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

Learn more at Review Commons

---

Reply to the reviewers

A detailed point wise response has been uploaded along with revised manuscript files

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

Learn more at Review Commons

---

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.

Learn more at Review Commons

---

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

---

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.

Learn more at Review Commons

---

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.

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

Learn more at Review Commons

---

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.”

---

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.”

---

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.

Learn more at Review Commons

---

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.

Learn more at Review Commons

---

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.

Learn more at Review Commons

---

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

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

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.

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 #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.

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

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.

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

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.

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

Learn more at Review Commons

---

Dear Editor,

Herewith we submit our fully revised peer-reviewed preprint that had been reviewed by Review Commons. We thank the Review Commons team and reviewers for thoroughly commenting on our preprint and providing very useful additional points for consideration and discussion.

You will find - the revised manuscript (third preprint version uploaded on biorxiv)<br /> - two reviewer letters (through Review Commons), - our rebuttal letter<br /> - a revised manuscript version with highlighted changes.

Our manuscript reports that an active form of FIT, an essential transcription factor for root iron acquisition in plants, forms dynamic nuclear condensates in response to a blue light stimulus.<br /> A hallmark of our work is the thorough investigation of the nature of the FIT nuclear bodies in plant cells, that we were able to characterize as highly dynamic condensates in which active FIT homo- and heteromeric protein complexes can accumulate preferentially. Through co-localization with nuclear body markers, we found that these FIT condensates are related to speckles, which are a sub-type of nuclear bodies connected with splicing activities. This suggests that FIT condensates are linked with post-transcriptional regulation mechanisms.

The reviewers highlight that an “impressive set of microscopic techniques” has been combined to study in a unique manner the characteristics and functionalities of FIT nuclear bodies in living plant cells. We show that FIT nuclear bodies can be formed in roots of Arabidopsis thaliana. The microscopic imaging techniques we used to characterize the nature and functionalities of FIT nuclear bodies in plant cells have several constraints related to sensitivity and a required strength of fluorescent protein signal. For technical reasons to be able to apply qualitative and quantitative imaging techniques, we conducted the investigation of FIT condensates in Nicotiana benthamiana, a classical and widely used plant protein expression system.

As stated in the reviews, the connection between plant nutrition and nuclear bodies is an “unprecedented” new mode of regulation. The significance of our work is underlined by the fact that we report a “very precise cellular and molecular mechanism in nutrition” that is as yet “still largely unexplored in this context”. Therefore, our study “sheds light on the functional role of this membrane-less compartment and will be appreciated by a large audience.”

We propose that condensate formation is a mechanism that may steer iron nutrition responses by providing a link between iron and light signaling. For sessile plants, it is absolutely essential that environmental signals are sensed and integrated with developmental and physiological programs so that plants can rapidly adjust to a changing environment and potential stress situations. Since iron is a micronutrient that may be toxic when present in excess, e.g. through catalyzing oxidative stress, plants strictly control the acquisition and allocation of iron. Hence, FIT nuclear bodies may be regulatory hubs that integrate at the sub-nuclear level environmental signaling inputs in the control of micronutrient uptake, possibly connected with splicing.

Our work lays the ground for future studies that can address the proof of concept in more detailed manner in plants exposed to varying environmental conditions to reveal the interconnection of environmental and nutritional signaling.

We prepared a revised preprint in which we address all reviewer comments. Please find our revision and our detailed response to all reviewer comments.

With these changes, we hope that our peer-reviewed preprint can receive a positive vote,

We are looking forward to your response,

Sincerely

Petra Bauer and Ksenia Trofimov on behalf of all authors

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity):

In this paper entitled " FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) accumulates in homo- and heterodimeric complexes in dynamic and inducible nuclear condensates associated with speckle components", Trofimov and colleagues describe for the first time the function of FIT in nuclear bodies. By an impressive set of microscopies technics they assess FIT localization in nuclear bodies and its dynamics. Finally, they reveal their importance in controlling iron deficiency pathway. The manuscript is well written and fully understandable. Nonetheless, at it stands the manuscript present some weakness by the lack of quantification for co-localization and absence controls making hard to follow authors claim. Moreover, to substantially improve the manuscript the authors need to provide more proof of concepts in A. thaliana as all the nice molecular and cellular mechanism is only provided in N. bentamiana. Finally, some key conclusions in the paper are not fully supported by the data.<br /> Please see below:

Main comments:

1) For colocalization analysis, the author should provide semi-quantitative data counting the number of times by eyes they observed no, partial or full co-localization and indicate on how many nucleus they used.

Authors:

We have added the information in the Materials and Method section, lines 731-734:

In total, 3-4 differently aged leaves of 2 plants were infiltrated and used for imaging. One infiltrated leaf with homogenous presence of one or two fluorescence proteins was selected, depending on the aim of the experiment, and ca. 30 cells were observed. Images are taken from 3-4 cells, one representative image is shown.

In all analyzed cases, except in the case of colocalization of FIT and PIF4 fusion proteins, the ca. 30 cells had the same localization and/or colocalization patterns. This information has also been added in the figure legends. Each experiment was repeated at least 2-3 times, or as indicated in the figure legend.

2) Do semi-quantitative co-localization analysis by eyes, on FIT NB with known NB makers in the A. thaliana root. For now, all the nicely described molecular mechanism is shown in N. benthamiana which makes this story a bit weak since all the iron transcriptional machinery is localized in the root to activate IRT1.

Authors:

The described approach has been very optimal, and we were able to screen co-localizing marker proteins in FIT NBs in N. benthamiana to better identify the nature of FIT NBs. This has been successful as we were able to associate FIT NBs with speckles. The N. benthamiana system allowed optimal microscopic observation of fluorescence proteins and quantification of FIT NB characteristics in contrast to the root hair zone of Arabidopsis where Fe uptake takes place. FIT is expressed at a low level in roots and also in leaves, whereby fluorescence protein expression levels are insufficient for the here-presented microscopic studies. The tobacco infiltration system is also well established to study FIT-bHLH039 protein interaction and nuclear body markers. We discuss this point in the discussion, see line 489-500.

3) The authors need to provide data clearly showing that the blue light induce NB in A. thaliana and N. benthamiana.

Authors:

For tobacco, see Figure 1B (t = 0, 5 min) and Supplemental Movies S1. For Arabidopsis, please see Figure 1A (t = 0, 90 and 120 min) and Supplemental Figure S1A. We provide an additional image of pFIT:cFIT-GFP Arabidopsis control plants, showing that NB formation is not detected in plants that were grown in white light and not exposed to blue light before inspection (Supplemental Figure S1B). We state, that upon blue light exposure, plants had FIT NBs in at least 3-10 nuclei of 20 examined nuclei in the root epidermis in the root hair zone (in three independent experiments with three independent plants). White-light-treated plants showed no NB formation unless an additional exposure to blue light was provided (in three independent experiments, three independent plants per experiment and with 15 examined nuclei per plant).

4) Direct conclusion in the manuscript:

• Line 170: At this point of the paper the author cannot claim that the formation of FIT condensates in the nucleus is due to the light as it might be indirectly linked to cell death induced by photodamaging the cell using a 488 lasers for several minutes. This is true especially with the ELYRA PS which has strong lasers made for super resolution and that Cell death is now liked to iron homeostasis. The same experiment might be done using a spinning disc or if the authors present the data of the blue light experiment mentioned above this assumption might be discarded. Alternatively, the author can use PI staining to assess cell viability after several minutes under 488nm laser.

Authors:

As stated in our response to comment 3, we have included now a white light control to show that FIT NB formation is not occurring under the normal white light conditions. Since the formation of FIT NBs is a dynamic and reversible process (Figure 1A), it indicates that the cells are still viable, and that cell death is not the reason for FIT NB formation.

• Line 273: I don't agree with the first part of the authors conclusion, saying that "wild-type FIT had better capacities to localize to NBs than mutant FITmSS271AA, presumably due its IDRSer271/272 at the C-terminus. This is not supported by the data. In order to make such a claim the author need to compare the FA of FIT WT with FITmSS271AA by statistical analysis. Nonetheless, the value seems to be identical on the graphs. The main differences that I observed here are, 1) NP value for FITmSS271AA seems to be lower compared to FIT-WT, suggesting that the Serine might be important to regulate protein homedimerization partitioning between the NP and the NB. 2) To me, something very interesting that the author did not mention is the way the FA of FITmSS271AA in the NB and NP is behaving with high variability. The FA of those is widely spread ranging from 0.30 to 0.13 compared to the FIT-WT. To me it seems that according to the results that the Serine 271/272 are required to stabilize FIT homodimerization. This would not only explain the delay to form the condensate but also the decreased number and size observed for FITmSS271AA compared to FIT-WT. As the homodimerization occurs with high variability in FITmSS271AA, there is less chance that the protein will meet therefore decreasing the time to homodimerize and form/aggregate NB.

Authors:

We fully agree. We meant to describe this result it in a similar way and thank you for help in formulating this point even better. Rephrasing might make it better clear that the IDRSer271/272 is important for a proper NB localization, lines 272-278:

“Also, the FA values did not differ between NBs and NP for the mutant protein and did not show a clear separation in homodimerizing/non-dimerizing regions (Figure 3D) as seen for FIT-GFP (Figure 3C). Both NB and NP regions showed that homodimers occurred very variably in FITmSS271AA-GFP.

In summary, wild-type FIT could be partitioned properly between NBs and NP compared to FITmSS271AA mutant and rather form homodimers, presumably due its IDRSer271/272 at the C-terminus.”

• Line 301: According to my previous comment (line 273), here it seems that the Serine 271/272 are required only for proper partitioning of the heterodimer FIT/BHLH039 between the NP and NB but not for the stability of the heterodimer formation. However, it might be great if the author would count the number of BHLH039 condensates in both version FITmSS271AA and FIT-WT. To my opinion, they would observe less BHLH039 condensate because the homodimer of FITmSS271AA is less likely to occur because of instability.

Authors:

bHLH039 alone localizes primarily to the cytoplasm and not the nucleus, and the presence of FIT is crucial for bHLH039 nuclear localization (Trofimov et al., 2019). Moreover, bHLH039 interaction with FIT depends on SS271AA (Gratz et al., 2019). We therefore did not consider this experiment for the manuscript and did not acquire such data, as we did not expect to achieve major new information.

5) To wrap up the story about the requirements of NB in mediating iron acquisition under different light regimes, provide data for IRT1/FRO2 expression levels in fit background complemented with FITmSS271AA plants. I know that this experiment is particularly lengthy, but it would provide much more to this nice story.

Authors:

Data for expression of IRT1 and FRO2 in FITmSS271AA/fit-3 transgenic Arabidopsis plants are provided in Gratz et al. (2019). To address the comment, we did here a NEW experiment. We provide gene expression data on FIT, BHLH039, IRT1 and FRO2 splicing variants (previously reported intron retention) to explore the possibility of differential splicing alterations under blue light (NEW Supplemental Figure S6 and S7, lines 454-466). Very interestingly, this experiment confirms that blue light affects gene expression differently from white light in the short-term NB-inducing condition and that blue light can enhance the expression of Fe deficiency genes despite of the short 1.5 to 2 h treatment. Another interesting aspect was that the published intron retention was also detected. A significant difference in intron retention depending on iron supply versus deficiency and blue/white light was not observed, as the pattern of expression of transcripts with respective intron retentions sites was the same as the one of total transcripts mostly spliced.

Minor comments

In general, I would suggest the author to avoid abbreviation, it gets really confusing especially with small abbreviation as NB, NP, PB, FA.

Authors:

We would like to keep the used abbreviations as they are utilized very often in our work and, in our eyes, facilitate the understanding.

Line 106: What does IDR mean?

Authors:

Explanation of the abbreviation was added to the text, lines 105-108:

“Intrinsically disordered regions (IDRs) are flexible protein regions that allow conformational changes, and thus various interactions, leading to the required multivalency of a protein for condensate formation (Tarczewska and Greb-Markiewicz, 2019; Emenecker et al., 2020).”

Line 163-164: provide data or cite a figure properly for blue light induction.

Authors:

We have removed this statement from the description, as we provide a white light control now, lines 157-158:

“When whole seedlings were exposed to 488 nm laser light for several minutes, FIT became re-localized at the subnuclear level.”

Line 188: Provide Figure ref.

Authors:

Figure reference was added to the text, lines 184-185:

“As in Arabidopsis, FIT-GFP localized initially in uniform manner to the entire nucleus (t=0) of N. benthamiana leaf epidermis cells (Figure 1B).”

Line 194: the conclusion is too strong. The authors conclude that the condensate they observed are NB based on the fact the same procedure to induce NB has been used in other study which is not convincing. Co-localization analysis with NB markers need to be done to support such a claim. At this step of the study, the author may want to talk about condensate in the nucleus which might correspond to NB. Please do so for the following paragraph in the manuscript until colocalization analysis has not been provided. Alternatively provide the co-localization analysis at this step in the paper.

Authors:

We agree. We changed the text in two positions.

Lines 176-178__: “__Since we had previously established a reliable plant cell assay for studying FIT functionality, we adapted it to study the characteristics of the prospective FIT NBs (Gratz et al., 2019, 2020; Trofimov et al., 2019).”

Lines 192-193: “__We deduced that the spots of FIT-GFP signal were indeed very likely NBs (for this reason hereafter termed FIT NBs).”

Line 214: In order to assess the photo bleaching due to the FRAP experiment the quantification of the "recovery" needs to be provided in an unbleached area. This might explain why FIT recover up to 80% in the condensate. Moreover, the author conclude that the recovery is high however it's tricky to assess since no comparison is made with a negative/positive control.

Authors:

In the FRAP analysis, an unbleached area is taken into account and used for normalization.

We reformulated the description of Figure 1F, lines 212-214:

“According to relative fluorescence intensity the fluorescence signal recovered rapidly within FIT NBs (Figure 1F), and the calculated mobile fraction of the NB protein was on average 80% (Figure 1G).”

Line 220-227: The conclusion it's too strong as I mentioned previously the author cannot claim that the condensate are NBs at this step of the study. They observed nuclear condensates that behave like NB when looking at the way to induce them, their shape, and the recovery. And please include a control.

Authors:

Please see the reformulated sentences and our response above.

Lines 176-178: “Since we had previously established a reliable plant cell assay for studying FIT functionality, we adapted it to study the characteristics of the prospective FIT NBs (Gratz et al., 2019, 2020; Trofimov et al., 2019).”

Lines 192-193: “__We deduced that the spots of FIT-GFP signal were indeed very likely NBs (for this reason hereafter termed FIT NBs).”

Line 239: It's unappropriated to give the conclusion before the evidence.

Authors:

Thank you. We removed the conclusion.

Line 240: Figure 2A, provide images of FIT-G at 15min in order to compare. And the quantification needs to be provided at 5 minutes and 15 minutes for both FIT-G WT and FIT-mSS271AA-G counting the number of condensates in the nucleus. Especially because the rest of the study is depending on these time points.

Authors:

This information is provided in the Supplemental Movie S1C.

Line 241: the author say that the formation of condensate starts after 5 minutes (line 190) here (line 241) the author claim that it starts after 1 minutes. Please clarify.

Authors:

In line 190 we described that FIT NB formation occurs after the excitation and is fully visible after 5 min. In line 241 we stated that the formation starts in the first minutes after excitation, which describes the same time frame. We rephrased the respective sentences.

Lines 185-188: “A short duration of 1 min 488 nm laser light excitation induced the formation of FIT-GFP signals in discrete spots inside the nucleus, which became fully visible after only five minutes (t=5; Figure 1B and Supplemental Movie S1A).”

Lines 239-242: “While FIT-GFP NB formation started in the first minutes after excitation and was fully present after 5 min (Supplemental Movie S1A), FITmSS271AA-GFP NB formation occurred earliest 10 min after excitation and was fully visible after 15 min (Supplemental Movie S1C).”

Line 254: Not sure what the authors claim "not only for interaction but also for FIT NB formation ". To me, the IDR is predicted to be perturbed by modeling when the serines are mutated therefore the IDR might be important to form condensates in the nucleus. Please clarify.

Authors:

The formation of nuclear bodies is slow for FITmSS271AA as seen in Figure 2. Previously, we showed that FITmSS271AA homodimerizes less (Gratz et al., 2019.) Therefore, the said IDR is important for both processes, NB formation and homodimerization. We have added this information to make the point clear, lines 253-255:

“This underlined the significance of the Ser271/272 site, not only for interaction (Gratz et al., 2019) but also for FIT NB formation (Figure 2).”

Line 255: It's not clear why the author test if the FIT homodimerization is preferentially associated with condensate in the nucleus.

Authors:

We test this because both homo- and heterodimerization of bHLH TFs are generally important for the activity of TFs, and we unraveled the connection between protein interaction and NB formation. We state this in lines 228-232.

Line 269-272: It's not clear to what the authors are referring to.

Authors:

We are describing the homodimeric behavior of FIT and FITmSS271AA assessed by homo-FRET measurements that are introduced in the previous paragraph, lines 256-268.

Line 309: This colocalization part should be presented before line 194.

Authors:

We find it convincing to first examine and characterize the process underlying FIT NB formation, then studying a possible function of NBs. The colocalization analysis is part of a functional analysis of NBs. We thank the reviewer for the hint that colocalization also confirms that indeed the nuclear FIT spots are NBs. We will take this point and discuss it, lines 516-522:

“Additionally, the partial and full colocalization of FIT NBs with various previously reported NB markers confirm that FIT indeed accumulates in and forms NBs. Since several of NB body markers are also behaving in a dynamic manner, this corroborates the formation of dynamic FIT NBs affected by environmental signals.”

“In conclusion, the properties of liquid condensation and colocalization with NB markers, along with the findings that it occurred irrespective of the fluorescence protein tag preferentially with wild-type FIT, allowed us to coin the term of ‘FIT NBs’.”

Line 328: add the ref to figure, please.

Authors:

Figure reference was added to the text, lines 330-332:

“The second type (type II) of NB markers were partially colocalized with FIT-GFP. This included the speckle components ARGININE/SERINE-RICH45-mRFP (SR45) and the serine/arginine-rich matrix protein SRm102-mRFP (Figure 5).”

Line 334: It seems that the size of the SR45 has an anormal very large diameter between 4 and 6 µm. In general a speckle measure about 2-3µm in diameter. Can the author make sure that this structure is not due to overexpression in N. benthamiana or make sure to not oversaturate the image.

Authors:

Thank you for this hint. Indeed, there are reports that SR45 is a dynamic component inside cells. It can redistribute depending on environmental conditions and associate into larger speckles depending on the nuclear activity status (Ali et al., 2003). We include this reference and refer to it in the discussion, lines 557-564:

“Interestingly, typical FIT NB formation did not occur in the presence of PB markers, indicating that they must have had a strong effect on recruiting FIT. This is interesting because the partially colocalizing SR45, PIF3 and PIF4 are also dynamic NB components. Active transcription processes and environmental stimuli affect the sizes and numbers of SR45 speckles and PB (Ali et al., 2003; Legris et al., 2016; Meyer, 2020). This may indicate that, similarly, environmental signals might have affected the colocalization with FIT and resulting NB structures in our experiments. Another factor of interference might also be the level of expression.”

Line 335: It seems that the colocalization is partial only partial after induction of NB. The FIT NB colocalize around SR45. But it's hard to tell because the images are saturated therefore creating some false overlapping region.

Authors:

The localization of FIT with SR45 is partial and occurs only after FIT has undergone condensation, see lines 335-338.

Line 344-345: It's unappropriated to give the conclusion before the evidence.

Authors:

We explain at an earlier paragraph that we will show three different types of colocalization and introduce the respective colocalization types within separate paragraphs accordingly, see lines 314-321.

Line 353: increase the contrast in the image of t=5 for UAP56H2 since it's hard to assess the colocalization.

Authors:

This is done as noted in the figure legend of Figure 6.

Line 381-382: "In general" does not sound scientific avoid this kind of wording and describe precisely your findings.

Authors:

We rephrased the sentence, line 387-388:

Localization of single expressed PIF3-mCherry remained unchanged at t=0 and t=15 (Supplemental Figure S5A).

Line 384-385: Provide the data and the reference to the figure.

Authors:

We apologize for the misunderstanding and rephrased the sentence, line 389-391:

After 488 nm excitation, FIT-GFP accumulated and finally colocalized with the large PIF3-mCherry PB at t=15, while the typical FIT NBs did not appear (Figure 7A)

Line 386: The structure in which FIT-G is present in the Figure 7A t=15 is not alike the once already observed along the paper. This could be explained by over-expression in N. benthamiana. Please explain.

Authors:

Thank you for the hint. We discuss this in the discussion part, see lines 555-568.

Line 393: Explain and provide data why the morphology of PIF4/FIT NB do not correspond to the normal morphology.

Authors:

Thank you for the valuable hints. Several reasons may account for this and we provide explanations in the discussion, see lines 555-568.

Line 396-398: It seems also from the data that co-expression of PIF4 of PIF3 will affect the portioning of FIT between the NP and the NB.

Authors:

We can assume that residual nucleoplasm is depleted from protein during NB formation. This is likely true for all assessed colocalization experiments. We discuss this in lines 492-494.

The discussion is particularly lengthy it might be great to reduce the size and focus on the main findings.

Authors:

We shortened the discussion.

Referees cross-commenting

All good for me, I think that the comments/suggestions from Reviewer #2 are valid and fair. If they are addressed they will improve considerably the manuscript.

Reviewer #1 (Significance):

This manuscript is describing an unprecedent very precise cellular and molecular mechanism in nutrition throughout a large set of microscopies technics. Formation of nuclear bodies and their role are still largely unexplored in this context. Therefore, this study sheds light on the functional role of this membrane less compartment and will be appreciated by a large audience. However, the fine characterization is only made using transient expression in N. Bentamiana and only few proofs of concept are provided in A. thaliana stable line.

