to be further validated, the data shown is of high quality, revealing an important conceptual advance. The paper is interesting and of general relevance beyond the signalling and autophagy community.

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Reply to the reviewers

We thank the reviewers for their positive comments on our manuscript with the reviewer’s cross-commenting:

‘It is a scholarly work that makes a significant contribution detailing how network entropy and curvature relate in the context of biological networks.’

While reviewer #2 further comments:

‘This is an important paper…it will be of interest to a wide range of scientists that are involved in stem cell differentiation and biological networks.’

We also thank both reviewers for their constructive and insightful comments. In what follows we present a point-by-point response to the reviewer comments.

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

Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated. In the case of cell differentiation networks, a positive correlation is likely across highly promiscuous signalling regimes such as stem cells. While a negative correlation is likely for deterministic signalling (differentiated cells). Additionally, they found that cancer cells have a higher network entropy, but lower total Forman-Ricci curvature compared to healthy differentiated cells. This contrasts with stem cells, where both network entropy and total Forman-Ricci curvature are higher than healthy differentiated cells. They demonstrated this on the k-star network as a toy example, as well as different real-world biological datasets (embryonic stem cell differentiation, melanoma patients, and colorectal cancer patients) where the Forman-Ricci curvature and the network entropy follow two distinct regimes.

Finally, they apply their defined normalized discrete Ricci flow to predict the intermediate time-points of cellular differentiation by deriving the biological network rewiring trajectories based only on the first and last time points of time courses of cellular differentiation. Their approach accurately predicted intermediate time-points compared to the null Euclidean model, where a straight-line trajectory is considered. The proposed Ricci flow correctly orders the differentiation time courses during both the embryonic stem cell (ESC) differentiation and myoblast differentiation.

• The paper is well-written and easy to follow.

• The paper presents that the network entropy and the FRC do not always result in a positive correlation. This has been shown in a network toy example and several real-world cellular differentiation datasets.

We thank the reviewer for their positive and accurate summary of our manuscript’s results.

• [p.8-9, {section sign}2.1.2] "We note that these studies also employ slightly different constructions of Forman-Ricci curvature than our own." The Forman-Ricci curvature is formulated by defining the edge weights and node weights as in {section sign}2.1.2/{section sign}4.2.
• The node weights are defined as the reciprocal of the node degree. It is explained that this is "to normalize the sums in (4), preventing connected high-degree vertices from dominating the total FRC." Does this pay less importance to hub nodes? Is this consistent with the cellular differentiation biological application?

The reviewer raises the important point that computation of edge-wise Forman-Ricci Curvature (FRC) defined in eq. 4 requires stipulation of node and edge weights, which must be carefully defined to match the context of our problem of cellular differentiation.

We specify the edge weights to match the computation of network entropy (Banerji et al., Sci. Rep., 2013), which is based on a mass-action principle assumption. This assumes that the rate of reaction between two interacting proteins is proportional to the product of the concentration of the reactants. We assume that gene expression is a proxy for protein concentration and define interaction strength between nodes i and j by x_i*x_j, where x_i is the expression of gene i. In the context of edge-wise FRC, edge weights equate to a distance, and so nodes which have a strong interaction should have a low edge weight, implying close proximity. Hence we define edge weights for FRC as a_ij/x_i*x_j, where a_ij is the (i,j)^{th} element of the protein interaction network adjacency matrix.

For node weights we selected the reciprocal of the node degree as the reviewer recognises. The FRC computed on an edge (i,j) is essentially a pair of sums over edges connected to nodes i and j, with each sum normalised by the corresponding node weight W_i and W_j. The number of elements in each sum is deg(i) and deg(j) respectively. Thus choosing W_i as anything other than 1/deg(i), introduces a node degree bias to the edge-wise curvature measure.

Rather than paying less importance to hub nodes as the reviewer suggests, by selecting W_i=1/deg(i) we are ensuring that node degree does not alter the edge-wise Forman-Ricci curvature, and equal importance is paid to all nodes. This property is essential in assessing the correlation between network entropy and network average Forman-Ricci curvature, as it prevents trivial correlation.

To elaborate, to compute network average FRC we introduce a node wise FRC (eq. 24) as the mean edge-wise FRC over edges connected to a given vertex. Network average FRC (eq. 25) is then defined as a weighted sum over node averaged FRCs, where the weights employed are elements of the stationary distribution of P=(p_ij)_{ij \in V}. This construction mirrors the calculation of network entropy, where node-wise entropies are first computed (eq. 7) and an identical weighted sum over node-wise entropies (using the stationary distribution) defines network entropy.

If we consider eq. 7 for computing the node wise entropy of node i:

S_i = - \sum_{j \in V} p_{ij} log(p_{ij})

we see that S_i is bounded between [0,deg(i)]. Hence the node-wise of elements of the sum defining network entropy have a degree dependence. If we employ a different node weight in our definition of edge-wise FRC, we ensure that the node-wise elements of network average FRC also have a degree dependence. Such a dependency could lead to a trivial correlation between network entropy and network average Forman-Ricci curvature, confounding our results.

The reviewer makes the important observation that node degree often correlates with importance in biological networks and should be considered in a network average measure. We emphasise, that though we are removing degree dependency in the computation of edge-wise FRC, by using the stationary distribution for our network average FRC we re-introduce the importance of hub nodes (as high degree/strength nodes will have higher stationary distribution probabilities).

Thus, to answer the two questions raised by the reviewer: (1) The choice of node weights in eq. 4 ensure that edge-wise FRC is independent of node degree and does not pay more or less importance to hub nodes, this prevents trivial correlation between network average FRC and network entropy. (2) The fact that higher degree nodes are more biologically relevant is respected by the use of the stationary distribution in calculation of network average FRC, which gives higher weighting to hub nodes, without introducing the possibility of trivial correlation with network entropy.

We thank the reviewer for these important questions and will clarify this in an updated manuscript.

• How is the Forman-Ricci curvature/flow definition in earlier works applied to biological networks different? How does it impact the current conclusions if such FRC definitions were used?

Previous works investigating Forman-Ricci curvature in biological networks have defined edge-wise FRC using different node and edge weights, with various justification. All methods investigating gene expression data have used the same method to convert edge-wise FRC to network average FRC.

In our manuscript, for reasons explained above, we defined edge weights as \omega_{ij}=a_{ij}/(x_i*x_j) and node weights as W_i=1/deg(i). As with all prior studies we then compute node average FRCs and define network average FRC via a stationary distribution weighted sum of node-wise FRCs.

Murgas et al., Complex Networks & Their Applications X, 2021 defined edge weights via \omega_{ij}=x_i*x_j and node weights via W_i=x_i. Network average FRCs were computed via the same method we employ. The authors demonstrate a positive correlation between network average FRC and network entropy on stem cells. We note, however, that as the node weights do not depend on the node degree, the edge-wise FRCs have a degree dependence, leading to a possibly trivial correlation with network entropy as explained above. We further note that in this construction edge weights cannot be considered as “distances” between nodes, as they are large when interaction propensity is high and vice versa. This leads to a conceptual difficulty in interpreting the curvature. Finally, as the node weight depends on a temporal variable (gene expression), this construct is incompatible with a computable Ricci flow. To elaborate, at each time step of the Ricci flow equation we must compute all updated edge-weights and all updated edge-wise FRCs. However, the Ricci flow equation is defined on edges and thus produces a system of |E| equations at each time point, allowing us to solve for a maximum of |E| parameters. If the node weights also change over the time step, to compute the updated edge-wise FRCs, we will need to solve for |E|+|V| variables, which the system is insufficient to achieve. Thus the FRC defined by Murgas et al., 2021 is not suitable for our investigation as it may introduce trivial correlation with network entropy, is not well defined as a curvature in the biological context and is not compatible with a computable Ricci flow.

Murgas et al., Sci Rep. 2022 employed a different construction with edge weights \omega_{ij}=a_{ij}/(p_{ij}*deg(i) and node weights again W_i=x_i. Network average FRCs were computed via the same method we employ. The authors demonstrate a negative correlation between network average FRC and network entropy on stem cells. The fact that deg(i) is incorporated in the edge weight definition makes the degree dependence of edge wise FRC non-trivial, however the choice of node weights independent of degree still introduces the possibility of a correlation with node degree, which could confound any correlation with network entropy. The explanation given for a negative rather than positive correlation observed in this study, was the selection of edge weights, which the authors’ chose to better represent a ‘distance’ between nodes. While this permits a more well defined, intuitive curvature, we have shown here that both positive and negative correlations are possible with edge weights defined to represent distances. We also note that as the node weight is again a temporal variable, computation of a Ricci flow using these parameters is intractable.

Other studies investigating FRC in biological networks (e.g., Weber et al., Journal of Complex Networks, 2016) do so in a different context and do not consider single sample gene expression weighting, and so cannot be directly compared to our approach.

To our knowledge no other study has employed a Forman-Ricci flow on a biological network, but Forman-Ricci flow has been employed in the context of the Gnutella peer-to-peer file sharing network (Weber et al., Journal of Complex Networks, 2016). The major difference between the flow implemented by the authors and the one we present is that it was not normalised, but rather evolved the network towards a flat zero curvature. This would be suboptimal for our goal, which is to infer rewiring from one biological state to the another. We note that the concept of a normalised Ricci flow was introduced by Weber et al., 2016, but was not implemented in their example.

Other studies have employed Ollivier-Ricci curvature in the investigation of gene expression weighted biological networks, including using phenotype average curvatures (Sandhu et al Sci. Rep. 2015) and single sample curvatures (Elkin et al., NPJ Genom Med, 2021). We note that Ollivier-Ricci curvature is significantly more computationally expensive than Forman-Ricci curvature, and for our Ricci flow we must compute ~150,000 curvatures per time-step making Ollivier-Ricci curvature impractical. While Ollivier-Ricci curvature and Forman-Ricci curvature have been compared on biological networks, with some overlap in conclusions, the correlation between the two measures is not strong and our results cannot be generalised to cover this measure (Pouryahya et al., 2021, https://arxiv.org/abs/1712.02943).

We will update the discussion of our manuscript with details of these prior studies.

• Along these lines and especially considering the importance of curvature plays in network science and biology, the authors have to better discuss the existing work in developing various differential geometry approaches for molecular and system biology such as: "Ollivier-ricci curvature-based method to community detection in complex networks." Scientific reports 9, no. 1 (2019): 9800. "Inferring functional communities from partially observed biological networks exploiting geometric topology and side information." Scientific Reports 12, no. 1 (2022): 10883.

We thank the reviewer for this suggestion and will include a discussion of these studies in the updated manuscript.

• [{section sign}2.3, Fig. 3cd] For the cancer datasets shown in Fig. 3, I believe Pearson's r is shown for all the samples (malignant and healthy cells). It is mentioned that for cancerous cells, the network entropy is higher while the total FRC is lower. Is there a significant difference in correlation values if the healthy and cancerous cells are considered separately?

We thank the reviewer for this suggestion and indeed there is a difference in this correlation when healthy and cancerous samples are considered separately. Healthy samples have a stronger negative correlation between network entropy and total FRC than cancerous samples. Our theoretical results show that as intracellular signalling becomes less deterministic the correlation between our two measures becomes less negative. The findings of the analysis suggested by the reviewer, therefore supports the conclusion that intracellular signalling becomes less deterministic during oncogenesis.

For Figure 3C across control (healthy) samples there is a significant negative correlation between network entropy and total FRC (n=3256, Pearson’s r=-0.83, p-16), while there is no correlation between the two measures across melanoma (cancerous) samples (n=1257, Pearson’s r=-0.009, p=0.76). Using Fisher’s z-transformation to compare these correlations we find a highly significant difference (p-16).

For Figure 3D, the difference in more subtle. Across control (healthy) samples there is significant negative correlation between network entropy and total FRC (n=160, Pearson’s r=-0.90, p-16), while across colorectal cancer samples the negative correlation is slightly weaker (n=272, Pearson’s r=-0.83, p-16). Using Fisher’s z-transformation to compare these correlations we again find a significant difference (p-3).

We will include these results in an updated manuscript.

• [Methods, p.23] "For each gene expression time course we implemented one time step of the Ricci flow from the first time point $\mathbf{x^0}$ using each $\Delta t$ value and selected the optimal $\Delta t$ as the largest which does not admit negative values of $d_{0+\Delta t}$..." It seems that choosing the optimal $\Delta t$ is a heuristic; I wonder how sensitive is the proposed framework based on the choice of $\Delta t$ in accurately identifying the trajectory. i.e., assuming that the choice of $\Delta t$ does not admit to negative d values, are there choices of $\Delta t$ that are still too large that it does not correctly identify the intermediate time points?

The reviewer raises an important point on the selection of the parameter \Delta t in our Ricci flow equation. Selection of the optimal \Delta t is a trade off. Too small a \Delta t means many time-steps before convergence and the computational cost of implementing our flow (requiring the calculation of 150,000 Forman-Ricci curvatures per time step) becomes unmanageable. While too large a \Delta t may mean too coarse grain an approximation, inhibiting inference of the true underlying continuous network rewiring trajectory.

As the reviewer notes we are also subject to the constraint that edge weights (which in the context of Ricci flow correspond to distances) must be non-negative. If \Delta t is sufficiently small this guarantees non-negative edge weights are output from the Ricci flow. The precise \Delta t which prevents negative edge weights depends on the differences between the edge wise FRCs of the network at each time point and those of the normaliser. As these differences get smaller as we progress the flow and converge to the normaliser, we can determine the optimal \Delta t from the first time step as:

1/min_{(i,j)\in E}(Ric_{(i,j)}(x^0) - \bar{Ric_{(i,j)})

This is the largest possible value of \Delta t we can use in our framework without introducing negative values, and thus the \Delta t most likely to inhibit our approach from approximating the true intermediate time points.

In our paper, rather than using this formula we empirically searched for the largest \Delta t which admits only positive values. This was done by considering a range of possible values of \Delta t calculating one step of the Ricci flow equation for each value and identifying the largest \Delta t for which the smallest updated edge weight was non-negative. For both our time courses this identified \Delta t=0.06 as optimal, while the above formula gives maximal values for \Delta t at between 0.06 and 0.065.

The results we present are therefore calculated using very close to the maximum value of \Delta t possible in our framework and as the reviewer comments we

‘accurately predicted intermediate time-points compared to the null Euclidean model’

using this \Delta t.

Thus to answer the reviewer’s question, subject to the constraint of non-negative d values, there does not appear to be a choice of \Delta t which is too large to correctly identify the intermediate time points.

We will clarify this point in the updated manuscript.

Reviewer #1 (Significance (Required)):

Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated.

We thank the reviewer for their kind and positive comments on our work.

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

The paper brings in new insights, and corrects earlier ones, on how certain measures quantify structure and functionality of biological networks. It starts with the hypothesis that network entropy is a proxy for 'height' in Waddington's Landscape, with higher values on stem cells and malignant cells compared to healthy ones. In this context, it explores different notions of network curvature (one in particular, Forman-Ricci, in depth) in how it correlates with entropy for various types of networks. It draws a potentially transformative conclusion that promiscuity in signalling, that characterizes less differentiated and malignant cell, is also a key factor in how curvature and entropy relate to each other. Specifically, it observes for cases, and explains with an academic example that correlation between network curvature and entropy may change sign as e.g., stem cells differentiate.

It supports the conclusion that Forman-Ricci curvature has indeed a great biological relevance, and that curvature and entropy are complementary, and not interchangeable measures of cell potency, as suggested in earlier studies. It also hypothesizes that a version of Ricci flow (a dynamical model for how networks change in the strength of interactions and correlations; or, the metric in continuous spaces) correctly captures likely trajectories in cell differentiation. This provides an additional argument that highlights the importance of curvature as some sort of driving potential that effects, and is defined by, structural changes in biological networks.

The paper is well written, with a fairly accessible account of pertinent mathematics, in the body of the paper and the materials and methods. The component on the analysis of data is also well explained and supported. Minor suggestion, please point to the formula for S_R in the materials and methods when it is first referred to in the paper.

We thank the reviewer for their kind comments on our paper and will point to the formula for S_R as suggested in the updated manuscript.

**Referees cross-commenting**

It is a scholarly work that makes a significant contribution detailing how network entropy and curvature relate in the context of biological networks

We thank both referees for their kind and positive assessment of our work.

Reviewer #2 (Significance (Required)):

This is an important paper that explores in a systematic way certain mathematical concepts, as indicators of biological network functionality, to distinguish stages in cell differentiation that may be significant in understanding e.g., malignant versus healthy cells and the flow in stem cell differentiation. It supports its conclusions with an academic example as well as analysis of biological data sets. It draws a balanced view on how entropy and curvature relate and gives an accessible account to the relevant mathematics.

It will be of interest to a wide range of scientists that are involved in stem cell differentiation and biological networks.

We thank the referee for their kind words on our work and for emphasising the broad range of interest.

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Referee #2

Evidence, reproducibility and clarity

The paper brings in new insights, and corrects earlier ones, on how certain measures quantify structure and functionality of biological networks.

It starts with the hypothesis that network entropy is a proxy for 'height' in Waddington's Landscape, with higher values on stem cells and malignant cells compared to healthy ones. In this context, it explores different notions of network curvature (one in particular, Forman-Ricci, in depth) in how it correlates with entropy for various types of networks. It draws a potentially transformative conclusion that promiscuity in signaling, that characterizes less differentiated and malignant cell, is also a key factor in how curvature and entropy relate to each other. Specifically, it observes for cases, and explains with an academic example that correlation between network curvature and entropy may change sign as e.g., stem cells differentiate.

It supports the conclusion that Forman-Ricci curvature has indeed a great biological relevance, and that curvature and entropy are complementary, and not interchangeable measures of cell potency, as suggested in earlier studies. It also hypothesizes that a version of Ricci flow (a dynamical model for how networks change in the strength of interactions and correlations; or, the metric in continuous spaces) correctly captures likely trajectories in cell differentiation. This provides an additional argument that highlights the importance of curvature as some sort of driving potential that effects, and is defined by, structural changes in biological networks.

The paper is well written, with a fairly accessible account of pertinent mathematics, in the body of the paper and the materials and methods. The component on the analysis of data is also well explained and supported. Minor suggestion, please point to the formula for S_R in the materials and methods when it is first referred to in the paper.

Referees cross-commenting

It is a scholarly work that makes a significant contribution detailing how network entropy and curvature relate in the context of biological networks

Significance

This is an important paper that explores in a systematic way certain mathematical concepts, as indicators of biological network functionality, to distinguish stages in cell differentiation that may be significant in understanding e.g., malignant versus healthy cells and the flow in stem cell differentiation. It supports its conclusions with an academic example as well as analysis of biological data sets. It draws a balanced view on how entropy and curvature relate, and gives an accessible account to the relevant mathematics.

It will be of interest to a wide range of scientists that are involved in stem cell differentiation and biological networks.

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

Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated. In the case of cell differentiation networks, a positive correlation is likely across highly promiscuous signaling regimes such as stem cells. While a negative correlation is likely for deterministic signaling (differentiated cells). Additionally, they found that cancer cells have a higher network entropy but lower total Forman-Ricci curvature compared to healthy differentiated cells. This is in contrast to stem cells, where both network entropy and total Forman-Ricci curvature are higher than healthy differentiated cells. They demonstrated this on the k-star network as a toy example, as well as different real-world biological datasets (embryonic stem cell differentiation, melanoma patients, and colorectal cancer patients) where the Forman-Ricci curvature and the network entropy follow two distinct regimes.

Finally, they apply their defined normalized discrete Ricci flow to predict the intermediate time-points of cellular differentiation by deriving the biological network rewiring trajectories based only on the first and last time points of time courses of cellular differentiation. Their approach accurately predicted intermediate time-points compared to the null Euclidean model, where a straight-line trajectory is considered. The proposed Ricci flow correctly orders the differentiation time courses during both the embryonic stem cell (ESC) differentiation and myoblast differentiation.

• The paper is well-written and easy to follow.
• The paper presents that the network entropy and the FRC do not always result in a positive correlation. This has been shown in a network toy example and several real-world cellular differentiation datasets.
• [p.8-9, {section sign}2.1.2] "We note that these studies also employ slightly different constructions of Forman-Ricci curvature than our own." The Forman-Ricci curvature is formulated by defining the edge weights and node weights as in {section sign}2.1.2/{section sign}4.2.
• The node weights are defined as the reciprocal of the node degree. It is explained that this is "to normalize the sums in (4), preventing connected high-degree vertices from dominating the total FRC." Does this pay less importance to hub nodes? Is this consistent with the cellular differentiation biological application?
• How is the Forman-Ricci curvature/flow definition in earlier works applied to biological networks different? How does it impact the current conclusions if such FRC definitions were used?
• Along these lines and especially considering the importance of curvature plays in network science and biology, the authors have to better discuss the existing work in developing various differential geometry approaches for molecular and system biology such as: "Ollivier-ricci curvature-based method to community detection in complex networks." Scientific reports 9, no. 1 (2019): 9800. "Inferring functional communities from partially observed biological networks exploiting geometric topology and side information." Scientific Reports 12, no. 1 (2022): 10883.
• [{section sign}2.3, Fig. 3cd] For the cancer datasets shown in Fig. 3, I believe Pearson's r is shown for all the samples (malignant and healthy cells). It is mentioned that for cancerous cells, the network entropy is higher while the total FRC is lower. Is there a significant difference in correlation values if the healthy and cancerous cells are considered separately?
• [Methods, p.23] "For each gene expression time course we implemented one time step of the Ricci flow from the first time point $\mathbf{x^0}$ using each $\Delta t$ value and selected the optimal $\Delta t$ as the largest which does not admit negative values of $d_{0+\Delta t}$..." It seems that choosing the optimal $\Delta t$ is a heuristic; I wonder how sensitive is the proposed framework based on the choice of $\Delta t$ in accurately identifying the trajectory. i.e., assuming that the choice of $\Delta t$ does not admit to negative d values, are there choices of $\Delta t$ that are still too large that it does not correctly identify the intermediate time points?

Significance

Contrary to earlier studies, which proposed a positive correlation between network entropy and the total discrete curvature of biological networks, the paper proves that the network entropy and their defined total Forman-Ricci curvature are not guaranteed to be positively correlated.

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Reply to the reviewers

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

This work by Bruckman et al. showed how sperm can induce somatic cell syncytium upon binding. This finding has tremendous implications for the field. Although gamete fusion is one of the fundamental events in biology, the molecular details of this process in mammals is not well understood. As briefly mentioned in this work, spermatozoa contribute Izumo1 which forms a complex with SPACA6 and TMEM81; and probably with other proteins. On the other hand, the egg counterpart is JUNO, a GPI- anchored protein, and CD9. IZUMO1 and JUNO have been shown to interact and this interaction is essential for fusion. However, it is still not clear the extent that this binding is needed for attachment of for fusion between gametes. The authors have published before that somatic cells expressing IZUMO1 can fuse with other cells expressing JUNO; and that sperm can fuse to JUNO-expressing cells. This previous work strongly suggests that IZUMO and JUNO are sufficient for fusion; however, the efficiency of the fusion assay was low. In the present work, formation of syncytium is highly efficient, making possible to envision applications of this assay to research of the proteins involved in sperm-egg fusion as well as for molecular mechanisms. In addition, as mentioned in this work, the assay can be translated for the use for clinical diagnosis.