Reviewer #2 (Evidence, reproducibility and clarity):

The manuscript of Trofimov et al shows that FIT undergoes light-induced, reversible condensation and localizes to nuclear bodies (NBs), likely via liquid-liquid phase separation and light conditions plays important role in activity of FIT. Overall, manuscript is well written, authors have done a great job by doing many detailed and in-depth experiments to support their findings and conclusions.

However, I have a number of questions/comments regarding the data presented and there are still some issues that authors should take into account.

Major points/comments:

1) Authors only focused on blue light conditions. Is there any specific reason for selecting only blue light and not others (red light or far red)?

Authors:

There are two main reasons: First, in a preliminary study (not shown) blue light resulted in the formation of the highest numbers of NBs. Second, iron reductase activity assays and gene expression analysis under different light conditions showed a promoting effect under blue light, but not red light or dark red light (Figure 9). This indicated to us, that blue light might activate FIT, and that active FIT may be related to FIT NBs.

2) Fig. 3C and D: as GFP and GFP-GFP constructs are used as a reference, why not taking the measurements for them at two different time points for example t=0 and t=5 0r t=15???

Authors:

Free GFP and GFP-GFP dimers are standard controls for homo-FRET that serve to delimit the range for the measurements.

3) Line 27-271: Acc to the figure 3d, for the Fluorescence anisotropy measurement of NBs appears to be less. Please explain.

Authors:

FA in NBs with FITmSS271AA is variable and the value is lower than that of whole nucleus but not significantly different compared with that in nucleoplasm. We describe the results of Figure 3D in lines 272-275.

4) Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.

Authors:

Neither for FIT/bHLH039 nor the FITmSS271AA/bHLH039 pair, there is a significant decrease in the fluorescence lifetime values between t=0 and t=5/15. FIT-G is a control to delimit the range. The interesting experiment is to compare the protein pairs of interest between the different nuclear locations at t=5/15.

5) Line 300-301: In Figure 4D and 4E. Fluorescence lifetime of G measurement at t=0 seems very similar for both FIT-G as well as FITmSS but if we look at the values of t=0 for FIT-G+bhlh039 it is greater than 2.5 and for FITmSS271AA-G+bhlh039 it is less which suggests more heterodimeric complexes to be formed in FITmSS271AA-G+bhlh039. Similar pattern is observed for NBs and NPs, according to the figure 4d and E.

Therefore, heterodimeric complexes accumulated more in case of FITmSS271AA-G+bhlh039 as compared to FIT-G+bhlh039 (if we compare measurement values of Fluorescence lifetime of G of FITmSS271AA-G+bhlh039 with FIT-G+bhlh039).

Please comment and elaborate about this further.

Authors:

These conclusions are not valid as the experiments cannot be conducted in parallel. Since the experiments had to be performed on different days due to the duration of measurements including new calibrations of the system, we cannot compare the absolute fluorescence lifetimes between the two sets.

6) Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.

Authors:

Please see our response to your comment 4).

7) Line 439-400: As iron uptake genes (FRO2 and IRT1) are more induced in WT under blue light conditions and FRO2 is less induced in case of red-light conditions. So, what happens to Fe content of WT grown under blue light or red light as compared to WT grown under white light. Perls/PerlsDAb staining of WT roots under different light conditions will add more information to this.

Authors:

We focused on the relatively short-term effects of blue light on signaling of nuclear events that could be related to FIT activity directly, particularly gene expression and iron reductase activity as consequence of FRO2 expression. These are both rapid changes that occur in the roots and can be measured. We suspect that iron re-localization and Fe uptake also occur, however, in our experience differences in metal contents will not be directly significant when applying the standard methods like ICP-MS or PERLs staining.

Minor comments:

Line 75-76: Rephrase the sentence

Authors:

We rephrased the sentence, lines 73-74:

“As sessile organisms, plants adjust to an ever-changing environment and acclimate rapidly. They also control the amount of micronutrients they take up.”

Line 119: Rephrase the sentence

Authors:

We rephrased the sentence, line 118-119:

“Various NBs are found. Plants and animals share several of them, e.g. the nucleolus, Cajal bodies, and speckles.”

Line 235-236: rephrase the sentence

Authors:

We rephrased the sentence, line 232-234:

“In the work of Gratz et al. (2019), the hosphor-mimicking FITmS272E protein did not show significant changes in its behavior compared to wild-type FIT.”

Line 444: Correct the sentence “Fe deficiency versus sufficiency”

Authors:

We corrected that, line 449-451:

“In both, the far-red light and darkness situations, FIT was induced under iron deficiency versus sufficiency, while on the other side, BHLH039, FRO2 and IRT1 were not induced at all in these light conditions (Figure 9I-P).”

Referees cross-commenting

I agree with R1 suggestions/comments and i think manuscript quality will be much better if authors carry out the experiments suggested by R1. I believe this will also strengthen their conclusions.

Reviewer #2 (Significance):

Overall, manuscript is well written, authors have done a nice job by doing several key experiments to support their findings and conclusions. However, the results and manuscript can be improved further by addressing some question raised here. This study is interesting for basic scientists which unravels the crosstalk of light signaling in nutrient signaling pathways.

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

_The manuscript of Trofimov et al shows that FIT undergoes light-induced, reversible condensation and localizes to nuclear bodies (NBs), likely via liquid-liquid phase separation and light conditions plays important role in activity of FIT. Overall, manuscript is well written, authors have done a great job by doing many detailed and in-depth experiments to support their findings and conclusions.<br /> However, I have a number of questions/comments regarding the data presented and there are still some issues that authors should take into account.

Major points/comments:

1. Authors only focused on blue light conditions. Is there any specific reason for selecting only blue light and not others (red light or far red)?
2. Fig. 3C and D: as GFP and GFP-GFP constructs are used as a reference, why not taking the measurements for them at two different time points for example t=0 and t=5 0r t=15???
3. Line 27-271: Acc to the figure 3d, for the Fluorescence anisotropy measurement of NBs appears to be less. Please explain.
4. Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.
5. Line 300-301: In Figure 4D and 4E. Fluorescence lifetime of G measurement at t=0 seems very similar for both FIT-G as well as FITmSS but if we look at the values of t=0 for FIT-G+bhlh039 it is greater than 2.5 and for FITmSS271AA-G+bhlh039 it is less which suggests more heterodimeric complexes to be formed in FITmSS271AA-G+bhlh039. Similar pattern is observed for NBs and NPs, according to the figure 4d and E.<br /> Therefore, heterodimeric complexes accumulated more in case of FITmSS271AA-G+bhlh039 as compared to FIT-G+bhlh039 (if we compare measurement values of Fluorescence lifetime of G of FITmSS271AA-G+bhlh039 with FIT-G+bhlh039).<br /> Please comment and elaborate about this further.
6. Figure 4: For the negative controls, data is shown at only t=0, data should be shown at t=5 also to prove that there is no decrease in fluorescence in these negative controls when they are expressed alone without bhlh39 as there is no acceptor in this case.
7. Line 439-400: As iron uptake genes (FRO2 and IRT1) are more induced in WT under blue light conditions and FRO2 is less induced in case of red-light conditions. So, what happens to Fe content of WT grown under blue light or red light as compared to WT grown under white light. Perls/PerlsDAb staining of WT roots under different light conditions will add more information to this.

Minor comments:

Line 75-76: Rephrase the sentence

Line 119: Rephrase the sentence

Line 235-236: rephrase the sentence

Line 444: Correct the sentence "Fe deficiency versus sufficiency"

Referees cross-commenting

I agree with R1 suggestions/comments and i think manuscript quality will be much better if authors carry out the experiments suggested by R1. I believe this will also strengthen their conclusions.

Significance

Overall, manuscript is well written, authors have done a nice job by doing several key experiments to support their findings and conclusions. However, the results and manuscript can be improved further by addressing some question raised here. This study is interesting for basic scientists which unravels the crosstalk of light signaling in nutrient signaling pathways.

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

In this paper entitled " FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR (FIT) accumulates in homo- and heterodimeric complexes in dynamic and inducible nuclear condensates associated with speckle components", Trofimov and colleagues describe for the first time the function of FIT in nuclear bodies. By an impressive set of microscopies technics they assess FIT localization in nuclear bodies and its dynamics. Finally, they reveal their importance in controlling iron deficiency pathway. The manuscript is well written and fully understandable. Nonetheless, at it stands the manuscript present some weakness by the lack of quantification for co-localization and absence controls making hard to follow authors claim. Moreover, to substantially improve the manuscript the authors need to provide more proof of concepts in A. thaliana as all the nice molecular and cellular mechanism is only provided in N. bentamiana. Finally, some key conclusions in the paper are not fully supported by the data.<br /> Please see below:

Main comments:

1. For colocalization analysis, the author should provide semi-quantitative data counting the number of times by eyes they observed no, partial or full co-localization and indicate on how many nucleus they used.
2. Do semi-quantitative co-localization analysis by eyes, on FIT NB with known NB makers in the A. thaliana root. For now, all the nicely described molecular mechanism is shown in N. benthamiana which makes this story a bit weak since all the iron transcriptional machinery is localized in the root to activate IRT1.
3. The authors need to provide data clearly showing that the blue light induce NB in A. thaliana and N. benthamiana.
4. Direct conclusion in the manuscript:
5. Line 170: At this point of the paper the author cannot claim that the formation of FIT condensates in the nucleus is due to the light as it might be indirectly linked to cell death induced by photodamaging the cell using a 488 lasers for several minutes. This is true especially with the ELYRA PS which has strong lasers made for super resolution and that Cell death is now liked to iron homeostasis. The same experiment might be done using a spinning disc or if the authors present the data of the blue light experiment mentioned above this assumption might be discarded. Alternatively, the author can use PI staining to assess cell viability after several minutes under 488nm laser.
6. Line 273: I don't agree with the first part of the authors conclusion, saying that "wild-type FIT had better capacities to localize to NBs than mutant FITmSS271AA, presumably due its IDRSer271/272 at the C-terminus. This is not supported by the data. In order to make such a claim the author need to compare the FA of FIT WT with FITmSS271AA by statistical analysis. Nonetheless, the value seems to be identical on the graphs. The main differences that I observed here are, 1) NP value for FITmSS271AA seems to be lower compared to FIT-WT, suggesting that the Serine might be important to regulate protein homedimerization partitioning between the NP and the NB. 2) To me, something very interesting that the author did not mention is the way the FA of FITmSS271AA in the NB and NP is behaving with high variability. The FA of those is widely spread ranging from 0.30 to 0.13 compared to the FIT-WT. To me it seems that according to the results that the Serine 271/272 are required to stabilize FIT homodimerization. This would not only explain the delay to form the condensate but also the decreased number and size observed for FITmSS271AA compared to FIT-WT. As the homodimerization occurs with high variability in FITmSS271AA, there is less chance that the protein will meet therefore decreasing the time to homodimerize and form/aggregate NB.
7. Line 301: According to my previous comment (line 273), here it seems that the Serine 271/272 are required only for proper partitioning of the heterodimer FIT/BHLH039 between the NP and NB but not for the stability of the heterodimer formation. However, it might be great if the author would count the number of BHLH039 condensates in both version FITmSS271AA and FIT-WT. To my opinion, they would observe less BHLH039 condensate because the homodimer of FITmSS271AA is less likely to occur because of instability.
8. To wrap up the story about the requirements of NB in mediating iron acquisition under different light regimes, provide data for IRT1/FRO2 expression levels in fit background complemented with FITmSS271AA plants. I know that this experiment is particularly lengthy, but it would provide much more to this nice story.

Minor comments

In general, I would suggest the author to avoid abbreviation, it gets really confusing especially with small abbreviation as NB, NP, PB, FA.

Line 106: What does IDR mean?

Line 163-164: provide data or cite a figure properly for blue light induction.

Line 188: Provide Figure ref.

Line 194: the conclusion is too strong. The authors conclude that the condensate they observed are NB based on the fact the same procedure to induce NB has been used in other study which is not convincing. Co-localization analysis with NB markers need to be done to support such a claim. At this step of the study, the author may want to talk about condensate in the nucleus which might correspond to NB. Please do so for the following paragraph in the manuscript until colocalization analysis has not been provided. Alternatively provide the co-localization analysis at this step in the paper.

Line 214: In order to assess the photo bleaching due to the FRAP experiment the quantification of the "recovery" needs to be provided in an unbleached area. This might explain why FIT recover up to 80% in the condensate. Moreover, the author conclude that the recovery is high however it's tricky to assess since no comparison is made with a negative/positive control.

Line 220-227: The conclusion it's too strong as I mentioned previously the author cannot claim that the condensate are NBs at this step of the study. They observed nuclear condensates that behave like NB when looking at the way to induce them, their shape, and the recovery. And please include a control.

Line 239: It's unappropriated to give the conclusion before the evidence.

Line 240: Figure 2A, provide images of FIT-G at 15min in order to compare. And the quantification needs to be provided at 5 minutes and 15 minutes for both FIT-G WT and FIT-mSS271AA-G counting the number of condensates in the nucleus. Especially because the rest of the study is depending on these time points.

Line 241: the author say that the formation of condensate starts after 5 minutes (line 190) here (line 241) the author claim that it starts after 1 minutes. Please clarify.

Line 254: Not sure what the authors claim "not only for interaction but also for FIT NB formation ". To me, the IDR is predicted to be perturbed by modeling when the serines are mutated therefore the IDR might be important to form condensates in the nucleus. Please clarify.

Line 255: It's not clear why the author test if the FIT homodimerization is preferentially associated with condensate in the nucleus.

Line 269-272: It's not clear to what the authors are referring to.

Line 309: This colocalization part should be presented before line 194.

Line 328: add the ref to figure, please.

Line 334: It seems that the size of the SR45 has an anormal very large diameter between 4 and 6 µm. In general a speckle measure about 2-3µm in diameter. Can the author make sure that this structure is not due to overexpression in N. benthamiana or make sure to not oversaturate the image

Line 335: It seems that the colocalization is partial only partial after induction of NB. The FIT NB colocalize around SR45. But it's hard to tell because the images are saturated therefore creating some false overlapping region.

Line 344-345: It's unappropriated to give the conclusion before the evidence.

Line 353: increase the contrast in the image of t=5 for UAP56H2 since it's hard to assess the colocalization.

Line 381-382: "In general" does not sound scientific avoid this kind of wording and describe precisely your findings.

Line 384-385: Provide the data and the reference to the figure.

Line 386: The structure in which FIT-G is present in the Figure 7A t=15 is not alike the once already observed along the paper. This could be explained by over-expression in N. benthamiana. Please explain.

Line 393: Explain and provide data why the morphology of PIF4/FIT NB do not correspond to the normal morphology.

Line 396-398: It seems also from the data that co-expression of PIF4 of PIF3 will affect the portioning of FIT between the NP and the NB.

The discussion is particularly lengthy it might be great to reduce the size and focus on the main findings.

Referees cross-commenting

All good for me, I think that the comments/suggestions from Reviewer #2 are valid and fair. If they are addressed they will improve considerably the manuscript.

Significance

This manuscript is describing an unprecedent very precise cellular and molecular mechanism in nutrition throughout a large set of microscopies technics. Formation of nuclear bodies and their role are still largely unexplored in this context. Therefore, this study sheds light on the functional role of this membrane less compartment and will be appreciated by a large audience. However, the fine characterization is only made using transient expression in N. Bentamiana and only few proofs of concept are provided in A. thaliana stable line.

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

Learn more at Review Commons

---

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.

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 #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.

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

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.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

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.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

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.

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 #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.

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

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.

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

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.

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

Learn more at Review Commons

---

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.

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

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.

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

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

Learn more at Review Commons

---

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.

Learn more at Review Commons

---

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.

Learn more at Review Commons

---

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).

---

“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.

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 #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

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 #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.

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

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

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

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.

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

Learn more at Review Commons

---

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.

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 #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.

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

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.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

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.

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

Learn more at Review Commons

---

Reply to the reviewers

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

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 #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.

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

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.

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

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.

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

Learn more at Review Commons

---

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.

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 #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.

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

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.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary

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.

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

Learn more at Review Commons

---

Reply to the reviewers

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

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 #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.

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

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.

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

In this manuscript, Morgenstern et al investigated the molecular mechanism by which LINC00941 regulates keratinocyte differentiation. They found the LINC00941 interacts with the NuRD chromatin remodeling complex in human primary keratinocytes. Furthermore, LINC00941 silencing by RNAi results in changes in the genomic occupancy of MTA2, a core NuRD subunit, especially near a number of bivalent genes. In particular, they showed that LINC00941 depletion resulted in reduced MTA2 occupancy at the EGR3 gene, increased EGR3 expression, and increased expression of EGR3-regulated epidermal differentiation genes. Together, they propose that LINC00941 prevents premature differentiation of human epidermal tissue by repressing EGR3 expression in non-differentiated keratinocytes via NuRD. The interaction between LINC00941 and NuRD is a novel finding and will likely provide new insights for the function of LINC00941, which has been implicated in keratinocytes, tissue homeostasis and cancer. It will also shed light on the role of lncRNAs in epigenetic gene regulation and cell fate transition in general. The conclusion of this study can be much strengthened if the authors can identify LINC00941-occupied genomic regions by ChIRP (PMID: 21963238) or RAP (PMID: 23828888). In addition, the authors are also encouraged to address the following questions and comments to further improve the manuscript.

Fig 1C: Since multiple NuRD subunits were identified in the LINC00941 pull-down (Fig 1B), can the authors validate at least one other subunit? CHD4 is also a NuRD-specific subunit and appears to be a strong hit based on supplemental Fig 1.

Fig 1D: Can the authors also try RNA-IP on MTA2 and endogenous LINC00941?

Fig 2B: It seems that MTA2 protein level still remains reasonably high at day-4.

Fig 3C: How many bivalent promoters are there in keratinocytes? How many of those are bound by MTA2?

Fig S3A: Can the authors examine MTA2 occupancy at TSS and bivalent TSS in control vs. siLINC00941 cells (by meta-gene analysis)? This will show whether LINC00941 KD affects MTA2 occupancy at bivalent TSS in general.

Fig 4B: Does LINC00941 KD only affect 33 out of the 3613 MTA2 peaks? If yes, can the authors comment on why only such a small fraction of MTA2 occupied regions are affected?

Fig 4C: The authors only examined a small number of MTA2-associated genes. To provide a more complete view of the potential involvement of LINC00941-regulated genes in keratinocytes differentiation, can the authors provide the total number of differentially expressed genes (DEGs) in LINC00941 KD, the total number of DEGs during keratinocytes differentiation, and the overlap between the two (maybe using a venn diagram)? In addition, among all the overlapping DEGs from above, how many of them have MTA2 peaks nearby? Finally, in the overlapping DEGs occupied by MTA2, can the authors compare MTA2 occupancy at up- vs. down-regulated DEGs caused by LINC00941 KD, to see whether reduced MTA2 occupancy associates with increased expression after LINC00941 KD?

Fig 4D: Can the authors add the H3K4me3 track to the figure? Can the authors provide ChIP-qPCR result to validate the changes in MTA2 occupancy near EGR3 after LINC00941 KD?

Fig 5A: Some of the EGR3 target genes (eg., GJB4, SERTAD1) appear to be expressed before EGR3 up-regulation in siCtrl, and some of them (eg. HMOX1, ESYT3, SMPD3) appear to show stronger up-regulation than EGR3 in siLINC00941. This is not entirely consistent with the idea that they are regulated by LINC00941 via EGR3.

Significance

The interaction between LINC00941 and NuRD is a novel finding and will likely provide new insights for the function of LINC00941, which has been implicated in keratinocytes, tissue homeostasis and cancer. It will also shed light on the role of lncRNAs in epigenetic gene regulation and cell fate transition in general. The conclusion of this study can be much strengthened if the authors can identify LINC00941-occupied genomic regions by ChIRP (PMID: 21963238) or RAP (PMID: 23828888). In addition, the authors are also encouraged to address the above-mentioned questions and comments to further improve the manuscript.

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

Learn more at Review Commons

---

Reply to the reviewers

[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.

1. General Statements [optional]

In this paper we describe the new finding that the epicardial deposits the extracellular matrix component laminin onto the apical ventricular surface during cardiac development. We identify a novel role for the apicobasal polarity protein Llgl1in timely emergence of the epicardium and deposition of this apical laminin, alongside a requirement for Llgl1 in maintaining integrity of the ventricular wall at the onset of trabeculation.

We thank the reviewers for their very positive appraisal of our manuscript, and for their helpful suggestions for useful revisions. In particular we would like to highlight the broad interest they feel this manuscript holds, not only contributing conceptual advances to our understanding of multiple aspects of cardiac development, but also to cell and developmental biologists working in epithelial polarity and extracellular matrix function. We also note their positive appraisal of the rigor of the study and quality of the manuscript.

2. Description of the planned revisions

Reviewer 1

1a) It is mentioned that llgl1 CRISPR/Cas9 mutants are viable as adults on pg. 3 of the Results section. Have the authors examined heart morphology in these mutants in juvenile or adult fish?

We have some historical data on adult llgl1 mutant survival that we plan to include in the study.

Reviewer 2

2a) The authors note an interesting observation with apical and basal laminin deposition dynamics surrounding cardiomyocytes, and that Llg1 has a role in apical Laminin deposition (however, highly variable at 80 hpf as Figure 3M shows). They carry out a very nice study in which they overexpress Llgl1 tagged with mCherry in the myocardium and show that there is no rescue of the extruding cardiomyocyte defect or Laminin deposition. However, there is still a possibility that the tagged Llgl1 in the transgene Tg(myl7:Llg1-mCherry)sh679 might not be functional due to improper protein folding or interference by the mCherry tag. The authors should supplement their approach with a transplantation experiment to generate mosaic llgl1 mutant animals and assess whether llgl1 mutant cardiomyocytes extrude at a higher rate than the control. This would provide definitive evidence that Llg1l acts in a cell non-autonomous manner.

We agree with the reviewer, and propose to perform transplant experiments, transplanting cells from llgl1 mutants into wild type siblings, and quantify cell extrusion to determine whether llgl1 mutant cells are extruded more frequently than wild type.

2b) The data in this manuscript appears to point that Llgl1 regulates Laminin deposition mainly in epicardial cells to regulate their dissemination/migration across the ventricular myocardial surface. It would be important to test this cell-autonomous function with the transplant experiment (above point) and examine whether llgl1 mutant epicardial cells fail to migrate and deposit Laminin. It might be possible to perform a rescue experiment through overexpression of Llgl1 in epicardial cells (if possible, there is a tcf21:Gal4 line available).

Similar to above, we propose to perform transplant experiments, transplanting cells from llgl1 mutants or wild type siblings into wild type siblings or llgl1 mutants, respectively, and in this instance quantify contribution of transplanted cells to epicardial coverage.

2c) In the Discussion, the authors propose that Llgl1 acts in two ways: Laminin deposition in epicardial cells that suppress cell extrusion and polarity regulation in cardiomyocytes to promote trabeculation. It would be important to test the second hypothesis on trabeculation and polarity regulation by using the myocardial-specific overexpression/rescue of Llgl1 in llgl1 mutants, and then quantifying the trabeculating cardiomyocytes and analyze Crb2a localization. This experiment can distinguish whether this trabeculation phenotype is rescued independently of the apical Laminin deposition that has been included in Figure S5.

To help address the second part of our hypothesis laid out in the discussion, we propose to quantify trabecular organisation and Crb2a localisation in llgl1 mutants either carrying the myl7:llgl1-mCherry construct, or mCherry-negative controls.

2d) The potential mis-localization of Crb2a in the llgl1 mutants is interesting, but this effect appears to be quite mild, and as the authors note, resolve by 80 hpf. Considering the role of Lgl in Drosophila in shifting Crb complex localization during early epithelial morphogenesis, it would be worth performing the analysis at earlier timepoints (between 55 and 72 hpf) to determine whether Llgl1 is indeed important for the progressive apical relocalization of Crb2a.

We will expand our description of this in the mutants by performing analysis of Crb2a at earlier timepoints in the llgl1 mutant (55hpf and 60hpf).

2e) OPTIONAL: It might be worth testing other antibodies that could mark the apical (particularly aPKC which is known to phosphorylate and regulate the Crb complex) and basolateral domains (Par1, Dlg) of the cardiomyocytes to definitively conclude that the epithelial integrity of the cells is affected. Although there are no reports of working antibodies marking the basal domain in zebrafish, there is at least a Tg(myl7:MARCK3A-RFP) line published (Jimenez-Amilburu et al. (2016)) - which the authors can inject to examine the localization in mosaic hearts.

We plan to assess localisation of aPKC (see section 4 for response to other suggested polarity protein analyses).

2f) Have the authors quantified the numbers of total cardiomyocytes in llgl1 mutants to correlate how many cells are lost as a consequence of extrusion? What is the physiological impact of this extrusion (ejection fraction, total cardiac volumes, sarcomere organization)?

We have some of this data already which we will include in the manuscript (cell number, myocardial volume). We agree that the analysis of cardiac function could be more extensive, and we will perform more detailed analysis of cardiac function, including e.g. ejection fraction. Sarcomere organisation has been previously described in llgl1 mutants by Flinn et al, 2020, so we do not plan to replicate this data.

2g) The lamb1a and lamc1 mutant phenotypes were nicely analyzed. However, there is basement membrane deposition on both the apical and basal sides of the cardiomyocytes. Therefore, it is unclear whether the cardiomyocyte extrusion is completely caused by loss of apical basement membrane, or whether the loss of basal basement membrane could compromise the myocardial tissue integrity. The authors should clarify this conclusion in the text.

We will address this further in the text, but will also include 55hpf Laminin staining data for llgl1 mutants to reinforce our message.

2h) The authors note that Llgl1-mCherry in the Tg(myl7:Llg1-mCherry)sh679 line localizes to the basolateral domain of the cardiomyocytes, which is valuable confirmation that Llgl1 protein is spatially restricted. However, only 1 timepoint (55 hpf) is noted. It would be important to perform Llgl1 localization across different developmental timepoints (at least until 80 hpf) to examine the dynamics of this protein during trabeculation and apical extrusion, and potentially correlate it with Crb2a localization for a better understanding of the apicobasal machinery in cardiomyocytes.

We already have some of this data and will include extra timepoints in a revised version of the manuscript

2i) The phenotypes of llgl1 mutants described here differ compared to the previous study by Flinn et al. (2020). In particular, whereas the mutants generated in this study have only mild pericardial edema and are adult viable, approximately one third of llgl1mw3 (Flinn et al. (2020)) died at 6 dpf. Is this caused by the different natures of the mutations in the llgl1 gene? Is there a possibility that the llgl1sh598 is a hypomorphic allele since the targeted deletion is in a more downstream sequence (in exon 2) compared to the llgl1mw3 (deletion in exon 1) allele?

We thank the reviewer for noticing these subtle differences between the two llgl1 mutants. Indeed, while we occasionally see llgl1sh598 mutants with the severe phenotype described by Flinn et al, this is a small minority which we did not quantify. Our mutation is indeed slightly further downstream than that described by Flinn et al, however we believe that this will have a neglible effect on Llgl1 function. Our llgl1sh589 mutation results in truncation shortly into the WD40 domain, and importantly completely lacks the Lgl-like domain, which is responsible for the specific function of Llgl1 likely through its ability to interact with SNAREs to regulate cargo delivery to membranes (Gangar et al, Current Biology 2005).

Interestingly, Flinn et al report no increased phenotypic severity in their maternal-zygotic llgl1 mutants when compared to zygotic mutants. Conversely, we often observed very severe phenotypes in MZ llgl1sh589 mutants, including failure of embryos during blastula stages, apparently through poor blastula integrity. We did not include this information in the manuscript due to space constraints. However, we argue that together these differences between the two alleles may not be due to hypomorphism of our llgl1sh589 allele, but rather differences in genetic background that may amplify specific phenotypes. We plan to include a short sentence summarising the above in combination with planned experiments described below to address the reviewer’s next comment.

2j) Suggested experiment: qPCR of regions downstream of the deletion to make sure that the transcript is absent/reduced in the llgl1sh598 mutants. Alternatively, immunostaining or Western blot would be an even better option to ensure there is no Llgl1 protein production - there is an anti-Llgl1 antibody available that works for Western blots in zebrafish (Clark et al. (2012)).

We plan to analyse llgl1 expression in llgl1 mutants using qPCR.

Reviewer 3

3a) Major - the authors describe that llgl1 mutants exhibit transient cardiac edema at 3 dpf, which is resolved by 5 dpf, and claim that the mutants are viable. This statement needs to be better supported - What is the proportion of mutants that survive to adulthood? The embryonic phenotypes are pretty variable - are the mutants that survive the ones with a less severe phenotype? Is there a gross defect in the adult heart of these animals?

In line with comments from Reviewers 1 and 2 above, we will include a description of the data we have from adult animals (historical data, not generation of new animals).

3b) Major - Many of the phenotypes described here -most importantly, the defects on epicardial development- could result from hemodynamic defects in llgl1 mutants. The authors claim that function is unaffected in these animals, but this has only been addressed by measuring heartbeat. The observation that the cardiac function in these animals is normal would conflict with a previous description (PMID: 32843528) that demonstrates that llgl1 mutant animals show significant hemodynamic defects, which would cause epicardial defects. Thus, this aspect of the work needs to be better addressed.

In line with our comments to point 2f) from Reviewer 2, we will perform a more in-depth functional analysis on llgl1 mutant larvae.

3c) The phenotypes related to forming multiple layers in the heart (Fig. 1) could be more convincing. In some figures, the authors use a reporter that labels the myocardial cell membrane, but in Figure 1 this is not used. Showing a myocardial membrane marker (for example, the antibody Alcama, Zn-8) would significantly strengthen this observation.

We will describe trabecular phenotypes in more detail using the suggested antibody to highlight membranes.

3d) The analysis of Crumbs redistribution (Fig. 2) is quite interesting. Still, given that the authors have a transgenic model to rescue llgl1 expression in cardiomyocytes, they could move from correlative evidence to experimental demonstration of the role of llgl1 in Crumbs localization.

Similar to our response to comment 2c) from Reviewer 2, we plan to address this

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

Reviewer 1:

Although information is provided in the introduction and discussion on the role of the Llgl1 homolog in Drosophila and speculation on LLGL1 contributing to heart defects in SMS patients in the discussion, have Llgl1 homologs been examined in other vertebrate animal models during heart development or regeneration?

With the exception of the Flinn et al paper, we find no published studies assessing the role of Llgl1 in heart development or regeneration in other vertebrates, and have updated the introduction to highlight this fact:

‘Zebrafish have two Lgl homologues, llgl1 and llgl2, and llgl1 has previously been shown to be required for early stages of heart morphogenesis (Flinn et al. 2020). However, although Llgl1 expression has also been reported in the developing mouse heart and both adult mouse and human hearts (Uhlén et al. 2015; Klezovitch et al. 2004), whether llgl1 plays a role in ventricular wall development has not been examined.’

In Fig. 4J-M', there is no Cav1 signals after wt1a MO but still laminin signals. Where these laminins come from?

The residual laminin staining observed in wt1a morphants is located at the basal surface of cardiomyocytes (while the apical laminin signal is lost, in line with the epicardial deposition of laminin at the apical ventricle surface). This basal laminin is likely deposited earlier during heart tube development by either the myocardium, endocardium or both, and thus unaffected by later formation of the epicardium. We reason this since a) it is present at the basal cardiomyocyte surface at 55hpf (see Fig 2); b) we have previously identified both myocardial and endocardial expression of laminin subunits at 26hpf and 55hpf (Derrick et al, Development, 2021); c) sc-RNA-seq analysis of hearts at 48hpf demonstrates that laminin subunits, e.g. lamc1 are expressed in myocardial and endocardial cells (Nahia et al, bioRxiv, 2023), also in line with our previous ISH analysis. We have included a sentence to reflect this in the results section:

Conversely, *wt1a* morphants retain deposition of laminin at the basal CM surface, likely from earlier expression and deposition of laminin by either myocardial or endocardial cells (Derrick et al. 2021; Nahia et al. 2023), which is unaffected by later epicardial development.

On page 3 of the manuscript, Fig. 1A should be included with Fig. 1B in the first sentence of paragraph 2 of the Results subsection "Llgl1 regulates ventricular wall integrity and trabeculation".

Amended

It would be beneficial to readers to briefly describe what cell type the transgenic reporters label when mentioned in the Results section to help readers unfamiliar with zebrafish.

We have updated the text to read:

We further analysed heart morphology using live lightsheet microscopy of *Tg(myl7:LifeActGFP);Tg(fli1a:AC-TagRFP)* double transgenic wild-type and *llgl1* mutant embryos, allowing visualisation of myocardium (green) and endocardium (magenta) respectively. Comparative analysis of overall heart morphology between 55hpf and 120hpf when looping morphogenesis is complete, revealing that *llgl1* mutants continue to exhibit defects in heart morphogenesis (Fig S1S-X).

Reviewer 3

(Optional) There is laminin in the luminal side of the heart before there is any epicardial invasion. What is the source of this laminin? The techniques the authors have used (i.e., chromogenic ISH) are fine, but a more detailed analysis using fluorescent ISH (i.e., RNAScope) would be much more definitive.

This is related to our response to Reviewer 1 (above) – where we have included the following text included in manuscript: Conversely, *wt1a* morphants retain deposition of laminin at the basal CM surface, likely from earlier expression and deposition of laminin by either myocardial or endocardial cells (Derrick et al. 2021; Nahia et al. 2023), which is unaffected by later epicardial development. We hope this clarifies our proposed origins for the earlier laminin deposition.

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

Reviewer 1:

As pan-epicardial transgenes like tcf21 reporters have been widely used, the authors should use such reporters to verify the expression of laminin gene expression in epicardial cells, and the efficacy and efficiency of depleting epicardial cells after wt1 MO injection.

Several studies have demonstrated that the epicardium is not a heterogeneous population – for example, tcf21 is not expressed in all epicardial cells and thus not a pan-epicardial reporter (Plavicki et al, BMC Dev Biol, 2014, Weinberger et al, Dev Cell, 2020) – the suggested analysis would not necessarily be conclusive, and more detailed study would require acquisition of three new transgenic lines. Furthermore, we believe the evidence we present in the paper supports our claim: 1) We show expression of two laminin subunits in a thin mesothelial layer directly adjacent to the myocardium, specifically in the location of the epicardium; 2) sc-RNA seq analyses have also identified laminin expression in epicardial cells at 72hpf (where lamc1a is identified as a marker of the epicardium); 3) We demonstrate 100% efficacy of our wt1a knockdown as assayed by Cav1 expression, an established epicardial marker (Grivas et al, 2020, Marques et al, 2022) which in sc-RNA seq data is expressed at high levels broadly in the epicardial cell population (Nahia et al, 2023), representing a good assay for presence of epicardium. However, we propose to perform ISH analysis of laminin subunit expression in wt1a MO to investigate whether the mesothelial laminin-expressing layer we observe adjacent to the myocardium is absent upon loss of wt1a.

Reviewer 2:

The data in this manuscript appears to point that Llgl1 regulates Laminin deposition mainly in epicardial cells to regulate their dissemination/migration across the ventricular myocardial surface. It would be important to test this cell-autonomous function with the transplant experiment (above point) and examine whether llgl1 mutant epicardial cells fail to migrate and deposit Laminin. It might be possible to perform a rescue experiment through overexpression of Llgl1 in epicardial cells (if possible, there is a tcf21:Gal4 line available).

We do not propose to perform this experiment using a tcf21:Gal4 line, as this would likely require at least 6 months to either import and quarantine, or generate the necessary stable lines. Furthermore, as mentioned above, tcf21 is not a pan-epicardial marker, and the extent and timing of the Gal4:UAS system may make this challenging to determine whether llgl1 has been expressed early or broadly enough. We will instead attempt transplantation experiments.

OPTIONAL: It might be worth testing other antibodies that could mark the apical (particularly aPKC which is known to phosphorylate and regulate the Crb complex) and basolateral domains (Par1, Dlg) of the cardiomyocytes to definitively conclude that the epithelial integrity of the cells is affected. Although there are no reports of working antibodies marking the basal domain in zebrafish, there is at least a Tg(myl7:MARCK3A-RFP) line published (Jimenez-Amilburu et al. (2016)) - which the authors can inject to examine the localization in mosaic hearts.

We will assess localisation of aPKC, but we do not plan to analyse the other components. Analysis of basolateral domains (Par1, Dlg, Mark3a-RGP), will not necessarily assess epithelial integrity, as suggested, but rather apicobasal polarity – which we already assess using Crb2a, and additionally plan to assess aPKC to accompany the Crb2a analysis. Since the reviewer suggests this as an optional experiment we prioritise their other suggested experiments that we think more directly address the main messages of the manuscript.

OPTIONAL: Gentile et al. (2021) found that reducing heartbeat led to decreased cardiomyocyte extrusion in snai1b mutants. The authors could look into the contribution of mechanical pressure through contraction in the apical cardiomyocyte extrusion, and test whether reducing contraction (tnnt2 morpholino, chemical treatments) partly rescues the llgl1 mutant phenotypes.

The relationship between cardiac function and myocardial wall integrity appears to be complex. The paper referred to by the reviewer indeed finds that reduction in heartbeat leads to decreased CM extrusion upon loss of the EMT-factor Snai1b. Previous studies have also found that endothelial flow-responsive genes klf2a/b are required to maintain myocardial ventricular wall integrity at later stages in a contractility-dependent manner (Rasouli et al, 2018). However, contractility is also required early for pro-epicardial emergence, but plays a lesser role in expansion of the epicardial layer on the myocardial surface (Peralta, 2013). Unpicking the relationship between the forces induced by mechanical contraction of the ventricular wall, contractility-based induction of e.g klf2 expression, and the impact of contractile forces on proepicardial development or epicardial expansion will be complex. We therefore think the proposed experiment will be difficult to interpret whatever the outcome, and argue that dissecting this relationship is beyond the scope of revisions for this paper.

Reviewer 3

How llgl1 relates to epicardial biology is left entirely unexplored in this work. Do proepicardial cells show any defect in cell polarization related to llgl1 absence?

We agree with the reviewer that we do not delve into the mechanisms underlying regulation of epicardial development by llgl1, and that this is an interesting question. Our scope for this manuscript was to understand the mechanisms by which llgl1 regulates integrity of the ventricular wall, and feel that uncovering the molecular mechanisms by which llgl1 regulates timely epicardial emergence is a larger question that would require substantial investigation (for example, if and when llgl1 PE cells do exhibit apicobasal defects, how this impacts timing of cluster release etc). We think these are important questions that would be better answered in detail in a separate manuscript.

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 #3

Evidence, reproducibility and clarity

This manuscript from Pollitt and colleagues analyzes the role of lethal(2) giant larvae homolog 1 (llgl1) in cardiac development in zebrafish. Llgl1 has been previously involved in regulating epithelial polarity, which raises the possibility that this gene might play a role during the trabeculation of the zebrafish heart. To examine the role of llgl1 in this phenomenon, the authors generated a new loss of function mutant using CRISPR/Cas9. Animals lacking llgl1 initially exhibited abnormal cardiac development, manifested by defects in cardiac looping and pericardial edema. This phenotype, however, was transient. A detailed analysis of the developing heart showed, albeit with significant variability, defects in trabeculation and an interesting cardiomyocyte extrusion phenotype, described before in mutants that lack epicardium. During their analysis, the authors discovered a switch in the localization of the extracellular matrix protein laminin from the luminal to the apical side of the cardiomyocytes that temporally correlates with the process of trabeculation. The accumulation of laminin in the epicardial side was affected in llgl1 mutants, which also showed a defect in epicardial development. Coincidentally, the epicardial cells appear to be the primary source of laminin. This work suggests that llgl1 acts in epicardial cells to maintain ventricular wall integrity during heart development.

Major Comments:

1. Major - the authors describe that llgl1 mutants exhibit transient cardiac edema at 3 dpf, which is resolved by 5 dpf, and claim that the mutants are viable. This statement needs to be better supported - What is the proportion of mutants that survive to adulthood? The embryonic phenotypes are pretty variable - are the mutants that survive the ones with a less severe phenotype? Is there a gross defect in the adult heart of these animals?
2. Major - Many of the phenotypes described here -most importantly, the defects on epicardial development- could result from hemodynamic defects in llgl1 mutants. The authors claim that function is unaffected in these animals, but this has only been addressed by measuring heartbeat. The observation that the cardiac function in these animals is normal would conflict with a previous description (PMID: 32843528) that demonstrates that llgl1 mutant animals show significant hemodynamic defects, which would cause epicardial defects. Thus, this aspect of the work needs to be better addressed.
3. The phenotypes related to forming multiple layers in the heart (Fig. 1) could be more convincing. In some figures, the authors use a reporter that labels the myocardial cell membrane, but in Figure 1 this is not used. Showing a myocardial membrane marker (for example, the antibody Alcama, Zn-8) would significantly strengthen this observation.
4. The analysis of Crumbs redistribution (Fig. 2) is quite interesting. Still, given that the authors have a transgenic model to rescue llgl1 expression in cardiomyocytes, they could move from correlative evidence to experimental demonstration of the role of llgl1 in Crumbs localization.
5. (Optional) There is laminin in the luminal side of the heart before there is any epicardial invasion. What is the source of this laminin? The techniques the authors have used (i.e., chromogenic ISH) are fine, but a more detailed analysis using fluorescent ISH (i.e., RNAScope) would be much more definitive.
6. How llgl1 relates to epicardial biology is left entirely unexplored in this work. Do proepicardial cells show any defect in cell polarization related to llgl1 absence?

Significance

General Assessment. Overall, this is an interesting manuscript put together with rigor. The strongest aspect of this work is the discovery of a switch in the localization of laminin in the developing heart and the potential implications of this process in regulating correct trabeculation versus cardiomyocyte extrusion. Although the text itself is very well written, with clear statements of the hypotheses and the findings that led the authors to each experiment, I found myself wondering what the unifying theme and central message of the manuscript is and whether this has been appropriately supported with experimental data. Specifically, although the authors included a very detailed analysis of the myocardium, their results (including the last supplementary figure) suggest that these phenotypes might be secondary to a defect in epicardial development. Still, it is entirely unclear how the loss of llgl1 would affect epicardial development.

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

Summary: The manuscript by Pollitt et al. explores the functions of llgl1, which encodes a critical component of the basolateral domain complex, during cardiac development in zebrafish. The authors observed that llgl1 mutants exhibited compromised myocardial tissue integrity with significantly higher numbers of apically extruding cardiomyocytes. Llgl1 appears to primarily function during epicardial cell spreading on the myocardial tissue, as myocardial-specific overexpression of llgl1 did not rescue llgl1 mutant phenotypes. llgl1 mutants exhibited impaired epicardial coverage and subsequently Laminin deposition on the apical side of the cardiomyocytes. Functional linkage between Laminin/the basement membrane was identified, as extruding cardiomyocytes were also observed in mutants of two core laminin genes, lamb1a and lamc1. The epicardial defects were transmitted to myocardial tissue defects, marked by mis-localization of the apical polarity protein Crumbs2a during early heart development. Overall, the authors provide a nice study that strengthens the role of apicobasal factors in myocardial tissue morphogenesis and that sheds light on the role of epicardial-derived basement membrane in maintaining myocardial tissue integrity.