• We would like to thank the reviewer for the insightful comments that improved our manuscript. Furthermore, we appreciate the acknowledgment of the value of our findings for the field. Overall, the manuscript is straightforward, and the conclusions are sound. I have only a couple of comments:

1) As mentioned in this work, fusion of human sperm with hamster eggs was used in the past to evaluate human sperm fusogenic properties. Although this assay is not longer recommended, still highlight the fact that hamster egg's oolemma is promiscuous regarding species-specific fusion. Although I don't think the experiment is essential, if it were possible, trying hamster IZUMO1 in this assay will be a good addition to this manuscript.

• We have performed this experiment and it is now included in the revised manuscript (Figure 4). As discussed in the new version, hamster JUNO is as efficient as its murine counterpart in facilitating SPICER, confirming the well-known promiscuous nature of hamster eggs. 2) Is it possible to know how many sperm form part of the syncytium>?

• We have included in the revised manuscript a Figure showing the number of sperm fused in syncytia of different sizes (Figure S1). 3) Although I understand the relevance of this assay for diagnostic use, still the major contribution of this manuscript is to the sperm-egg fusion field of study. I encourage the authors to start the discussion with a more basic approach and add at least one paragraph summarizing how their discovery advanced the field. From my perspective the translational application should be at the end of the Discussion section, not at the start.

• We have modified the Introduction and Discussion sections according to this comment. In addition, we are now including new data further supporting that the SPICER assay can be used to predict sperm fertilizing capacity (Figure 3). **Referee Cross-Commenting**

The syncytia described in this work support the hypothesis that once Izumo and partners in sperm interact with Juno, the cells become able to undergo fusion events. It does not claim that this type of event happens in vivo. Although Juno is expressed in other cell types and not only in the egg, it is too soon to claim a physiological role for syncytia formation in vivo.

I agree with reviewer 2 comments. Both experiments proposed (paragraph 2 and 3) are relatively straightforward.

In general, I also agree with reviewer 3 comments, one of the experiments proposed is similar to the one proposed by reviewer 2. Having said this, I disagree that the human sperm experiment is needed for this paper to be significant. Although the diagnostic claim is important, the major contribution of this work is at the basic research level. Toning down the clinical relevance would be sufficient to make this paper a significant contribution to the field.

Mechanisms of sperm-egg fusion have been elusive for the last decades. In my view, syncytia formation and I don't expect a single paper to elucidate such a complex event.

Reviewer #1 (Significance (Required)):

General Assessment: This is an excellent manuscript contributing to our understanding of sperm-egg fusion. In addition, the tools described here warrant new approaches to study the molecules involved in this process using genetically modified mouse models.

Advance: Although the relevance of IZUMO1 and JUNO for sperm-egg fusion have been previously reported, as far as I know, this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells. This finding will provide new tools to study the molecular basis of sperm-egg fusion as well as generate translational tools to evaluate human sperm ability to fertilize.

Audience: I believe that this manuscript will have broad interest. Although initially, the main audience will be related to reproductive biologists, this manuscript will be also highly relevant for other scientists studying fusion. In addition, this work has clear translational applications.

Keywords describing my expertise: sperm, eggs, in vitro fertilization, reproductive biology, embryos

We are pleased and grateful to read this reviewer’s comments. We have now implemented the suggestions in the revised version.

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

This is a very nice manuscript that continues previous work of the lab. Authors in this manuscript goes further, showing that proteins from the plasma membrane of sperm end up in somatic cells plasma membrane after fusing, conferring them fusogenic capacity. The manuscript is very elegantly written, and of interest to the reproductive community. I have only some minor concerns to be addressed:

• We would like to thank the reviewer for the positive feedback as well as for the valuable suggestions.
• The authors indicate that this test "can help embryologists to better predict the success of the different ARTs for each individual.". This assumption should be taken as it is, an assumption. In order to state that in can be used, authors should first demonstrate, maybe with a correlation test hand by hand with a reproductive clinic, that this procedure indeed works for diagnosis. There is no doubt that it is a very nice proof of principle.
• We have changed the Introduction and Discussion to be more cautious about this point. Unfortunately, we currently do not have permission to work with human samples, however, we have added new data showing a positive correlation between the extent of cell-cell fusion and the fertilizing potential of mouse sperm (Figure 3).

Fig S1C. Although I agree with the ayuthors that the two models are possible, have the authors tried to wash sperm away from the fusing assay, and add new cells, maybe differentially tagged, to see whether they fuse to those already fused with sperm? This could shed light onto the mechanism.

• We have performed this experiment that was requested by Reviewer #1 and Reviewer #2 (Figure S3). The results show that when the sperm are added to the plate before the addition of the second population of cells, no fusion is observed. This argues against the model in which there is transfer of the fusion machinery to the somatic cells. We have now adjusted the manuscript accordingly.

Fig 3. Sperm incubated in the absence of BSA, failed tu fuse to cells. I wonder whether the lack of BSA precludes fusogenicity. Have authors tried removing HCO3 and keeping BSA? The presence of HCO3 still enables PKA activation even in the absence of BSA, also allowing hyperpolarization of the plasma membrane. Have authors evaluated acrosomal status after the selected treatments?

• As the co-incubation of the sperm and the cells is performed in the cell-culture incubator with CO2 supply, we were not able to evaluate a condition without HCO3. However, we have analyzed the levels of acrosome reaction after the incubation in media without calcium or BSA (Figure S4). For both conditions, the levels of acrosome reaction are significantly lower than the control consistent with previous reports (Visconti et al., 1995 PMID: 7743926). **Referee Cross-Commenting**

I thank reviewer 3 for his/her insight. I agree on being cautious about the relevance of this finding, as it is unclear if anything similar happens in vivo.

I agree with comments raised by rev 1. This reviewer clearly points out that the clinical relevance of the manuscript is not a strength of the manuscript. And I agree with reorganizing the discussion as suggested. In this line, I consider very important to tone down the clinical aspect of the manuscript. In this regard, I completely understand the experiments suggested by reviewer 3, but still, I think that the major contribution of this manuscript is to the sperm-egg fusion field of study. Thus, my suggestion is to tone down to a minimum level the clinical relevance of the study.

Reviewer #2 (Significance (Required)):

The work shows an interesting advance in the field. However, caution should be taken when referring to "capacitated sperm", since no controls are taken, and methods to inhibit capacitation are not solid.

• We thank the reviewer for this comment. We have improved the manuscript following their recommendations and now the pertinent controls are included (Fig. S4). Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary:

The authors find that mouse sperm can fuse to hamster fibroblasts (Baby Hamster Kidney, BHK) and that consequently these cells form large multinucleated syncytia. They describe and quantify the formation of syncytia and suggest that the sperm ability to induce formation of syncytia is associated with sperm functionality.

Based on these findings, the authors propose a novel method for the diagnosis of male infertility.

Overall, the manuscript is clearly written, and the experimental procedures are described in detail.

• We sincerely appreciate the important comments and suggestions made by Reviewer #3. Major comments:

The main limitation of the study is that the data is obtained with mouse sperm, while the proposed application is for the evaluation of human sperm fertilizing ability. The authors need to repeat the assay of sperm fusion to somatic cells with human samples.

Furthermore, to correlate sperm functionality with cell fusion induction, it would be necessary to compare sperm from fertile and infertile men. The experiment reported in figure 3, where non-capacitated sperm, maintained in media without Calcium or without BSA, are used, does not mimic real cases of human male infertility where even fully capacitated sperm are unable to fertilize eggs.

The experiments with human sperm should not take longer than 6 months, depending on the local legislation regulating human sample collection and usage.

• We understand the point made by Reviewer #3 and agree to tone down the clinical significance of the finding. Unfortunately, we do not have permission to perform experiments with human samples.
• We also agree with Reviewers #1 and #2 in the sense that these experiments are out of the scope of this study. However, we have now included new data directly correlating mouse sperm fertilizing ability and syncytia formation (Figure 3) that further support our conclusions. Minor comments:

Figure 1. The % of multinucleation in panel C (~ 55%) and in panel D (25%) are substantially different. The authors should explain better how the percentages were calculated.

• As explained in the Results section, within the panels and in the legend of Figure 1, in panel C we quantify multinucleation separately for cells with and without sperm fused to them to distinguish the effect of sperm fusion. Instead, in panel D we quantify multinucleation in the whole population. This is better clarified in the revised version in the legend of Figure 1. Figure 2., panel C. It is unclear how the number of 'fluorescent cells IN CONTACT that do not fuse (NuC)' are evaluated given the lack of a marker for cell membranes or for actin. It is challenging to visualize all the filopodia at the edges of cells just with light microscopy, therefore adding a cell membrane dye could be helpful for visualization and counting.

• We agree with the reviewer that we cannot confirm that the cells are projecting filopodia between them. When we stated “in contact” we referred to the cell body, as we have done in several publications to date: Podbilewicz et al., 2006 (DOI 10.1016/j.devcel.2006.09.004); Avinoam et al., 2011 (DOI: 10.1126/science.1202333); Valansi et al., 2017 (DOI: 10.1083/jcb.201610093); Moi et al., 2022 (DOI: 10.1038/s41467-022-31564-1); and Brukman et al., 2023 (DOI: 10.1083/jcb.202207147). To be more specific we have now explained this in the revised version as “cells whose cell bodies are in contact”. Figure 3, panel A. The authors should specify if the 500 cells are single cells or nuclei, including those in syncytia.

• The figure refers to cells, not nuclei. It is better explained in the revised version of the Materials and Methods. Figure S1 panel C. To discern between the mechanism proposed in i) and ii), a possibility would be to add sperm to JUNO(GFPnes) cells, wash them out carefully after 4 hours in order to remove non-fused and unbound sperm, and then to add JUNO(H2B-RFP) cells. If sperm induce multinucleation by fusing with more than one cell, only GFP syncytia will be obtained. On the other hand, if fusion is due to the transfer of sperm fusing machinery, syncytia will be GFP positive and have red nuclei too.

• We performed this important experiment that was also requested by Reviewers #1 and #2 (Figure S3). We found that when the sperm were added to the plate before the addition of the second population of cells, no hybrid syncytia were formed, only multinucleated GFP cells. The manuscript has been modified to include this observation that points against the model in which there is transfer of the fusion machinery to the somatic cells. **Referee Cross-commenting**

As pointed out by reviewer 1 'this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells' therefore if the clinical relevance of the manuscript is toned down, I believe that the authors need to add a more in-depth investigation of the cell fusion mechanism. Particularly in consideration of the data shown by other groups indicating that IZUMO1 and JUNO are not sufficient for cell fusion. In that regard, the experiments suggested by reviewers 1 and 2 would be helpful to start getting a better understanding of the mechanism. Anyhow, I would be extremely cautious about the relevance of this finding because it is unclear whether anything similar happens in vivo.

Reviewer #3 (Significance (Required)):

The mechanism of sperm-egg cell membrane fusion is still unclear, and the entire molecular machinery orchestrating fertilization is yet to be unveiled.

The sperm protein IZUMO1 and its egg receptor JUNO have been shown to be essential for sperm-egg binding in mouse, human and rats. Whether IZUMO1 also mediates membrane fusion is still debated because research groups who have adopted various assays came to different conclusions (PMID: 24739963; PMID: 35096839; PMID: 36394541; PMID: 26568141).

In a previous paper published earlier this year Brukman and colleagues showed that IZUMO1 is able to induce membrane fusion in a heterologous cell system (PMID: 36394541). In the current manuscript, they expand on this observation and report the formation of syncytia (large multinucleated cells) induced by the fusion of sperm with somatic cells. Whether this event that is observed in vitro has any relevance in vivo has to be investigated.

The large amount of work done on an unrelated cell membrane protein, the fibroblast growth factor receptor-like 1 (FGRL1), that also induces cell fusion in vitro, suggests a possible interpretation for the results described by Brukman et al. 'Cell fusion might just be the most extreme result of very tight cell adhesion [...] Under normal conditions, FgfrL1 might only bring together the cell membranes into intimate contact' https://link.springer.com/article/10.1007/s00018-012-1189-9 (PMID:23112089)

Therefore, it is reasonable to think that cell-cell fusion observed in vitro does not recapitulate the fusion of sperm and egg.

Regardless of whether sperm can induce fusion of somatic cells in vivo, the SPICER method proposed in this manuscript needs to be validated with human sperm in order to become a valuable tool for the assessment of male fertility.

We are truly grateful for the comments and suggestions made by Reviewer #3 that allowed us to improve our manuscript. We agree about the important questions that arise from our discovery and we believe that SPICER will be a powerful tool to address them in the future. We would only like to mention that the FGRL1-induced fusion was observed only when at least one of the fusing cells was a CHO cell (PMCID: PMC2988375 DOI: 10.1074/jbc.M110.140517). In our case, the fact that SPICER, as well as IZUMO1-induced fusion (Brukman et al., 2023, J Cell Biol.), was detected in two unrelated cell lines (i.e. hamster BHK and human HEK cells) argues against a cell-specific effect and suggests the involvement of a conserved mechanism.

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

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Referee #3

Evidence, reproducibility and clarity

Summary:

The authors find that mouse sperm can fuse to hamster fibroblasts (Baby Hamster Kidney, BHK) and that consequently these cells form large multinucleated syncytia. They describe and quantify the formation of syncytia and suggest that the sperm ability to induce formation of syncytia is associated with sperm functionality. Based on these findings, the authors propose a novel method for the diagnosis of male infertility. Overall, the manuscript is clearly written, and the experimental procedures are described in detail.

Major comments:

The main limitation of the study is that the data is obtained with mouse sperm, while the proposed application is for the evaluation of human sperm fertilizing ability. The authors need to repeat the assay of sperm fusion to somatic cells with human samples.

Furthermore, to correlate sperm functionality with cell fusion induction, it would be necessary to compare sperm from fertile and infertile men. The experiment reported in figure 3, where non-capacitated sperm, maintained in media without Calcium or without BSA, are used, does not mimic real cases of human male infertility where even fully capacitated sperm are unable to fertilize eggs. <br /> The experiments with human sperm should not take longer than 6 months, depending on the local legislation regulating human sample collection and usage.

Minor comments:

Figure 1. The % of multinucleation in panel C (~ 55%) and in panel D (25%) are substantially different. The authors should explain better how the percentages were calculated.

Figure 2., panel C. It is unclear how the number of 'fluorescent cells IN CONTACT that do not fuse (NuC)' are evaluated given the lack of a marker for cell membranes or for actin. It is challenging to visualize all the filopodia at the edges of cells just with light microscopy, therefore adding a cell membrane dye could be helpful for visualization and counting.

Figure 3, panel A. The authors should specify if the 500 cells are single cells or nuclei, including those in syncytia.

Figure S1 panel C. To discern between the mechanism proposed in i) and ii), a possibility would be to add sperm to JUNO(GFPnes) cells, wash them out carefully after 4 hours in order to remove non-fused and unbound sperm, and then to add JUNO(H2B-RFP) cells. If sperm induce multinucleation by fusing with more than one cell, only GFP syncytia will be obtained. On the other hand, if fusion is due to the transfer of sperm fusing machinery, syncytia will be GFP positive and have red nuclei too.

Referee Cross-commenting

As pointed out by reviewer 1 'this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells' therefore if the clinical relevance of the manuscript is toned down, I believe that the authors need to add a more in-depth investigation of the cell fusion mechanism. Particularly in consideration of the data shown by other groups indicating that IZUMO1 and JUNO are not sufficient for cell fusion. In that regard, the experiments suggested by reviewers 1 and 2 would be helpful to start getting a better understanding of the mechanism. Anyhow, I would be extremely cautious about the relevance of this finding because it is unclear whether anything similar happens in vivo.

Significance

The mechanism of sperm-egg cell membrane fusion is still unclear, and the entire molecular machinery orchestrating fertilization is yet to be unveiled.

The sperm protein IZUMO1 and its egg receptor JUNO have been shown to be essential for sperm-egg binding in mouse, human and rats. Whether IZUMO1 also mediates membrane fusion is still debated because research groups who have adopted various assays came to different conclusions (PMID: 24739963; PMID: 35096839; PMID: 36394541; PMID: 26568141).

In a previous paper published earlier this year Brukman and colleagues showed that IZUMO1 is able to induce membrane fusion in a heterologous cell system (PMID: 36394541). In the current manuscript, they expand on this observation and report the formation of syncytia (large multinucleated cells) induced by the fusion of sperm with somatic cells. Whether this event that is observed in vitro has any relevance in vivo has to be investigated.

The large amount of work done on an unrelated cell membrane protein, the fibroblast growth factor receptor-like 1 (FGRL1), that also induces cell fusion in vitro, suggests a possible interpretation for the results described by Brukman et al. 'Cell fusion might just be the most extreme result of very tight cell adhesion [...] Under normal conditions, FgfrL1 might only bring together the cell membranes into intimate contact' https://link.springer.com/article/10.1007/s00018-012-1189-9 (PMID:23112089)

Therefore, it is reasonable to think that cell-cell fusion observed in vitro does not recapitulate the fusion of sperm and egg.

Regardless of whether sperm can induce fusion of somatic cells in vivo, the SPICER method proposed in this manuscript needs to be validated with human sperm in order to become a valuable tool for the assessment of male fertility.

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

This is a very nice manuscript that continues previous work of the lab. Authors in this manuscript goes further, showing that proteins from the plasma membrane of sperm end up in somatic cells plasma membrane after fusing, conferring them fusogenic capacity. The manuscript is very elegantly written, and of interest to the reproductive community. I have only some minor concerns to be addressed:

1. The authors indicate that this test "can help embryologists to better predict the success of the different ARTs for each individual.". This assumption should be taken as it is, an assumption. In order to state that in can be used, authors should first demonstrate, maybe with a correlation test hand by hand with a reproductive clinic, that this procedure indeed works for diagnosis. There is no doubt that it is a very nice proof of principle.
2. Fig S1C. Although I agree with the ayuthors that the two models are possible, have the authors tried to wash sperm away from the fusing assay, and add new cells, maybe differentially tagged, to see whether they fuse to those already fused with sperm? This could shed light onto the mechanism.
3. Fig 3. Sperm incubated in the absence of BSA, failed tu fuse to cells. I wonder whether the lack of BSA precludes fusogenicity. Have authors tried removing HCO3 and keeping BSA? The presence of HCO3 still enables PKA activation even in the absence of BSA, also allowing hyperpolarization of the plasma membrane. Have authors evaluated acrosomal status after the selected treatments?

Referee Cross-Commenting

I thank reviewer 3 for his/her insight. I agree on being cautious about the relevance of this finding, as it is unclear if anything similar happens in vivo.

I agree with comments raised by rev 1. This reviewer clearly points out that the clinical relevance of the manuscript is not a strength of the manuscript. And I agree with reorganizing the discussion as suggested. In this line, I consider very important to tone down the clinical aspect of the manuscript. In this regard, I completely understand the experiments suggested by reviewer 3, but still, I think that the major contribution of this manuscript is to the sperm-egg fusion field of study. Thus, my suggestion is to tone down to a minimum level the clinical relevance of the study.

Significance

The work shows an interesting advance in the field. However, caution should be taken when referring to "capacitated sperm", since no controls are taken, and methods to inhibit capacitation are not solid.

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 work by Bruckman et al. showed how sperm can induce somatic cell syncytium upon binding. This finding has tremendous implications for the field. Although gamete fusion is one of the fundamental events in biology, the molecular details of this process in mammals is not well understood. As briefly mentioned in this work, spermatozoa contribute Izumo1 which forms a complex with SPACA6 and TMEM81; and probably with other proteins. On the other hand, the egg counterpart is JUNO, a GPI- anchored protein, and CD9. IZUMO1 and JUNO have been shown to interact and this interaction is essential for fusion. However, it is still not clear the extent that this binding is needed for attachment of for fusion between gametes. The authors have published before that somatic cells expressing IZUMO1 can fuse with other cells expressing JUNO; and that sperm can fuse to JUNO-expressing cells. This previous work strongly suggests that IZUMO and JUNO are sufficient for fusion; however, the efficiency of the fusion assay was low. In the present work, formation of syncytium is highly efficient, making possible to envision applications of this assay to research of the proteins involved in sperm-egg fusion as well as for molecular mechanisms. In addition, as mentioned in this work, the assay can be translated for the use for clinical diagnosis.

Overall, the manuscript is straightforward, and the conclusions are sound. I have only a couple of comments:

1. As mentioned in this work, fusion of human sperm with hamster eggs was used in the past to evaluate human sperm fusogenic properties. Although this assay is not longer recommended, still highlight the fact that hamster egg's oolemma is promiscuous regarding species-specific fusion. Although I don't think the experiment is essential, if it were possible, trying hamster IZUMO1 in this assay will be a good addition to this manuscript.
2. Is it possible to know how many sperm form part of the syncytium>?
3. Although I understand the relevance of this assay for diagnostic use, still the major contribution of this manuscript is to the sperm-egg fusion field of study. I encourage the authors to start the discussion with a more basic approach and add at least one paragraph summarizing how their discovery advanced the field. From my perspective the translational application should be at the end of the Discussion section, not at the start.

Referee Cross-Commenting

The syncytia described in this work support the hypothesis that once Izumo and partners in sperm interact with Juno, the cells become able to undergo fusion events. It does not claim that this type of event happens in vivo. Although Juno is expressed in other cell types and not only in the egg, it is too soon to claim a physiological role for syncytia formation in vivo.

I agree with reviewer 2 comments. Both experiments proposed (paragraph 2 and 3) are relatively straightforward.

In general, I also agree with reviewer 3 comments, one of the experiments proposed is similar to the one proposed by reviewer 2. Having said this, I disagree that the human sperm experiment is needed for this paper to be significant. Although the diagnostic claim is important, the major contribution of this work is at the basic research level. Toning down the clinical relevance would be sufficient to make this paper a significant contribution to the field.

Mechanisms of sperm-egg fusion have been elusive for the last decades. In my view, syncytia formation and I don't expect a single paper to elucidate such a complex event.

Significance

General Assessment: This is an excellent manuscript contributing to our understanding of sperm-egg fusion. In addition, the tools described here warrant new approaches to study the molecules involved in this process using genetically modified mouse models.

Advance: Although the relevance of IZUMO1 and JUNO for sperm-egg fusion have been previously reported, as far as I know, this is the first study showing somatic cell fusion and syncitium formation using sperm and somatic cells. This finding will provide new tools to study the molecular basis of sperm-egg fusion as well as generate translational tools to evaluate human sperm ability to fertilize.

Audience: I believe that this manuscript will have broad interest. Although initially, the main audience will be related to reproductive biologists, this manuscript will be also highly relevant for other scientists studying fusion. In addition, this work has clear translational applications.

Keywords describing my expertise: sperm, eggs, in vitro fertilization, reproductive biology, embryos

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

To,

The editors,

Review Commons.

                                                                                                        31st Oct, 2023

We thank the editor and all the reviewers for the detailed critical feedback on our manuscript. We have substantially revised the manuscript to address all the queries, and have incorporated changes that address most of the suggestions made by the reviewers. The revised manuscript includes new experimental data, as well as text changes that address and clarify comments raised by the reviewers. The manuscript has been significantly strengthened by these revisions. A detailed response to reviewer comments is included below.

In the response letter below as well as the revised manuscript, we have addressed all the concerns raised by reviewers 1, 3, and 4, and most comments of reviewer 2. Some of the experiments suggested by reviewer #2 (related to an in-depth phosphoproteomics analysis) are unfortunately well beyond the scope of this manuscript, and infeasible at this stage (with the explanations provided below). We have divided our response document into three sections. In the first paragraph of the response, we briefly summarize the new data that we have included in the revision in response to reviewer queries. In the second section of this document, we have addressed the common comments raised by all the reviewers, which mostly address comments regarding the identification of Far11 phosphosites and mechanistic details about Far complex assembly. In the third part of this response, we include a detailed, point-by-point response to each of the reviewer's concerns, pointing to new data and specific changes made in the main manuscript. We also include a marked-up (in blue) version of the manuscript text, for easier follow up.