Major comments

• The authors note an interesting observation with apical and basal laminin deposition dynamics surrounding cardiomyocytes, and that Llg1 has a role in apical Laminin deposition (however, highly variable at 80 hpf as Figure 3M shows). They carry out a very nice study in which they overexpress Llgl1 tagged with mCherry in the myocardium and show that there is no rescue of the extruding cardiomyocyte defect or Laminin deposition. However, there is still a possibility that the tagged Llgl1 in the transgene Tg(myl7:Llg1-mCherry)sh679 might not be functional due to improper protein folding or interference by the mCherry tag. The authors should supplement their approach with a transplantation experiment to generate mosaic llgl1 mutant animals and assess whether llgl1 mutant cardiomyocytes extrude at a higher rate than the control. This would provide definitive evidence that Llg1l acts in a cell non-autonomous manner.
• The data in this manuscript appears to point that Llgl1 regulates Laminin deposition mainly in epicardial cells to regulate their dissemination/migration across the ventricular myocardial surface. It would be important to test this cell-autonomous function with the transplant experiment (above point) and examine whether llgl1 mutant epicardial cells fail to migrate and deposit Laminin. It might be possible to perform a rescue experiment through overexpression of Llgl1 in epicardial cells (if possible, there is a tcf21:Gal4 line available).
• In the Discussion, the authors propose that Llgl1 acts in two ways: Laminin deposition in epicardial cells that suppress cell extrusion and polarity regulation in cardiomyocytes to promote trabeculation. It would be important to test the second hypothesis on trabeculation and polarity regulation by using the myocardial-specific overexpression/rescue of Llgl1 in llgl1 mutants, and then quantifying the trabeculating cardiomyocytes and analyze Crb2a localization. This experiment can distinguish whether this trabeculation phenotype is rescued independently of the apical Laminin deposition that has been included in Figure S5.
• The potential mis-localization of Crb2a in the llgl1 mutants is interesting, but this effect appears to be quite mild, and as the authors note, resolve by 80 hpf. Considering the role of Lgl in Drosophila in shifting Crb complex localization during early epithelial morphogenesis, it would be worth performing the analysis at earlier timepoints (between 55 and 72 hpf) to determine whether Llgl1 is indeed important for the progressive apical relocalization of Crb2a. OPTIONAL: It might be worth testing other antibodies that could mark the apical (particularly aPKC which is known to phosphorylate and regulate the Crb complex) and basolateral domains (Par1, Dlg) of the cardiomyocytes to definitively conclude that the epithelial integrity of the cells is affected. Although there are no reports of working antibodies marking the basal domain in zebrafish, there is at least a Tg(myl7:MARCK3A-RFP) line published (Jimenez-Amilburu et al. (2016)) - which the authors can inject to examine the localization in mosaic hearts.
• Have the authors quantified the numbers of total cardiomyocytes in llgl1 mutants to correlate how many cells are lost as a consequence of extrusion? What is the physiological impact of this extrusion (ejection fraction, total cardiac volumes, sarcomere organization)?
• The lamb1a and lamc1 mutant phenotypes were nicely analyzed. However, there is basement membrane deposition on both the apical and basal sides of the cardiomyocytes. Therefore, it is unclear whether the cardiomyocyte extrusion is completely caused by loss of apical basement membrane, or whether the loss of basal basement membrane could compromise the myocardial tissue integrity. The authors should clarify this conclusion in the text.

Minor comments

• The authors note that Llgl1-mCherry in the Tg(myl7:Llg1-mCherry)sh679 line localizes to the basolateral domain of the cardiomyocytes, which is valuable confirmation that Llgl1 protein is spatially restricted. However, only 1 timepoint (55 hpf) is noted. It would be important to perform Llgl1 localization across different developmental timepoints (at least until 80 hpf) to examine the dynamics of this protein during trabeculation and apical extrusion, and potentially correlate it with Crb2a localization for a better understanding of the apicobasal machinery in cardiomyocytes.
• The phenotypes of llgl1 mutants described here differ compared to the previous study by Flinn et al. (2020). In particular, whereas the mutants generated in this study have only mild pericardial edema and are adult viable, approximately one third of llgl1mw3 (Flinn et al. (2020)) died at 6 dpf. Is this caused by the different natures of the mutations in the llgl1 gene? Is there a possibility that the llgl1sh598 is a hypomorphic allele since the targeted deletion is in a more downstream sequence (in exon 2) compared to the llgl1mw3 (deletion in exon 1) allele? Suggested experiment: qPCR of regions downstream of the deletion to make sure that the transcript is absent/reduced in the llgl1sh598 mutants. Alternatively, immunostaining or Western blot would be an even better option to ensure there is no Llgl1 protein production - there is an anti-Llgl1 antibody available that works for Western blots in zebrafish (Clark et al. (2012)).
• Closeups needed for Figure 3I-L' - difficult to assess mis-localization or differences in Laminin staining. Contrary to the quantification or conclusion, the Laminin staining appears stronger in llgl1 mutants compared to wild types in Figure 3I' and J'.
• OPTIONAL: Gentile et al. (2021) found that reducing heartbeat led to decreased cardiomyocyte extrusion in snai1b mutants. The authors could look into the contribution of mechanical pressure through contraction in the apical cardiomyocyte extrusion, and test whether reducing contraction (tnnt2 morpholino, chemical treatments) partly rescues the llgl1 mutant phenotypes.

Significance

As someone with expertise in cardiac development and cellular behaviours, I find this study provides strong and convincing quantitative data on the role of Llgl1 in suppressing cardiomyocyte extrusion and promoting epicardial dissemination on the ventricular surface. The genetic experiments, including mutant analysis and myocardial-specific rescue, were carefully performed in a region-specific manner, which provides much insight into the non-uniformity of myocardial tissue integrity. The generation of Tg(myl7:llgl1-mCherry) line is also a valuable tool for researchers in the field interested in understanding apicobasal polarity and cardiomyocyte development and regeneration.

A limitation of the study is the unclear link between epithelial polarity and basement membrane deposition, and how they synchronize to regulate cardiomyocyte integrity. The llgl1 mutant phenotype in increasing cardiomyocyte apical extrusion and Crb2 localization is interesting; however, the authors note that this appears to be a phenotype induced by epicardial defects. Epicardial cells are not known to exhibit apicobasal polarity and are fibroblastic by nature. Thus, the cellular mechanisms by which Llg1 regulates epicardial cell morphology or behaviours, and how it functions to regulate polarity in cardiomyocytes are not clearly defined in this work. In addition, clarification of the cell autonomous functions of Llgl1 in epicardial cells and/or cardiomyocytes would strengthen the findings.

Overall, the findings of this study would be of interest to cell and developmental biologists in the fields of epithelial polarity, cardiac morphogenesis, and extracellular matrix function. It provides nice conceptual advance in further elucidating the mechanisms that underlie myocardial tissue integrity and epicardial-myocardial interactions.

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

Pollitt et al. investigated the role of Llgl1 in maturation of the ventricular wall in zebrafish. They examined zebrafish heart morphology with microscopy analysis of fluorescent reporter lines crossed with their CRISPR/Cas9 llgl1 line and observed apically extruding CMs in llgl1 mutant embryos, implicating a role for llgl1 in ventricular wall integrity. Further, they examined apicobasal polarity in the ventricular wall through quantification of Crb2a distribution at the apical membrane surface, with enhanced Crb2a retention at CM junctions in llgl1 mutant embryos in comparison to WT at 72 hpf but similar levels at 80 hpf. Further analyses indicated a requirement for llgl1 for the temporal establishment of the apical laminin sheath, llgl1 is required for the timely dissemination of epicardial cells which deposit laminin to maintain the integrity of the ventricular wall. This work is written well and provides new information on heart development in zebrafish, and provides additional information on the role of the epicardium in supporting the integrity of the ventricular wall during trabeculation.

Major:

1. Although information is provided in the introduction and discussion on the role of the Llgl1 homolog in Drosophila and speculation on LLGL1 contributing to heart defects in SMS patients in the discussion, have Llgl1 homologs been examined in other vertebrate animal models during heart development or regeneration?
2. In Fig. 3I-O: The authors described the spatial dynamics of laminin in llgl1 mutants at 72 and 80m hpf. However, it is hard to say the schematic depicting of laminin for llg1 mutant in Fig. 3O reflect the real laminin staining signals in Fig. 3J' and 3L'.
3. It is mentioned that llgl1 CRISPR/Cas9 mutants are viable as adults on pg. 3 of the Results section. Have the authors examined heart morphology in these mutants in juvenile or adult fish?
4. In Fig. 4J-M', there is no Cav1 signals after wt1a MO but still laminin signals. Where these laminins come from?
5. As pan-epicardial transgenes like tcf21 reporters have been widely used, the authors should use such reporters to verify the expression of laminin gene expression in epicardial cells, and the efficacy and efficiency of depleting epicardial cells after wt1 MO injection.

Minor:

1. On page 3 of the manuscript, Fig. 1A should be included with Fig. 1B in the first sentence of paragraph 2 of the Results subsection "Llgl1 regulates ventricular wall integrity and trabeculation".
2. Fig. 2E: Is Fig. 2E from WT or llgl1 embryos? This information isn't indicated in the image panels or the figure legend. It might also be beneficial to include a similar representative image for the WT or llgl1 mutant embryo as this was used for quantification.
3. Fig. 3G: As the entirety of Fig. 3 used violet coloring to depict Laminin, it would be more consistent to change the blue coloring used to depict Laminin in panel G to the same violet coloring used for Laminin in the other panels of Fig. 3.
4. It would be beneficial to readers to briefly describe what cell type the transgenic reporters label when mentioned in the Results section to help readers unfamiliar with zebrafish.

Significance

This work is written well and provides new information on heart development in zebrafish, and provides additional information on the role of the epicardium in supporting the integrity of the ventricular wall during trabeculation.

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

Learn more at Review Commons

---

Reply to the reviewers

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

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 #3

Evidence, reproducibility and clarity

Summary:

In this paper, Hama et al look to address ongoing questions regarding the recruitment of core autophagy factors to protein aggregates during aggrephagy. Using cells that lack all known aggrephagy receptors (PentaKO HeLa cells) provides the authors with a 'blank canvas' with which to cleanly dissect a process otherwise fraught with mechanistic redundancy. By this approach, the authors isolate a previously unidentified mechanism by which TAX1BP1 recruits ATG9A vesicles to ubiquitin-positive aggregates. Using mass spectrometry, the authors identify SCAMP3 as a component of ATG9 vesicles that is responsible for recruiting the vesicles to cargo. Moreover, they provide additional mechanistic insight through the subsequent identification and mutagenesis of a putative interaction interface between TAX1BP1 and SCAMP3.

Major comments:

1. Statistics:
   • a) The description of the statistical methods is sparse. In the methods section, the authors' state that statistical methods are described in each figure legend. However, for most figures they were excluded.
   • b) Where statistics are discussed, Mann-Whitney was used. However, this appears to be the incorrect test in many cases. Per GraphPad (the author's preferred statistical package) "Use Mann-Whitney test only to compare two groups. To compare three or more groups, use the Kruskal-Wallis test followed by post tests. It is not appropriate to perform several Mann-Whitney (or t) tests, comparing two groups at a time."
2. Rigor and reproducibility - The authors report n=~30 cells in most experiments. However, it's unclear if these 30 cells represent more than a single experimental replicate. While the trends in the data are quite convincing, this a significant limitation of this study.
3. Puncta identification - it's unclear how the authors called puncta. The quantification of these images (i.e. the box and whiskers plots) makes compelling points that support the authors' interpretation. However, in many cases the authors are calling puncta amidst a fairly speckled image. How were puncta distinguished from speckles? When did something rise to the level of a puntum? If it was computationally called, please provide the methods and pipeline. If data were manually scored, then the lack of replicates rises to a more significant concern - would other investigators have scored the data similarly? Is it possible that the data were scored with implicit bias based on expected outcomes of the investigator? Where data scored in a blinded fashion?
4. Do the findings presented here have functional implications? While the data on recruitment of ATG9A to TAX1BP1 is clear, it's unclear whether the SCAMP3/TAX1BP1 interaction is functionally important for lysosomal delivery of cargo. In part, this is because the authors must work in autophagy-deficient cells (ATG9AKO or FIP200KO) to observed many of their effects. One way to address function might be to ask whether lysosomal delivery of TAX1BP1 is affected upon SCAMP3KO (e.g. in PentaKO cells to remove the effects of other receptors).

Minor comments:

1. The authors IP TAX1BP1CC1 and SCAMP3, but an IP between full length TAX1BP1 (WT and K248E) and SCAMP3 would more fully demonstrate the sufficiency of this binding site.
2. Optional: Is the TAX1BP1 and SCAMP3 interaction direct?. The data are suggestive, but this question is not fully resolved due to the in vivo nature of the assays. Formally, there could be an adapter that facilitates TAX1BP1/SCAMP3 interaction. This could be formalized by testing binding between purified soluble domains of SCAMP3 and TAX1BP1CC1.
3. Figure 4F - the y axis has a typographical error

Significance

This is a crisply written manuscript with a generally clean experimental approach. While there are many directions the authors could take this work in the future, the data largely stand on their own as a short, concise advance. The work adds to our current understanding of the regulation of ATG9A recruitment during selective autophagy, which is under-explored in comparison to starvation-induced autophagy. Mechanistic insights are provided in the form of a new interacting protein SCAMP3, which is present in ATG9A vesicles and is required for the recruitment of ATG9A vesicles to TAX1BP1. The data are predominantly convincing, albeit with significant caveats. Limited sample sizes and lack of functional effects (see 'major concerns') limit the impact of this work. However, the data have merit on their own. This is a specialized study that will be appreciated by those interested in selective autophagy and the mechanism of autophagosome formation.

Reviewer expertise: selective autophagy and autophagosome formation

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

Ubiquitin-binding adaptors are a group of autophagy adaptor proteins that facilitates the formation of isolating membrane around a specific substrate during selective autophagy. At least two ubiquitin-binding adaptors, NBR1 and OPTN, have previously been demonstrated to recruit ATG9-positive vesicles to ubiquitin-positive biomolecular condensates. It is postulated that this recruitment mediates the formation of the isolating membrane around the ubiquitinated biomolecular condensates. In this manuscript, the authors utilized the HeLa cells deficient in the expression of five ubiquitin-binding adaptors, p62, NBR1, OPTN, NDP52 and TAX1BP1, to show that expressing TAX1BP1 colocalizes with ATG9A. The authors interpreted this observation to suggest that TAX1BP1 can recruit ATG9A vesicles independent of other adaptors. Interestingly, the TAX1BP1 positive structures differ from that of OPTN and NBR1 because they do not contain ubiquitin, suggesting a difference in substrate specificity. They further show that TAX1BP1 differs from OPTN and NBR1 because ATG9 recruitment required SCAMP3 to recruit ATG9A. Based on these observations, the authors propose that TAX1BP1 recruits ATG9A via SCAMP3.

Major comments

• The work presented in this manuscript is of high quality, however, the reliance on a single experimental assay (colocalization microscopy studies) limits the relevance of its findings. The conclusions are based on colocalization studies in wild-type and CRISPR/cas9 knockout HeLa cell lines. Therefore, it is not certain that their findings are restricted to these PentaKO cell lines. Given that these adaptors are required for selective autophagy, the pentaKO cells may have adapted. Unfortunately, the current study lacks additional systems physiologically relevant models or systems. Such experiments are needed to validate their findings in the HeLa pentaKO cell. (Optional) Alternatively, identify the substrate(s) specific to the TAX1BP1-SCAMP3-ATG9A mediated autophagosome vs. that of NBR1 and/or OPTN in these cell lines.
• There are several issues with the immunofluorescence data throughout the manuscript. For example, in Figure 1C, the authors quantify 30 cells per condition and perform a Mann-Whitney test, presumably using each cell as a data point, as this is not specified in the legend. There are two problems with this approach, the first being there is only one indicated independent trial performed, and the second that treating each cell as a data point for statistical analysis overinflates the power. These experiments should instead be performed across at least 3 independent trials (especially given the wide range of values seen in some conditions such as Fig. 2b) with 30 cells counted per trial, and statistical analysis should be performed using the means from each trial. See Lord, S.J. et al. (JCB 2020, PMID: 32346721) for a detailed explanation. Further, how each statistical test is performed (using means vs. all points) and the number of independent trials conducted should be communicated in the figure captions. Finally, beyond Fig. 1, the statistical test used is not reported. If a Mann-Whitney test was used for all statistical analyses, this should be revisited as with two or more variables (cell type, treatment, etc.), this is not the appropriate test.

Minor Points

• The experimental design rationale is not clear throughout the manuscript. For example, why FIP200 KO cells are used in Fig. 3c and Fig. 4 is not apparent, and only in Fig. 6 (Lines 211-213) is it clearly stated. The authors should revisit the results section and ensure the experimental rationale is clearly explained.
• It is curious that condensates formed in muGFP-NDP52 and muGFP-TAX1BP1 cells lack ubiquitin (Fig. 3c). Is this image representative of most cells? if so it should be discussed.
• The authors are missing some relevant citations:
  • Lines 57-58, the authors may consider citing more recent literature (Olivas, T.J. et al., JCB 2023; Broadbent, D.G. et al., JCB 2023; Nguyen, A. et al., Mol Cell 2023) investigating ATG9 vesicles in autophagosome biogenesis.
  • In lines 58-66, the authors do not mention that NBR1 was also shown to bind FIP200 (Turco, E. et al., Nat Communications 2021, PMID: 34471133).
• Some minor changes are recommended for clarity:
  • In lines 106-110, the authors may consider explaining that "growing" conditions are cells grown in regular culture conditions, as it is a bit unclear whether these cells are treated with a drug, etc., to induce p62-condensate formation. Something as simple as "...p62-double-positive structures under normal culture conditions, hereafter referred to as growing conditions," would help the reader.
  • The general axis labelling of "Ubiquitin-positive rate of FIP200 puncta (%)" (Fig 1D) is confusing wording. The authors may consider changing to "% of ubiquitin-positive FIP200 puncta" for readability.
  • The axis title in Fig. 4F has a typo.
• Several figure legends include data interpretation, which should generally be excluded from figure legends. For example, the first line of Fig. 4B should be removed.
• The authors may consider how ATG9A is recruited to ubiquitin-independent selective autophagy cargoes that are degraded independently of NBR1, p62, OPTN, NDP52, and TAX1BP1 in their discussion.
• Supplementary figure S2-right side blot. For best practice in publications, I encourage the authors to provide a single blot of FIP200, ATG13, and ATG9A immunoblot for Penta KO and its KO derivatives. Presently, the blot appears to have been spliced together

Significance

Although different ubiquitin-binding adaptor proteins have been shown to have some substrate specificity, how they mediate the selective degradation of a substrate remains unclear. This manuscript provides evidence that TAX1BP1, like NBR1 and OPTN, can also recruit ATG9A positive structures independent of the autophagosome initiation complex ULK1-complex. However, they do show that the mechanism of ATG9A vesicles differs from the other two adaptors in that it requires SCAMP3, a membrane protein found in endosomes. The data provided by the authors are of high quality. However, the study relies on only one experimental system/assay, which limits the relevance of its findings and could benefit from testing these findings using additional systems and/or physiologically relevant models, as well as increasing the rigor of the quantitative analysis.

Besides these two major shortcomings, the finding is novel and adds to our current understanding of autophagy adaptor proteins. The difference in the mechanism of ATG9 vesicles compared to NBR1 and OPTN may contribute to the selective nature of these autophagy adaptors.

This manuscript is most appropriate for specialized basic researchers in the field of selective autophagy.

Our expertise is in selective autophagy regulation and substrate selectivity of selective autophagy.

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

Macroautophagy is a catabolic pathway for various intracellular components mediated by the formation of autophagosomes followed by lysosomal degradation. ATG9 vesicles provide the initial autophagosomal membrane source and thereby recruitment of ATG9 vesicles to the autophagosome formation sites serves as a critical step for autophagy induction. However, the precise molecular mechanism of the ATG9A recruitment is not fully understood. In this study, Hama et al. report two distinct pathways; ULK complex-dependent ATG9 vesicle recruitment during starvation-induced autophagy, and selective autophagy receptor TAX1BP1-dependent ATG9 vesicle trafficking through the binding to SCAMP3, which was identified as an ATG9 vesicle component by the authors. Unfortunately, the authors were unable to demonstrate the impact of the TAX1BP1-SCAMP3-ATG9 vesicle axis on cellular physiology, presumably due to the existence of compensatory mechanisms mediated by other selective autophagy receptors and ULK complex, which limits the impact of the findings presented. Having said that, the study is technically well executed and provides a new insight into the regulation of cargo/receptor-mediated ATG9 vesicle recruitment. This reviewer has a few comments that should be addressed to strengthen the authors' conclusions.

1. In the Co-IP experiments (Fig 5A-C), binding of TAX1BP1 to SCAMP3 is assessed by using the CC1 domain fraction of TAX1BP1, which may yield an artificial binding to SCAMP3. Could the authors confirm binding of full length TAX1BP1 wild-type and K248E mutant to SCAMP3?
2. In Fig 5D, the authors showed that SCAMP3 localises to immuno-isolated ATG9A-positive vesicles. Is it a direct interaction between the two proteins? Could the authors provide the evidence that the interaction is retained in the presence of a detergent by immunoprecipitation? If the interaction is indirect, can the authors discuss candidate proteins that mediate binding of SCAMP3 to ATG9 vesicles?
3. Related to the comment #2, it is interesting that the knockout of ATG9A does not affect SCAMP3-positive "ATG9 vesicle" formation. What is the nature of "ATG9 vesicles" lacking ATG9A?
4. Could the authors confirm that K284E mutation in TAX1BP1 abrogates the localisation of SCAMP3 to the TAX1BP1 condensates as in Fig 6E? This will reinforce the claim that TAX1BP1 binding to SCAMP3 facilitates ATG9 vesicle recruitment.
5. Could the authors discuss the potential reasons differentiating TAX1BP1 from other CC-domain containing autophagy receptor proteins (NDP52, OPTN and NBR1), which enables it to bind to SCAMP3. For instance, does TAX1BP1 have charged residues facing outwards in its CC domain that could be responsible for this specificity?
6. In Fig 3C and 6E, no colocalisation of TAX1BP1 and ubiquitin was observed in TAX1BP1 condensates. In the context of "cargo-driven recruitment" of ATG9 vesicles, what cellular component(s) could trigger TAX1BP1-mediated SCAMP3/ATG9 vesicle recruitment? In the Discussion, authors mentioned that ferritin-NCOA4 was not the target of the TAX1BP1-SCAMP3 axis. Could the authors test if any of the other known TAX1BP1 cargo proteins localise to TAX1BP1 condensates in Penta KO/FIP200 KO/muGFP-TAX1BP1 cells?