Our responses to queries are provided in blue text.

__Section I: A Brief summary of all new experimental data included in this manuscript __

• In this revised version of our study, we assessed the impact of the combined deletion of Ppg1 and components of Far complex. Our analysis revealed that double deletion of Ppg1 and Far complex does not result in any additive effect on metabolic regulation, indicating Ppg1 and Far function sequentially within the same pathway. Given these observations, the expectation will be that cells lacking Far complex will also have similar effects as the Ppg1 knock-out to adapt to glucose depletion. To assess this hypothesis, we conducted a competition experiment and indeed found that Far complex is required for proper adaptation to glucose depletion, very similar to Ppg1.
• To understand if Ppg1-Far regulate gene expression changes to modulate gluconeogenic outputs, we assessed transcript levels of gluconeogenic enzymes from ppg1D cells. Interestingly we observed that the transcript levels of these enzymes remain unchanged in ppg1D cells, suggesting mechanisms involving other modes of regulation.
• Additionally, our investigations revealed that the increased carbon allocation to cell wall precursors in ppg1D cells also makes them more susceptible towards the cell wall stress agent, Calcofluor white, similar to results obtained with Congo red.
• We have also carried out growth and competitive growth experiments with Far complex mutants, similar to those carried out with ppg1D cells, and find that these phenocopy ppg1D cells with respect to adapting to growth in glucose-depleted conditions. Section II: Regarding the identification of Far11 phosphosites dephosphorylated by Ppg1

Our response to this concern:

We agree with the reviewers that in order to obtain the full mechanism of Far complex assembly, it will be important to identify the Far11 phosphosites regulated by Ppg1. However, current evidence already suggests that it will be very challenging to identify specific phospho-site targets of Ppg1. Currently available data from extensive phosphoproteomics studies in Saccharomyces cerevisiae have identified a very large number of phosphorylation sites on Far11 (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). The identified Ser/Thr residues on Far11 that are known to be phosphorylated are shown in the figure below, and include at least 19 Ser/Thr residues. Considering that so many Ser/Thr residues of Far11 are phosphorylated, it is hard to pinpoint specific phosphosites that are recognized and uniquely dephosphorylated by Ppg1. Additionally, it is widely established that Ser/Thr phosphatases, especially the PP2A family, have little selectivity. At least in vitro, as well as often in vivo, multiple PP2A family members non-selectively target the same residue. Therefore, most of the function is only revealed by a combination of genetic and biochemical data that identify contextual phenotypes (as we have done so, and established in this manuscript). At this stage of this study, these phosphoproteomics experiments are well beyond the scope of the current manuscript.

Identified Ser/Thr residues known to be phosphorylated in only Far11

__However, we acknowledge this important point raised, and now include it in the discussion in Line 507. __

The revised text now reads:

“Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”

We also now have an extensive discussion section included, on the challenges of identifying specific phosphatase sites on proteins (and the contrast with kinase dependent phosphorylation sites). This section reads as (Line 500):

“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge. Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”

Section III: Point-by-point response to all individual reviewer comments:

Reviewer #1:

The authors study metabolic adaptation to glucose depletion in budding yeast. A non-essential protein phosphatase mutant screen reveals adaptation to glucose depletion (growth in post-diauxic phase) requires Ppg1. The authors show i) that, in post-diauxic phase, cells lacking Ppg1 accumulate more trehalose, glycogen, UDP-glucose, UDP-GlcNAc (i.e., gluconeogenic outputs) than wild-type cells, ii) that, in post-diauxic phase, cells expressing a catalytically inactive version of Ppg1 accumulate more trehalose and iii) that Ppg1 is required for adaptation and growth post-glucose depletion. The authors find that Ppg1 interacts with Far11 (a member of the Far complex) in cells growing in the post-diauxic phase and that Ppg1 promotes Far complex stability. Finally, the authors conclude that the Ppg1 promotes Far complex stability to maintain gluconeogenic outputs after glucose depletion.

We thank the reviewer for a careful reading of this manuscript, and many constructive comments.

Major comments:

• Figures 1 to 4. The authors show that loss of Far components phenocopies loss of Ppg1 and conclude that Ppg1 is upstream of Far. However, the authors do not determine the combined effect of the two mutations. The authors should assess the phenotype (e.g., gluconeogenic output levels) of cells lacking both Ppg1 and Far (or in far9_deltaTA far10_deltaTA cells lacking Ppg1). The authors' conclusion would be strengthened if there was no additive effect between the mutations. Thank you for raising this point. We have now investigated the combined effect of deletion of Far11 and Ppg1 on post-diauxic carbon metabolism by measuring trehalose amounts. From these measurements, we did not observe any additional increase in trehalose amounts in the cells lacking both Ppg1 and Far11. We also assessed the growth of these cells in the presence of the cell wall stress agent, Congo red. There was no additional growth defect in the double deletion strain in the presence of Congo red. This data strongly indicates that the double deletion of Ppg1 and Far11 does not have an additive effect, indicating that both proteins function in the same pathway. This data is now included in Fig. S3 D and E. Text changes are made accordingly in the results section line 300:

“To determine if Ppg1 and Far complex independently regulate carbon metabolism or function within the same pathway, we generated double deletion mutants of Ppg1 and Far11 (ppg1Dfar11D). We assessed trehalose accumulation in ppg1Dfar11D cells and found no additional increase in trehalose levels (Figure 3H). Furthermore, we studied the growth of these ppg1Dfar11D cells in the presence of Congo red and observed no additional growth defects (Figure 3I). Overall, these findings strongly indicate that Ppg1 and Far complex function sequentially within the same pathway, with Ppg1 upstream of Far.”

Data showing trehalose levels from WT, far11D and ppg1Dfar11D cells after 24hrs of growth in YPD medium (now new Figure 3H):

Data showing the growth of WT, far11D and ppg1Dfar11D cells in the presence of Congo red (now new Figure 3I):

• Related to Figure 6A-C. One would expect that cells lacking Far components (or far9_delta TA far10_deltaTA cells) show a similar phenotype (fail to adapt to growth in changing glucose compared with wild-type cells) as cells lacking Ppg1. Is this the case? We agree with this expectation. The cells with loss of components of Far complex fully phenocopy ppg1D cells, and have an imbalanced carbon metabolism. Therefore, the expectation is that these cells will exhibit similar defects as ppg1D to properly adapt to glucose depletion. To address this question, we carried out a competition experiment with wild-type and Far9DTAFar10DTA cells (where the Far complex can now no longer be anchored, and therefore assemble properly, as shown in Fig. 4F, G), specifically assessing adaptation to environments as glucose depletes (and done identically to those with ppg1D). Note: the choice of this strain was to enable quantitative estimation, since we needed strains that had a ‘fluorescence’ mark (mNeonGreen or mCherry) to quantitatively assess changes in each genotype. Similar to ppg1D cells, the relative proportion of Far9DTAFar10DTA cells decreased during the competition experiment (Fig S6A). Independently, we estimated changes in post-diauxic growth of far11D cells, starting in glucose-rich conditions. In batch culture, the far11D cells showed reduced growth specifically in the post-diauxic phase (Fig S6B). Effectively, the loss of the Far complex nearly perfectly phenocopies the loss of Ppg1 in enabling effective adaptation to glucose-depleting environments. This data reiterates the importance of the Far complex in adaptation to glucose depletion, as the mechanistic target of Ppg1 function. This data is now included in the new Fig. S6 A and B. The text changes are made accordingly (Line 423):

---

“To concurrently address the role of Far complex in enabling cells to adapt to glucose depletion, we carried out a similar competition experiment (as with ppg1D) with WT and Far9DTA10DTA cells. Note: the Far9DTA10DTA cells will not allow the Far complex to anchor and assemble within cells, as shown earlier, and therefore phenocopies far9D, and was utilized in this experiment for easier quantitative estimations based on fluorescence. Expectedly, the relative proportion of Far9DTA10DTA cells decreased during the course of the competition experiment (Figure S6A). We next examined the effect of loss of Ppg1 on steady-state batch culture growth, starting from a glucose-replete medium. The loss of Ppg1 did not affect growth in the glucose-replete log phase, but after cells entered the post-diauxic (glucose-depleted) phase, ppg1D cells showed reduced growth and a reduction in biomass accumulation (Figure 6D). Independently, we assessed the growth of far11D cells starting in glucose-replete conditions, and observed reduced growth of these cells specifically in the post-diauxic phase (Figure S6B), similar to ppg1D cells. Effectively, the loss of Ppg1 or the Far complex phenocopied each other, and collectively, these data reveal that Ppg1-Far mediated regulation enables adaptation and competitive growth fitness after glucose depletion.”

Data showing growth competition between WT and Far9DTA10DTA cells in changing glucose conditions (now new Fig. S6A):

Data showing growth dynamics of WT and far11D cells in the YPD medium (now new Fig. S6B):

• The manuscript would be considerably strengthened if the authors provided more information on the mechanism by which Ppg1 controls Far complex stability, e.g., can the authors about the phosphosite(s) in Far11 regulated by Ppg1? As the authors mention, it has been already suggested that Ppg1 is required for Far complex assembly (PMID: 33317697). This comment has been commonly addressed in the section 2 of this document. Briefly, while we are equally excited about this direction, given the complexity of phosphorylation of the Far11 protein (and the challenges specifically in the context of PP2A family phosphatase action), this component is likely to take years to address and is beyond the scope of this manuscript. We do include a more speculative section in the discussion in this regard.

Minor comments:

• The authors may consider to include data from Figures 6A, 6B and 6C (failure to adapt to glucose-changing conditions) after Figure 2 to show a complete characterization of the phenotype of cells lacking Ppg1. Figure 6 could show only the "proposed model". We appreciate the reviewer’s suggestion and had indeed considered this possibility in an early version of the writing of the manuscript. However, we prefer the current flow of this manuscript, where we identify Ppg1 as a putative regulator of gluconeogenic flux, and end with the mechanistic confirmation of function that links Ppg1 and Far complex to the same adaptation function. We hope the reviewer appreciates this viewpoint.

• Related to Figure 1. The authors mention that Ppg1 is a "notable hit" and that the "increase in post-diauxic trehalose levels are considerable". However, there is no reference to use as a comparison. Is there any other mutant strain known to accumulate trehalose at the post-diauxic shift? If yes, it would be informative if the authors compared the effect of such mutant strain to a ppg1-delta mutant. For this experiment, we did not employ another mutant as a reference for increased trehalose accumulation. However, the point raised by the reviewer in itself was interesting in itself. Looking into the literature, we find that there are no reliable, quantitative estimates of how much trehalose increases in yeast in the post-diauxic phase (compared to the log phase), although numerous manuscripts (including some of our own earlier work) allude to this point. Therefore, we did this experiment, to obtain absolute quantitative information on the trehalose amounts in YPD in cells after 4 hrs of growth in 2% glucose, vs. in cells the post-diauxic phase (24 hrs after starting growth in 2% glucose). The amounts of trehalose increase >10-fold in the post-diauxic phase compared to the log phase. We now mention this in the text, and include absolute quantitation of trehalose amounts in Fig S1B. __Hence, a ~1.5-fold further increase in trehalose amounts in the post-diauxic ppg1D cells compared to post-diauxic wild-type cells is considerable. The revised text now reads (Line 121):__

• *

*“We initially estimated how much trehalose amounts increased in the post diauxic phase. Trehalose amounts increased over 10-fold in the post-diauxic phase after 24 hours of growth starting in 2% glucose, compared to cells after 4 hours of growth in the same condition (Figure S1B).” *

• *

*“Compared to the ~10 fold increase in trehalose (as shown in Figure S1B), the further increase of 1.5-fold in trehalose accumulation in the post-diauxic phase in cells lacking Ppg1 (Figure 1D) is substantial.” *

Data showing trehalose accumulation in log and post-diauxic phase wild-type cells (Figure S1B):

• Figures 4D and 4F. Regarding sensitivity to Congo Red and compared to wild-type cells, it seems that cells lacking Far9 are much more sensitive to Congo Red in Figure 4F than in Figure 4D. Is this just an image quality issue? The authors should address this apparent discrepancy. The small visual difference is likely because the images (which come from experiments done at different times) were taken at slightly different time points after spotting. To avoid any confusion, in the figure legends we have now mentioned the precise time at which each of the images were taken.

Reviewer #2:

Summary: In this manuscript, the authors screened the yeast phosphatase mutant that shows defective in metabolic adaptation and found that PP2A-like phosphatase Ppg1 is required for the appropriate gluconeogenic outputs after glucose depletion. Furthermore, they showed that Far complex which assembles with Ppg1 is also required to maintain gluconeogenic outputs. They also found that Ppg1 is required for assembly of Far complex and the assembly on the ER or mitochondrial membrane is important for their function. Ppg1 and Far complex dependent control of gluconeogenic outputs had important role on adaptive growth under glucose depletion.

Major comments:

In this study, the authors report new evidence that the Ppg1 and Far complexes are involved in the regulation of gluconeogenic outputs. However, the mechanism by which the Ppg1-Far complex is involved in gluconeogenic outputs has not been fully analyzed, and further analysis of the role of Ppg1 in Far complex assembly and the significance of Far11 phosphorylation is needed. The authors should consider the following points,

We thank the reviewer for valuable, constructive comments. Investigating the mechanism through which Ppg1, via the Far complex, regulates gluconeogenesis outputs and unravelling the added mechanism of Far complex assembly in this context are exciting areas of future research, and indeed where we hope to go. However, at this stage addressing some of these follow-up questions is beyond the scope of this manuscript. Our current findings unambiguously identify Ppg1 as a phosphatase that controls post-glucose depletion gluconeogenic flux, also identifies this mechanism to function through the proper assembly of the Far complex, and show that cells function through a Ppg1 - Far axis to adapt to glucose depletion. At this stage, we do not know what the Far complex might help assemble, and while this is an obvious follow-up, we anticipate years of effort to unravel this next question.

• In the mutant screen, both pph21Δ and pph22Δ cells showed increased levels of trehalose (figure 1C). Pph21 and Pph22 are catalytic subunits of protein phosphatase 2A (PP2A) and function redundantly. Thus, it may be possible that PP2A is more involved in gluconeogenic outputs regulation than Ppg1. In S. cerevisiae, the PP2A phosphatases regulate phosphorylation of transcription factors that control storage carbohydrate synthesis, and thereby regulate carbon metabolism (Bontron et al, 2013; Clotet et al, 1995; Dokládal et al, 2021). Notably, the deletion of Pph21 and Pph22 results in increased transcription of glucose repressed genes (Castermans et al, 2012), consequently resulting in increased gluconeogenesis and storage carbohydrate synthesis in these mutants. Additionally, our screen data also found an increase in trehalose accumulation in mutants of Pph21 and Pph22 (which were less than that of the Ppg1 mutants). Collectively, these observations emphasize the role of PP2A phosphatases in regulating gluconeogenic outputs, primarily through transcriptional control.

In notable contrast to the transcriptional changes observed with Pph21/22 mutants, the Ppg1-mediated regulation described in this manuscript does not involve any transcriptional changes of the enzymes of gluconeogenesis and related carbon metabolism (now included in Fig 2E ). This excitingly points towards regulation by some combination of post-translational modifications, allostery, or mass action. In light of these disparities, we believe that the function of Ppg1 elucidated in this study, operates independently of PP2A-mediated regulation of carbon metabolism. This point is now included in the discussion (Line 456). The revised text now reads:

“There are well studied examples of signaling systems regulating metabolic adaptation, which have typically focused on understanding the repression or activation of relevant transcriptional outputs. For example, upon glucose depletion, the Snf1 kinase activates transcription factors such as Cat8 and Rds2, resulting in an increase in transcripts of key gluconeogenic enzymes (Turcotte et al, 2010; Rashida et al, 2021; Vengayil et al, 2019). In this context, phosphatases belonging to PP2A family, particularly Pph21 and Pph22, regulate transcriptional outputs of glucose repressed genes (Bontron et al, 2013; Castermans et al, 2012). Interestingly, and in contrast to this, the Ppg1-mediated regulation we uncover in this study does not rely on changes in gene expression (Fig. 2E). Instead, this points towards regulation through other mechanisms that are driven by post-translational modifications, mass action, or enzyme concentration etc. This function of Ppg1, as uncovered in this study, differs from regulation mediated by related phosphatases.”

• Is it the Far complex or Ppg1 activity that is required for the regulation of gluconeogenic outputs? It seems that assembly of the Far complex requires Ppg1 and Ppg1 activity requires the Far complex. However, either one should be involved in the regulation of gluconeogenic outputs. For example, Innokentev et al, 2020 concluded that Ppg1 activity is critical for the regulation of mitophagy and that the Far complex serves only as a scaffold for Ppg1. by Ppg1 dephosphorylating an unidentified protein. The possibility that Ppg1 may be involved in the regulation of glycolytic output by dephosphorylating unidentified substrates needs to be fully tested. We agree with the reviewer's point. Our data very clearly now demonstrate that the Ppg1 activity is required for the assembly of Far complex (Fig. 3D). Subsequently, our data shows that the Far complex is required for regulation of gluconeogenic outputs. These observations together suggest the following two hypotheses:

• Ppg1 is required for assembly of the Far complex. The assembled Far complex could transiently interact with other proteins that regulate gluconeogenic outputs (as would be possible for a ‘scaffolding system’). Ppg1 is primarily required for the assembly of the Far complex, and may not directly regulate signaling proteins that control gluconeogenesis.

• Alternately, the Far complex only serves as a scaffold, and enables Ppg1 to interact and dephosphorylate as yet unidentified substrate(s). These two possibilities are also not mutually exclusive. Both these possibilities merit further investigation, but at this stage, all our direct biochemical experiments (including isolating the Far complex and Ppg1, in order to identify interacting proteins) have not yielded more than this connection between Ppg1 and Far itself. Given this (and what would be a reasonable expectation for a dynamic scaffold), it is likely that the subsequent targets of Ppg1 and Far are transient interactions. A future effort would involve creating proximity-based target identification systems in S. cerevisiae (that work effectively in glucose-depleted conditions) and identifying such mechanisms. Currently, no such system exists, and we are building these kinds of tools for future studies. Exploring such mechanisms of gluconeogenic outputs is therefore a very interesting area of future investigation, but well beyond the scope of this manuscript. We include an acknowledgement of the same in the discussion section____ (Line 490). The revised text now reads:

“Notably, the Ppg1 phosphatase regulates post-diauxic carbon metabolism by modulating the assembly of the Far complex (Fig. 3). Considering this requirement of Ppg1 to assemble this scaffolding complex and thereby constrain gluconeogenic flux, our study presents two intriguing possibilities: first, the Far complex scaffold could act as a facilitator, enabling interaction between Ppg1 and its other substrates (which regulate gluconeogenic outputs); and second, the primary function of Ppg1 is to facilitate Far complex assembly, which transiently brings to proximity other signaling proteins and enzymes that control gluconeogenesis. Both these possibilities (which are not mutually exclusive) merit detailed investigation. However, exploring these would require the development of new, proximity-based target identification systems for yeast that can identify transient protein-protein interactions. Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge.”

• Although there are no known substrates of Ppg1 other than Atg32, Atg32 is not involved in the regulation of gluconeogenic outputs. The identification of substrates of Ppg1 involved in the regulation of gluconeogenic outputs will help to elucidate the molecular mechanism of gluconeogenesis. We completely agree with the reviewer's point (and also see our response to the previous point). It’s important to note that the involvement of Ppg1 in regulating mitophagy is entirely independent of its role in regulating gluconeogenic outputs. This is something we firmly establish in this study, in Fig. S1C. This indicates that in addition to its recognized role in Atg32 dephosphorylation specific to extreme starvation conditions of mitophagy, Ppg1 activity functions to regulate gluconeogenesis, a critical homeostatic function. Our response to the previous comment indicates our future lines of inquiry, which are currently well beyond the scope of this manuscript. Included in the discussion (Line 500). The revised text now reads:

---

“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge.”

• The authors conclude that Ppg1 dephosphorylates Far11 and that dephosphorylated Far11 assembles with the Far complex. However, there is a possibility that Ppg1 activity is required for Far complex assembly independently of dephosphorylation of Far11. To prove the authors' assertion, it is necessary to identify the phosphorylation site of Far11 and show that its phosphorylation affects the binding of Far11 to Far8. We address this point in the earlier section 2 of this document and hope that the reviewer will recognize the extreme challenges in the feasibility of these experiments at the current stage

• Several kinases have been reported to be involved in gluconeogenic outputs regulation. The initial aim of this study was to identify phosphatases involved in gluconeogenic outputs regulation by antagonizing these kinases. However, Ppg1 has not been shown to be involved in transcriptional regulation to control carbon metabolism by antagonizing any kinase. We thank the reviewer for raising this important point, which is one of the highlights of the findings of this manuscript. The regulation of gluconeogenesis enzymes by dephosphorylation/phosphorylation is not yet known at all, nor has there been a prior reason to look for such regulation. This paper will now provide strong reasons to look for such regulation. Separately, almost all past effort has been to look at transcriptional responses to changes in carbon availability. This is despite overwhelming evidence (Hackett et al, 2016) that over 50% of metabolic regulation in yeast can be direct - at the level of flux regulation by mass action, substrate availability and/or allostery- and precludes transcriptional changes. Since this interesting point was raised, we compared the expression of transcripts of the enzymes of gluconeogenesis, storage carbohydrate metabolism and cell wall synthesis in wild-type and ppg1D cells. Notably, we did not observe significant changes in transcript levels of these enzymes in ppg1D cells. This provides additional evidence that suggests the regulation of gluconeogenic flux (which we quantitatively demonstrate in Fig 2) must be via alternate mechanisms that involve increasing local substrate concentrations/PTMs/enzyme scaffolds or other mechanisms that need not invoke transcription. We therefore believe that it will be very interesting to study these possibilities in the future, and this can lead to a rich line of future inquiry. Our study also opens the possibility that Ppg1 might counteract kinase-mediated signaling (which, in the context of glucose, is better studied with PKA, TORC1 and other outputs). We have now included the transcript analysis of gluconeogenic enzymes in WT and ____ppg1____D cells, now included in the new Fig 2E.

We also include this revised text, to reiterate this point (Line 212):

---

“Finally, we asked if Ppg1 regulates the expression of transcripts of enzymes involved in gluconeogenesis, storage carbohydrate metabolism and cell wall synthesis proteins to modulate gluconeogenic outputs. For this, we measured the transcript levels of these enzymes from post-diauxic WT and ppg1D cells. Notably, the transcript levels of these enzymes remain unchanged in ppg1D cells (Figure 2E). This data suggests that Ppg1-mediated carbon flux regulation does not involve any transcript level changes, indicating that Ppg1 regulates gluconeogenic flux via mechanisms that involve allosteric, post-translational or mass action-based regulation.”

Data showing transcript levels of enzymes of gluconeogenesis, storage carbohydrate metabolism, and cell wall synthesis proteins in WT and ppg1D cells (now new Fig. 2E):

We also include a few lines in discussion, where we reiterate that while much of our understanding of the regulation of gluconeogenesis comes from changes in transcriptional programs, substantial regulation of metabolic flux involves direct regulation via allostery, post-translational modifications, mass action and concentration (Hackett et al, 2016). Ppg1, via the Far complex, appears to participate in one such example of regulation (Line 463).

“the Ppg1-mediated regulation we uncover in this study does not rely on changes in gene expression (Fig. 2E). Instead, this points towards regulation through other mechanisms that are driven by post-translational modifications, mass action, or enzyme concentration etc. This function of Ppg1, as uncovered in this study, differs from regulation mediated by related phosphatases. How might this occur? An underappreciated but important mediator of metabolic adaptation is the direct modulation of metabolic outputs or flux, through a combination of mass action and allosteric regulation (and without invoking transcriptional changes). Even in unicellular organisms like S. cerevisiae, over 50% of metabolic regulation occurs through such mechanisms (Hackett et al, 2016).”

• If the assembly of the Far complex is involved in gluconeogenic outputs regulation, what is the mechanism? The Far complex is a scaffold for enzymes. Therefore, the role of the Far complex in gluconeogenic outputs regulation will not be elucidated until the enzymes that function there are identified. We agree that the specific mechanism of how the Far complex functions, after being assembled by Ppg1, cannot be understood unless we find those targets. Indeed, the Far complex may potentially interact with signaling proteins or metabolic enzymes involved in post-diauxic carbon metabolism. However, many of these interactions are transient and identifying these interactions is an extremely challenging task. As pointed earlier, we will now have to establish effective methods in yeast for proximity-based substrate/target identification, establish effective mass spectrometry-based pipelines for the same, and then screen for new regulators. This is by no means a trivial task, and is well beyond the scope of this manuscript, and we hope the reviewer recognizes the same.

Recognizing this point, we had mentioned this in the discussion (Line 553):

“In order to understand how dynamically assembled scaffolds with varying localizations and modifications can regulate homeostatic outputs such as metabolic adaptations, we require new chemical biology approaches that stabilize low-affinity protein-protein interactions, or substrate-trapping mutants to identify transient substrates that are brought together by such signaling hubs (Qin et al, 2021). This remains a key challenge in the context of protein phosphatases, which naturally interact with substrates with low affinities (Bonham et al, 2023).”

Minor comments:

• Because TA of Far10 can tether Far complex on the membrane, Mito-Far and ER-Far experiments (Figure 4A-D) should be performed under Far10ΔTA conditions. We agree with the reviewers' comment. In the Mito-Far and ER-Far cells, the Far10 protein (with intact TA domain) can localize to the surface of both organelles. Our careful microscopy images show clear localization/targeting of components of Far complex to respective compartments in both Mito-Far and ER-Far cells. This data strongly indicates that regulating localization of Far9 by itself (at either surface location) is sufficient for Far complex to localise to these compartments.

• Figure 6B and 6C, total culturing time (hours) should be shown on X-axis in addition to number of transfers. We now include this in the figure legends.

• Figure 3E, additional explanation is needed as to why the molecular mobility of Far11-FLAG after CIP treatment differs between Ppg1-H111N and Ppg1. The difference in mobility of Far11 in wild-type and Ppg1-H111N cells can be because of post-translational modifications other than phosphorylation. However, these modifications are regulated in a Ppg1-dependent manner. It will be interesting to identify these modifications on Far11 and their role in stabilizing the Far complex assembly. We now include this in the text (Line 285):

“At this stage, while these experiments are consistent with a role of dephosphorylation in Far11 function and the assembly of the Far complex, these do not preclude other post-translational modifications in addition to phosphorylation.”

Reviewer #3:

The study performed by Niphadkar et al. seeks to uncover the role of the phosphatase Ppg1 in regulating gluconeogenesis during post-diauxic shift in S. cerevisiae. The authors show that loss or inactivation of Ppg1p affects production of gluconeogenic products incl. trehalose and glycogen. The authors show that assembly of the Far complex required the activity of Ppg1 and is required to maintain gluconeogenic

outputs after glucose depletion.

The manuscript is clearly written and methods well considered, no omics-methods have been included. Especially phosphoproteomics would be relevant to include. Specifically, the tracing experiments are an interesting and appropriate approach to confirm effects on gluconeogenesis etc. Yet, working with regulation of posttranslational modifications (phosphorylations) it is surprising that the authors only to a limited extent examine phosphorylation events, and not all examine or discuss specific phosphorylation events of e.g. Far11.

The study is interesting and provides new insights into regulation of glucose metabolism in yeast, however, there are serious concerns that need to be addressed before it can be reconsidered for publication.

Major points:

• The authors use electrophoretic mobility assays w/wo CIP to address the phosphorylation state of Far11. They show in figure 3E that the mobility of Far11 depends on Ppg1 activity and can be affected by CIP. Why is the mobility of Far11 not affected in e.g. figure 3D? For these experiments, protein gels with different acrylamide concentrations were used. For the shift experiment, proteins were resolved using 7% gel for the duration of 5 hours. In Fig. 3D, 4-12% gradient gels were used and proteins were resolved for the duration of 2 hours. We now mention this in the figure legends and methods section.

• There are several sites in Far11 previously reported to be phosphorylated, see e.g. Bodenmiller et al 2010 (Science Signal.) Are there sites that are specifically regulated (dephosphorylated) by Ppg1? or by other phosphatases? kinases? This is addressed in section 2 of this document. In that section, we summarize the very large number of putative Ser/Thr residues that are phosphorylated in Far11, and while there is no clear information on which kinases might act on these, it is extremely complex to identify specific phosphatase roles in dephosphorylating these.

• Here, it would be appropriate to apply phosphoproteomics to examine Far11 phosphorylation in Ppg1 knock out cells or in cells with inactivated Ppg1. We agree with the reviewer's comment. It will be very interesting to implement phosphoproteomics to identify phosphosites regulated by Ppg1. However, unlike kinases, the changes in the phosphoproteome with phosphatase knock-outs are very challenging to interpret, especially for a family of phosphatases from the PP2A family. Due to a range of overlapping substrate recognition sites, as well as a change in kinase outputs when a phosphatase is missing, interpreting phosphoproteomes with phosphatase knockouts, which function conditionally (during say post-diauxic conditions, like this study), will have substantial challenges. See for example this very recent, exhaustive, state-of-the-art study in yeast, quantifying kinase and phosphatase mutant phosphoproteomes (Li et al, 2019). While the analysis for kinase mutants were substantially more revealing, the data from phosphatase mutants were very convoluted, and could identify very little specific outputs of phosphatase function. This set of experiments is beyond the scope of this manuscript, but this manuscript provides compelling reasons to do so. We have included a couple of lines in the discussion, related to this specific component. Included in the discussion (Line 500).

“Separately, phosphoproteomics-based studies could provide avenues for identifying as yet unidentified substrates of Ppg1. However, phosphoproteomics approaches have been far more suited for elucidating kinase-mediated regulation, due to high substrate specificity of kinases (Li et al, 2019). Identifying the specific substrates of phosphatases has posed significant challenges because of the nature of phosphatases like PP2A, which exhibit low substrate specificity and often have overlapping and compensatory outputs (Virshup & Shenolikar, 2009; Millward et al, 1999). Hence, determining the specific outputs or substrates of phosphatases through these methods presents a formidable challenge. Additionally, large-scale studies suggest over 19 putatively phosphorylated Ser/Thr residues in Far11 alone, indicating multiple kinase-phosphatase interactions on this protein (Bodenmiller et al, 2010; Swaney et al, 2013; Soulard et al, 2010). Phosphoproteomics experiments with Ppg1 mutants are therefore a good starting point, but in themselves may be insufficient to specifically identify Ppg1 specific phosphosites on Far11.”

• The authors show that the levels of Ppg1 remain constant during growth in YPD medium, while the levels of Far11 increased after 24hrs of growth in YPD medium, and thus argue that the amount of Far complex itself increases in post-diauxic phase. The authors need to show that the level of complex indeed increases. In addition to Far11, we also compared the amounts of Far8 - another core component of Far complex. Similar to Far11, we observed an increase in the amounts of Far8 specifically in the post-diauxic phase. We also assessed the amounts of Far8 in response to glucose availability, and find that Far8 also decreases when glucose is added to the system. These data support our findings that the amounts of Far complex increase in the post-diauxic phase. These data are included in Fig. S5 B.

In the text, we reiterate (Line 396):

“Furthermore, the amounts of Far8 also were reduced after addition of glucose to post-diauxic cells (Figure S5B). Together, we infer that the activity and amounts of Ppg1 are constitutive, but the amounts of the Far proteins are glucose-responsive (Figure 5E).”

Data showing the effect of glucose availability on Far8 protein amounts are included in (Fig. S5B):

• The authors also apply fluorescence microscopy to address the localization of the Far11 complex etc. The quality of the shown images should be improved, also merged images should be shown. Only one single image containing one cell is shown, images should ideally show additional cells in the same image, alternatively, additional images should be shown. Good point. We have now included higher quality images which show more cells in each frame, as well as include the merge/overlap, in Fig. 4B. This should satisfy concerns.

For the reviewer’s reference, some additional images is are shown here:

Reviewer #4:

General comments

This paper reports a critical function of PP2A-like phosphatase encoded by PP1G in the post-diauxic shift of the yeast Saccharomyces cerevisiae. This function is mediated via the assembly of FAR complex that naturally sites at the ER-mitochondria outer membranes to ensure proper onset of growth at the diauxic shift by appropriate carbon allocation through gluconeogenesis. The identification of this PPase was based on a screen of yeast mutant defective in non-essential PPases for trehalose accumulation.

This is a bit surprising as it is known that trehalose accumulation sets in as soon as glucose is depleted and continues steadily during growth on other carbon source, which is merely ethanol, although it may depend whether the experiment was carried out in YPD or in mineral synthetic medium as YNB.

Although the work seems experimentally well conducted, in particular for the demonstration that bPP1G interacts with FAR complex, it raises several issues requiring a thorough revision and additional experiments to truly support the role of PP1F in regulating post-diauxic shift.

• all experiments were done using YPD medium and only a single value of trehalose at 24 h was recorded! It will be important to ensure that all mutant had exactly same growth rate, that at 24 h, glucose was totally gone. It should be relevant to have a more complete kinetic analysis of trehalose/ glycogen accumulation along growth, monitoring as well glucose consumption in WT and the pp1gD, to really convince that the metabolic difference is not associated with a difference in glucose consumption, such as that at 24 h, there is still some glucose remaining in the culture of the mutant! We thank the reviewer for raising these interesting points. We address this comment in following two points:

• In Fig.2 C, we have shown the kinetics of trehalose accumulation during the course of growth. Additionally, we measure amounts of other gluconeogenic outputs as cells grow and deplete glucose. Only after cells deplete glucose and enter the post-diauxic phase do we observe an increase in amounts of these metabolites in ppg1D cells.

We measured extracellular glucose concentration at 24hrs, and there was no detectable glucose present in the medium. This indicates that cells have completely consumed available glucose and have shifted to gluconeogenic metabolism. We now mention this in the text (Line 117):

“For the screen output, trehalose accumulation was assessed from these mutants after 24hrs. At this 24hrs time point, no glucose was detected in the medium, confirming that cells are in the post-diauxic phase. Trehalose synthesis increases in the post-diauxic phase and is a reliable readout of a gluconeogenic state.”

Note for further references: in several earlier studies, we have systematically established that trehalose production is a very reliable indicator of gluconeogenic flux (e.g. see PMID: 32876564, PMID: 31758251, PMID: 27090086, and PMID: 31241462). In addition, we have extensively described ways to look at trehalose production in this methods paper PMID: 32181267.

Finally, we will note that there are very few studies that actually have estimated flux through gluconeogenesis, and have made most inferences using only the expression of transcripts of gluconeogenic genes, and hence the interpretations have to be made accordingly. Our study, to our knowledge, is the first to provide quantitative carbon flux measurements through gluconeogenic intermediates, in the context of any phosphatase mutant studied in yeast. All other studies have not measured flux, but rely on changes in transcripts, or steady-state amounts of storage carbohydrates to draw conclusions.

• The experiment at least with pp1gD and WT should be redo in mineral synthetic medium with 2% glucose. There, only ethanol can be the sole carbon for growth resumption and thus this will ensure that the effects is linked to growth resumption at diauxic growth as YPD is a rich medium that contains excess of many amino acids and peptides that may interfere with your phenotype. We have examined the growth of ppg1D cells in a synthetic medium with glucose as the sole carbon source. These data indicate that ppg1D cells grow similar to wild-type cells in the log phase (glucose-replete). However, these cells show reduced growth post-glucose depletion, where the only available carbon source is secreted ethanol. Here, as expected in a synthetic minimal medium, the extent of difference in growth of ppg1D cells is not as pronounced as seen in YPD medium as the spent post-diauxic medium here may not provide enough carbon source to fuel further growth.

Data showing the growth dynamics of ppg1D cells in SD medium:

Additionally, to study the role of Ppg1 in regulating post-diauxic metabolism in cells growing in SD medium, we measured amounts of gluconeogenic outputs from the post-diauxic cells. Notably, after 18 hours of growth in the SD medium, the ppg1D cells showed increased amounts of gluconeogenic outputs (UDP-GlcNAc, F16BP). Collectively, this data suggests that Ppg1 regulates gluconeogenic outputs in cells growing in a synthetic medium with glucose as the only carbon source.

Data showing the relative levels of UDP-GlcNAc and F16BP in ppg1D cells after 18 hours of growth in SD medium:

__Given that all the experiments conducted in this manuscript were performed using YPD medium, and YPD is better reflective of a complex nutrient medium that natural yeasts would be exposed to (and more relevant to adaptation in changing nutrient sources), we feel that the manuscript remains more readable and relevant in YPD (complex medium). Incorporating these data from minimal medium in the manuscript would disrupt its coherence and the overall flow. In addition, the very elaborate estimates of carbon flux through gluconeogenesis, using 13C label tracking, have been done in this medium only. As noted earlier, this is the first study (to our knowledge) to do this in the context of any phosphatase regulating gluconeogenic flux. Repeating the entire study in minimal, defined medium is therefore impractical. However, we have included these data for the reference of the reviewer, and believe it addresses the primary concerns. __

• While loss of PP1G does not affect growth on glucose, cells entered post-diauxic shift show some latency, suggesting that they would resume more slowly on gluconeogenic substrates, which is mainly ethanol. Thus, it might be relevant to check whether this Ppase is not important growth on gluconeogenic substrate, such as ethanol and acetate (not glycerol at least if using mineral synthetic medium such as YNB as this is not a good substrate), and clearly do this minimal medium (YNB) to get rid of other carbon substrates. Our results (included in the manuscript) indicate that the ppg1D cells show reduced growth in the post-diauxic phase. We carried out a shift experiment to investigate if these cells resume growth slowly when shifted to gluconeogenic substrates. We cultured ppg1D cells in glucose-replete conditions and shifted them to a medium with ethanol as sole carbon source. As anticipated, we observed that the ppg1D cells resumed growth at a slower rate in ethanol containing medium.

Data showing the growth dynamics of ppg1D cells after shift to ethanol containing medium:

Given that all the experiments conducted in this manuscript were performed using YPD medium, and no shift experiments were included (as explained earlier), we believe that incorporating this data in the manuscript could disrupt its coherence, cause confusion to readers, and disrupt the overall flow. However, we include this for the reviewer, to address this point, but would prefer to not include it in the manuscript.

• Technical methods for quantifying intracellular metabolites are missing! There is a link to a paper from the same authors that is even not accessible! Measuring intracellular metabolites is very tricky as how quenching, sampling and extraction have been made are critical to get reliable data. We apologize for this. Having studied trehalose and flux towards this for so long (e.g. see PMID: 32876564, PMID: 31758251, PMID: 27090086, and PMID: 31241462), we inadvertently took for granted some of the details of these methodologies. We have extensively described many ways to quantitatively estimate trehalose production and flux in this methods paper PMID: 32181267 (also see some other references particularly PMID: 32876564, PMID: 31758251, PMID: 27090086, and PMID: 31241462. We have now modified the methods section and mentioned the detailed extraction protocols and methods to measure trehalose (Line 668).

“The metabolite extraction and analysis were carried out following protocols described in (Walvekar et al, 2019). For each experiment, 10 OD600 cells were used for metabolite extraction. First, the cells were quenched for 5 minutes in 60% methanol (maintained at -45oC). After centrifugation, the cell pellet was resuspended in the extraction buffer (75% ethanol) and kept at 80oC followed by incubation on ice and centrifugation. The supernatant was collected, dried, and then stored at -80oC till further use.”

In addition, we have now included all the mass spectrometry parameters, as well as all the mass spectrometry raw data values as supplementary tables, so that any reader can analyse and quantify these metabolites.

• Taking into account metabolites levels reported, the 3 to 4 fold levels of G6P and UDPGlc can account for higher capacity of trehalose accumulation because the trehalose synthase (TPS) displays Km that are in mM range for these metabolites and thus any increase of these metabolites will increase rate of TPS (old publication by {Vandercammen, 1989 #3278;Londesborough, 1993 #3899} Thanks for this note. Indeed, a major idea that is nucleated by our study is the possible roles of mass action based control of gluconeogenic flux. This is also related to some of our responses included earlier. We have now contextually discussed the importance of mass action-based regulation in the discussion section, and included key references. ____Included in the discussion (Line 466).

“This function of Ppg1, as uncovered in this study, differs from regulation mediated by related phosphatases. How might this occur? An underappreciated but important mediator of metabolic adaptation is the direct modulation of metabolic outputs or flux, through a combination of mass action and allosteric regulation (and without invoking transcriptional changes). Even in unicellular organisms like S. cerevisiae, over 50% of metabolic regulation occurs through such mechanisms (Hackett et al, 2016). In this study, the loss of Ppg1 increases the levels of gluconeogenic intermediates, precursors of cell wall and storage carbohydrates (Fig. 2A). Increasing flux towards G6P and UDP-glucose would be one way of supporting the increased synthesis of storage carbohydrates without requiring alterations in enzyme levels, driven primarily by mass action. Classic studies of the trehalose synthesis enzymes in yeast (Vandercammen et al, 1989; Londesborough & Vuorio, 1993) indicate this possibility.”

• 13C-labelling indicates a higher GNG flux in a pp1gD strain. Thus, one might expect faster growth resumption, which is the opposite that what is observed in a pp1g deletion strain? How to reconcile these data? In the post-diauxic phase, the ppg1D cells exhibit increased gluconeogenic flux, suggesting an imbalanced carbon allocation. However, this increased gluconeogenic flux need not necessarily support better adaptation on gluconeogenic substrates. What is really important to a cell is the balance of allocation of carbon resources (discussed more extensively in a recent study from our lab: (Rashida et al, 2021 PMID: 33853774), which we now contextually cite here). In ppg1D cells, this imbalance in carbon allocation results in increased consumption of amino acids towards gluconeogenic outputs and might limit their availability for other cellular processes resulting in reduced growth and biomass production. Hence, even though the gluconeogenic flux is higher in ppg1D cells, these cells have reduced growth in post-diauxic phase. Achieving a precise equilibrium of flux towards different outputs is crucial for optimum growth or appropriate adaptation. This is an interesting and non-intuitive point, hence we now more extensively discuss this in the manuscript. Included in the discussion (Line 477).

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“This increase in gluconeogenic flux in ppg1D cells indicates an imbalance in carbon allocations, resulting in increased consumption of amino acids towards gluconeogenic outputs, and therefore might limit their availability for other cellular processes. Hence, even though the gluconeogenic flux is higher in ppg1D cells, these cells have reduced growth in the post-diauxic phase. This plausible mode of regulation via Ppg1 could be systematically investigated in future studies, as an example of regulation mediated via some combination of mass action, concentration, allostery and enzyme regulation. These additional mechanisms (through scaffolding systems working together with signaling systems) to mediate overall metabolic outputs might be more prevalent than currently appreciated. In this context, we recently identified a signaling axis with Snf1 (AMPK) and TORC1 (via Kog1) in enabling precise carbon allocations, ensuring optimum growth and adaptation during nutrient limitation (Rashida et al, 2021).”

• Sensibility of mutant cells to CR is borderline. Could you confirm with Calcofluor white which usually is more sensitive to minor cell wall modification, and notably when chitin is increased This is a good suggestion. We studied the growth of ppg1D cells in the presence of Calcofluor white and observed a similar growth defect as seen with CR, but the images are very clear. Some of this is a reflection of the nature of our light-box black-and white camera (and visibly and in color, the plates look much better!). We have now added this data in Fig S2D and mentioned this in the text (Line 206).

“Increased chitin accumulation is known to sensitize cells to cell wall stress (Ram & Klis, 2006; Vannini et al, 1983); hence, we studied the growth of ppg1D cells in the presence of two cell wall stress agents, Congo red and Calcofluor white. Expectedly, (and as observed earlier (Hirasaki et al, 2010)) the growth of ppg1D cells was reduced in the presence of either Congo red or Calcofluor white (Figure S2C, D).”

Data showing the growth of ppg1D cells in the presence of Calcofluor white (now new Fig. S2D):

• Is there any idea about the phosphorylation site on far11. I did not check on the phosphoproteomes data, but this might be worth to do and in that case, the loss of this phosphorylation shall be similar to loss of pp1G (no necessary to do that in this report) Addressed extensively in the section 2 of this document.

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Referee #4

Evidence, reproducibility and clarity

General comments

This paper reports a critical function of PP2A-like phosphatase encoded by PP1G in the post-diauxic shift of the yeast Saccharomyces cerevisiae. This function is mediated via the assembly of FAR complex that naturally sites at the ER-mitochondria outer membranes to ensure proper onset of growth at the diauxic shift by appropriate carbon allocation through gluconeogenesis. The identification of this PPase was based on a screen of yeast mutant defective in non-essential PPases for trehalose accumulation. This is a bit surprising as it is known that trehalose accumulation sets in as soon as glucose is depleted and continues steadily during growth on other carbon source, which is merely ethanol, although it may depend whether the experiment was carried out in YPD or in mineral synthetic medium as YNB.

Although the work seems experimentally well conduct, in particular for the demonstration that bPP1G interacts with FAR complex, it raises several issues requiring a thorough revision and additional experiments to truly support the role of PP1F in regulating post-diauxic shift

• all experiments were done using YPD medium and only a single value of trehalose at 24 h was recorded! It will be important to ensure that all mutant had exactly same growth rate, that at 24 h, glucose was totally gone. It should be relevant to have a more complete kinetic analysis of trehalose/ glycogen accumulation along growth, monitoring as well glucose consumption in WT and the pp1g, to really convince that the metabolic difference is not associated with a difference in glucose consumption, such as that at 24 h, there is still some glucose remaining in the culture of the mutant!
• The experiment at least with pp1g and WT should be redo in mineral synthetic medium with 2% glucose. There, only ethanol can be the sole carbon for growth resumption and thus this will ensure that the effects is linked to growth resumption at diauxic growth as YPD is a rich medium that contains excess of many amino acids and peptides that may interfere with your phenotype
• While loss of PP1G does not affect growth on glucose, cells entered post-diauxic shift show some latency, suggesting that they would resume more slowly on gluconeogenic substrates , which is mainly ethanol. Thus, it might be relevant to check whether this Ppase is not important growth on gluconeogenic substrate, such as ethanol and acetate (not glycerol at least if using mineral synthetic medium such as YNB as this is not a good substrate), and clearly do this minimal medium (YNB) to get rid of other carbon substrates
• Technical methods for quantifying intracellular metabolites are missing! There is a link to a paper from the same authors that is even not accessible! Measuring intracellular metabolites is very tricky as how quenching, sampling and extraction have been made are critical to get reliable data
• Taking into account metabolites levels reported, the 3 to 4 fold levels of G6P and UDPGlc can account for higher capacity of trehalose accumulation because the trehalose synthase (TPS) displays Km that are in mM range for these metabolites and thus any increase of these metabolites will increase rate of TPS (old publication by {Vandercammen, 1989 #3278;Londesborough, 1993 #3899}
• 13C-labelling indicates a higher GNG flux in a pp1g strain. Thus, one might expect faster growth resumption, which is the opposite that what is observed in a pp1g deletion strain? How to reconcile these data?
• Sensibility of mutant cells to CR is borderline. Could you confirm with Calcofluor white which usually is more sensitive to minor cell wall modification, and notably when chitin is increased
• Is there any idea about the phosphorylation site on far11. I did not check on the phosphoproteomes data, but this might be worth to do and in that case, the loss of this phosphorylatuion shall be similar to loss of pp1G (no necessary to do that in this report)

The references should be carefully revised because many of them are incomplete, lacking journal name or wrong name, number, issues, pages etc.