Minor:

1. Fig 4F: Typo in y axis.

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 main finding of the study is a new pathway of ATG9 vesicle recruitment through the interaction of TAX1BP1 with SCAMP3, which provides a novel insight into molecular mechanisms of autophagosome biogenesis. However, the axis is implied to be redundant for functional autophagy in wild-type cells, and lack of data providing a biological function of the axis in cellular physiology will limit impact attracting broader readers outside of molecular mechanism of autophagy.

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,...).

Interaction between ATG9A and selective autophagy receptors OPTN and NBR1 has been reported (doi: 10.1083/jcb.201912144; 10.15252/embr.201948902). This study provides an additional mechanistic insight into the regulation of ATG9 vesicle recruitment through another autophagy receptor TAX1BP1 interacting with SCAMP3 which was newly identified as an ATG9 vesicle component in this study. Given the predominant functions of ATG9A in TNF cytotoxicity and plasma membrane integrity as well as TAX1BP1 in neuronal proteostasis and iron homeostasis (doi: 10.1126/science.add6967; 10.1038/s41556-021-00706-w; 10.1016/j.molcel.2020.10.041; 10.15252/embr.202154278), the interaction between TAX1BP1 and ATG9A would potentially have uncovered but important role in mammals. Autophagy-independent lysosomal degradation regulated via ULK component, ATG9 and TAX1BP1 might be related in this context (10.1016/j.celrep.2017.08.034).

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 will be of interest to the basic researchers working on molecular and structural mechanisms of bulk and selective macroautophagy. Unfortunately, the lack of data demonstrating the relevance of the findings for cellular physiology will limit the impact on researchers in broader fields such as pathology and drug discovery.

Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

Molecular cell biology of autophagy; neurodegenerative and lysosomal storage disorders.

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

Learn more at Review Commons

---

Reply to the reviewers

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

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 #3

Evidence, reproducibility and clarity

Summary:

The article describes a study on two effector nucleases, V2c and V2a, encoded by the T6SS cluster in Agrobacterium tumefaciens 1D1609. The study shows that V2c is a DNase belonging to the Tox-SHH clade of His-Me finger superfamily, exhibiting DNase activity in vivo and in vitro. The SHH and HNH motif of V2c were found to be involved in DNase activity. The study also demonstrates that V2c induces DNA degradation and cell elongation, which can be neutralized by its cognate immunity protein V3c. Furthermore, V2a, also exhibits DNase activity-dependent cell elongation phenotype. Both V2a and V2c nucleases function synergistically for antibacterial activity against Dickeya dadantii, resulting in elongated and lysed cells. The study suggests that 1D1609 uses V2a and V2c DNase effectors with synergistic antibacterial activity against Dickeya dadantii.

Major issues:

1. The cell elongation phenotype was an important focus of the paper and the authors did a solid job quantifying this phenotype. However, cell elongation does not seem to be associated with the mechanism of toxicity. Just a small part of the cells are elongated while you have more than one log of killing (Figure 5C and 5D).<br /> Many stresses can result in cell elongation, such as cell-wall targeting antibiotics. The signal narrows down to the very well-known mechanism of SulA activated by SOS response. There is no evidence "cell elongation independent of nuclease activity may represent a new mechanism of stress response #339". I strongly suggest the authors to use an SOS like pPrecA-gfp from ref 17 if they want to invest in the cell elongation phenotype. As it is, cell elongation is a distraction from the most exciting things in the manuscript. One potential hypothesis is that the catalytic mutants may bind DNA without cleaving, and while bound to the DNA, the mutants may interfere with DNA metabolism, replications, transcription, etc... This interference could create breaks in the DNA and cause cell wall elongation.
2. The manuscript does not have biological replicates in many experiments. At first glance, the tiny error bars in many graphs raised a red flag. It is very difficult, if not impossible, to have the date so tight when working with bacterial cultures. Many of these graphs have "independent experiments", but in Fig 5 the authors mention " 6 repeats from three independent experiments". My understanding is that the independent experiments are from the same cultures, which makes them technical and not biological replicates. The authors need to have replicates from independent cultures. I ask the authors to explain better what they mean by replicates and their rationale.

Minor issues:

1. Many figures have all the data with conditions with and without arabinose. This makes the figure very polluted and difficult to follow. I suggest the authors keep only the data from induced cultures and move the figures with all the data to supplement. This is a big issue with Fig 3
2. The entire "V2c V2a nucleases function synergistically for antibacterial activity against the soft rot phytopathogen, Dickeya dadantii" #212 section is difficult to follow because the name of the toxins are not in the name of the strains. The reader should be able to associate the toxin with the strain.
3. The in vitro essay #427 should have information about the amount of enzyme used.
4. The competition essay #474 method does not have enough information to allow reproducibility and must be expanded.
5. Figure 2B Y-axis needs one log increase between marks, and not two.

6. 256 is difficult to understand and not very scientific. DNses are potent because they target essential molecules, the same for lipases and muramidases. This is not related to the origin of life.

Significance

Strengths:

Data is clear about the toxicity and DNA degradation phenotypes The synergy of toxins is a very exciting topic; it helps to explain why some strains have redundant toxins

Weakness:

Cell elongation claims are not supported Replicates are inadequate Methods needs to be more specific

The manuscript provides incremental data about bacteria-to-bacteria toxins. The major finding of predicted nuclease acting as nuclease toxins is not particularly innovative. This work will benefit a smaller audience in the field of toxins.

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

Santos et al demonstrate the activity and confirm predictions of the mechanism of action of SHH nuclease toxin encoded in auxiliary T6SS cluster of Agrobacterium tumefaciens. Study includes demonstration of nuclease activity in vitro and its manifestation in vivo, mutational analysis of the predicted catalytic site, and assessment of its role in inter-bacterial competition using strains deleted for the T6SS effector(s).

Major comments: major issues affecting the conclusions

Lines 159 and further - I am not sure that large deletion as an entire region between residues 380-409 is a very healthy approach. Without showing that such protein can be produced and fold, it can be very risky to draw any conclusions. While expression of other mutants are shown in Figure 2D, this construct is not included. Also, it is not clear (starting from line 160) what is the utility of double catalytic mutant if single mutants are already inactive. Was there residual activity of single mutants after all?

Line 187 and Figure 3 - cell elongation of CR mutant and HAHA mutant seems to be independent of expression of the construct. It is therefore incorrect to write that "E. coli expressing ...", since theoretically it should not express anything in the absence of arabinose. Also, how do would authors interpret these findings? Again, at Line 211 - in the same paragraph authors say "V2c shows DNase activity-independent cell elongation" and then conclude "cell elongation phenotype may be specific to toxicity of nuclease effectors". These two phrases seem to contradict each other.

Line 272 on - authors speculate about dependency on the metals, which is a hallmark of the his-me finger nucleases. A simple test could have been adding the EDTA control to chelate the metal in the in vitro experiment such as one presented in figure 2D. (OPTIONAL)

Minor comments

Figure 1 - Auxiliary cluster with accession numbers, in addition to the domain composition of the toxin could be demonstrated. This would help the readers to identify which effector is studied and link it to other studies.

Line 127 and Figure 1 C and line 303 - the last option, named "FIX RhsA AHH HNH-like" seems to be a mix of multiple things, First, FIX domain is already included as a first option; AHH HNH-like corresponds to toxic domain (although it often ends up in annotation of the entire protein). All His-Me finger nucleases at some point were annotated as HNH-like and thus HNH, AHH, SHH, ... all belong to the same clade of nuclease domains; RhsA is probably a correct domain/protein detected here and to be represented here as a separate option. I would call it "Rhs", not RhsA, since RhsA,B,C,... is part of an historical systematics from E.coli, but is probably true for one strain only, since Rhs ends (C-terminal toxic domains) are highly variable between species and even strains. To my knowledge, Rhs do encode AHH-like domains and quite often. In conclusion, this is just a mess of protein naming that was picked up from databases, I would correct this name for "Rhs".

Line 181-182 - text is speaking about v2c H383A v2c H384A, but in figure 3, it is v2c HAHA. It might be just naming differences, but it should be consistent.

Line 246 - expression "detoxify D. dadantii" is unclear and a little confusing here, did authors mean kill or eliminate?

Line 263 - "that consisting of" should be "that comprise" or "that possess"

Referees cross-commenting

I agree with the reviewer #1 that the text could be improved by better explaining the nomenclature, and reasoning behind certain experiments such as choice of prey cells. Regarding the novelty, to my opinion it is a choice of authors - either limit their study as it is now and it does not stand out by neither approach nor subject, or as suggested by the reviewers explore the mode of action, specificity and the reason behind the cell filamentation.

I agree with the reviewer #3 that the cell elongation seems to be central interest but it is not investigated properly. I do think, that the SOS reporter would strengthen the study and would help to support (or not) some of the statements. I appreciate the scrutiny of reviewer #3 and I agree that the replicates seem to have extremely low variation and authors should provide precise explication on the reproducibility.

Overall, I agree with the two other reviewers that the weakness of this manuscript is lack of innovation and to some extent lack of support for certain claims. The study could be improved by making it more profound, but a lot of additional work will be needed to bring it to another level.

Significance

Overall, this study is rather detailed, but not very novel - SHH and other HNH nucleases have been already assessed in literature using very similar methods, even by the same authors (Santos et al., Front Microbiol 2020). On the other hand, the study presents in depth investigation of auxiliary toxin, but shows that it is fully functional and has a role in killing, which is important and interesting for the field of Agrobacterium.

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 study characterised one of the four previously identified T6SS effectors of Agrobacterium tumefaciens strain 1D1609. The effector, named V2c, is a His-Me finger nuclease likewise the previously characterised V2a, although V2c has a distinct SHH motif. V2c was found to induce growth inhibition, plasmid degradation, and cell elongation in E. coli. The cognate immunity protein V3c can neutralise the DNase activity of V2c. V2a and V2c are found to function synergistically to exhibit stronger antibacterial activity against a phytopathogen, Dickeya dadantii.

Although the manuscript provides a decent characterisation of the T6SS effector V2c of strain 1D1609 of A. tumefaciens, there are several areas where the authors could make significant improvements to their work.

• The introduction section of the manuscript would benefit from a more detailed background of the research to situate the reader in the appropriate context for a better understanding of the results of this study. Specifically, the authors could provide more information regarding the four effectors of A. tumefaciens 1D1609, their domains, their genetic context, and their immunity proteins. In fact, three of the effectors are, to a different extent, named in the manuscript, whereas the fourth one is not mentioned at all. The authors should also aim to provide more context and explanation of the nomenclature of the effectors. This will make the paper more understandable for readers unfamiliar with the terminology of Agrobacterium effectors. In addition, the authors should consider giving more attention to the phenotype of previously described nucleases, such as cell elongation, so that the reader would better understand the result section when encountering this phenotype. The authors only explain this in the discussion, and it would be helpful to have more clarification in the Introduction as well. The authors should also explain the reasoning behind using that prey cell (Dickeya) and not E. coli or another organism for the antibacterial activity experiments.
• The novelty of the study could be improved by providing a better explanation of the specific mechanism or mode of action through which V2c works. For example, the authors could study more deeply the puzzling fact that the elongation phenotype is independent of the nuclease activity for this effector but not for V2a. Another interesting approach would be to study the putative specificity of these nuclease effectors, as not all of them are effective against bacterial targets. This is an unexplored area of great interest for a more innovative study because nucleases do not seem to be specific, they all degrade nucleic acids, and somehow they have different capacities to kill different prey cells.
• In Figure 1B, it would be convenient to include in the alignment the two nucleases of the same family as V2c that have been already described in the literature and are named in the main text (Tke4 and Txe4) to better illustrate their similarities and differences. In Figure 1C, it would be convenient to add the PAAR-RHS domain to the list of N-terminal domains found with Tox-SHH nucleases.
• When it comes to the microscopy experiments (Figure 5), the authors should work to improve the relevance and quantification of the data they present. This could involve using more rigorous analysis techniques, such as statistical analysis, to support their findings more convincingly. In fact, the authors could enhance the rigour of their analysis (Dickeya cell lysis) by gathering more supporting evidence before drawing their conclusions. The authors should explain why they are using two identical strains (attackers) in the competition assays (Fig 5C) d3EIbcd 1 and 2.
• Regarding the inter-bacterial competition settings between Agrobacterium and Dickeya, the authors should explain why they used TssB-GFP prey cells when this is not necessary for the assays they performed. Furthermore, the authors should clarify the statement in the discussion (lane 353-355) regarding the absence of antibacterial activity of V2c against E. coli, while the results of this work show inhibition of E. coli cells (Fig. 2). The authors should rethink whether the video is necessary for this section, as it does not provide additional relevant information.
• To ensure that the paper is easily comprehensible and effectively conveys its message, the authors should meticulously examine all aspects of their writing, including language usage, grammatical accuracy, and syntax structure. The authors should also ensure consistency in their writing style and language usage throughout the paper. As one example of writing style inconsistency, the authors used both Gram-negative and gram-negative in the manuscript.

Significance

The study expands the knowledge in the specific field of SHH nucleases and T6SS effectors. This could be of interest to T6SS researchers and more broadly to researchers in the field of bacterial toxins.

The novelty of the study is very limited since the authors functionally characterised a T6SS effector with a well-described function, a DNAse (DNA degradation) and a recognised structure (SHH domain). As expected for a nuclease, it degrades DNA and provokes cell elongation, which has also been described before.

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

Learn more at Review Commons

---

Reply to the reviewers

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

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

Summary:

The authors provide insight into the gaps within BRAFi research in an effort to further understand how elements such as mechanisms of resistance and clinically observed adverse events in melanoma patients occur. This manuscript more specifically highlights the effects of BRAFi treatment on endothelial cells in the context of vasculature. The authors begin to explore how traditional BRAFi therapies may lend to such adverse events due to the role they play alongside that of targeting melanoma cells such as off-target effects, paradoxical endothelial signaling, and inducing a pro-tumorigenic microenvironment. The conducted studies demonstrate simple and effective methodology, focusing on proteomic and phosphoproteomic analysis, to elucidate the endothelial consequences of BRAFi treatment. The authors provide sound conclusions from the presented data and validate their in vitro findings with clinical observations using patient tissue. The analysis within this manuscript is just scratching the surface and leaves the authors with much to explore in future manuscripts.

Major comments:

The authors provide a solid story outlining the pitfalls in BRAFi therapy research and the consequences on endothelial vasculature in the treatment of BRAF mutant melanoma. The manuscript details clinical relevance of the research, functional impact to the field, and a thorough discussion on the scope of this work and where it may be lacking, which allows for the opportunity for future directions.

Minor comments:

The authors may consider revising minor errors within the Discussion as indicated below. Discussion - Paradoxical MAPK activation Missing comma between cells and the; "For endothelial cells, the concentration of BRAFi measured in the patient circulation is critical." Discussion - Off targets in endothelial cells Missing comma between range and it; "At concentrations in the low µM range, it inhibits numerous other kinases." Missing commas around apart from MAPK; "This suggests that, apart from MAPK, other signaling pathways would also be affected by BRAFi treatment"

Significance

This manuscript poses a key discussion in the importance of expanding research of molecular targeted therapies on more than just the target cells as the consequences to surrounding cell types can give vital insight into potential adverse effects in the clinic. The authors note that while this is not a novel concept, there are still gaps that prove vital in understanding clinical impact, which they hope to fill with this manuscript. They provide support to their conclusions using primarily proteomic approaches with the addition of some comparative analysis of a publicly available dataset, and patient tissue samples in order to validate their findings. Whether in the context of treating melanoma or any other disease. this manuscript serves as a helpful reminder to pre-clinical and clinical researchers alike in how important it is to factor in the patient as a whole, not just the disease when identifying effective treatment options.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary:

The author Bromberger and colleagues have submitted a MS # RC-2023-02152 entitled "Off-targets of BRAF inhibitors disrupt endothelial signaling and differentially affect vascular barrier function" for review via Review Commons. In the MS they have investigated four BRAF inhibitors with different pharmacodynamics; Vemurafenib, Dabrafenib, Encorafenib and PLX8394 and their specific effect on vascular endothelial cells but also on melanoma cells. The study is composed of in vitro studies using in-house isolated human dermal endothelial cells. Also, melanoma cells and skin biopsies from 5 melanoma patients were analysed. The authors conclude that the BRAF inhibitor Vemurafenib caused strong effect on the endothelial cells' barrier function in comparison to the other three BRAF inhibitors.

Major comments: major issues affecting the conclusions:

In general; a major issue that is affecting the whole story is the rather high concentration of Vemurafenib (100 uM) used in the study. The authors do not provide any data describing the viability and function of the endothelial cells after exposure to 100 uM of Vemurafenib. Instead they have chosen two concentrations with a large (10X) difference. Where the cells viable at 100 uM of Vemurafenib? If the endothelial cells were suffering from 100 uM Vemurafenib, they will immediately loose the cell-cell contacts/junctions and thereby any performed permeability assay would be pointless. Furthermore, isolation of skin endothelial cells is at risk to be accompanied with lymphatic endothelial cell contamination. The authors should provide data ensuring that the cells are of >90% endothelial cell purity by checking for PROX1-positive cells together with endothelia cells markers (CD31, VE-cadherin, uptake of AcLDL etc).

The work is of importance in understanding consequences for endothelial cells exposed to BRAF inhibitors used in the clinic using clinically relevant concentrations of the drugs investigated in vitro. If the authors provide with a major revision, the work could be acceptable for publication.

1. The Western blots in this manuscript are in general overexposed (saturated) and therefore differences between treatment conditions are not possible to be clearly defined. Therefore, quantifications of the experiments should be done and combined with representative Western blots.
2. Figure 1A-C n=?, notice no standard deviations in 1C. Does this mean that in 1C n=1?
3. Figure 1D, no significant differences? If there is no significant difference, then there is no difference between the treatments.
4. Regarding concentrations of Vemurafenib; it is needed that the authors define endothelial cell viability (proliferation, Caspase-9 staining or LIVE/DEAD fixative stains) at this high concentration. Then 10 uM is probably a too low concentration (see data in figure 2 where 10 uM gives no data of relevance). Cell toxic effects could be the reason of increased passage of N-Fluorescein upon 100 uM Vemurafenib treatment or the cause of cell-cell gaps (Figure 5). If 100 uM truly shows that the cells are viable without any signs of toxicity, the paper would be more clear if main figures contain only 100 uM Vemurafenib. It is recommended that cell cytotoxicity is tested for all compounds in this short- and long-term treatments
5. Figure 5; the authors should demonstrate the effect of BRAF inhibitors using a different approach. Trans-endothelial migration (trans-well), or similar methods would enforce the main message. Furthermore, migration defects could be evaluated by scratch-wound assay. Comment: the imaging in figure 5A is not clear enough to truly show the cell morphology and to define the cell status (see point 4 related to cell viability). We also advice that figure 5A also contains stainings for all other treatment conditions (or included in a supplement figure). What´s the mechanism behind junctional rearrangement? Internalization, degradation or actin cytoskeleton-dependent mechanisms? Figure 5A, stainings should be quantified. Figure 5C; with three asterisks in the figure, what is the actual significance and is it compared to DMSO? With the large SD the significance can hardly fit with three asterisks (<0.001).
6. Valuable skin biopsies of patients before and after treatment have been used for figure 6. The authors should pay more careful attention to what vasculature they are investigating in the biopsy material. The authors mainly focus on large arteries (large vascular lumens with a thick layer of ASMA-positive cells). We recommend that they investigate capillaries (5-10 um in diameter) which are more plastic and susceptible towards treatment. Claudin-5 is a vascular marker but the antibody chosen clearly provides with high autofluorescence stains detecting blood cells in the vascular lumen and not only the endothelial cells. We therefore recommend to use another claudin-5 antibody that will stain dermal vasculature better. Which patient is imaged in figure 6? Please prepare a supplement figure with patient 1-4 to show representative images of the main differences. Do the authors expect that Vemurafenib 100µM will also decrease VE-cadherin and claudin-5 total protein levels?
7. Table 2: The quantification is not clear. The authors should describe the data in a more descriptive way. For example, what does it means to have more than 100% (181.41% of claudin-5 for patient 5) of the vascular markers? Also, it is not realistic to describe percentage data with 2 decimals. The authors should also classify their quantification based on vessel type (large caliber vessels vs capillaries), cancer and pseudo-normal tissue. As a way to validate their in vitro findings (permeability and junctional disruption in these patient tissue biopsies), the authors should check for leakage by staining for serum proteins like IgG, fibrinogen or serum albumin.

Minor comments: important issues that can confidently be addressed:

1. The authors want to fill a gap in knowledge related to BRAF inhibitors effect on endothelial cells, which a limited number of publications are available.
2. Why are the authors using CellTracker for visualize cell morphology. It would be better if cells were stained for VE-cadherin and beta-actin including nuclear stain with DAPI. This would far better define the cell morphology after treatments.
3. Please in Material & Methods describe KinSwing activity predictions index to help the reader to follow the results better.
4. Table 1 could be reformatted to be more easily to read.
5. Figure 1, is ERK= ERK1/2?
6. The discussion text should be shortened and more focused towards their findings and with conclusions of performed experiments. How is the paradoxical effect of Vemurafenib (figure 1) related to their later findings (Figure 2 and 3)? In other words, what is the relation between figure 1 and figure 2 and 3?
7. For the discussion; is the result in figure 5C supported by data that patients on Vemurafenib treatment would be exposed to a higher risk of metastasis?
8. Figure 3B, resolution of text needs to be improved and the full compound names could be written in figure 3A.
9. Figure 4A are any of the results statistically significant? If not, then there is no difference.
10. The authors should elaborate a hypothesis based on their phosphoproteomics data. Which of the off-targeted molecule(s) could impact endothelial barrier?