Significance

Although the work seems experimentally well conduct, in particular for the demonstration that bPP1G interacts with FAR complex, it raises several issues requiring a thorough revision and additional experiments to truly support the role of PP1F in regulating post-diauxic shift

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Referee #3

Evidence, reproducibility and clarity

The study performed by Niphadkar et al. seeks to uncover the role of the phosphatase Ppg1 in regulating gluconeogenesis during post-diauxic shift in S. cerevisiae. Thea authors show that loss or inactivation of Ppg1p affects production of gluconeogenic products incl. trehaloase and glycogen. The authors show that assembly of the Far complex required the activity of Ppg1 and is required to maintain gluconeogenic outputs after glucose depletion.

The manuscript is clearly written and methods well considered, no omics-methods have been included. Especially phosphoproteomics would be relevant to include. Specifically, the tracing experiments are an interesting and appropriate approach to confirm effects on gluconeogeneisis etc. Yet, working with regulation of posttranslational modifications (phosporylations) it is surprising that the authors only to a limited extent examine phosphorylation events, and not all examine or discuss specific phosphorylation events of e.g. Far11.

The study is interesting and provides new insights into regulation of glucose metabolism in yeast, however, there are serious concerns that need to be addressed before it can be reconsidered for publication.

Major points:

The authors use electrophoretic mobility assays w/wo CIP to address the phosphorylation state of Far11. They show in figure 3E that the mobility of Far11 depends on Ppg1 activity and can be affected by CIP. Why is the mobility of Far11 not affected in e.g. figure 3D?

There are several sites in Far11 previously reported to be phosphorylated, see e.g. Bodenmiller et al 2010 (Science Signal.) Are there sites that are specifically regulated (dephosphorylated) by Ppg1? or by other phosphatases? kinases?

Here, it would be appropriate to apply phosphoproteomics to examine Far11 phosphorylation in Ppg1 knock out cells or in cells with inactivated Ppg1.

The authors show that the levels of Ppg1 remain constant during growth in YPD medium, while the levels of Far11 increased after 24hrs of growth in YPD medium, and thus argue that the amount of Far complex itself increases in post-diauxic phase. The authors need to show that the level of complex indeed increases.

The authors also apply fluorescence microscopy to address the localization of the Far11 complex etc. The quality of the shown images should be improved, also merged images should be shown. Only one single image containing one cell is shown, images should ideally show additional cells in the same image, alternatively, additional images should be shown.

Minor points:

Does the FLAG tag affect activity of Ppg1?

Significance

The study is interesting and provides new insights into regulation of glucose metabolism in yeast, however, there are serious concerns that need to be addressed before it can be reconsidered for publication.

The manuscript is of broad interests for an audience primarily interested glucose metabolism and signalling in yeast.

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Referee #2

Evidence, reproducibility and clarity

Summary:

In this manuscript, the authors screened the yeast phosphatase mutant that shows defective in metabolic adaptation and found that PP2A-like phosphatase Ppg1 is required for the appropriate gluconeogenic outputs after glucose depletion. Furthermore, they showed that Far complex which assembles with Ppg1 is also required to maintain gluconeogenic outputs. They also found that Ppg1 is required for assembly of Far complex and the assembly on the ER or mitochondrial membrane is important for their function. Ppg1 and Far complex dependent control of gluconeogenic outputs had important role on adaptive growth under glucose depletion.

Major comments:

In this study, the authors report new evidence that the Ppg1 and Far complexes are involved in the regulation of gluconeogenic outputs. However, the mechanism by which the Ppg1-Far complex is involved in gluconeogenic outputs has not been fully analyzed, and further analysis of the role of Ppg1 in Far complex assembly and the significance of Far11 phosphorylation is needed. The authors should consider the following points,

1．In the mutant screen, both pph21Δ and pph22Δ cells showed increased level of trehalose (figure 1C). Pph21 and Pph22 are catalytic subunits of protein phosphatase 2A (PP2A) and function redundantly. Thus, it may be possible that PP2A is more involved in gluconeogenic outputs regulation than Ppg1. 2. Is it the Far complex or Ppg1 activity that is required for the regulation of gluconeogenic outputs? It seems that assembly of the Far complex requires Ppg1 and Ppg1 activity requires the Far complex. However, either one should be involved in the regulation of gluconeogenic outputs. For example, Innokentev et al, 2020 concluded that Ppg1 activity is critical for the regulation of mitophagy and that the Far complex serves only as a scaffold for Ppg1. by Ppg1 dephosphorylating an unidentified protein. The possibility that Ppg1 may be involved in the regulation of glycolytic output by dephosphorylating unidentified substrates needs to be fully tested. 3．Although there are no known substrates of Ppg1 other than Atg32, Atg32 is not involved in the regulation of gluconeogenic outputs. The identification of substrates of Ppg1 involved in the regulation of gluconeogenic outputs will help to elucidate the molecular mechanism of gluconeogenesis. 4. The authors conclude that Ppg1 dephosphorylates Far11 and that dephosphorylated Far11 assembles with the Far complex. However, there is a possibility that Ppg1 activity is required for Far complex assembly independently of dephosphorylation of Far11. To prove the authors' assertion, it is necessary to identify the phosphorylation site of Far11 and show that its phosphorylation affects the binding of Far11 to Far8. 5. Several kinases have been reported to be involved in gluconeogenic outputs regulation. The initial aim of this study was to identify phosphatases involved in gluconeogenic outputs regulation by antagonizing these kinases. However, Ppg1 has not been shown to be involved in transcriptional regulation to control carbon metabolism by antagonizing any kinase. 6. If the assembly of the Far complex is involved in gluconeogenic outputs regulation, what is the mechanism? The Far complex is a scaffold for enzymes. Therefore, the role of the Far complex in gluconeogenic outputs regulation will not be elucidated until the enzymes that function there are identified.

Minor comments:

1. Because TA of Far10 can tether Far complex on the membrane, Mito-Far and ER-Far experiments (Figure 4A-D) should be performed under Far10ΔTA conditions.
2. Figure 6B and 6C, total culturing time (hours) should be shown on X-axis in addition to number of transfers.
3. Figure 3E, additional explanation is needed as to why the molecular mobility of Far11-FLAG after CIP treatment differs between Ppg1-H111N and Ppg1.

Significance

General assessment:

The discovery that Ppg1 and the Far complex are involved in the regulation of gluconeogenic outputs is novel. However, other studies on the assembly of the Far complex and the role of Ppg1 are very superficial and do not support the authors' claims.

Advance:

There are few reports of phosphatases involved in the regulation of gluconeogenesis. In this regard, the identification of Ppg1 and its involvement in the regulation of gluconeogenesis is a precedent.

Audience:

Cellular metabolism, yeast genetics

The expertise of this reviewer: yeast genetics, cell biology

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Referee #1

Evidence, reproducibility and clarity

The authors study metabolic adaptation to glucose depletion in budding yeast. A non-essential protein phosphatase mutant screen reveals adaptation to glucose depletion (growth in post-diauxic phase) requires Ppg1. The authors show i) that, in post-diauxic phase, cells lacking Ppg1 accumulate more trehalose, glycogen, UDP-glucose, UDP-GlcNAc (i.e., gluconeogenic outputs) than wild-type cells, ii) that, in post-diauxic phase, cells expressing a catalytically inactive version of Ppg1 accumulate more trehalose and iii) that Ppg1 is required for adaptation and growth post-glucose depletion. The authors find that Ppg1 interacts with Far11 (a member of the Far complex) in cells growing in post-diauxic phase and that Ppg1 promotes Far complex stability. Finally, the authors conclude that the Ppg1 promotes Far complex stability to maintain gluconeogenic outputs after glucose depletion.

Major comments:

1. Figures 1 to 4. The authors show that loss of Far components phenocopies loss of Ppg1 and conclude that that Ppg1 is upstream of Far. However, the authors do not determine the combined effect of the two mutations. The authors should assess the phenotype (e.g., gluconeogenic outputs levels) of cells lacking both Ppg1 and Far (or in far9_deltaTA far10_deltaTA cells lacking Ppg1). The authors' conclusion would be strengthened if there was no additive effect between the mutations.
2. Related to Figure 6A-C. One would expect that cells lacking Far components (or far9_delta TA far10_deltaTA cells) showing a similar phenotype (fail to adapt to growth in changing glucose compared with wild type cells) as cells lacking Ppg1. Is this the case?
3. The manuscript would be considerably strengthened if the authors provided more information on the mechanism by which Ppg1 controls Far complex stability, e.g., can the authors about the phosphosite(s) in Far11 regulated by Ppg1? As the authors mention, it has been already suggested that Ppg1 is required for Far complex assembly (PMID: 33317697).

Minor comments:

1. The authors may consider to include data from Figures 6A, 6B and 6C (failure to adapt to glucose changing conditions) after Figure 2 to show a complete characterization of the phenotype of cells lacking Ppg1. Figure 6 could show only the "proposed model".
2. Related to Figure 1. The authors mention that Ppg1 is a "notable hit" and that the "increase in post-diauxic trehalose levels are considerable". However, there is no reference to use as a comparison. Is there any other mutant strain known to accumulate trehalose at the post-diauxic shift? If yes, it would be informative if the authors compared the effect of such mutant strain to a ppg1-delta mutant.
3. Figures 4D and 4F. Regarding sensitivity to Congo Red and compared to wild type cells, it sems that cells lacking Far9 are much more sensitive to Congo Red in Figure 4F than in Figure 4D. Is this just an image quality issue? The authors should address this apparent discrepancy.

Significance

The manuscript is well written and easy to follow. Data and methods are presented in a clear way. It provides interesting and relatively novel insights on the function of Ppg1, a poorly characterized protein phosphatase. It will be interesting mainly for the yeast community working on metabolism.

This reviewer's area of expertise is budding yeast cation homeostasis, protein phosphatases, TOR signaling and nutrient sensing.

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Reply to the reviewers

Response to reviewer comments

R: We really appreciate the reviewer positive comments and consideration, and we believe that the review process has significantly strengthened our manuscript.

We have responded to all the reviewer comments, as follows:

Response (R)

From Reviewer #1

Major comments: The manuscript is mostly well written (it could use a few minor grammatical corrections), the significance of the problem is well described, and the results are clearly presented with adequate controls. The movies, provided as supplementary material, are of the highest quality and are essential additions to the stills provided in the figures. The data convincingly support the key conclusions of the manuscript.

R: We really appreciate the reviewer positive comments, and we have revised the manuscript accordingly

1) Does the MO knockdown both S and L homoeologs of X. laevis? Since the level of GAPDH in Figure 1H also looks reduced in Gai2 MO lane, it should be made clear that the apparent knockdown of Gai2 was normalized to GAPDH, rather than being the results of unequal loading of the gel. Yes, I recognize that Figure 1I says normalized, but this is not stated in the results or the methods. Also, was this experiment done with X. laevis or X. tropicalis? I could imagine that if done in X. laevis, the lack of complete knockdown might be due to only one homoeolog being affected.

R: We appreciate the reviewer comment, and we described in Material and Methods section the region targeted by the morpholino, in both Xenopus species. We added the next paragraph in the Material and Methods section, see page 23 paragraph 1 lines: 7-11.

“The Gαi2 morpholino (Gαi2MO) was designed as described in the results section to target Gαi2 in both Xenopus species (Xenopus tropicalis and Xenopus laevis). Specifically, it hybridizes with the 5’ UTR of X. tropicalis Gαi2 (NM_203919), 17 nucleotides upstream of the ATG start codon. For X. laevis Gαi2, the morpholino hybridizes with both isoforms described in Xenbase. It specifically targets the 5' UTR of the Gαi2.L isoform (XM_018258962), located 17 nucleotides upstream of the ATG start codon, and the 5' UTR of the Gαi2.S isoform (NM_001097056), situated 275 nucleotides upstream of the ATG.”

With respect to Figure 1H and 1I, we have specified in the Figure 1 legend that we normalized the data to GAPDH to quantifying the decrease in Gαi2 expression induced by the morpholino.

See page 37, Figure 1H-I, Legends section.

2) The knowledge of the efficacy of knockdown in each Xenopus species provided by the information requested in the previous point, would allow the reader to assess the level of knockdown in the remaining assays. To do this, the authors should tell us which assays were done in which species. I am not suggesting that each experiment needs to be done in each species, only that the information should be provided. If the MO is more effective in X. tropicalis - which assays used this species? If the knock down is partial, as shown in Figure 1H-I, which species this represents in the remaining assays would be useful knowledge.

R: We greatly appreciate the reviewer's valuable comments and suggestions, and as a response, we have incorporated a new supplementary figure (Figure S1). This figure includes a western blot and an in situ hybridization assay illustrating the efficiency of the knockdown in Xenopus laevis. The results presented in Figure S1 demonstrate that the knockdown efficiency is similar in both Xenopus species, allowing for a comparison between Figure 1A-I (X. tropicalis) and Figure 1S (X. laevis).

To complement this information, we have also improved the section of Material and Methods regarding the experiments in both Xenopus species (Xenopus tropicalis and Xenopus laevis). As detailed in the Materials and Methods section, we employed 20 ng of Gai2MO for Xenopus tropicalis embryos and 35 ng of Gai2MO for Xenopus laevis embryos to deplete cell migration. In both species, in vivo migration was analyzed, resulting in a substantial inhibition of cranial neural crest (NC) migration, ranging from 60% to 80%. Additionally, we conducted dispersion assays in both species. In X. laevis, in vitro migration was monitored for 10 hours, while in X. tropicalis, it was tracked for 4 hours, both yielding the same phenotype. We also studied cell morphology and microtubule dynamics in both Xenopus models. However, we used different tracer concentrations for each, with 200 pg for X. laevis and 100 pg for X. tropicalis, as specified in the Materials and Methods section. Our Rac1 and RhoA timelapse experiments were conducted in both species as well, employing pGBD-GFP and rGBD-mCherry probes, respectively, and different probe concentrations as outlined in the Materials and Methods section. These experiments revealed polarity impairment and consistent Rac1 behavior in both Xenopus species. The study of focal adhesion in vivo dynamics using the FAK-GFP tracer was carried out also in both species, resulting in the same phenotype. It is worth noting that the only experiment conducted exclusively in X. tropicalis was the focal adhesion disassembly assay with nocodazole.

Regarding the improvements of the Materials and Method section see page 22, paragraph 2.

We want to highlight that at the beginning of the Materials and Methods section, we incorporated a paragraph to clarify that “all experiments were conducted in both Xenopus species (X.t and X.l) using distinct concentrations of the morpholino (MO) and mRNA, as specified in each respective methodology description”. This approach consistently yielded similar results. It is important to note that for the figures, we selected the most representative images.

We have also specified in each figure legend which Xenopus species is depicted.

Minor comments: While prior studies are referenced appropriately, and the text and figures are mostly clear and accurately presented, the following are a few suggestions that would help the authors improve the presentation of their data and conclusions:

1) The cell biological experiments convincingly demonstrate that knockdown of Gai2 causes cells to move more slowly. It would be a nice addition to bring the explant experimental data back to the embryo by showing whether the slower moving NC cells in morphants eventually populate the BA. DO they cease to migrate or are they just slower getting to their destination? This could be done by performing snail2 ISH at a later stage (34-35?)

R: We appreciate the reviewer's insightful point and are currently conducting the in situ hybridization assay at stages 32-36 to address this question. Our plan includes incorporating a supplementary figure showing the results of this assay and integrating this information into both the results and discussion sections.

2) There are places in the manuscript where the authors use the terms "silencing" or "suppression" of Gai2, when they really mean reduced translation - their system is not a genetic knockout, as clearly demonstrated in Figure 1H-I. I suggest that more accurate wording be used.

R: We appreciate the reviewer's comment, and we agree that the Gαi2 morpholino impedes Gαi2 translation, leading to a reduction in Gαi2 protein expression. Consequently, we have revised the entire manuscript, replacing the terms “silencing” and “suppression” with “knockdown”.

3) In Figures 1-5 there are scale of bars on the cell images, but these are not defined in any of the figure legends.

R: We value the reviewer's comment, and we have revised all the figure legends by including the scale information. Each image has been scaled to 10 µm with varying magnifications.

4) The abstract is the weakest section of the manuscript, and would have greater impact if it were more clearly written.

R: We appreciate the reviewer's comment on the abstract, and we have revised and edited it to enhance its quality.

Abstract:

“Cell migration is a complex and essential process in various biological contexts, from embryonic development to tissue repair and cancer metastasis. Central to this process are the actin and tubulin cytoskeletons, which control cell morphology, polarity, focal adhesion dynamics, and overall motility in response to diverse chemical and mechanical cues. Despite the well- established involvement of heterotrimeric G proteins in cell migration, the precise underlying mechanism remains elusive, particularly in the context of development.

This study explores the involvement of Gαi2, a subunit of heterotrimeric G proteins, in cranial neural crest cell migration, a critical event in embryonic development. Our research uncovers the intricate mechanisms underlying Gαi2 influence, revealing its interaction with tubulin and microtubule-associated proteins such as EB1 and EB3, suggesting a regulatory function in microtubule dynamics modulation. Gαi2 knockdown leads to microtubule stabilization, alterations in cell morphology and polarity, increased Rac1-GTP concentration at the leading edge and cell-cell contacts, impaired cortical actin localization and focal adhesion disassembly. Interestingly, RhoA-GTP was found to be reduced at cell-cell contacts and concentrated at the leading edge, providing evidence of Gαi2 significant role in polarity. Remarkably, treatment with nocodazole, a microtubule-depolymerizing agent, effectively reduces Rac1 activity, restoring cranial NC cell morphology, actin distribution, and overall migration. Collectively, our findings shed light on the intricate molecular mechanisms underlying cranial neural crest cell migration and highlight the pivotal role of Gαi2 in orchestrating microtubule dynamics through EB1 and EB3 interaction, modulating Rac1 activity during this crucial developmental process.”

The molecular regulation of cell movement is a key feature of a number of developmental and homeostatic processes. While many of the proteins involved have been identified, how they interact to provide motility has not been elucidated in any great detail, particularly in embryo-derived cells (as opposed to cell lines). The results obtained from the presented experiments are novel, in-depth and provide a novel paradigm for how G proteins regulate microtubule dynamics which in turn regulate other components of the cytoskeleton required for cell movement. The results will be applicable to many migrating cell types, not just neural crest cells.

Because of the application of the data to many types of cells that migrate, the audience is expected to include a broad array of developmental biologists, basic cell biologists and those interested in clinically relevant aberrant cell migrations.

R: We really appreciate the reviewer positive comments and consideration

From Reviewer 2

Major comments

The authors aim to address two issues in this manuscript: a) the role of Gai2 in neural crest development; and b) the mechanism of Gai2 function. While they have done a good job demonstrating a role of Gai2 in NC migration both in vivo and in vitro as well as the effects of Gai2 knockdown on cytoskeleton dynamics, protein distribution of selected polarity and focal adhesion molecules, and Rac1 activation, the link between Gai2 and the downstream effectors is largely correlative. Because of this, the model suggesting the sequential events flowing from Gai2 to microtubule to Rac1 to focal adhesion/actin should be modified to allow room for direct and indirect regulation at potentially multiple entry points.

R: We appreciate the reviewer's valuable comments. We concur with the reviewer's observation that our experiments do not establish a causal link between Gαi2, EB1/EB3, and Rac1. We established a relationship between Gαi2 and microtubule dynamics (EB1 and EB3) to regulate Rac1 polarity through co-immunoprecipitation assays, which reveal protein interactions within an interactor complex. Therefore, while our findings support the involvement of Gαi2 in coordinating cranial NC cell migration alongside EB1, EB3, and Rac1, we cannot exclude the possibility that this regulation may occur through other intermediary proteins, such as GEFs, GAPs, GDIs, and others. As a result, we have revised our model and its description in accordance with the reviewer suggestion.

We have edited the discussion/conclusion, model and the legend at Figure 6. See page 16 (paragraph 2, last line), 17 (paragraph 1, last line), 22 (paragraph 1, last line 17-20), 42 (Legend Fig. 6).

Specific major comments are as the following: Strengths: -Determination of a role of Gai2 in neural crest migration is novel. -The effect of Gai2 knockdown on membrane protrusion morphology and microtubule stability and dynamics are demonstrated nicely. -Quantification of experimental perimeters has been performed throughout the manuscript in all the figures, and statistical analysis is included in the figures.

R: We appreciate the reviewer positive comments

Weaknesses: -The heavy focus of the study on microtubule is due to the previous publication on the function of Gai2 in regulation of microtubule during asymmetrical cell division. However, the activity of Gai2 is likely cell type-specific, as it has not been shown to control microtubule during cytokinesis in general. It is equally likely that Gai2 primarily regulates Rac1 or actin regulators to influence both microtubule and actin dynamics. The tone of the discussion should therefore be softened.

R: We greatly appreciate and agree with the comment from the reviewer, highlighting the possibility that Gαi2 primarily regulates Rac1 or actin regulators to influence both microtubule and actin dynamics. In this regard, we have revised our manuscript to include a discussion of this point. We added the next paragraph in the Discussion/Conclusion section, page 20-21.

“It is well established that the activity from the Rho family of small GTPases is controlling cytoskeletal organization during migration (Ridley et al., 2015). Contrariwise, it has been described in many cell types, that microtubules dynamic polymerization plays a crucial role in establishing the structural foundation for cell polarization, consequently influencing the direction of cell motility (Watanabe et al., 2005). Our results appear to align with this latter view. While it is reasonable to postulate the possibility that Gαi2 regulates Rac1 activity, subsequently influencing actin and microtubule dynamics, our findings in the context of cranial NC cells, lend support to an alternative sequence of events. Initially, Gαi2 knockdown leads to a decrease in microtubule dynamics, which in turn increase Rac-GTP towards the leading edge. This shift is accompanied by reduced levels of cortical actin and impaired focal adhesion disassembly, culminating in compromised cell migration. Notably, nocodazole, a microtubule-depolymerizing agent, not only diminishes Rac-GTP localization at the leading edge but also rescues cell morphology, restores normal cortical actin localization, and promotes focal adhesion disassembly, thereby facilitating cell migration. If Rac1 activity were indeed upstream of microtubules, it would be expected that nocodazole would not reduce Rac-GTP levels at the cell leading edge. These results suggest that the regulation of Rac1 activity may follow, rather than precede, alterations in microtubule dynamics, in the context of NC cells. Furthermore, in support of our model, our protein interaction analysis demonstrates Gαi2 interacting with microtubule components such as EB proteins and tubulin. As we already mention above, earlier studies have reported that microtubule dynamics promote Rac1 signaling at the leading edge and by releasing RhoGEFs promote RhoA signaling as well (Best et al., 1996; Garcin and Straube, 2019; Moore et al., 2013; Waterman-Storer et al., 1999). In addition, it is well-documented that RhoGEFs interact with microtubules, including bPix, a GEF for Rac1 and Cdc42, which, in turn, promotes tubulin acetylation (Kwon et al., 2020). Interestingly, in ovarian cancer cells, Gαi2 has been shown to activate Rac1 through an interaction with bPix, thereby jointly regulating migration in response to LPA (Ward et al., 2015). Taken together, these findings further support our proposed model (refer to Fig. 6).”

-The effect of rescue of NC migration with Rac1 inhibitor is marginal and the result is hard to interpret considering the inhibitor also blocks control NC migration. Either lower doses of Rac1 inhibitor can be used or the experiment can be removed from the manuscript, as Rac1 is required for membrane protrusions and the inhibitor doses can be hard to titrate.

__ R: We appreciate and agree with the reviewer's comments. To address this concern and enhance clarity, we have incorporated the following paragraph into the manuscript within the Discussion section. Additionally, we have included information on the range of NSC23766 concentrations used for this analysis in the Materials and Methods section. Page 24, __Explants and microdissection.

“It is worth noting that we conducted Rac inhibitor NSC23766 trials at concentrations ranging from 20 nM to 50 nM for X. laevis and between 10 nM to 30 nM for X. tropicalis. In both cases, higher concentrations of the Rac inhibitor proved to be lethal, underscoring the essential role of Rac1 in both cell migration and cell survival. Specifically, the described concentrations of 20 nM for X. laevis and 10 nM for X. tropicalis led to a partial rescue of the observed phenotype. This suggests that these concentrations are sufficient to demonstrate that the increase in Rac1-GTP resulting from Gαi2 morpholino knockdown impairs cell migration. The partial rescue can be attributed to the crucial role of microtubule dynamics in cell migration, which acts upstream of Rac activity. Additionally, Rac is pivotal for the modulation of cell polarity at the leading edge of migration. It is worth emphasizing that Rac1 levels are critical for cell migration, as demonstrated by other researchers. Lower concentrations of Rac1-GTP have been shown to hinder cell migration in cells deficient in Rac1, leading to a significant reduction in wound closure and random cell migration (Steffen et al., 2013).

Therefore, we believe that the lower concentration of NSC23766 used in our assay was adequate to reduce the abnormal Rac1-GTP activity in the morphant NC cells. However, it is important to note that for normal NC cell, this level of reduction in Rac1-GTP activity is critical and sufficient to impair normal migration”.

See page 12, paragraph 2, lines 8-11, 14-16, 23-25.

Steffen A, Ladwein M, Dimchev GA, Hein A, Schwenkmezger L, Arens S, Ladwein KI, Margit Holleboom J, Schur F, Victor Small J, Schwarz J, Gerhard R, Faix J, Stradal TE, Brakebusch C, Rottner K. Rac function is crucial for cell migration but is not required for spreading and focal adhesion formation. J Cell Sci. 2013 Oct 15;126(Pt 20):4572-88. doi: 10.1242/jcs.118232. Epub 2013 Jul 31. PMID: 23902686; PMCID: PMC3817791.

-Since the defects seem to result partially from the inability of the NC cells to retract and move away, it may help to either include some data on Rho activation patterns in knockdown cells or simply add some discussion about the issue.

R: We acknowledge and sincerely appreciate the reviewer's valuable comments on this pivotal aspect, which significantly enhances our capacity to elucidate the impact of Gαi2 knockdown on cell polarity. To address this crucial point, we have introduced an experiment that examines RhoA-GTP localization under Gαi2 knockdown conditions, and we have incorporated a supplementary figure S3 into our manuscript. This newly added figure clearly demonstrates that, under Gαi2 knockdown conditions, and in contrast to control cells, RhoA-GTP localization is substantially disrupted at cell-cell contacts and now detected at the leading edge of the cell, providing compelling evidence of cell polarity defects (refer to Figure S3). In response to these results, we have included a description of these findings in the Results section (please see page 11-12) and a dedicated paragraph in the Discussion section (please see page 18, paragraph 2, line 15-16, page 19, paragraph 1, lines 6-12).

Results section 1: “To achieve this, we explored whether Gαi2 regulates the subcellular distribution of active Rac1 and RhoA in cranial NC explants under Gαi2 loss-of-function conditions, considering their pivotal roles in cranial NC migration and contact inhibition of locomotion (CIL) (Carmona-Fontaine et al., 2011; Moore et al., 2013; Leal et al., 2018). Hence, we employed mRNA encoding the small GTPase-based probe, enabling specific visualization of the GTP-bound states of these proteins.”

Results section 2: “Consistent with earlier observations by Carmona-Fontaine et al. (2011), in control cranial NC cells, active Rac1 displayed prominent localization at the leading edge of migrating cells, whereas its presence was reduced at cell-cell contacts, coincident with an increase in RhoA-GTP levels (white arrows in Fig. 4A, supplementary material Figure S3A). On the contrary, in comparison to the control cells, Gαi2 morphants exhibit a pronounced accumulation of active Rac1 protein in the protrusions at cell-cell contacts, where active RhoA localization is conventionally expected (white arrow in Fig. 4B, supplementary material Figure S3A and movie S6). In contrast to control cells, a notable shift in the localization of active RhoA protein was observed, with its predominant accumulation now detected at the leading edge of the cell, instead of the typical localization towards the trailing edge or cell-cell contacts (__supplementary material Figure S3B). __These findings suggest a dysregulation of contractile forces that align with the observed distribution of active RhoA, cortical actin disruption, and diminished retraction in cell treated with Gαi2MO.”

Discussion section:

“Other studies have reported that microtubule assembly promotes Rac1 signaling at the leading edge, while microtubule depolymerization stimulates RhoA signaling through guanine nucleotide exchange factors associated with microtubule-binding proteins controlling cell contractility, via Rho-ROCK and focal adhesion formation (Krendel et al., 2002; Ren et al., 1999; Best et al., 1996; Garcin and Straube, 2019; Waterman-Storer et al., 1999; Bershadsky et al., 1996; Moore et al., 2013). This mechanism would contribute to establishing the antero-posterior polarity of cells, crucial for maintaining migration directionality, underscoring the significance of regulating microtubule dynamics in directed cell migration. These findings closely align with the results obtained in this investigation, demonstrating that Gαi2 loss of function reduces microtubule catastrophes and promotes tubulin stabilization, resulting in increased localization of active Rac1 at the leading edge and cell-cell contacts, while decreasing active RhoA at the cell-cell contact but increasing it at the leading edge. This possibly reinforces focal adhesion, which is consistent with the presence of large and highly stable focal adhesions under Gαi2 knockdown conditions. This finding also suggests a dysregulation of contractile forces in comparison to control cells, a result that aligns with the observed distribution of active RhoA, cortical actin distribution and diminished retraction in cells treated with Gαi2MO. This strikingly contrasts with the normal cranial NC migration phenotype, where Rac1 is suppressed while active RhoA is increased at cell-cell contacts during CIL, leading to a shift in polarity towards the cell-free edge to sustain directed migration (Theveneau et al., 2010; Shoval and Kalcheim, 2012; Leal et al., 2018).”

-To consider focal adhesion dynamics, live imaging should be used in the analysis. The fixed samples are different from each other, and natural variations of focal adhesion may exist among the samples. This can obscure data collection and quantification.

R: We agree with the reviewer that focal adhesion (FA) dynamics need to be analysed using live imaging. Indeed, Fig 5E-H shows an extensive analysis of FA using live imaging of neural crest expressing FAK-GFP. As complement to this live imaging analysis, and in order to analyse the effect on the endogenous levels of FA proteins, we performed immunostaining against FA. Both experiments using live imaging or fixed cells produce similar results, and they are consistent with our model on the role of Gαi2 on FA dynamics.

Minor comments -Fig. 2, the centrosomes in control cells are not always obvious. The microtubules simply seem to be more networked and more fluid in control cells. This should be clarified with either marking the centrosomes in the figure or modifying the wording in the manuscript.

R: We appreciate and concur with the reviewer's comment on this matter. As pointed out by the reviewer, the precise localization of the centrosome is not consistently clear in all cells. In response to this observation, we have revised the manuscript to emphasize this aspect solely as “microtubule morphology”. Please refer to the Results section description Figure 2.

-In Fig. 3, a better negative control for co-IP should be using anti-V5 antibody to IP against tubulin/EB1/EB3 in the absence of Gai2-V5.

R: We appreciate the reviewer's comment, and we agree about the controls that the reviewer suggest. We can inform that we have done by triplicate all the Co-IPP. Although, if is necessary we will do the controls suggested. We present this assay as a plan.

-The data for cell polarity proteins Par3 and PKC-zeta seem to be out of place. It is unclear whether mis-localization of these proteins has anything to do with NC migration defects induced by Gai2 knockdown. The conclusion does not seem to be affected if the data are taken out of the manuscript.

R: We appreciate the reviewer's concern, and we would like to highlight two points in this regard. Firstly, we have included these results as additional data to support the impact of Gai2 knockdown on cell polarity, given that these two proteins are commonly used polarity markers. Secondly, we have discussed this aspect extensively in the Discussion section of the manuscript. (See page 19, paragraph 1, lines 18-28)

In that section, we delve into the relationship between aPKC, Par3, and Gαi2 in controlling cell polarity during asymmetric cell division, as described in Hao et al., 2010. Par3 is known to play a role in regulating microtubule dynamics and Rac1 activation through its interaction with Rac-GEF Tiam1 (Chen et al., 2005). Additionally, it has been shown to promote microtubule catastrophes and inhibit Rac1/Trio signaling, regulating Contact Inhibition of Locomotion (CIL) as demonstrated in Moore et al., 2013. Thus, we believe that the data we present support the relationship between Par3 and aPKC localization changes and the neural crest migration defects induced by Gαi2 knockdown, probably by controlling microtubule dynamics. However, we have moved these results as part of the supplementary Figure S3.

-In Suppl. Fig. 1, protrusion versus retraction should be defined more clearly. The retraction shown in this figure seems to be just membrane between protrusions instead of actively retracting membrane.

R: We appreciate the reviewer's comments, and here we aim to provide a clearer description of our approach to this analysis. For the measurement of protrusion extension/retraction, we conducted two distinct experiments. The first, as described in Figure 1, involved measuring membrane extension and retraction in live cell using membrane-GFP by utilizing the image subtraction tool in ImageJ, which highlights changes in the membrane in red. Secondly, we employed ADAPT software to quantify cell perimeter based on fluorescence intensity in live cell using lifeactin-GFP, distinguishing membrane extension in green and retraction in red (as has been shown similarly in Barry et al., 2015). In both approaches, we observed a substantial increase in membrane protrusion (both in area and extension) and protrusion stability in Gαi2 morphants. Hence, we have revised the Materials and Methods section of the manuscript and included this clarification.

See Materials and Methods section, Cell dispersion and morphology, page 25-26.

Barry DJ, Durkin CH, Abella JV, Way M. Open source software for quantification of cell migration, protrusions and fluorescence intensities. J Cell Biol. 2015. Doi: 10.1083/jcb.201501081

-Discussion can be improved by better incorporating all the components to make a cohesive story on how Gai2 works to regulate migration in the context of the neural crest cells.

R: We appreciate the reviewer's comment and agree. To enhance the manuscript, we have included a new paragraph at the end of the Discussion/Conclusion section specifically addressing this point. For more details, please refer to page 21-22.

“In the context of collective cranial NC cells migration, our findings reveal the pivotal role played by Gαi2 in orchestrating the intricate interplay of microtubule dynamics and cellular polarity. When Gαi2 levels are diminished, we observe significant impediments in the ability of cells to efficiently navigate through their environment, resulting in a range of distinct effects. First and foremost, Gαi2 deficiency leads to the diminished ability of cells to adjust and reorient new protrusions effectively. Primary protrusions exhibit higher stability and heightened levels of active Rac1/RhoA when compared to control conditions in the leading edge. In addition, we observe a notable increase in protrusion area, a decrease in retraction velocity, and an enhanced level of cell-matrix adhesion in Gαi2 knockdown cells. These findings underscore the pivotal role that Gαi2 plays in the modulation of various cellular dynamics essential for collective cranial NC cells migration. Notably, the application of nocodazole, a microtubule-depolymerizing agent, and NSC73266, a Rac1 inhibitor, to Gαi2 knockdown cells leads to the rescue of the observed effects, thus facilitating migration. This observed response closely mirrors the outcomes associated with Par3, a known regulator of microtubule catastrophe during contact inhibition of locomotion (CIL) in NC cells. This parallel implies that there exists a delicate equilibrium between microtubule dynamics and Rac1-GTP levels, crucial for the establishment of proper cell polarity during collective migration. Our findings collectively position Gαi2 as a central master regulator within the intricate framework of collective cranial NC migration. This master regulator's role is pivotal in orchestrating the dynamics of polarity, morphology, and cell-matrix adhesion by modulating microtubule dynamics through interactions with EB1 and EB3 proteins, possible in a protein complex involving other intermediary proteins such as GDIs, GAPs and GEFs, thus fostering crosstalk between the actin and tubulin cytoskeletons. This orchestration ultimately ensures the effective collective migration of cranial NC cells (Fig. 6).”

From Reviewer #3

Major comments: 1. The authors focus exclusively on the analysis of the subcellular levels of Rac1, which is probably related to the fact that they observe large extended protrusions with high Rac1 activity. However, as the authors note, a global fine-tuning of Rho GTPase activity is required for neural crest migration. One of the observed phenotypes of Gαi2-morphant neural crest cells is a decrease in cell dispersion, which may be caused by defects in contact inhibition of locomotion (CIL). This process involves a local activation of RhoA at cell-cell contact sites (Carmona-Fontaine et al., 2008). Furthermore, in fibroblast, RhoA/ROCK activity is required for the front-rear polarity switch during CIL (Kadir et al., 2011). Interestingly, similar to the Gαi2 loss of function phenotype, ROCK inhibition leads to microtubule stabilization, which can be rescued by nocodazole treatment, restoring microtubule dynamics and CIL. Therefore, it would also be interesting to know how RhoA activity is affected in Gαi2-morphant NC cells. At a minimum, this point should be be included in the discussion.

R: We acknowledge and sincerely appreciate the reviewer's valuable comments on this pivotal aspect, which significantly enhances our capacity to elucidate the impact of Gαi2 knockdown on cell polarity. To address this crucial point, we have introduced an experiment that examines RhoA-GTP localization under Gαi2 knockdown conditions, and we have incorporated a supplementary figure S3 into our manuscript. This newly added figure clearly demonstrates that, under Gαi2 knockdown conditions and in contrast to control cells, RhoA-GTP localization is substantially disrupted at cell-cell contacts and now detected at the leading edge of the cell, providing compelling evidence of cell polarity defects (refer to Figure S2). In response to these results, we have included a description of these findings in the Results section (please see page 11-12) and a dedicated paragraph in the Discussion section (please see page 18, paragraph 2, line 15-16, page 19, paragraph 1, lines 6-12).

Results section 1: “To achieve this, we explored whether Gαi2 regulates the subcellular distribution of active Rac1 and RhoA in cranial NC explants under Gαi2 loss-of-function conditions, considering their pivotal roles in cranial NC migration and contact inhibition of locomotion (CIL) (Carmona-Fontaine et al., 2011; Moore et al., 2013; Leal et al., 2018). Hence, we employed mRNA encoding the small GTPase-based probe, enabling specific visualization of the GTP-bound states of these proteins.”

Results section 2: “Consistent with earlier observations by Carmona-Fontaine et al. (2011), in control cranial NC cells, active Rac1 displayed prominent localization at the leading edge of migrating cells, whereas its presence was reduced at cell-cell contacts, coincident with a increase in RhoA-GTP levels (white arrows in Fig. 4A, supplementary material Figure S2A). On the contrary, in comparison to the control cells, Gαi2 morphants exhibit a pronounced accumulation of active Rac1 protein in the protrusions at cell-cell contacts, where active RhoA localization is conventionally expected (white arrow in Fig. 4B, supplementary material Figure S3B and movie S6). In contrast to control cells, a notable shift in the localization of active RhoA protein was observed, with its predominant accumulation now detected at the leading edge of the cell, instead of the typical localization towards the trailing edge or cell-cell contacts (__supplementary material Figure S2). __These findings suggest a dysregulation of contractile forces that align with the observed distribution of active RhoA, cortical actin disruption, and diminished retraction in cell treated with Gαi2MO.”

Discussion section:

“Other studies have reported that microtubule assembly promotes Rac1 signaling at the leading edge, while microtubule depolymerization stimulates RhoA signaling through guanine nucleotide exchange factors associated with microtubule-binding proteins controlling cell contractility, via Rho-ROCK (cita) and focal adhesion formation (Krendel et al., 2002; Ren et al., 1999; Best et al., 1996; Garcin and Straube, 2019; Waterman-Storer et al., 1999; Bershadsky et al., 1996; Moore et al., 2013). This mechanism would contribute to establishing the antero-posterior polarity of cells, crucial for maintaining migration directionality, underscoring the significance of regulating microtubule dynamics in directed cell migration. These findings closely align with the results obtained in this investigation, demonstrating that Gαi2 loss of function reduces microtubule catastrophes and promotes tubulin stabilization, resulting in increased localization of active Rac1 at the leading edge and cell-cell contacts and decreasing active RhoA at the cell-cell contact but increasing at the leading edge, possibly reinforcing focal adhesion, which align with our result here that show large and highly stable focal adhesions under Gαi2 knockdown conditions. This finding also suggests a dysregulation of contractile forces in comparison to control cells, a result that aligns with the observed distribution of Active RhoA, cortical actin distribution and diminished retraction in cells treated with Gαi2MO. This strikingly contrasts with the normal cranial NC migration phenotype, where Rac1 is suppressed while active RhoA is increased at cell-cell contacts during CIL, leading to a shift in polarity towards the cell-free edge to sustain directed migration (Theveneau et al., 2010; Shoval and Kalcheim, 2012; Leal et al., 2018).”

The co-Immunoprecipitation data lack marker bands (larger images/sections of the blots would be preferable) and the labelling is not clear. What do the white arrows in Fig. 3H,I mean? What does "elu" and "non eluted" mean? Did the reverse IP work as well?

R: We appreciate the reviewer's comments, and here we intend to provide a more detailed explanation of our approach to this analysis. Since we do not possess a secondary antibody specific to the heavy chain, our method involves eluting the co-immunoprecipitated proteins to visualize those with weights close to that of the light chain (such as EB1). We have outlined this elution step in the “Cell lysates and co-immunoprecipitation” protocol in the Materials and Methods section. To ensure proper control, we load both fractions - the eluted (or supernatant) and non-eluted (or resin) fractions - to monitor the amount of protein extracted from the resin using a 1% SDS solution. It's important to note that the elution step, as indicated by the V5 signal, is not entirely efficient, and a significant portion of the protein remains bound to the resin. This issue may also apply to the EB1 protein; however, it is still possible to visualize both bands (Gαi2V5 and EB1).

We have revised the legend for Figure 3 to include an explanation of the terms 'elu' (eluted fraction) and 'non-eluted' (non-eluted fraction). We have also included the explanation of the white arrows’ significance in the legends for Figure 3H and 3I. These arrows indicate the bands corresponding to the immunoprecipitated proteins.

We also agree with the reviewer’s suggestion to conduct the reverse IP. We can inform that we have done by triplicate all the Co-IPP. Although, if is necessary we will do the controls suggested. We present this assay as a plan.

The presentation of the Delaunay triangulations varies in quality. In Fig. 1 J/K the cells are clearly visible in the images, while this is not the case in Fig. 3 J-M and Fig. 4K-N. Conversely, the Delaunay triangulations in Fig. 1L are mainly black, while they are clear in Fig. 3 and 4. Perhaps the authors could find a more consistent way to present the data. Were the explants all approximately the same size at the beginning of the experiment? The Gαi2-morphant explant in Fig. 3K appears to be unusually small.

R: We appreciate the reviewer’s concerns and have taken steps to address them. To improve the quality of our data, we have made enhancements to the presentation of Figures 3 (panels J-M) and Figure 4 (panels K-N). Specifically, we have standardized the Delaunay triangulation representations.

Regarding the size of the explants at the beginning of the experiments, they were indeed approximately similar in size. We confirmed this by including a reference point (point 0) for each condition in the figures 3. However, in the panels presented, we show the results after 10 hours (Figure 3, X. laevis) and 4 hours (Figure 4, X. tropicalis) to assess cell dispersion, as indicated in the respective figure legends. This uniformity in size was further ensured by the calculation used to quantify dispersion. For the dispersion assay, we normalized each initial size of the explant upon the control, and we have added another representative explant of Gαi2 morpholino with its Delaunay triangulation to facilitate the experiment interpretation. Every Delaunay triangulation calculates the area generated between three adjacent cells and it grows depending on how much disperse are the cells between each other in the final point. (See Material and Methods section, Cell dispersion and morphology). As we can see in the manuscript, in every dispersion experiment that we have performed with Gαi2 morpholino, the cells cannot disperse at all. Furthermore, to analyze the dispersion rate in this experiment we use Control n= 21 explants, Gαi2MO n= 24 explants, Gαi2MO + 65 nM Nocodazole n= 36 explants, Control + 65 nM Nocodazole n= 30 explants (as we mentioned in the manuscript legend).

Why was the tubulin distribution in Fig. 2F measured from the nucleus to the cell cortex? Would it not make more sense to include cell protrusions? This does not seem to be the case in the example shown in Fig. 2F.

R: We appreciate the reviewer's concern. We would like to clarify that for the tubulin distribution measurements, we indeed measured from the nucleus to the cell protrusion. As a result, we have made an edit to Figure 2 (panel 2F) to provide further clarity on this matter.

The immunostaining for acetylated tubulin (Fig. 3A,B) looks potentially unspecific and seems to co-localize with actin (for comparison see Bance et al., 2019). For imaging and quantification, it may be better to use tubulin co-staining to calculate the percentage of acetylated tubulin. Please also add marker bands to the Western blot in Fig. 3C. If this issue cannot be resolved it may be better to only include the Western blot data.

R: We appreciate the reviewer's concern about the potential unspecific nature of acetylated-tubulin and its co-localization with actin, particularly in Figure 3. Regarding the co-localization with actin, it is predominantly observed in panel B, and we attribute this phenomenon to the Gαi2 morphant phenotype, where cortical actin is notably reduced, creating the appearance of co-localization. However, we will assess the experiment as suggested by the reviewer. Therefore, our plan is to conduct an immunostaining for acetylated tubulin and tubulin in both control and Gαi2 knockdown conditions. This will allow us to calculate the percentage of acetylated tubulin and complement the western blot analysis.

We have included marker weight indications on the western blot panel in Figure 3C.

The model in Fig.6 indicates that Gαi2 inhibits EB1/3. What is the experimental evidence and the proposed mechanism for this? In the discussion, the authors cite evidence that Gαi activates the intrinsic GTPase activity of tubulin, which would destabilize microtubules by removing the GTP cap. However, this mechanism would not directly affect EB1 and EB3 stability as the Fig. 6A seems to suggest. The authors also mention that EB3 appears to be permanently associated with microtubules in Gαi2-morphant cells. How would this work, given that end-binding proteins bind to the cap region? Are the authors suggesting that there is an extended cap region in Gαi2 morphants?