Referees cross-commenting

With our deep knowledge in endothelial cell biology, we would like to emphasize the need of Bromberger et al to reply to our comments. Additional experiments and verifications will improve the impact of the performed research. With reviewer 2 demanding far less additional work to be done there is a discrepancy between the two reviewers of the estimated time needed for performing a revision (1-3 months for reviewer 1 versus 1 month for reviewer 2). I (reviewer 1) believe that at least three (3) moths will be needed to collect additional data to reply to the questions.

Significance

This manuscript addresses an important question; how is the vasculature affected by cancer treatments? It is not unusual that the vascular status is neglected in clinical treatment studies. The manuscript provides valuable phosphoproteomics data of great interest related to this topic. The major weakness of the work is the lack of data verifying the chosen concentrations for the BRAF inhibitors used in the study. There is a great risk that several results based on the 100 uM Vemurafenib treatment (of high impact for the story) are based on cell toxicity due to a high concentration treatment in vitro. Also, the link between the strategy of performed in vitro experiments isn't clear and there is a lack of connecting the in vitro data to the validation performed on melanoma patient tissue biopsies. It is a great strategy to investigate skin biopsies before and after treatment. The precious biopsy material should be more carefully investigated and evaluated.

Audience: after improvement of the manuscript by better presentation of existing data and by additional experiments the work presented would be of interest to a pre-clinical and clinical audience investigating cancer treatments.

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

Learn more at Review Commons

---

Reply to the reviewers

Please see below for the detailed description of the changes made in response to the reviewers’ comments.

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

The manuscript investigated the composition of the plastid proteomes of seven distantly-related kareniacean dinoflagellates, including newly-sequenced members of three genera (Karenia, Karlodinium, and Takayama). Using a custom plastid-targeting predictor, automatic single-gene tree building and phylogenetic sorting of plastid-targeted proteins for plastid proteome construction, the authors suggest that the haptophyte order Chrysochromulinales is the closest living relative of the fucoxanthin plastid donor. Interestingly, the N-terminal targeting sequences of kareniacean plastid signal peptides, reveal a high sequence conservation. Moreover, ecological and mechanistic factors are suggested that may have driven the endosymbiotic acquisition of the fucoxanthin plastid. Overall, this is a comprehensive and interesting analysis.

Other comments.

1. For analyses of N-terminal targeting sequences, why did the authors not consider to employ Predalgo as an additional tool? Author response: We thank the reviewer for their suggestion. To our understanding, PredAlgo is a targeting predictor trained on primary green algae, which have two-membrane bound plastids and purely hydrophilic N-terminal plastid targeting sequences. It thus would be expected to perform poorly for the prediction of N-terminal targeting sequences in complex plastids such as those of the Kareniaceae bound by three or more membranes, who are located within endomembrane-derived compartments and which utilise plastid-targeting sequences based on an N-terminal hydrophobic signal peptide for ER import.

We considered the application of PredAlgo for the identification of downstream hydrophilic transit peptide regions in Kareniacean presequences, but note that the specific residue positioned after the signal peptidase cleavage site is typically a much better predictor than transit peptide hydrophobicity for identifying plastid-targeting sequences (Gruber et al., Plant J 2015, and citing references). We found that other targeting prediction tools based primarily on hydrophobicity (e.g., HECTAR) performed poorly in identifying probable plastid-targeting sequences in our control Kareniacean dataset, and therefore chose to prioritise a modified version of ASAFind that takes into account the residue context of Kareniacean signal peptidase cleavage site for our targeting predictor, which works with high sensitivity and specificity on our control dataset. We summarise these observations in Fig. S15.

Given the fact that peridinin or fucoxanthin pigment binding is in the focus of the paper, a more detailed introduction of the peridinin and fucoxanthin light-harvesting systems should be given.

Author response: A brief introduction to the pigment-binding proteins in dinoflagellates was added, “These include a unique carotenoid pigment… massively paralogized and synthesized as polyproteins” (lines 86-89).

The authors state "It is also possible that there has been a direct niche competition between the peridinin and fucoxanthin plastid that may have coexisted in the same host for a period of time with possibly different selective pressure on retention of their respective proteins based on their interaction with plastid-encoded components, e.g., extrinsic photosystem subunits not assembling correctly with their intrinsic haptophyte-like counterparts." It is tempting to ask, whether peridinin light-harvesting systems have left traces in the fucoxanthin plastid, possibly due to mistargeting of peridinin light-harvesting systems into the fucoxanthin plastid? Are some photosynthetic subunits "in-between" peridinin and fucoxanthin plastids?

Author response: We did not identify any other peridinin-like photosystem subunits than the ones visualized in the map schematic (i.e., ferredoxin/PetF in both Karenia and Karlodinium and PsaD of Karlodinium micrum) and discussed in the supplementary text. PetF is the only consistently retained peridinin-like photosystem protein, likely due to the fact that it is not strictly linked to photosynthesis: it is expressed in plant leucoplasts, and plastid-encoded in some non-photosynthetic chrysophytes. We have added a sentence in Supporting Text 6.4 that “we detect no possible homologues of peridinin-chlorophyll binding proteins (PCP) in any kareniacean transcriptome” (line 91).

Figure 3 is difficult to understand, e.g. for PSI and PSII which subunits are shown, why has PSI "more" contribution from dinoflagellates as compared to PSII?

Author response: The photosystem subunits are ordered numerically in the schematic, and detailed information on each protein and the corresponding sequences with their origin are included in the supplementary table S3. A single subunit of photosystem I (PsaD) was determined to be of plastid-early (peridinin-like) origin in Karlodinium (while the same protein is plastid-encoded in Karenia and undetermined in Takayama). We believe this may be simply due to an evolutionarily neutral differential loss / non-adaptive retention of photosynthesis-related proteins in a secondarily non-photosynthetic host before the acquisition of a replacement plastid. We note that there are only two (incomplete) kareniacean plastid genomes available so we cannot rule out the possibility of this subunit being plastid-encoded in Karlodinium as well (which would mean that both plastid-late and plastid-early homologs co-occur in this genus).

Fig. 3 is necessarily complex due to the size and multiplicity of the dataset considered. To facilitate reader navigation, we have added the following text to the figure legend (lines 1128-1140) text “Plastid proteins are arranged by major metabolic pathway or biological process, with each protein shown as rosettes … Proteins of plastid-late (haptophyte) origin, such as are concentrated in photosystem and ribosomal processes, are coloured red; and proteins of plastid-early (dinoflagellate) origin, such as are concentrated in carbon and amino acid metabolism are coloured blue. … In certain cases (shown as rosettes with multiple colours), homologues from different species have different evolutionary origins, e.g. Karenia possessing plastid-late and Karlodinium/ Takayama plastid-early”.

Data shown in figure 4, is there experimental evidence for signal peptide cleavage site(s). Could these data been used to predict mature plastid targeted protein sequence?

Author response: We were able to determine the conserved motives in signal peptide, including its cleavage site (GRR) which we exploited in the design of kareniaceae-specific matrix for ASAFind. We show these residues in Fig. 4. We note that these motifs were identified based on homology to known signal processing peptidase recognition sites, as opposed to experimentally determined protein N-termini.

Consistent with previous studies (e.g. Yokoyama et al., J Phycol 2011) we see limited evidence for consensus plastid transit peptide cleavage motifs in kareniacean presequences, and do not discuss this further as a result.

The authors state "Partial Least Square (PLS) analysis shows a set of environmental variables (salinity, silicate, iron) positively correlated with abundances of both Karenia and Takayma and also haptophytes as a whole, but at the same time negatively correlated to Karlodinium (Figure S8), further illustrating that the latter genus is quite distant from the rest in its biogeographical pattern." How could this be interpreted in the light of the plastid proteomes

Author response: We believe that this may be due to the more cosmopolitan distribution of Karlodinium, and possibly also a result of bias stemming from our strategy of grouping the organisms at the genus level (as not enough data was available at species level) which may obscure the potential outlier status of only some species/ subpopulations. This is particularly true for the haptophytes, where in the absence of specific ancestry for individual kareniacean plastids we are only able to consider distributions at the levels of entire orders. We now acknowledge this in the Discussion: “specific ecological interactions between the progenitors … via ancestral niche reconstruction for each lineage” (lines 473-475).

Please note, that the results might have changed slightly from the previous version due to the re-calculation following additional normalization of the data (see below).

Reviewer #1 (Significance (Required)):

The current manuscript gives insights into the endosymbiotic acquisition of the fucoxanthin plastids.

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

This is a well done, detailed bioinformatic analysis of genomic and transcriptomic data from an important lineage of dinoflagellates that have undergone serial substitution of their plastid. On the whole I am enthusiastic about the paper; it presents valuable new insights, and is rigorously performed. However, I have to object to the way the term "proteome" is used in the paper; the manuscript is talking about the predicted proteome, not a measured proteome. This is something of a technical distinction, but it is an important one because the transcriptome and the proteome don't necessarily track each other, and there is little or no actual proteomic data available from dinoflagellates. We assume that transcript abundance has something to do with proteome abundance, but this is often violated. What this paper is really addressing is the potential proteome, because if a given gene is completely absent from the genome and the transcriptome we can be confident it will not be present in the proteome. The converse is not true. For this reason I feel it is important to be clear on the distinction. I would be satisfied in this regard by minor modifications, using the term "predicted proteome" in the title, and being more direct in the introduction about the distinction.

Author response: We agree that the usage of the word proteome for in silico predictions is not entirely correct, and have used the term “predicted proteome” where possible in the text to clarify this.

We have also, as described in our response to Reviewer 1 above, included a statement in the Discussion that our largely bioinformatic results will be transformed by an experimentally realised kareniacean plastid proteome, which we nonetheless feel goes beyond the scope of our manuscript.

Overall the analyses are impressive. I do have to squirm a little when I see automated analyses generating alignments where the threshold is less than 75% gaps and at least 100 nucleotides aligned. I looked at the supplementary data and the figshare files and could not find the alignments themselves, so I don't know what fraction of the sequences are in that territory. Because phylogenetic analysis (as performed here) treats the alignments as an observation, and because the alignments include sequences with more than 50% gaps, it is entirely possible that some taxa, or even whole segments of the tree, are based on non-overlapping data.

Author response: We thank the reviewer for their comment and have added in three new supplementary figures (S16-S18) providing statistics on alignment size, length, and average gap percentage distribution. We report that most of the alignments contained relatively little gaps: 90% of the alignments contained between 1.1 and 24.5% of gaps with median value of 6.6%.

Mind you, we have done similar analyses, and I don't think this invalidates the results, but it does open up the possibility of some dramatic artifacts. Consequently, I would recommend a) making the alignments available (or more obvious where to find them), and b) providing more detail on the alignments, including, if possible, to add a figure (probably in the supplementary data) that visualizes them. It is not given in the text itself, but according to the figure 2 caption there are 22 sequences thought to be "plastid late", and 241 in the pan-eukaryotic dataset. This is a scale that is feasible to put in a figure showing, for example, each aligned residue as a color and indels as grey. Such a figure is readable even when the individual residues are only a few pixels in size (less than a millimeter when printed). I also recommend describing the final alignments more fully in the text. Most of the summary statistics are presented in normalized form, and that can obscure patterns that come from poorly sampled taxa. Better clarify on the characteristics of the alignments will make it easier to interpret the findings overall. Although this is critical to interpreting the results, gappy alignments are not uncommon in analyses of this sort, and setting that aside the analyses presented are comprehensive and thorough. The discussion does a good job of addressing the significance of the work, and potential causes of error are addressed adequately (aside from the matter of the alignments).

Author response: We thank the reviewer for their comment and have provided alignments for all single-gene trees, in a dedicated online supporting repository (https://figshare.com/articles/dataset/all-automatically-generated-alignments_rar/24347032). The datasets and alignments used for PhyloFisher and plastid-encoded gene trees are included directly in the supplementary files (phylofisher_files.tar, plastid_genome_phylogeny_files.tar and plastid_protein_phylogeny_files.tar).

We have additionally included three new supporting figures (S16-S18) showing the distributions of lengths, gaps and homologues in each single-gene tree. These data project largely completion of individual alignments, with only 5% containing > 20% gapped positions (see Fig. S18), for example. We have additionally clarified in the Methods that “The trimmed alignments were then filtered by a custom python script that discarded sequences comprising of more than 75% gaps and then rejected alignments shorter than 100 positions or containing fewer than 10 taxa.” (lines 571-573).

For the two concatenated trees presented, we have clarified in the Methods the alignment lengths (PhyloFisher: 72, 162 positions; plastid genes: 2,404 positions), and that we removed sequences containing >66% of gaps from the final alignment. Reflecting on the congruency assumptions required to concatenated alignments, we have chosen to replace the plastid-late concatenated tree (which may group proteins with multiple phylogenetic signals) with a new main text figure 2 providing an overview of the plastid signals we observe across the entire dataset (see comments below to Reviewer 3).

Reviewer #2 (Significance (Required)):

I find the paper to be exciting and important. These organisms are economically important, particularly as potential nuisance organisms, but also because of their role in primary productivity. They also have extremely complex evolutionary histories and similarly complex genomes. performing any bioinformatic analysis of these organisms is a substantial challenge because almost every gene exists in high copy number and with complex and often obscure patterns of homology. The manuscript brings forward these challenges, and makes a substantial step forward in elucidating the evolution of a group that is fascinating and important, but remarkably difficult to work with. I feel that it is an important analysis, and should be of interest to a broad audience.

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

Summary

This manuscript entitled "Divergent and diversified proteome content across a serially acquired plastid lineage" by Novak Vanclova et al. proposes the origin and evolution of plastids in kareniacean dinoflagellates. The authors generated new transcriptome data from Karenia mikimotoi, Karenia papilionacea, Karlodinium micrum, Karlodinium armiger, and Takayama helix. Combining them to the previously published transcriptome data from kareniacean dinoflagellates, they constructed the pan-kareniacean transcriptome library. They surveyed plastid-targeted protein-coding transcripts in the dataset, and consequently they estimated ~14.5% of the transcriptome data were of plastid-targeted ones. Of them, 65-80% were derived from a peridinin-containing dinoflagellate ancestor while ~15% were derived from EGTs from a haptophyte endosymbiont of the current plastid origin. By using the plastid-targeted transcript dataset, they investigated 1) origins of the plastid-targeted protein-coding transcripts by single gene-trees, 2) the plastid origin and evolution by the multigene dataset of 22 conserved plastid-targeted protein-coding transcripts and of 3) plastid genome-derived transcripts, 4) plastid functions, 5) diversity of plastid-targeted signals in kareniacean dinoflagellates, and 6) the distributions of kareniacean species by using the Tara Oceans database. On the basis of their results, they proposed many hypotheses regarding kareniacean dinoflagellate evolution, such as i) the chrysochromulinales-origin of the plastids, ii) more recent acquisition of the plastid than previously thought, iii) a plastid replacement within kareniaceae evolution, iv) the strict selection of signal peptides but non-conserved transit peptides in the kareniacean plastid-targeted proteins, and v) correlated or non-correlated distribution patterns of kareniaceaen dinoflagellates to specific haptophyte lineages.

Although their proposals are interesting, I have many concerns to be addressed. Especially, their analyses on which the above proposals are based seem to be still preliminary and inconclusive. To support their proposals more confidently, I also suggest some additional analyses.

Major comments

1. seemingly inconsistency between the authors' claims The most striking is inconsistency of the authors' claims proposed in this manuscript. Their proposals include a) the common ancestor of kareniaceans has not possessed a fucoxanthin plastid but the plastid has been acquired more recently, b) an ancestor of Takayama and Karlodinium has gained a fucoxanthin plastid from a (chrysochlomulinales) haptophyte, c) an ancestor of Karenia has gained a fucoxanthin plastid from Karlodinium. However, they also demonstrate a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama. If I understand correctly, "a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama" would seemingly be inconsistent to and challenge two of the authors' claims: no haptophyte-derived plastid in the common ancestor of kareniacean dinoflagellates and a Karlodinium-to-Karenia plastid transfer (Fig. 7). If the Karenia plastid is derived from Karlodinium, I have no idea why haptophyte-derived plastid proteome of Karenia is larger than that of Karlodinium. After the plastid acquisition in Karenia, Karenia might have gained more genes for plastid-targeted proteins from haptophytes by LGTs. If this is true, many single gene trees would suggest different origins of plastid-targeted proteins between Karenia and Karlodinium/Takayama. Can we see it in the single gene analyses? I would like authors to rationalize the inconsistency in the main text.

Author response: We agree with the reviewer that the evolutionary origins and dynamics of the kareniacean plastid proteome are complex, and thank them for their suggestion.

First, to take into account the different evolutionary scenarios that could explain the present-day distribution of the kareniacean plastids, including the new plastid genome sequences identified in response to the reviewer’s suggestions, we have made a revised version of Fig. 8 evaluating three different hypotheses (see below). Nonetheless, we feel that the Karlodinium-to-Karenia model we propose is plausible, based on the following observations:

• We identify 1,418 plastid protein gene trees in which at least two of the three studied genera (Karenia, Karlodinium, Takayama), and 748 in which all three resolve as monophyletic, and with a haptophyte sister-group (i.e., a common plastid-late origin; Fig. S2). This points to a common haptophyte ancestry in all three groups, as opposed to independent endosymbiotic consumptions of free-living haptophytes in Karenia and Karlodinium micrum.

• We see no such shared signal with the RSD, which shares only 42 proteins with at least two other kareniacean genera (Fig. S4). Thus, and consistent with previous studies (Hehenberger et al., PNAS 2019) we cannot invoke an ancestral presence of a fucoxanthin plastid shared with the RSD in the last common kareniacean ancestor. This discrepancy thus likely points to a serial transfer of the kareniacean plastid from either Karlodinium into Karenia or vice versa (Fig. 8).

• Concerning the direction of this transfer, among 1,059 gene trees of plastid-late origin found in both Takayama, Karenia and Karlodinium, 873 place Takayama as basal to a monophyletic clade of Karenia and Karlodinium, i.e. support a specific plastid transfer between the latter two genera. The most parsimonious explanation for this is the origin of the fucoxanthin plastid in the common Takayama/ Karlodinium ancestor, which was subsequently transferred into Karenia. It is true that Karenia contains both a greater absolute proportion of predicted plastid-targeted proteins (Fig. 1) and greater number of unique KO number annotations (Table S4) of plastid-late origin than either Karlodinium or Takayama. That said, this signal may be influenced by multiple other factors beyond how old the given endosymbiosis is (i.e., longer coexistence implies more EGT). For example, the number of plastid-late gene in a host genome may depend on the frequency of duplication of plastid-late genes and the receptiveness of the host nuclear genome to incoming horizontally derived genes. It may further be influenced by the presence and relative selective advantage or disadvantage of competing genes of host nuclear origin (i.e. plastid-early genes) that may be differentially selected over plastid-late genes, which might vary between Karenia and Karlodinium due to differential retention of the ancestral peridinin-type plastid in each lineage.

We have elaborated on this point in the Discussion, noting that there may have been “a direct niche competition between the peridinin and fucoxanthin plastid … with possibly different selective pressure on retention of individual imported proteins” (lines 370-372), “relatively recent origin and spread throughout the kareniacean genome, e.g., via gene duplications” (line 459), and finally that precedent for divergent evolutionary trajectories in different Kareniaceae exists from the Karenia and Karlodinium plastid genomes that “contain partially non-overlapping sets of genes that suggest independent post-endosymbiotic plastid genome reduction” (lines 403-404). Nonetheless, we acknowledge that the evolutionary model we propose is not definitive, and that alternative explanations may find more favour with increased genome data.

Signal peptide prediction I think the modified ASAFind would be greatly helpful for future studies on automatic prediction of plastid proteomes in kareniacean dinoflagellates. However, I found no data on selection criteria for the signal peptide prediction program SignalP5.0 used. I believe such data would be very important to interpret the previously published paper by Gruber et al. in which prediction methods for plastid-targeting sequences are compared to each other to see how sensitively and specifically they can capture the plastid proteomes.

Gruber et al. 2020. Comparison of different versions of SignalP and TargetP for diatom plastid protein predictions with ASAFind.

According to Gruber et al. (2020), signalP5.0 is not suitable for prediction of signal peptides for diatoms, in consistent with the authors' claim for kareniacean dinoflagellates. This inconsistency would be difference of the nature in signal peptides between diatoms and kareniacean dinoflagellates. Even if so, it would be useful to see quantitatively how much different their signal peptides are in terms of their suitable prediction programs.

Author response: In our preliminary benchmarking using only the previously published transcriptomes (see additional sheet in Supplementary tables), SignalP 5.0 performed substantially better in terms of specificity than SignalP 3.0 (i.e., 22 versus 34/ 728 retrieved positive hits of proteins with uniquely non-plastidial functions), with comparable sensitivity in the correct prediction of positive control proteins. Given the size of our dataset, and the substantial risk of false positive detection in the highly expanded and redundant dinoflagellate transcriptomes we have used, we feel that the greater specificity of SignalP 5.0 is important to integrate in our model selection. We have clarified this position in the Methods, stating “First, the relative effectiveness of two SignalP versions … SignalP 5.0 was used for all subsequent analysis.” (lines 525-529).

I also have a concern about use of the combination of PrediSI and ChloroP, combination which is suitable for the plastid proteome prediction in Euglena gracilis. The authors should rationalize why the method for Euglena plastids can be applicable without any modification to the plastid proteome prediction in kareniacean dinoflagellates. Although Euglena plastids are enclosed by three membranes, kareniacean plastids are by four. Therefore, from the side of molecular mechanisms in protein import, the method suitable for Euglena plastids is not necessarily suitable for kareniacean dinoflagellate plastids.