R: We appreciate the reviewer's valuable comments. We agree with the reviewer's observation that our experiments do not establish a causal link between Gαi2, EB1/EB3, and Rac1. We established a relationship between Gαi2 and microtubule dynamics (EB1 and EB3) to regulate Rac1 polarity through co-immunoprecipitation assays, which reveal protein interactions within an interactor complex. In addition, in Gαi2 Knockdown conditions we have found a strong reduction in microtubules dynamics following EB-GFP comets. Regarding the observation that EB3 seems to be persistently associated with microtubules in Gαi2-morphant cells, we wish to clarify that this is a speculation based on the microtubule phenotype observed during our dynamic analysis, where they appear more like lines rather than comets. It is important to note that none of the experiments conducted in this study conclusively demonstrate this, and thus, it remains a suggestion. Therefore, while our findings support the involvement of Gαi2 in coordinating cranial NC cell migration alongside EB1, EB3, and Rac1, we cannot exclude the possibility that this regulation may occur through other intermediary proteins, such as GEFs, GAPs, GDIs, and others. As a result, we have revised our model in accordance with the reviewer suggestion.

We have edited both the model and the legend at Figure 6. Gαi2 controls cranial NC migration by regulating microtubules dynamics.

Considering this, we have reviewed the manuscript to provide clarity on this point. See page 16 (paragraph 2, last line), 17 (paragraph 1, last line), 22 (paragraph 1, last line 17-20), 42 (Legend Fig. 6).

Minor comments 1. The citation of Wang et al. 2018 in the introduction does not seem to fit.

R: We appreciate the correction provided by the reviewer. We have carefully reviewed the Introduction and Reference sections and have corrected this error.

2.Does the graph in Fig. 4S show average values for the three conditions? If so, what is the standard deviation?

R: We appreciate the reviewer’s concern and we have added the standard deviation to Figure 4S.

3.From the images in Fig. 2G and H, it is difficult to understand what the difference is between the four groups shown.

R: We appreciate the reviewer's comment, and to clarify this point, we would like to explain that the comparison has been made between each type of comet. The PlusTipTracker software separates comets based on their speed and lifetime, classifying them as fast long-lived, fast short-lived, slow long-lived, or slow short-lived. In both conditions (control and morphant cells), we compared the percentage of each type of comet, as previously described in Moore et al., 2013. The results demonstrate that morphant cells exhibit an increase in slow comets compared to control cells. The same explanation is described in the Material and Methods section on page 26, Microtubule dynamics analysis.

Reviewer #3 (Significance (Required)): Overall, the study is well executed and significantly advances our understanding of the control and role of microtubule dynamics in cell migration, which is much less understood compared to the function of the actin cytoskeleton in this process. The strength of the study is the use of state-of-the-art (live imaging) techniques to characterize the role of Gαi in neural crest migration at the cellular/subcellular level. This article will be of interest to a broad readership, including researchers interested in basic embryonic morphogenesis, cell migration, and cytoskeletal dynamics, as well as translational/clinical researchers interested in cancer progression or wound healing.

R: We really appreciate the reviewer positive comments and consideration. We believe that the review process has significantly strengthened our manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary: The manuscript by Villaseca et al. analyzes the role of Gαi2 in cranial neural crest migration and reveals a novel mechanistic link to microtubule dynamics. The authors nicely demonstrate that Gαi2 is required for Xenopus neural crest migration and affects cell dispersion, cell polarity, focal adhesion turnover, and microtubule dynamics. They find that Gαi2-morphant neural crest cells are elongated, have larger, more stable protrusions, higher active Rac1 levels, and a concentration of microtubules at the leading edge. Using co-immunoprecipitation, the authors show that Gαi2 forms a complex with α-tubulin and the microtubule plus-end binding proteins EB1 and EB3, which are known regulators of microtubule dynamics. Time-lapse imaging shows that Gαi2 loss of function increases microtubule stability, which is further supported by an increase in acetylated tubulin levels. Consistently, treatment with nocodazole, which inhibits microtubule polymerization, as well as treatment with a Rac1 inhibitor, is able to rescue cell dispersion and morphology of Gαi2-morphant neural crest cells. The authors propose a model, whereby Gαi2 interacts with components of the plus-tip microtubule-binding complex to control microtubule dynamics and Rac1 activity to establish cell polarity, disassemble focal adhesion, and thereby facilitate neural crest migration.

Major comments:

1. The authors focus exclusively on the analysis of the subcellular levels of Rac1, which is probably related to the fact that they observe large extended protrusions with high Rac1 activity. However, as the authors note, a global fine-tuning of Rho GTPase activity is required for neural crest migration. One of the observed phenotypes of Gαi2-morphant neural crest cells is a decrease in cell dispersion, which may be caused by defects in contact inhibition of locomotion (CIL). This process involves a local activation of RhoA at cell-cell contact sites (Carmona-Fontaine et al., 2008). Furthermore, in fibroblast, RhoA/ROCK activity is required for the front-rear polarity switch during CIL (Kadir et al., 2011). Interestingly, similar to the Gαi2 loss of function phenotype, ROCK inhibition leads to microtubule stabilization, which can be rescued by nocodazole treatment, restoring microtubule dynamics and CIL. Therefore, it would also be interesting to know how RhoA activity is affected in Gαi2-morphant NC cells. At a minimum, this point should be be included in the discussion.
2. The co-Immunoprecipitation data lack marker bands (larger images/sections of the blots would be preferable) and the labelling is not clear. What do the white arrows in Fig. 3H,I mean? What does "elu" and "non eluted" mean? Did the reverse IP work as well?
3. The presentation of the Delaunay triangulations varies in quality. In Fig. 1 J/K the cells are clearly visible in the images, while this is not the case in Fig. 3 J-M and Fig. 4K-N. Conversely, the Delaunay triangulations in Fig. 1L are mainly black, while they are clear in Fig. 3 and 4. Perhaps the authors could find a more consistent way to present the data. Were the explants all approximately the same size at the beginning of the experiment? The Gαi2-morphant explant in Fig. 3K appears to be unusually small.
4. Why was the tubulin distribution in Fig. 2F measured from the nucleus to the cell cortex? Would it not make more sense to include cell protrusions? This does not seem to be the case in the example shown in Fig. 2F.
5. The immunostaining for acetylated tubulin (Fig. 3A,B) looks potentially unspecific and seems to co-localize with actin (for comparison see Bance et al., 2019). For imaging and quantification, it may be better to use tubulin co-staining to calculate the percentage of acetylated tubulin. Please also add marker bands to the Western blot in Fig. 3C. If this issue cannot be resolved it may be better to only include the Western blot data.
6. The model in Fig.6 indicates that Gαi2 inhibits EB1/3. What is the experimental evidence and the proposed mechanism for this? In the discussion, the authors cite evidence that Gαi activates the intrinsic GTPase activity of tubulin, which would destabilize microtubules by removing the GTP cap. However, this mechanism would not directly affect EB1 and EB3 stability as the Fig. 6A seems to suggest. The authors also mention that EB3 appears to be permanently associated with microtubules in Gαi2-morphant cells. How would this work, given that end-binding proteins bind to the cap region? Are the authors suggesting that there is an extended cap region in Gαi2 morphants?

Minor comments

1. The citation of Wang et al. 2018 in the introduction does not seem to fit.
2. Does the graph in Fig. 4S show average values for the three conditions? If so, what is the standard deviation?
3. From the images in Fig. 2G and H, it is difficult to understand what the difference is between the four groups shown.

Referees cross-commenting The concerns raised by my colleagues are entirely valid.

Significance

Overall, the study is well executed and significantly advances our understanding of the control and role of microtubule dynamics in cell migration, which is much less understood compared to the function of the actin cytoskeleton in this process. The strength of the study is the use of state-of-the-art (live imaging) techniques to characterize the role of Gαi in neural crest migration at the cellular/subcellular level. This article will be of interest to a broad readership, including researchers interested in basic embryonic morphogenesis, cell migration, and cytoskeletal dynamics, as well as translational/clinical researchers interested in cancer progression or wound healing.

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Referee #2

Evidence, reproducibility and clarity

Summary

The manuscript by Villaseca et al. describes functional analysis of Gai2 in cranial neural crest (CNC) migration using the frog Xenopus as their model system. The authors performed the loss-of-function assay to knock down expression of endogenous of Gai2 and discovered that CNC migration was impaired in the absence of changes of CNC fate specification. Based on the literature on Gai2 activities in other cellular contexts, the authors speculated that Gai2 might regulate microtubule dynamics and Rac1 function. Their studies using immunofluorescence (IF) and live-cell imaging indeed showed that microtubules were stabilized in membrane protrusions with concurrent activation of Rac1 in Gai2 knockdown cells. In addition, focal adhesion turnover was reduced. They further demonstrated that the CNC migration defects could be partially rescued by destabilization of microtubules with chemical treatment. The authors conclude from the studies that Gai2 orchestrates microtubule dynamics and modulates Rac1 activation during neural crest migration.

Major comments

The authors aim to address two issues in this manuscript: a) the role of Gai2 in neural crest development; and b) the mechanism of Gai2 function. While they have done a good job demonstrating a role of Gai2 in NC migration both in vivo and in vitro as well as the effects of Gai2 knockdown on cytoskeleton dynamics, protein distribution of selected polarity and focal adhesion molecules, and Rac1 activation, the link between Gai2 and the downstream effectors is largely correlative. Because of this, the model suggesting the sequential events flowing from Gai2 to microtubule to Rac1 to focal adhesion/actin should be modified to allow room for direct and indirect regulation at potentially multiple entry points.

Specific major comments are as the following:

Strengths:

-Determination of a role of Gai2 in neural crest migration is novel. -The effect of Gai2 knockdown on membrane protrusion morphology and microtubule stability and dynamics are demonstrated nicely. -Quantification of experimental perimeters has been performed throughout the manuscript in all the figures, and statistical analysis is included in the figures.

Weaknesses:

• The heavy focus of the study on microtubule is due to the previous publication on the function of Gai2 in regulation of microtubule during asymmetrical cell division. However, the activity of Gai2 is likely cell type-specific, as it has not been shown to control microtubule during cytokinesis in general. It is equally likely that Gai2 primarily regulates Rac1 or actin regulators to influence both microtubule and actin dynamics. The tone of the discussion should therefore be softened.
• The effect of rescue of NC migration with Rac1 inhibitor is marginal and the result is hard to interpret considering the inhibitor also blocks control NC migration. Either lower doses of Rac1 inhibitor can be used or the experiment can be removed from the manuscript, as Rac1 is required for membrane protrusions and the inhibitor doses can be hard to titrate.
• Since the defects seem to result partially from the inability of the NC cells to retract and move away, it may help to either include some data on Rho activation patterns in knockdown cells or simply add some discussion about the issue.
• To consider focal adhesion dynamics, live imaging should be used in the analysis. The fixed samples are different from each other, and natural variations of focal adhesion may exist among the samples. This can obscure data collection and quantification.

Minor comments

• Fig. 2, the centrosomes in control cells are not always obvious. The microtubules simply seem to be more networked and more fluid in control cells. This should be clarified with either marking the centrosomes in the figure or modifying the wording in the manuscript.
• In Fig. 3, a better negative control for co-IP should be using anti-V5 antibody to IP against tubulin/EB1/EB3 in the absence of Gai2-V5.
• The data for cell polarity proteins Par3 and PKC-zeta seem to be out of place. It is unclear whether mis-localization of these proteins has anything to do with NC migration defects induced by Gai2 knockdown. The conclusion does not seem to be affected if the data are taken out of the manuscript.
• In Suppl. Fig. 1, protrusion versus retraction should be defined more clearly. The retraction shown in this figure seems to be just membrane between protrusions instead of actively retracting membrane.
• Discussion can be improved by better incorporating all the components to make a cohesive story on how Gai2 works to regulate migration in the context of the neural crest cells.

Referees cross-commenting I agree with other reviewers' comments.

Significance

The authors demonstrate a role of Gai2 in regulation of neural crest migration in Xenopus by modulating microtubule dynamics. In addition, they show an effect of Gai2 knockdown on Rac1 spatial activation and focal adhesion stability. These are novel discoveries of the study. Some limitations exist in linking Gai2 with downstream effectors that directly or indirectly impact on cytoskeleton and Rac1 small GTPase.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This manuscript examines the role of a G protein, Gai2, in regulating the migration of cranial neural crest cells. Although previous literature has established that heterotrimeric G proteins are involved in cell migration, a central process during embryogenesis and adult homeostasis, the underlying cell biological mechanisms of their activities have not been elucidated. This manuscript rigorously examines the various aspects of Gai2 protein interactions to generate an exciting new paradigm in which Gai2 maintains normal microtubule dynamics by binding to tubulin and EB proteins. This normally dynamic microtubular intracellular environment then promotes cortical actin formation in the leading edge of the migrating cell as well as rapid focal adhesion disassembly by controlling Rac1 activity. Under conditions in which the levels of Gai2 are reduced by MO-mediated knockdown, cells display reduced microtubule dynamics and a decreased catastrophe rate, resulting in slower and more stable microtubules to which EB3 is more persistently associated. A stable microtubule environment leads to enhanced Rac1 activation at the leading edge and stable and larger focal adhesions, resulting in reduced migration. The authors utilize cutting edge approaches to examine the interactions between Gai2 and these other cellular components, taking advantage of the well characterized cell migration model - the cranial neural crest - both in embryos and in cultured explants of these cells.

Major comments:

The manuscript is mostly well written (it could use a few minor grammatical corrections), the significance of the problem is well described, and the results are clearly presented with adequate controls. The movies, provided as supplementary material, are of the highest quality and are essential additions to the stills provided in the figures. The data convincingly support the key conclusions of the manuscript.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? No

Would additional experiments be essential to support the claims of the paper? No additional experiments are essential.

Are the experiments adequately replicated and statistical analysis adequate? The number of embryos/ explants per assay and the number of explant replicates for each assay and the statistical assessments are rigorous.

Are the data and the methods presented in such a way that they can be reproduced? Mostly, however, the description of the MO used for Gai2 knockdown needs more detail:

1. Does the MO knockdown both S and L homoeologs of X. laevis? Since the level of GAPDH in Figure 1H also looks reduced in Gai2 MO lane, it should be made clear that the apparent knockdown of Gai2 was normalized to GAPDH, rather than being the results of unequal loading of the gel. Yes, I recognize that Figure 1I says normalized, but this is not stated in the results or the methods. Also, was this experiment done with X. laevis or X. tropicalis? I could imagine that if done in X. laevis, the lack of complete knockdown might be due to only one homoeolog being affected.
2. The knowledge of the efficacy of knockdown in each Xenopus species provided by the information requested in the previous point, would allow the reader to assess the level of knockdown in the remaining assays. To do this, the authors should tell us which assays were done in which species. I am not suggesting that each experiment needs to be done in each species, only that the information should be provided. If the MO is more effective in X. tropicalis - which assays used this species? If the knock down is partial, as shown in Figure 1H-I, which species this represents in the remaining assays would be useful knowledge.

Minor comments:

While prior studies are referenced appropriately, and the text and figures are mostly clear and accurately presented, the following are a few suggestions that would help the authors improve the presentation of their data and conclusions:

1. The cell biological experiments convincingly demonstrate that knockdown of Gai2 causes cells to move more slowly. It would be a nice addition to bring the explant experimental data back to the embryo by showing whether the slower moving NC cells in morphants eventually populate the BA. DO they cease to migrate or are they just slower getting to their destination? This could be done by performing snail2 ISH at a later stage (34-35?)
2. There are places in the manuscript where the authors use the terms "silencing" or "suppression" of Gai2, when they really mean reduced translation - their system is not a genetic knockout, as clearly demonstrated in Figure 1H-I. I suggest that more accurate wording be used.
3. In Figures 1-5 there are scale of bars on the cell images, but these are not defined in any of the figure legends.
4. The abstract is the weakest section of the manuscript, and would have greater impact if it were more clearly written.

Referees cross-commenting

The concerns are fair assessments. However, most can be addressed in the text and by clearer presentation of existing data rather than more experimentation.

Significance

The molecular regulation of cell movement is a key feature of a number of developmental and homeostatic processes. While many of the proteins involved have been identified, how they interact to provide motility has not been elucidated in any great detail, particularly in embryo-derived cells (as opposed to cell lines). The results obtained from the presented experiments are novel, in-depth and provide a novel paradigm for how G proteins regulate microtubule dynamics which in turn regulate other components of the cytoskeleton required for cell movement. The results will be applicable to many migrating cell types, not just neural crest cells.

Because of the application of the data to many types of cells that migrate, the audience is expected to include a broad array of developmental biologists, basic cell biologists and those interested in clinically relevant aberrant cell migrations.

Reviewer keywords: Xenopus embryology; neural crest gene expression; use of MO-mediated knockdown of gene expression. Not an expert in microtubule dynamics.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

This manuscript presents the cis-regulatory analysis of the enhancers controlling prox1a gene in zebrafish. Authors used both evolutionary conservation and existing single-cell ATAC data to highlight the major role of two elements. I feel that the transgenesis work is quite solid and the main conclusions interesting. However, I feel the authors need to provide some extra validations for some of the analysis.

1. the authors did not discuss the fact that euteleosts underwent an extra whole genome duplication and that prox1a might have a paralogue. They also perform genome alignment using non-duplicated outgroups (gar, xenopus) without discussing. I am a bit skeptical about the use of mVISTA on relatively short expert of sequence aroudn a gene, as it is not able to capture the global molecular evolution parameters. I think the authors should also examine some of the precomputed phastCons / phylocons data performed and available on UCSC to confirm their findings. probably they should also examien a few more fish genome. I don't find this evolutioanry analysis extremely convinced and careful - which doesn't mean that the conclusions are wrong.
2. I find the presentation, fairly obscure, the writing is quite convoluted, and the figures are very dense and not super explanatory, I would urge to improve (this is not helped by the fact that figure are their leged and presented at distinct places of this manuscript). For instance, I think having. a figure summarising signal from evolutionary conservation, scATAC and chromatin marks altogether would be quite essential.
3. I also find the reanalysis of the single-cell ATAC described too scarcely: which are the genes used to identify the different cell populations?
4. I feel the one additional experiment that the authors could have done would have been to use their construct to isolate the different cells population of interest and perform some regulatory profiling scuh as ATAC-seq or cut-and-tag on this population, to have a direct, in situ evidence of the activity of these regulatory elements.?

I also feel that the evolutionary aspect could be discussed a bit more, what are the differences between the diffeerent vertebrate lineage, etc...

(p7) active enhancer in a tissue: while ATAC gives a good indicated of accessibility it is not an indicate of activity as for instance H3K27Ac would be.

Significance

I think this is an interesting piece of work, which elaborates on previous studies on prox1a involvment in the lymphatic system but it doesn not bring essentially new perspective on the question.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Panara et al. identified the enhancer regions necessary for the tissue- and organ-specific expression of Prox1a, which is essential for lymphatic vessel development in zebrafish. The authors compared the sequences of eight species of osteichthyes, identified Conserved Non-Coding Elements (CNEs), and further predicted active enhancers by combining public databases. This sequence was analyzed using the ZED system. As a result, they confirmed that two out of ten sequences caused GFP expression in a subset of lymphatic endothelial cells. +15.2prox1a was required for the prox1a expression in the facial collecting lymphatics, while -2.1prox1a was required in the facial lymphatic valves. They also predicted transcription factor binding to these enhancer regions and identified enhancer regions of -14, -71, -87Prox1a based on chromatin state. Additionally, they identified a core element within -2.1Prox1a, performed its loss-of-function experiments, and analyzed its phenotype, revealing the functional importance of these enhancer regions. The paper is logically well-structured and informative concerning the expression control of Prox1a.

Major comments:

• Are the key conclusions convincing?

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

No need to qualify. - 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.

Figure 4H is probably incorrect. Looking at the morphology of the valve, it's inferred that the lymph flow goes from right to left. In this case, if the valve function is abnormal, the lymph flow that entered from the right should reflux back to the left. Is the reviewer misunderstanding something here? Clarity on this point could be provided with video images, similar to echocardiograms in mice. - 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.

Yes, they are realistic. I would like further clarification to ensure the data is correct.

Minor comments:

• Specific experimental issues that are easily addressable.

It's important where Prox1 is expressed in the lymphatic system, but if the identified enhancers also regulate Prox1 expression in other tissues like the myocardium, it would be a significant finding. Do these enhancers have a role in controlling Prox1 expression in lymphatics and non-lymphatics? - Are prior studies referenced appropriately?

Yes. - Are the text and figures clear and accurate?

Yes. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions? 1. Regarding Figure 4G, the actual image is unknown due to the red shading. Can you discuss or clarify how knocking out the Prox1 enhancer affects valve formation? If Prox1 expression is reduced, does the normal extracellular matrix change? Why do these morphological changes occur? 2. In mice, Prox1 is expressed in the heart valve endothelium. Have they checked the enhancer activity in other valves (heart valves or venous valves, if they exist)? 3. -2.1prox1a is said to be important for Prox1 expression in the facial lymphatic valves. Are valves only formed in this part of the lymphatics in zebrafish at this stage? Why is it important to identify the enhancer for this particular valve? Are there no other lymphatic valves? Please explain in the text. 4. They seem to emphasize +15.2's role in facial lymphatic expression. Compared to the trunk, why is it significant that there is a unique enhancer acting in this area? Is there something functionally or anatomically unique about facial lymphatics? Please discuss why the enhancer region's conservation is high in facial lymphatics.

Significance

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

In this paper, the authors have revealed previously unknown regulatory sequences of zebrafish Prox1a. They identified enhancer sequences that control Prox1a expression in the facial collecting lymphatics and lymphatic valves. The loss of the -2.1prox1a enhancer led to malformation in some lymphatic valves, suggesting the functional importance of this enhancer. - State what audience might be interested in and influenced by the reported findings.

Readers interested in vascular development and those interested in transcriptional control during development. - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Lymphatic development in mammals, cardiac development, cardiology, cardiovascular pathology.

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Referee #1

Evidence, reproducibility and clarity

Through the study of cis-regulatory element/enhancer activity in zebrafish, this study from Panara and colleagues provides insight into the transcriptional control of Prox1, a master regulator of lymphatic endothelial cell (LEC) fate. The authors used conservation of non-coding DNA, and chromatin accessibility data to identify enhancers that drive expression of a fluorescent reporter in anatomically distinct subsets of LECs. Analysis of transcription factor binding motifs in these enhancers suggests that differences in enhancer activity throughout the lymphatic vasculature may be due to binding of distinct transcription factors to these elements. Importantly, the authors identify a conserved 200 bp element within the -2.1kb enhancer that could drive expression in the lymphatic valve. Analysis of mutants carrying a 102 bp deletion in this region (including an Nfatc1 binding site), revealed reduced Prox1 expression and valve defects.

Major comments

• In the text it is suggested that sequence conservation was assessed across 8 species: "We aligned the region of the PROX1/prox1a locus in eight Osteichthyes species using mVISTA (Fig. 1A, Table S1)." Fig. 1A contains 7 species, and I am not able to find Table S1.
• It would be important to discuss reasons that the +15.2kb enhancer is not clearly identifiable in the scATAC-seq analyses but drives expression. Is this due to the relatively limited activity in facial lymphatics? Furthermore, given the degree of conservation, it would be useful to mutate specific transcription factor binding motifs (e.g. Mafb, Sox18, etc) in the -15.2kb enhancer and assess activity in the FCLV.
• OPTIONAL : Mutate Nr2f2 and Gata2 binding sites in -87kb enhancer to test for impact on activity. This would allow the authors to imply functional rather than sequence conservation. On a similar note, it would be interesting to understand if these enhancers are active in the context of mammalian lymphatic development.

Minor comments:

• It would be good to clarify in the following sentence that these enhancer marks are present at the whole embryo level and were not specifically identified in LECs : "Using zebrafish public databases for H3K4me1 and H3K27ac, we identified that ten of the selected prox1a CNEs were primed or active enhancers (Aday et al., 2011; Bogdanovic et al., 2012) (Fig. 1A, S1C)."
• "....(Gupta et al., 2007) to determine the motifs and putative transcription factor binding sides." - should read "....(Gupta et al., 2007) to determine the motifs and putative transcription factor binding sites".
• It might be more accurate to use zebrafish protein nomenclature for the transcription factor motifs in Fig1D, G and Fig2E (i.e. Gata2 not GATA2)

Significance

• While the roles of Prox1 in lymphatic vascular development and homeostasis are well established, relatively little is known about the cis-regulatory mechanisms governing it's expression; recent work has described an enhancer in the mouse Prox1 locus (Kazenwadel et al., 2023). This study from Panara and colleagues characterises numerous cis-regulatory elements in the zebrafish prox1a locus and demonstrates heterogeneous activity in anatomically distinct parts of the lymphatic vasculature. The imaging and characterisation of enhancer elements is of a high standard. Experiments that addressed the regulation of enhancers by upstream transcriptional or signalling cues would strengthen this study. Furthermore, to understand if there is functional conservation across evolution, analysis of activity in mouse embryos would be of interest.
• This should be of broad interest to the vascular biology and developmental biology fields.
• My expertise are in vascular biology, lymphatic development and developmental genetics.

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Reply to the reviewers

We thank all the reviewers for positively evaluating our work and for their valuable comments and constructive suggestions. We will revise the manuscript in accordance with the raised points and ensure that all comments are addressed.

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Referee #3

Evidence, reproducibility and clarity

Summary:

The study of the formation and maintenance of the blood-brain barrier (BBB) is a growing field of study, partly due to its strong link with neurological disorders. The BBB depends on the role of multiple cell types and mechanisms. Mutations in the conserved phospholipase NTE/SWS can lead to neurodegeneration, and previous work from the authors shows that SWS loss leads to abnormal glial morphology. In this work, authors use Drosophila to further study this phenotype, showing that SWS is mostly expressed in the BBB-related glia and that its loss leads to abnormal BBB permeability, increased inflammatory response and neural cell death. Interestingly, authors observed a dependence for the BBB-defective phenotype on aging, with important implications for SWS/NTE and neurodegeneration. Overall, the work represents a clear advance in the poorly explored role of NTE/SWS in neurodegeneration, with a broad impact on the understanding of BBB maintenance. This work shows a combination of multiple and appropriate experimental approaches, including confocal microscopy, EM, RT-qPCR, or gas chromatography-mass spectrometry among others.

Major comments:

The use of sws1 and sws1/sws4 transheterozygous animals, together with the use of sws RNAi is a solid approach to validate that the reported phenotypes are due to SWS loss. Using these models, the authors performed a convincing structural analysis of the subperineurial glia phenotype, and showed that it is accompanied by a defective BBB, inflammation and neuronal cell death. The key conclusions are properly supported by the data. However, there are some claims in the text that are not supported by any data in the Figures, but only qualifications. This needs to be fixed:

• Page 6, third paragraph: "...we specifically downregulated sws in the nervous system using the double driver line that allows downregulation of sws in glia and neurons (repo, nSyb-Gal4, Suppl. Fig. 2C-Cʹ). Since these animals had the same disorganized structure of brain surface as the loss-of-function mutant..." Supp. Fig. 2C-C' only shows expression of CD8:GFP and nlacZ reporters by repo and nSyb-Gal4, but there is no data showing sws RNAi expression by these drivers.

"...Moreover, downregulation of sws in all glial cells (repo>swsRNAi) resulted in the same phenotype. At the same time, upon sws downregulation in neurons,... (Suppl. Fig. 4)..." Suppl. Fig. 4 only shows nsyb>swsRNAi data but not repo>swsRNAi - Page 6, fourth paragraph: "Importantly, expression of Drosophila or human NTE in these glia cells rescued this phenotype (Fig. 2H)" In addition to the indicated quantifications, it is essential to show some representative data showing the phenotype when Drosophila or human NTE are expressed in glial cells of sws mutant animals. - Page 9, last paragraph: "We found that in moody mutants, the surface glia phenotype analyzed using CoraC as a marker could also be suppressed by NSAID and rapamycin (Fig. 5A)." In addition to the indicated quantifications, it is essential to show some representative data showing the phenotype with and without treatments.

A more detailed analysis of two aspects of the data would clearly improve the manuscript, whose findings are a bit superficial in the current state:

• The exact mechanism by which BBB permeability leads to brain inflammation remains unknown. Authors show that accumulation of polyunsaturated fatty acids (known to regulate inflammation) occurs in sws-depleted animals. However, they only observed a correlation between this phenotype and the inflammatory response, while is not clear whether the accumulation of polyunsaturated fatty acids causes inflammation in this model or is a consequence of it. An attempt to rescue the accumulation of polyunsaturated fatty acids (i.e., knocking down a required enzyme for their production) in sws mutants might help to understand this. Also, the fact that the defective BBB phenotype observed in either sws KO and glia-specific KD can only be partially rescued by the use of inflammation inhibitors, suggests that other pathways are involved.
• While the differences between the phenotypes caused by sws or moody loss are well characterized, it would be key for this work to further study the mechanisms by which sws controls septate junctions. The authors propose the organization of lipid rafts, but some experiments in that direction to check this hypothesis. For example, can authors reproduce the septate junction phenotype of sws mutant (Fig. 6C) by using a different approach to induce defective lysosomes in subperineurial glia?

The attempt of the proposed approaches above should require about 3-6 months of investment, with limited economic effort, given the availability and diversity of lines found in the existing stock centres such as Bloomington or Vienna.

The data is presented very clearly, and the methods are adequately detailed, and the experiments and statistical analysis are adequate.

Minor comments:

Prior studies are referenced appropriately, but there is a case that should be addressed. On Page 3, first paragraph, regarding the sentence: "However, the molecular mechanisms underlying inflammaging remain unclear". I recommend specifying what is known and what is unknown in the field. Ideally describing (briefly) the knowledge about lipids, inflammaging and neurodegeneration, which are the specific topics of the research. Otherwise, the current sentence is too vague, while there is a lot of work published about it.

The text and figures are clear and accurate. The logic of the experiments and the results are exposed very clearly (for example, the Suppl. Tables are very helpful). There are a few minor issues, however, that should be addressed:

• Page 4, first paragraph: regarding the sentence: "For various obvious reasons, humans are not ideal subjects for age-related research.", I recommend specifying the main reasons (i.e. life cycle, ethical issues, etc.?).
• I would recommend moving the text "For various obvious reasons...disrupted upon ageing." From its current position to just before "Drosophila melanogaster is an excellent...". This would keep a better logic in the text by explaining NTE first and later introducing the models to study its function. Presenting then Drosophila.
• To support the sentence "Together, Drosophila satisfies...neurodegeneration during aging", instead of citing so many papers, I recommend citing just one current review about it, since the amount of literature supporting the claim is huge and should not be limited to a few "random" articles. An alternative might be indicating that the lab has used Drosophila for this aim before, and then citing the examples from the literature.
• Page 4, second paragraph: if NTE/SWS is going to be used as a synonym for NTE/SWS loss of function (or other type) model, it needs to be specified. Otherwise, refers to the proteins and sentences like "NTE/SWS has been shown to result in lipid droplet accumulation..." are misleading.
• Page 4, last paragraph: the first time that "BBB" is used, its meaning should be specified. And three lines below use "BBB" instead of blood-brain barrier.

Referees cross-commenting

I agree with the comments provided by the other reviewers. They are well reasoned and cover some aspects of the work that I did not see. Regarding the main issue, the three revisions point at the same direction, that is the limited analysis about the mechanism underlying the phenotypes.

Significance

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

This work represents a substantial advance in the understanding of NTE/SWS function in the context of neurodegeneration, and opens potential approaches to treat related disorders (they successfully use anti-inflammatory compounds to ameliorate some of the key phenotypes). However, the findings are a bit superficial in terms of mechanisms, and further analysis (see major comments) would notably improve the significance of the manuscript. This should be realistic and suitable, given the advantages of the Drosophila model and the availability of tools. - Place the work in the context of the existing literature.

The role of SWS in regulating lysosomal function is potentially supported by NTE-deficient mice data (Akassoglou et al., 2004; Read et al., 2009), where different types of neurons show similar dense bodies containing concentrically laminated and multilayered membranes than those observed in this work in Drosophila sws mutant. Potentially, the rest of the work has a translation to mammals, which is supported by the fact that ectopic expression of NTE rescues some of the key phenotypes described in the manuscript. - State what audience might be interested in and influenced by the reported findings.

Neuroscience in general, since the study of BBB and neurodegeneration has a clear general interest in the whole field. - 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.

Drosophila; Neurodegeneration; Hereditary Spastic Paraplegia; Alzheimer's disease; Motor neurons; Microglia; Endoplasmic reticulum; Mitochondria.

Lipid metabolism is the part of the manuscript where I have less expertise to evaluate, only having general knowledge about it.

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Referee #2

Evidence, reproducibility and clarity

The manuscript by Tsap et al describes a role of NTE/SWS in forming the BBB in Drosophila. Disruption of the BBB in SWS mutants and knockdown flies results in morphological changes of the glia forming the BBB, increased brain permeability, altered lysosomes, and an upregulation of innate immune genes. The experiments to show a function of SWS in surface glia and the resulting changes in permeability are well supported by the experiments and the statistics appears appropriate. The authors also show changes in innate immune genes and some fatty acids and that similar changes are found in another mutant affecting the BBB. They discuss that these changes are a consequence of the disruptions of the BBB but also that these changes induce changes in the BBB. To address this and confirm that the changes in immune genes and fatty acids is a consequence of the altered BBB, they should include experiment expressing SWS in the surface glia and measure if that normalizes these changes. Another major aspect that should be addressed is the effect of aging. As the authors point out, loss of SWS causes age-dependent phenotypes (shown by the author and others) and with the exception of figure 3F, the age isn't even mentioned in any of the other figures. Furthermore, at least some of the experiments should be done at different ages to determine whether the phenotype is progressive; this includes the permeability assays and the measurements of immune genes (the latter could also support whether changes in the immune genes affect the BBB or vice versa the BBB changes cause the upregulation of immune genes). Lastly, the authors claim that septate junctions are defective in sws mutants. However, this should be confirmed by EM studies (which the authors have already done) besides immunohistochemistry which doesn't provide enough resolution.

Significance

A role of SWS in maintaining the BBB and what consequences this has provides another insight how this protein (and its homolog NTE) affects brain health. Although a function of SWS in glia (as well as in neurons) has previously been described, changes in the surface glia and the BBB is a novel aspect. However, the causative role of SWS on some of the described consequences (see above) should be confirmed. Although the manuscript can add to a better understanding of the connection between disruptions of the BBB and neurodegenerative diseases, which is of interest for a broader field of researchers, the discussion of the results is quite speculative.

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Referee #1

Evidence, reproducibility and clarity

Summary

Elucidating the cellular and molecular mechanisms underlying age-related neurodegeneration remains a key challenge for neurobiologists. In this manuscript, Mariana Tsap and colleagues in the team of Halyna Shcherbata focus on the function of the neuropathy target esterase NTE/Swiss Cheese (Sws) in the Drosophila brain. The authors use an elegant combination of genetics, light and electron microscopy, RT-qPCR and GS-MS mass spectrometry to determine the complex role of Sws in cellular blood brain barrier (BBB) integrity, the brain inflammatory response and fatty acid metabolism. The study provides a detailed characterisation as to how the loss of sws affects glial cell morphology in the BBB revealing abnormal membrane accumulations and tight junctions, and in consequence causing permeability issues. Importantly, they observed the upregulation of antimicrobial peptides in the brain, indicative of neuroinflammation, as well as of fatty acids, equally connected with the inflammatory response.

Major comments

The study provides a detailed and comprehensive characterization of the sws mutant phenotype, and in particular the role of this gene in blood-brain barrier forming glia. - The study connects neurodegeneration and inflammation, but also makes a particular point about "inflammaging". However, the age contribution has not been studied in detail. Indeed, the flies analyzed are 15 days old (according to the Material and Methods section, with the exception of Figure 1 where flies are 30 days old), and hence have not been compared with younger or older flies to make a point of age as evoked in the abstract, introduction or discussion. The authors should either add experiments comparing differently aged flies or de-emphasize this point to a brief consideration in the discussion. Instead, it would be very helpful to provide concise information about the current knowledge concerning the inflammatory response in the Drosophila brain. - Related to this point, the authors convincingly show that sws is required in surface glia using rescue experiments. Nevertheless, all experiments rely on drivers and mutants that could cause the emergence of phenotypes during development. Thus, to strengthen the causative link between the breakdown of the BBB and the neuroinflammatory response, it would be helpful to consider an acute knock-down in adults after BBB formation has been completed. - To test the brain permeability barrier, the study uses a 10 KDa dextran permeability assay. Almost 25% of brain in controls show a leaky barrier. It would be helpful to describe the causes for this relatively high occurrence. - An important point in the study concerns the increase of free fatty acids as cause of the inflammatory response. The measurements were based on measurements of whole heads, which could include the hemolymph and fat body within the head in addition to brain. However, the causative relationship remains unclear and the question why a leaky blood brain barrier would increase the free fatty acid levels in the body or brain remains mainly an observation at the descriptive level. Here, it would be helpful to design an experiment, which could test the causative links or to modify the interpretation in scheme 6D and adjust the wording in the text. - Related to this, how do the levels of AMP caused by a leaky BBB would compare to an elicited neuroinflammation by the presence of bacteria? The neuroinflammatory response can be accompanied by macrophage entry into the brain following AMP induction. Could the authors detect this response (which could be envisioned as manipulations include pupal development, provided macrophages would persist into adulthood)? This would make a strong point regardless of the outcome. - Expression of sws is determined using sws-Gal4 driving membrane-tethered GFP. As sws is expressed very widely and classical Gal4 lines tend to be active in the BBB, it is important to provide the exact information about the nature of this driver. - The Material and Methods section should contain a proper Quantification and Statistical analysis section. In the Figures, it would be helpful to refer to the Table reporting sample numbers. - In Figure 5, it would be important to indicate sample numbers, the nature of the error bar, and show data points together with columns.

Minor comments

• On page 8, cell death is visualized using "the apoptotic marker Cas3". It should be Caspase-3. Moreover, it is not clear whether this antibody (directed against vertebrate Caspase-3) recognizes indeed Caspase-3 in Drosophila? This should be formulated more carefully.
• On Page 9 (3rd paragraph), the authors report that they "want to understand what signaling pathway is activated." However, the described experiments do not lead to a signaling pathway, but conclude that an antiflammatory response is evoked. This should thus be reworded.
• Figure 1 reports the expression pattern and phenotype of sws; thus, the title of the figure should be extended.
• Concerning the description of phenotypes, the authors use the term "clumps", but it is not clear what this entails (e.g., Page 6, or Figure 6). For the reader, it is also necessary to refer to original studies of moody to understand the septate junction phenotype represented in the figure.

Referees cross-commenting

I fully agree with the comments of the other two reviewers, as they were complementary and overlapping with mine (e.g. the contribution of age).

Significance

This study provides a detailed cellular and functional characterization of the swiss cheese phenotype in the blood-brain barrier so far not reported in previous studies, including the team's own earlier publications (e.g., Kretzschmar et al., 1997; Melentev et al., 2021 and Ryabova et al., 2021). Furthermore, it uses cutting-edge technology to provide links to neuroinflammation and neurodegeneration, Previous studies explored neuroinflammation in the brain of Drosophila by challenging the organism with bacteria to mount an inflammatory response (Winkler et al., 2021). Intriguingly, this current study provides evidence, that a leaky blood brain barrier alone could lead to an inflammatory response, and that in turn, treatment with anti-inflammatory agents could reduce the cellular defects in glia and in consequence neurodegeneration. This represents an important conceptual advance that will be of wide interest to neurobiologists interested in glial biology, neuroinflammation and neurodegeneration in Drosophila and in vertebrates. One possible limitation of the study may be that while complex cellular processes have been pinpointed, some of the causative links of the BBB with neuroinflammation remain unexplored, in particular the aspect of elevated free fatty acids/antimicrobial peptides.

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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 the manuscript entitled "The long non-coding RNA LINC00941 modulates MTA2/NuRD occupancy to suppress premature human epidermal differentiation", Morgenstern and colleagues describe a novel mechanism by which Nur complex interacting with a ncRNA LINC00947 represses expression of a late differentiation transcription factor EGR3 in the skin. These findings are novel and will be of interest in the field. Of note, the authors use a plethora of biochemical and cell biology techniques such as chromatin occupancy, transcriptomics and organotypic skin culture. Both the Introduction and Discussion outline a necessary background in the field of ncRNAs and skin biology as well as clearly state and discuss the scientific problem. The Methods are comprehensive and accurate. Importantly, any possible ethical issues are discussed. The Results are clear and support the data shown. Also the Figures are well organised and easy to follow. Overall, the manuscript is well prepared and will be of interest for broad readership. Before publication, however, the authors should address some points to further enhance their work:

Major points:

1. It is peculiar that there is no evident K10 and FLG staining in the organotypic skin from siControl cells in Fig 2E but it is present on the Fig 5C. The authors should explain these inconsistences. Ideally the authors should provide a western blot analysis of these proteins which would help to give a more quantitative picture of the phenomenon.
2. There iare no evidences at protein level of an efficiency of MTA2 and EGR3 knock-down. The authors use an MTA2 for western blot, so it should not be difficult to confirm. For EGR3, it is essential, as EFR3 is expected to increase only during late differentiation, while experiments from Fig 5B are performed only at 3 days of differentiation. Is it enough to induce EGR3 expression? Is the knock-down efficient at protein level at that time point? This is important as the work by Kim et al 2019 shows a detectable expression of EGR3 only after 7 days of differentiation.
3. The authors should demonstrate an increase of EGR3 at protein level after LINC00947 knock-down (by western blot in vitro and/or in epidermal organotypic tissue).
4. A key experiment to confirm the proposed model should be a double knock-down of both LINC00941 and EGF3. Will it rescue the observed increase of pro-differentiation genes?

Minor points:

1. 4E - EGR3 or LINC00941 knock-down?
2. 4E - The RNA seq label states "RNA seq (siLINC00941 d3)" but apparently shows both scr and siLINC00941
3. Please add the recipe of the lysis buffer used for western blot analysis of keratinocytes lysates.
4. Page 10 - Fig 4D description - is it "chromatin conformation state" or just "chromatin state"?

Significance

These findings are novel and will be of interest in the field.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the authors investigate the role of LINC00941 epidermal differentiation. Specifically the authors show interaction with MTA2 and other NuRD subunits. Next, the authors show that LINC00941 and MTA2 restricts premature keratinocyte differentiation, where KD of either results in increased differentiation marker expression. To understand molecular impacts, the authors perform ChIPseq of MTA2 in control and LINC00941 depletion. Curiously, MTA2 binds in a trend differing from other cell types with predominant binding over active promoters. Upon LINC00941 KD, MTA2 binding is changed at 33 locations, where the majority show reduced binding. Overlapping binding changes with gene expression changes, the authors identify EGR3 as the only direct candidate upregulated upon LINC00941 KD and upregulated during differentiation. KD of EGR3 results in opposite trends of LINC00941 KD, suggesting the proposed mechanism of LINC00941 repressing EGR3 until appropriate time in differentiation. I have the following suggestions for this work:

1. While data support MTA2 acting in NuRD, beyond Fig 1, the authors exclusively use MTA2 as a proxy for NuRD. Of course there are some subunits that are within other complexes and should not be used, others are options. While I do not expect the authors to perform all experiments on an additional subunit of NuRD, I do think there are a few things the authors should consider:
   • a. Be more precise with language to point out only MTA2 rather than say NuRD complex throughout many aspects of the paper, and only assume the complex in limited settings and when it is clear it is speculative
   • b. Perform a subset of experiments on another subunit. For example, the Mass Spec in Fig 1A/B shows an interaction with other subunits, but the verification was only done for MTA2 (Fig 1C/D). This could easily be blotted (or another Western performed) and/or primers for other subunits for the qPCR for a couple additional subunits. Similarly straightforward, looking at MTA2 RNA expression changes during differentiation (Fig 2A): if additional primers were used to other subunits, these additional subunits could be used to verify.
2. Related to the above comment, does MTA2 KD (or LINC00941 KD for that matter) result in loss of NuRD complex formation? If so, this would be sufficient to address point 1.
3. Finally, in relation to NuRD complex here, it is important to note that mutually exclusive NuRD complexes (MBD2/NuRD and MBD3/NuRD) have been documented. Because the Mass Spec did not show interaction to MBD2 or MBD3, it is not clear if this is limited to one of these complexes. Related to this, the authors show by Mass spec that LINC00941 interacts with CHD4, but not CHD3. Is this because Chd3 is not expressed in these cells, or because there is some mutual exclusivity to CHD4 and LINC00941 is acting through this subcomplex?
4. Immunofluorescence images showing increased Keratin 10 and Filaggrin in LINC00941 or MTA2 KD (Fig 2E) and decreased Keratin 10 and Filaggrin in EGR3 KD (Fig 5C) are curious as the control look very different. In 2E, the control shows barely detectable levels, whereas in 5C the levels look similar to what is seen in Fig 2E KDs. Is this variability? If so, more representative images as well as quantification to the changes are necessary to make these two points.
5. In Figure 4, the authors present ChIPseq data for MTA2 in LINC00941 KD. One interesting trend is that the KD alters binding of MTA2 at mostly bivalent/repressed locations, rather than at active locations which is the majority of MTA2 binding (from Fig 3). It would be nice to show then these data rather than only stating it. The authors include a browser track for 2 genes (Fig 4D and S4C), but for the other 31 locations, a heatmap or something to show the level of K27me3 vs K27ac/K4me3 would be helpful to support this claim. Notably saying "Most of the differential MTA2/NuRD occupied sites were marked by repressive histone modification H3K27me3..." is the point that doesn't seem to be shown, and also a precise number should be included. a. Related to this, I believe the authors performed K27me3 ChIPseq in the KD, and if so, it would be nice to see more genome wide effects here.
6. This is perhaps beyond the scope of the paper, but the obvious question to me is if EGR3 is relocalized in LINC00941 KD. Specifically, we would anticipate that EGR3 localization in the KD would mimic that of a more differentiated cell (binding to differentiated genes). A quick ChIPqPCR experiment for a few locations would be sufficient to support this model.

Minor points:

1. Importantly, CHD5 can also be incorporated in NuRD, in place of CHD3 or CHD4.
2. The authors use heatmaps and metaplots in Fig S3 to show reproducibility of the ChIPseq datasets. Importantly, the PCA does show some variation. XY scatterplots for replicates vs one another would be a more robust QC.
3. In figure 4D, the authors present nice data showing changes in histone mods during differentiation, but it is very hard to see the color changes and the tracks as presented. (same point for Fig S4C)
4. it is unclear from the methods or the figure legend if RTqPCR data are biological or technical replicates.

Significance

In this manuscript, the authors present a molecular function for LINC00941 in epidermal differentiation, where it interacts directly with NuRD subunit MTA2. LINC00941 has been previously described but this activity was not described. LINC00941 seems to specifically help target or maintain MTA2 localization to EGR3 to promote repression of this gene. Then, the model suggests that during differentiation, LINC00941 and MTA2 levels decrease, permitting activation of EGR3 during epidermal differentiation and subsequent activation of appropriate genes. These findings will be of interest to individuals interested in NuRD function, lncRNA activity and/or epidermal cell fate.

My expertise is in chromatin biology, chromatin remodelers, epigenomics, and cell identity.