By using PrediSI and ChloroP, they detected additional "candidate plastid proteomes" including several proteins not detectable by SignalP5.0 and the modified ASAFind. That seems great. However, they did not seem to consider false positives since there is no mention on it. Although the additional candidates predicted by PrediSI and ChloroP included true plastid proteins of kareniacean dinoflagellates, many might not be. Nevertheless, the authors suggest 7.5 to 14.5% in K. micrum and K. brevis, respectively, are of plastid-targeted ones. I am so afraid if the proportions would be highly overestimated due to false positives by PrediSI and ChloroP. To rationalize the use of PrediSI and ChloroP, the authors should show sensitivity and specificity by quantitative analyses with a benchmark dataset.

Author response: We thank the author for this comment. The reasoning behind using the parallel PrediSI+ChloroP strategy was the previously reported similarity of the plastid signal structure between euglenids and peridinin dinoflagellates (c.f., Lukes et al., PNAS, 2009) and the previous observation that some kareniaceae posses plastid-targeting sequences resembling those of peridinin dinoflagellates (c.f., Hehenberger et al., PNAS, 2019). Per the reviewers’ suggestion, we present a modified sensitivity/ specificity testing PrediSI+ChloroP, alongside other alternative targeting predictors in Figure S15. While the PrediSI+ChloroP sensitivity is very low, its specificity is comparable with the modified ASAFind, and in this regard outperforms other targeting predictor tools, thus rationalising the use of both targeting prediction tools together.

Origin and evolution of kareniacean plastids The authors suggest the chrysochromulinales origin of the kareniacean dinoflagellate plastids and the Karlodinium-to-Karenia plastid transfer, on the basis of phylogenetic analyses using the concatenated datasets with the 22 conserved plastid-targeted proteins and with plastid-genome derived transcripts. It is very interesting that those plastid-targeted proteins in kareniacean dinoflagellates might be phylogenetically closely related to chrysochromulinales haptophyte I have suggestions on the analyses and interpretation

As the 22 analyzed genes are nuclear-encoded plastid targeted genes, they are a quite small portion of entire plastid proteins. I am not convinced by that evolution of the small number of genes reflects evolution of fucoxanthin plastids of which proteomes are comprised of >1000 proteins. How many genes for haptophyte-derived plastid-targeted proteins suggest the monophyly of kareniaceaen dinoflagellates and chrysochromulinales haptophytes should be investigated by, for example, a coalescence-based analysis such as Astral for all the detected haptophyte-derived plastid-targeted proteins including the 22 genes. This is because the monophyly could be reconstructed only by one or few, limited number of proteins even if the concatenated dataset is analyzed.

Relevant to this, plastid-targeted proteins derived from a peridinin-containing ancestor might still have phylogenetic signals of host evolution. I am interested in whether such analyses with peridinin plastid-derived plastid-targeted proteins reconstruct Takayama and Karlodinium as monophyletic but separate Karenia from them, as suggested in the phylogenomics with non-plastid proteins.

Author response: We agree with the reviewer concerning the problematic nature of concatenations with small numbers of genes, particularly if the underlying gene trees are not phylogenetically congruent to one another, and have chosen to replace the concatenation with a more global evaluation of the different plastid protein origins across our entire dataset. Using automated sorting approaches, we have evaluated the support for our evolutionary model across hundreds of gene trees. We feel that this approach supercedes coalescence-based techniques, as it enables us to treat each gene topology as an independent event, and to consider multiplicity in the origin of the kareniacean plastid proteome. We present these data in a new Fig. 2 and S2.

As stated above, these data strongly support monophyly of all three Kareniacean genera. Concerning the potential Chrysochromulinalean plastid signal in our dataset, we have reanalysed our data and quantify a substantial number of trees (220/ 1,418 of plastid-late origin) that specifically place multiple kareniacean genera within the Chrysochromulinales. This figure is more than twice the number (91) that place the kareniaceae with the next most occurrent haptophyte group in our dataset, Isochrysidales. We nonetheless have chosen to no longer present this as a cryptic plastid endosymbiosis, in the absence of clear examples of extant kareniaceae still possessing this plastid, saying purely in the Discussion that “a common ancestor of the studied organisms either possessed a stable plastid or had a long-term symbiotic relationship (e.g., kleptoplastidic) with a haptophyte lineage related to the extant Chrysochromulinaceae” (lines 363-365).

Concerning the phylogenetic placement of each karenicean genus, the majority of our plastid-late trees specifically recover the monophyly of Karenia and Karlodinium. Remarkably, we find that Takayama and Karlodinium only resolve together in 69/ 1,039 plastid-late gene trees in which all three genera are represented, strongly refuting a vertical origin of the haptophyte-derived components of their plastid proteome. This is not due to the Phaeocystales origin of the current Takayama plastid genome, which is found in only 21 of our plastid protein trees. Nonetheless, as the reviewer suggests, the opposite trend (1,505/ 2,804 gene trees grouping Takayama and Karlodinium as monophyletic) was observed amongst plastid-early gene trees, which might reflect a cryptic peridinin plastid shared between these groups. We expand on these results in the Discussion, stating “Many of the plastid-early gene trees copy the organismal topology …this awaits structural confirmation via microscopy” (lines 383-386).

Finally, to enable reviewer comprehension of the relationships shown, we have presented some exemplar topologies of some of the trees previously displayed in the concatenation, provided in a new Fig. S5.2.

For the phylogenetic analysis of plastid genome-derived transcripts, I might be wrong, but I could not find any information on dataset sizes (i.e., the numbers of sites) and evolutionary models for the analyses in the main text nor supplementary document. Although one may see the dataset sizes when looking at the original datasets in the supplementary files, such information is substantial and thus is to be described in the materials and methods section. I am afraid if this analysis was performed with a small dataset size. I would like to know total lengths of the concatenated sequences and especially that for Takayama. The phylogenetic position of Takayama, distantly related to the other kareniaceans, in this tree might be caused by a larger portion of gaps in the Takayama sequences than in the other kareniaceans.

Author response: As noted in our response to Reviewer 2, we have included three new supplementary figures (S16-S18) with statistics on alignment size, length, and average gap percentage distribution. The average and median values of these three measurements do not differ significantly when calculated separately for different organisms. We have clarified in the Methods that the concatenated alignments retained (PhyloFisher, and plastid-encoded genes) were “constructed by IQ-TREE with the LG+C60+F model for the plastid matrices and posterior mean site frequency (PMSF) model (LG+C60+F+G with a guide tree constructed with C20) for PhyloFisher matrix” (lines 630-632).

Moreover, due to lack of the plastid genome sequence of Takayama, no one could confidently identify plastid genome-derived transcripts: some of those could be derived from second, nuclear copies that might be pseudogenes. Otherwise, even if they are plastid-derived, no one can evaluate whether they are transcripts after or prior to RNA editing. I am afraid if the dataset used is comprised of a mixture of edited and non-edited sequences in kareniacean sequences. Either of sequences after or prior to RNA editing, latter of which are identical with DNA sequences, should be consistently used for the phylogenetic analysis. In any case, the plastid genomes are necessary for this analysis, and the authors can easily obtain them by DNAseq as they have the cultures.

Author response: We thank the Reviewer for their insightful response. We agree that understanding the evolution of kareniacean plastid genomes are crucial to understanding their evolutionary history.

We have accordingly, as described above, integrated a new main text Fig. 5 building a concatenated tree of plastid marker genes (psbA, psych, psbD, psaA, rbcL, and 16S rDNA) historically and commonly used to assess the evolutionary origins of fucoxanthin plastids (e.g., Takishita et al., Phycol Res 1999; Dorrell and Howe, PNAS 2012). These sequences were amplified cryopreserved stocks of total RNA and specific primers, amplified by RT-PCR. We have chosen here to use RNA sequences, to account for the presence of plastid RNA editing, which has been shown to play an important role in maintaining sequence identity between kareniaceaen plastids and haptophyte relatives despite a high DNA mutation rate in the former (Jackson et al., MBE 2013; Klinger et al., GBE 2018), rather than DNA sequences for this analysis.

Additionally, we would like to note that while plastid genomes are generally relatively simple to sequence and assemble, this is not the case in Kareniaceae. The existing plastid genome assemblies are partially incomplete and suggest more complex and possibly unstable structures (e.g., involving at least some minicircles in Karlodinium micrum, Espelund et al., PLoS One 2012; Richardson et al., MBE 2014). From personal communication with our colleagues, we are aware of some efforts to sequence additional kareniacean plastid genomes that unfortunately have not yielded satisfactory results and publications to this day. This strongly invites a separate project focused on kareniacean plastid genomes but is vastly out of scope of this study.

As described above, we have obtained striking new results which we are happy to report in the revised manuscript and which suggest even more, so far unnoticed, plastid replacements in the kareniacean lineage. In light of these finding, parts of the Results and Discussion sections have been extensively rewritten, and the schematic models presented in Fig. 8 has been updated to account for the distinct evolutionary origins of the Karlodinium armiger and Takayama helix plastids.

In addition, although I might be wrong, the phylogenomic analysis for plastid-encoded transcripts might be performed with their nucleotide sequences according to the figure title and legend of Figure S4 mentioning "nucleotide phylogenetic matrix" and the file name "plastid_coded_nt_concatenation_files.tar". If so, translated amino acid sequences should be subjected to phylogenetic analysis, to avoid a well-known artifact that is caused by saturation of substitutions at the 3rd codon.

Author response: With the exception of our 16S rDNA trees (in supporting data), all of our trees were generated with conceptual amino acid translations using a standard codon translation table, in accordance with previous studies (e.g., Klinger et al. GBE 2018). We have revised the file and figure names accordingly.

Duplication of an ATP synthase subunit Duplication and relocation of ATP synthase subunit delta seems interesting. In figure S6.4.1, could you clarify why the possible extensions containing signal peptides lack the initiation methionine at N-termini? I wonder they are 5′ UTRs but artifactually detected as signal peptides, if they all indeed lack Met. To evaluate this point, I recommend 5′ RACE followed by transformation into a model organism as performed in previous studies by some of the authors.

Author response: We reinvestigated these sequences more thoroughly using raw nucleotide data and conclude that the evidence for their retargeting to plastids is very weak and the reported extensions more likely represent untranslated regions some of which were falsely predicted as signal peptides. This section was removed from the new version of the manuscript, although we have noted in Supplementary Text 6.4 that: “A targeted HMMER search for possible distant homologs revealed that the distantly related functional analog of this protein in mitochondrial F-type ATP synthase (ATP5D, K02134) is duplicated in all species except Takayama. The additional copies, however, do not possess a detectable plastid-targeting signal and the specific functions of this duplicated subunit remain to be determined” (lines 107-111).

Comparison of transit peptides Amino acid compositions in transit peptides would vary when targeted compartments are different. In complex plastids, there are functionally distinct compartments: lumen, stroma, periplastidal compartment (PPC). Comparison should therefore be conducted separately for lumen-targeted, stroma-targeted and PPC-targeted proteins in order to claim their transit peptides are not conserved.

Author response: We acknowledge that this question was not explored in our analysis. We therefore re-analyzed our datasets taking the inferred sub-plastidial (thylakoid vs other, based on function) localization of the proteins into account. Our results showed no notable differences between these subsets and are reported in supplementary figure S10.

RDS never possessed a stable fucoxanthin plastid Although the authors cite Hehenberger et al. 2019 for that RDS never possessed a stable fucoxanthin plastid, as far as I know, that paper seems not to mention it. Could you let me know where that is mentioned in the paper? Hehenberger et al. instead proposed the retention of non-photosynthetic peridinin plastid.

Author response: We have modified the Results text, noting that we only identify 42 plastid-late proteins shared between RSD and other Kareniaceae, and in the Discussion that these data provide only limited support for a shared fucoxanthin plastid. We further clarify in the Introduction that “In some cases, the co-existence of a new organelle or endosymbiont with a remnant of the ancestral plastid has been proposed” (lines 106-108) and “It has previously been suggested that the RSD retains a non-photosynthetic form of peridinin plastid” (lines 378-379) with regard to the Hehenberger paper.

Regardless of whether Hehenberger et al. mentioned or not, Novák Vanclová et al. propose that RDS never possessed a stable fucoxanthin plastid because, if I understand correctly, they detected no or few haptophyte-derived RDS genes for plastid-targeted proteins of which origins are shared with those of Karlodinium, Karenia, and Takayama. What about the possibility that the last common ancestor of kareniacean dinoflagellates possessed a fucoxanthin plastid in addition to peridinin plastid followed by almost complete losses of those haptophyte-derived genes after loss of a fucoxanthin plastid in evolution leading to RSD? Free living eukaryotes were appeared to have lost a plastid in recent studies and they have only a few or no genes showing evidence of a plastid previously retained. We cannot rule out that an ancestor of kareniacean dinoflagellates possessed both of peridinin and fucoxanthin plastids, as the authors mention in the main text, and either plastid was inherited to each lineage by differential losses. Accordingly, I would say Fig. 7 is a too much strong proposal as alternative hypotheses are still present. They should be introduced equally.

Author response: We thank the reviewer for this comment. As discussed above, we evaluate the possibility of a cryptic peridinin plastid shared in different kareniaceae, which is suggested at a genetic level by our data but awaits structural confirmation.

We agree that alternative hypotheses may be invoked for the origins of the current kareniacean plastids, and have modified our Fig. 8 to present three alternative possibilities: serial transfer, independent acquisition, and coexistence of an ancestral peridinin and fucoxanthin plastid, as the reviewer suggests. The presence of an ancestral fucoxanthin plastid that was subsequently replaced in Takayama and Karlodinium armiger is strongly suggested by the monophyly of the plastid-late signal across all kareniacean species studied, except RSD. We nonetheless feel that the frequent monophyletic placement of the Karenia and Karlodinium micrum plastids to the exclusion of Takayama in our plastid-late gene trees strongly argues against a vertical inheritance of this plastid from the common kareniacean ancestor, and more likely reflects a serial transfer between the Karenia and Karlodinium / Takayama branches. We have evaluated the evidence for and against each hypothesis in the Discussion and in the Fig. 8 legend.

rRNA copy numbers in dinoflagellates It is known that the rRNA gene copy number varies among populations or strains in dinoflagellates; some possess several dozens of times as many rRNA gene copies as others (Galluzzi et al. 2010). Is it informative to see the ocean wide rRNA gene amplicon data for the kareniacean dinoflagellates? The numbers of rRNA gene-derived reads would not necessarily reflect the cell abundance of dinoflagellates.

Galluzzi et al. 2010. Analysis of rRNA gene content in the Mediterranean dinoflagellate Alexandrium catenella and Alexandrium taylori: implications for the quantitative real-time PCR-based monitoring methods. J Appl Phycol 22:1-9

Author response: We thank the reviewer for raising this point. The exploration of Kareniaceae distribution was intended primarily to investigate their respective ecological relevance in terms of niche diversity, in particular compared with the well-known cosmopolitan patterns of haptophytes, rather than comparing their abundance patterns. We feel that our approach, treating each Kareniacean genus independently, is sufficient for this, but have now clarified in the Results that the different abundances observed “may be biased by the different ribosomal DNA copy numbers in different genera” (lines 330-331) and have cited the reference the reviewer has kindly supplied.

We further note in the Discussion that “It will therefore be worthwhile in the future to assess the distributions of other more recently developed marker genes (Penot et al., 2022; Pierella Karlusich et al., 2023)” (lines 371-372).

Minor points

1. the dataset size for the 241 protein-based host phylogeny should also be described in the main text. Author response: The information (72,162 positions241 genes, removal of sequences with >66% gaps) has been included in the Materials and Methods.

The authors mention in Discussion "Thus, our results illuminate the mechanistics of a fundamental process that may under pin vast tracts of chloroplast evolution". If I understand correctly, I think this is based on "shopping bag model" when considering plastid replacements in dinoflagellates. It is helpful to add more details to clarify why the authors would like to claim so. "Chloroplast" should be replaced with "plastid".

Author response: We agree that the term plastid is more appropriate in this context, and have used it globally throughout the manuscript. We have mentioned once in the Introduction “primary plastids, i.e. chloroplasts” to orient the non-specialist reader.

We have elaborated on our definition of the Shopping Bag model, and the specific importance of the Kareniaceae, in the Discussion: “The idea that individual genes encoding plastid-targeted proteins may exhibit evolutionary affiliation with other groups than the plastid donor, typifying the “shopping bag” model (Larkum et al., 2007), is well-established in many plastid lineages” (lines 350-352).

Nonetheless, we feel that our data are in many ways different to those previously observed in other plastid lineages. This may reflect that the kareniacean plastid has undergone one, and potentially multiple, recent replacement events. Nonetheless, the predominant contribution of the host to the plastid proteome is striking, which we elaborate in the Discussion: “Our data show that the dinoflagellate host was the principal contributor of nucleus-encoded proteins supporting the kareniacean plastid proteome” (lines 352-353).

Supplementary document S6.6 I found the term nitrogen fixation, but should this be replaced with "nitrogen assimilation"?

Author response: We have corrected the text as requested.

Figure S5 For those LGTs, all the trees should be shown in supplementary text as they are only 11 or 12 trees. Especially, please add the chlorophyllide b reductase and chlorophyllase in the figure.

Author response: Trees for all laterally transferred genes mentioned in the text have been provided among supplementary figures (S7.1-10).

References I am not picky about a format of the reference list, but I think it should be consistent throughout the list. I recommend adding journals, volumes, and pages precisely for cited papers. I found lack of them at least in Novak Vanclova et al. and Pierella Karlusich et al.

Author response: We corrected the incomplete citations and will perform a complete reformatting of the references to comply with the requirements of a concrete affiliate journal.

Figures In figure 3, I strongly recommend adding RDS data, while distinguishing them by another color if they are derived from different origins from those of Karenia, Karlodinium, and Takayama. This would make the authors claim clearer that there are few haptophyte-derived genes for plastid targeted proteins of which origins are shared with those of the other kareniacean dinoflagellates.

Author response: We believe the comparison to RSD is not among the main stories of our study and adding this dimension to the already complex discussion and metabolic map schematic would compromise the overall clarity. This point is already noted by Reviewer 1 (above). However, this question may indeed be asked by some readers, therefore we decided to include the results for RSD as an additional column in the supplementary table S3 and as an additional graphical element in the supplementary version of the map schematic (figure S8). Per the reviewer’s comments above, we have further stated the number of plastid-late trees shared (42) between the RSD and other kareniaceae in the Results text.

In figures S5.1-2 showing LGTs, I found two paralogs of kareniacean dinoflagellates. What does "CP" mean? If "CP" means ChloroPlast-targeted, both paralogs of K. brevis in HARS and those of K. micrum are of plastid-targeted in TARS and they do not have cytosolic ones. I am afraid if these cases are caused by false positives of detection for plastid-targeted proteins by PredSI and ChloroP. Similarly, in figure S5.4, I found two distant paralogs of heam oxygenase in the tree and the taxon names for both types in kareniaceans include "CP." Are both targeted to the plastids or of false positives?

Author response: The annotation with “CP” and darker colour denotes proteins that were predicted as plastid-targeted by our pipeline. We have clarified in supporting text 6.8 that we investigated our aminoacyl-tRNA synthetases for possible dual targeting to both plastid and mitochondria but found no evidence for it.

We have searched the K. brevis SP3 HARS sequence (CAMPEP-0189291366) by CD-search and note that the conserved domain (underlined) starts at residue 24 after the first predicted methionine (bold), which is inconsistent with the probable length a plastid-targeting sequence, and we have noted in the figure legend that this is likely to represent a false positive.

CAMPEP_0189291366_Karenia-brevis-SP3-20130916

SWLVLLAFALTTPGPVVAVSATILRGLLVGLQRPCAAALRLSCCAATRALPLPGASELGSRFAAAAASSAR__M__GKEGKKKEDGKKKKDETKTEKLIGLEPPSGTRDFFPAEMRQQRYIFNKFRETANLYGFQEYDAPVLEHQELYIRKQGEEITDQMYSFDDKEGAKVTLRPEMTPTLARMVLNLMRVETGEMAAQLPLKWFSIPQCWRFETTQRGRKREHYQWNMDIVGVTSIYAEAELLSAICNFFESVGITSKDVGLRVNSRKVLNAVTKLAGVPDDRFAETCVIIDKLDKIGAEAVKTEMREKIGLPEEVGERIVKATGAKSLEEFADLAGVGQNNPEVLELKHLFELAEDYGYGDWLIFDASVVRGLGYYTGVVFEGFDRAGVLRAICGGGRYDRLLTKFGSPKEIPCVGFGFGDCVIAELLKEKGVTPSLPEHIDFVVAAFNSEMMGKAMNAARRLRLGGKSVDIFTEPGKKVGKAFNYADRVGADMVAFIAPDEWAKGLVRIKALRMGQDVPDDQKQKDVPLEDLANVDSYFGLAPAAAPVMSAAPAASTVKSTAPALAVPAAAKASAPKAAAPSGTGADVEAFLVDHPYVGGFRPCARDRTLFDELRLTSGRPSTPALGRWYDHIDSFPAVVRASWC

The green HARS sequences (including that of Karenia brevis SP1) in contrast typically have conserved domains starting after residues 50-60, and are likely to be genuinely plastid-targeted. Reflecting that the automated prediction approach used within our dataset may contain other such false positive results (c.f., Fig. S18), we have chosen for tree-sorting and pathway reconstruction analyses to only consider genes in which we can identify plastid-targeted homologues of the same inferred phylogenetic origin in at least two distinct Kareniacean genera (Figs. 2, 3).

For the Karlodinium micrum TARS sequence we have identified a second TARS sequence (CAMPEP_0200847158) that is of apparent dinoflagellate origin and lacks a credible targeting sequence, and have updated the tree accordingly.

In the case of heme oxygenases, we are convinced that (at least) two paralogs of distinct origins are indeed plastid targeted. The presence of multiple copies of this enzyme has been noticed in other organisms including some plants (e.g., Dammeyer and Frankenberg-Dinkel, Photochemical & Photobiological Sciences, 2008) and may be reflective of functional specialization or regulation / expression under different conditions. We have discussed this in the supporting text 6.1: “Two evolutionarily distinct versions of the biliverdin-producing haem oxygenase seem to be present …the specific metabolic functions of the green- and haptophyte-like haem oxygenases in the fucoxanthin plastid await experimental characterisation.” (lines 52-58).

Reviewer #3 (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?

This study by Novak Vanclova et al. provide new transcriptome datasets from multiple species in kareniacean dinoflagellates including harmful and toxic species. Their transcriptome datasets would help understand their biology, evolution, and ecology. The authors also provide a program that predicts plastid proteomes in those dinoflagellates, which would be useful for future studies to focus on kareniacean dinoflagellate plastids, after further refinement. The most important aspect of this study is that many plastid-targeted proteins might be derived from a particular haptophyte lineage, although it is still not sure whether they are derived from LGTs or EGTs. Phylogenetic analyses performed in this study should be improved by adding some plastid genomes, in order to gain more conclusive results. In addition to methods, interpretation of the current results and proposals on plastid evolution should be toned-down.

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,...).

Although there are technical issues, this study improves our conceptual understanding the plastid proteome evolution in Kareniacean dinoflagellates. The plastid proteomes are comprised of proteins with more various origins in those dinoflagellates, suggesting more complex plastid proteome evolution than previously thought.

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 seems to be "basic research".

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.

algal evolution, eukaryotic evolution, mitochondrial metabolisms, plastid metabolisms, phylogenomics

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 #3

Evidence, reproducibility and clarity

Summary

This manuscript entitled "Divergent and diversified proteome content across a serially acquired plastid lineage" by Novak Vanclova et al. proposes the origin and evolution of plastids in kareniacean dinoflagellates. The authors generated new transcriptome data from Karenia mikimotoi, Karenia papilionacea, Karlodinium micrum, Karlodinium armiger, and Takayama helix. Combining them to the previously published transcriptome data from kareniacean dinoflagellates, they constructed the pan-kareniacean transcriptome library. They surveyed plastid-targeted protein-coding transcripts in the dataset, and consequently they estimated ~14.5% of the transcriptome data were of plastid-targeted ones. Of them, 65-80% were derived from a peridinin-containing dinoflagellate ancestor while ~15% were derived from EGTs from a haptophyte endosymbiont of the current plastid origin. By using the plastid-targeted transcript dataset, they investigated 1) origins of the plastid-targeted protein-coding transcripts by single gene-trees, 2) the plastid origin and evolution by the multigene dataset of 22 conserved plastid-targeted protein-coding transcripts and of 3) plastid genome-derived transcripts, 4) plastid functions, 5) diversity of plastid-targeted signals in kareniacean dinoflagellates, and 6) the distributions of kareniacean species by using the Tara Oceans database. On the basis of their results, they proposed many hypotheses regarding kareniacean dinoflagellate evolution, such as i) the chrysochromulinales-origin of the plastids, ii) more recent acquisition of the plastid than previously thought, iii) a plastid replacement within kareniaceae evolution, iv) the strict selection of signal peptides but non-conserved transit peptides in the kareniacean plastid-targeted proteins, and v) correlated or non-correlated distribution patterns of kareniaceaen dinoflagellates to specific haptophyte lineages.

Although their proposals are interesting, I have many concerns to be addressed. Especially, their analyses on which the above proposals are based seem to be still preliminary and inconclusive. To support their proposals more confidently, I also suggest some additional analyses.

Major comments

1. seemingly inconsistency between the authors' claims The most striking is inconsistency of the authors' claims proposed in this manuscript. Their proposals include a) the common ancestor of kareniaceans has not possessed a fucoxanthin plastid but the plastid has been acquired more recently, b) an ancestor of Takayama and Karlodinium has gained a fucoxanthin plastid from a (chrysochlomulinales) haptophyte, c) an ancestor of Karenia has gained a fucoxanthin plastid from Karlodinium.

However, they also demonstrate a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama. If I understand correctly, "a higher proportion of plastid-late proteins in Karenia than Karlodinium and Takayama" would seemingly be inconsistent to and challenge two of the authors' claims: no haptophyte-derived plastid in the common ancestor of kareniacean dinoflagellates and a Karlodinium-to-Karenia plastid transfer (Fig. 7). If the Karenia plastid is derived from Karlodinium, I have no idea why haptophyte-derived plastid proteome of Karenia is larger than that of Karlodinium. After the plastid acquisition in Karenia, Karenia might have gained more genes for plastid-targeted proteins from haptophytes by LGTs. If this is true, many single gene trees would suggest different origins of plastid-targeted proteins between Karenia and Karlodinium/Takayama. Can we see it in the single gene analyses? I would like authors to rationalize the inconsistency in the main text. 2. Signal peptide prediction I think the modified ASAFind would be greatly helpful for future studies on automatic prediction of plastid proteomes in kareniacean dinoflagellates. However, I found no data on selection criteria for the signal peptide prediction program SignalP5.0 used. I believe such data would be very important to interpret the previously published paper by Gruber et al. in which prediction methods for plastid-targeting sequences are compared to each other to see how sensitively and specifically they can capture the plastid proteomes.

Gruber et al. 2020. Comparison of different versions of SignalP and TargetP for diatom plastid protein predictions with ASAFind.

According to Gruber et al. (2020), signalP5.0 is not suitable for prediction of signal peptides for diatoms, in consistent with the authors' claim for kareniacean dinoflagellates. This inconsistency would be difference of the nature in signal peptides between diatoms and kareniacean dinoflagellates. Even if so, it would be useful to see quantitatively how much different their signal peptides are in terms of their suitable prediction programs.

I also have a concern about use of the combination of PrediSI and ChloroP, combination which is suitable for the plastid proteome prediction in Euglena gracilis. The authors should rationalize why the method for Euglena plastids can be applicable without any modification to the plastid proteome prediction in kareniacean dinoflagellates. Although Euglena plastids are enclosed by three membranes, kareniacean plastids are by four. Therefore, from the side of molecular mechanisms in protein import, the method suitable for Euglena plastids is not necessarily suitable for kareniacean dinoflagellate plastids. By using PrediSI and ChloroP, they detected additional "candidate plastid proteomes" including several proteins not detectable by SignalP5.0 and the modified ASAFind. That seems great. However, they did not seem to consider false positives since there is no mention on it. Although the additional candidates predicted by PrediSI and ChloroP included true plastid proteins of kareniacean dinoflagellates, many might not be. Nevertheless, the authors suggest 7.5 to 14.5% in K. micrum and K. brevis, respectively, are of plastid-targeted ones. I am so afraid if the proportions would be highly overestimated due to false positives by PrediSI and ChloroP. To rationalize the use of PrediSI and ChloroP, the authors should show sensitivity and specificity by quantitative analyses with a benchmark dataset. 3. Origin and evolution of kareniacean plastids The authors suggest the chrysochromulinales origin of the kareniacean dinoflagellate plastids and the Karlodinium-to-Karenia plastid transfer, on the basis of phylogenetic analyses using the concatenated datasets with the 22 conserved plastid-targeted proteins and with plastid-genome derived transcripts. It is very interesting that those plastid-targeted proteins in kareniacean dinoflagellates might be phylogenetically closely related to chrysochromulinales haptophyte I have suggestions on the analyses and interpretation

As the 22 analyzed genes are nuclear-encoded plastid targeted genes, they are a quite small portion of entire plastid proteins. I am not convinced by that evolution of the small number of genes reflects evolution of fucoxanthin plastids of which proteomes are comprised of >1000 proteins. How many genes for haptophyte-derived plastid-targeted proteins suggest the monophyly of kareniaceaen dinoflagellates and chrysochromulinales haptophytes should be investigated by, for example, a coalescence-based analysis such as Astral for all the detected haptophyte-derived plastid-targeted proteins including the 22 genes. This is because the monophyly could be reconstructed only by one or few, limited number of proteins even if the concatenated dataset is analyzed.

Relevant to this, plastid-targeted proteins derived from a peridinin-containing ancestor might still have phylogenetic signals of host evolution. I am interested in whether such analyses with peridinin plastid-derived plastid-targeted proteins reconstruct Takayama and Karlodinium as monophyletic but separate Karenia from them, as suggested in the phylogenomics with non-plastid proteins.

For the phylogenetic analysis of plastid genome-derived transcripts, I might be wrong, but I could not find any information on dataset sizes (i.e., the numbers of sites) and evolutionary models for the analyses in the main text nor supplementary document. Although one may see the dataset sizes when looking at the original datasets in the supplementary files, such information is substantial and thus is to be described in the materials and methods section. I am afraid if this analysis was performed with a small dataset size. I would like to know total lengths of the concatenated sequences and especially that for Takayama. The phylogenetic position of Takayama, distantly related to the other kareniaceans, in this tree might be caused by a larger portion of gaps in the Takayama sequences than in the other kareniaceans. Moreover, due to lack of the plastid genome sequence of Takayama, no one could confidently identify plastid genome-derived transcripts: some of those could be derived from second, nuclear copies that might be pseudogenes. Otherwise, even if they are plastid-derived, no one can evaluate whether they are transcripts after or prior to RNA editing. I am afraid if the dataset used is comprised of a mixture of edited and non-edited sequences in kareniacean sequences. Either of sequences after or prior to RNA editing, latter of which are identical with DNA sequences, should be consistently used for the phylogenetic analysis. In any case, the plastid genomes are necessary for this analysis, and the authors can easily obtain them by DNAseq as they have the cultures.

In addition, although I might be wrong, the phylogenomic analysis for plastid-encoded transcripts might be performed with their nucleotide sequences according to the figure title and legend of Figure S4 mentioning "nucleotide phylogenetic matrix" and the file name "plastid_coded_nt_concatenation_files.tar". If so, translated amino acid sequences should be subjected to phylogenetic analysis, to avoid a well-known artifact that is caused by saturation of substitutions at the 3rd codon. 4. Duplication of an ATP synthase subunit Duplication and relocation of ATP synthase subunit delta seems interesting. In figure S6.4.1, could you clarify why the possible extensions containing signal peptides lack the initiation methionine at N-termini? I wonder they are 5′ UTRs but artifactually detected as signal peptides, if they all indeed lack Met. To evaluate this point, I recommend 5′ RACE followed by transformation into a model organism as performed in previous studies by some of the authors. 5. Comparison of transit peptides Amino acid compositions in transit peptides would vary when targeted compartments are different. In complex plastids, there are functionally distinct compartments: lumen, stroma, periplastidal compartment (PPC). Comparison should therefore be conducted separately for lumen-targeted, stroma-targeted and PPC-targeted proteins in order to claim their transit peptides are not conserved. 6. RDS never possessed a stable fucoxanthin plastid Although the authors cite Hehenberger et al. 2019 for that RDS never possessed a stable fucoxanthin plastid, as far as I know, that paper seems not to mention it. Could you let me know where that is mentioned in the paper? Hehenberger et al. instead proposed the retention of non-photosynthetic peridinin plastid. Regardless of whether Hehenberger et al. mentioned or not, Novák Vanclová et al. propose that RDS never possessed a stable fucoxanthin plastid because, if I understand correctly, they detected no or few haptophyte-derived RDS genes for plastid-targeted proteins of which origins are shared with those of Karlodinium, Karenia, and Takayama. What about the possibility that the last common ancestor of kareniacean dinoflagellates possessed a fucoxanthin plastid in addition to peridinin plastid followed by almost complete losses of those haptophyte-derived genes after loss of a fucoxanthin plastid in evolution leading to RSD? Free living eukaryotes were appeared to have lost a plastid in recent studies and they have only a few or no genes showing evidence of a plastid previously retained. We cannot rule out that an ancestor of kareniacean dinoflagellates possessed both of peridinin and fucoxanthin plastids, as the authors mention in the main text, and either plastid was inherited to each lineage by differential losses. Accordingly, I would say Fig. 7 is a too much strong proposal as alternative hypotheses are still present. They should be introduced equally. 7. rRNA copy numbers in dinoflagellates It is known that the rRNA gene copy number varies among populations or strains in dinoflagellates; some possess several dozens of times as many rRNA gene copies as others (Galluzzi et al. 2010). Is it informative to see the ocean wide rRNA gene amplicon data for the kareniacean dinoflagellates? The numbers of rRNA gene-derived reads would not necessarily reflect the cell abundance of dinoflagellates.

Galluzzi et al. 2010. Analysis of rRNA gene content in the Mediterranean dinoflagellate Alexandrium catenella and Alexandrium taylori: implications for the quantitative real-time PCR-based monitoring methods. J Appl Phycol 22:1-9

Minor points

1. the dataset size for the 241 protein-based host phylogeny should also be described in the main text.
2. The authors mention in Discussion "Thus, our results illuminate the mechanistics of a fundamental process that may under pin vast tracts of chloroplast evolution". If I understand correctly, I think this is based on "shopping bag model" when considering plastid replacements in dinoflagellates. It is helpful to add more details to clarify why the authors would like to claim so. "Chloroplast" should be replaced with "plastid".
3. Supplementary document S6.6 I found the term nitrogen fixation, but should this be replaced with "nitrogen assimilation"?
4. Figure S5 For those LGTs, all the trees should be shown in supplementary text as they are only 11 or 12 trees. Especially, please add the chlorophyllide b reductase and chlorophyllase in the figure.
5. References I am not picky about a format of the reference list, but I think it should be consistent throughout the list. I recommend adding journals, volumes, and pages precisely for cited papers. I found lack of them at least in Novak Vanclova et al. and Pierella Karlusich et al.
6. Figures In figure 3, I strongly recommend adding RDS data, while distinguishing them by another color if they are derived from different origins from those of Karenia, Karlodinium, and Takayama. This would make the authors claim clearer that there are few haptophyte-derived genes for plastid targeted proteins of which origins are shared with those of the other kareniacean dinoflagellates. In figures S5.1-2 showing LGTs, I found two paralogs of kareniacean dinoflagellates. What does "CP" mean? If "CP" means ChloroPlast-targeted, both paralogs of K. brevis in HARS and those of K. micrum are of plastid-targeted in TARS and they do not have cytosolic ones. I am afraid if these cases are caused by false positives of detection for plastid-targeted proteins by PredSI and ChloroP. Similarly, in figure S5.4, I found two distant paralogs of heam oxygenase in the tree and the taxon names for both types in kareniaceans include "CP." Are both targeted to the plastids or of false positives?

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?

This study by Novak Vanclova et al. provide new transcriptome datasets from multiple species in kareniacean dinoflagellates including harmful and toxic species. Their transcriptome datasets would help understand their biology, evolution, and ecology. The authors also provide a program that predicts plastid proteomes in those dinoflagellates, which would be useful for future studies to focus on kareniacean dinoflagellate plastids, after further refinement. The most important aspect of this study is that many plastid-targeted proteins might be derived from a particular haptophyte lineage, although it is still not sure whether they are derived from LGTs or EGTs. Phylogenetic analyses performed in this study should be improved by adding some plastid genomes, in order to gain more conclusive results. In addition to methods, interpretation of the current results and proposals on plastid evolution should be toned-down.

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,...).

Although there are technical issues, this study improves our conceptual understanding the plastid proteome evolution in Kareniacean dinoflagellates. The plastid proteomes are comprised of proteins with more various origins in those dinoflagellates, suggesting more complex plastid proteome evolution than previously thought.

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 seems to be "basic research".

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.

algal evolution, eukaryotic evolution, mitochondrial metabolisms, plastid metabolisms, phylogenomics

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 well done, detailed bioinformatic analysis of genomic and transcriptomic data from an important lineage of dinoflagellates that have undergone serial substitution of their plastid. On the whole I am enthusiastic about the paper; it presents valuable new insights, and is rigorously performed. However, I have to object to the way the term "proteome" is used in the paper; the manuscript is talking about the predicted proteome, not a measured proteome. This is something of a technical distinction, but it is an important one because the transcriptome and the proteome don't necessarily track each other, and there is little or no actual proteomic data available from dinoflagellates. We assume that transcript abundance has something to do with proteome abundance, but this is often violated. What this paper is really addressing is the potential proteome, because if a given gene is completely absent from the genome and the transcriptome we can be confident it will not be present in the proteome. The converse is not true. For this reason I feel it is important to be clear on the distinction. I would be satisfied in this regard by minor modifications, using the term "predicted proteome" in the title, and being more direct in the introduction about the distinction.

Overall the analyses are impressive. I do have to squirm a little when I see automated analyses generating alignments where the threshold is less than 75% gaps and at least 100 nucleotides aligned. I looked at the supplementary data and the figshare files and could not find the alignments themselves, so I don't know what fraction of the sequences are in that territory. Because phylogenetic analysis (as performed here) treats the alignments as an observation, and because the alignments include sequences with more than 50% gaps, it is entirely possible that some taxa, or even whole segments of the tree, are based on non-overlapping data.

Mind you, we have done similar analyses, and I don't think this invalidates the results, but it does open up the possibility of some dramatic artifacts. Consequently, I would recommend a) making the alignments available (or more obvious where to find them), and b) providing more detail on the alignments, including, if possible, to add a figure (probably in the supplementary data) that visualizes them. It is not given in the text itself, but according to the figure 2 caption there are 22 sequences thought to be "plastid late", and 241 in the pan-eukaryotic dataset. This is a scale that is feasible to put in a figure showing, for example, each aligned residue as a color and indels as grey. Such a figure is readable even when the individual residues are only a few pixels in size (less than a millimeter when printed). I also recommend describing the final alignments more fully in the text. Most of the summary statistics are presented in normalized form, and that can obscure patterns that come from poorly sampled taxa.

Better clarify on the characteristics of the alignments will make it easier to interpret the findings overall. Although this is critical to interpreting the results, gappy alignments are not uncommon in analyses of this sort, and setting that aside the analyses presented are comprehensive and thorough. The discussion does a good job of addressing the significance of the work, and potential causes of error are addressed adequately (aside from the matter of the alignments).

Significance

I find the paper to be exciting and important. These organisms are economically important, particularly as potential nuisance organisms, but also because of their role in primary productivity. They also have extremely complex evolutionary histories and similarly complex genomes. performing any bioinformatic analysis of these organisms is a substantial challenge because almost every gene exists in high copy number and with complex and often obscure patterns of homology. The manuscript brings forward these challenges, and makes a substantial step forward in elucidating the evolution of a group that is fascinating and important, but remarkably difficult to work with. I feel that it is an important analysis, and should be of interest to a broad audience.

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

The manuscript investigated the composition of the plastid proteomes of seven distantly-related kareniacean dinoflagellates, including newly-sequenced members of three genera (Karenia, Karlodinium, and Takayama). Using a custom plastid-targeting predictor, automatic single-gene tree building and phylogenetic sorting of plastid-targeted proteins for plastid proteome construction, the authors suggest that the haptophyte order Chrysochromulinales is the closest living relative of the fucoxanthin plastid donor. Interestingly, the N-terminal targeting sequences of kareniacean plastid signal peptides, reveal a high sequence conservation. Moreover, ecological and mechanistic factors are suggested that may have driven the endosymbiotic acquisition of the fucoxanthin plastid. Overall, this is a comprehensive and interesting analysis.

Other comments.

1. For analyses of N-terminal targeting sequences, why did the authors not consider to employ Predalgo as an additional tool?
2. Given the fact that peridinin or fucoxanthin pigment binding is in the focus of the paper, a more detailed introduction of the peridinin and fucoxanthin light-harvesting systems should be given.
3. The authors state "It is also possible that there has been a direct niche competition between the peridinin and fucoxanthin plastid that may have coexisted in the same host for a period of time with possibly different selective pressure on retention of their respective proteins based on their interaction with plastid-encoded components, e.g., extrinsic photosystem subunits not assembling correctly with their intrinsic haptophyte-like counterparts." It is tempting to ask, whether peridinin light-harvesting systems have left traces in the fucoxanthin plastid, possibly due to mistargeting of peridinin light-harvesting systems into the fucoxanthin plastid? Are some photosynthetic subunits "in-between" peridinin and fucoxanthin plastids?
4. Figure 3 is difficult to understand, e.g. for PSI and PSII which subunits are shown, why has PSI "more" contribution from dinoflagellates as compared to PSII?
5. Data shown in figure 4, is there experimental evidence for signal peptide cleavage site(s). Could these data been used to predict mature plastid targeted protein sequence?
6. The authors state "Partial Least Square (PLS) analysis shows a set of environmental variables (salinity, silicate, iron) positively correlated with abundances of both Karenia and Takayma and also haptophytes as a whole, but at the same time negatively correlated to Karlodinium (Figure S8), further illustrating that the latter genus is quite distant from the rest in its biogeographical pattern." How could this be interpreted in the light of the plastid proteomes?

Significance

The current manuscript gives insights into the endosymbiotic acquisition of the fucoxanthin plastids.

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

Learn more at Review Commons

---

General Statements

Ezrin, Radixin, and Moesin (ERMs) serve as crucial cytoskeletal linker proteins, connecting the actin cytoskeleton to the plasma membrane upon activation. ERMs are essential regulators of cell morphogenesis across every cell types reported so far, and have been implicated in vital cellular functions such as migration, and invasion. In our study, we discovered that ERMs are dispensable for the cortical organization of macrophages. In accordance with this surprising finding, we found that the migration of macrophages was not affected upon knock-out of the three ERMs. Our findings challenge the prevailing belief that ERMs universally regulate cortical organization. Instead, they indicate that the actin cortex of macrophages has evolved to possess a high degree of adaptability and plasticity, enabling these immune cells to function independently of ERM proteins.

We thank the editors of Review Commons that handled our manuscript and all three reviewers for their positive assessment of our manuscript and for their constructive suggestions.

Description of the planned revisions

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

In the manuscript, the authors systematically test the role of ERM proteins in macrophages using RNA silencing as well as the genetic knockout approaches. Previous studies have highlighted the fundamental importance of ERM proteins as structural and regulatory components of the cell cortex governing several essential functions such as the generation of surface features such as filopodia, maintenance of cortex-plasma membrane attachment, bleb retraction, cortical mechanics, and cell migration. The authors performed a series of experiments to comprehensively test each of these functions (including cell migration in 2D surface and 3D matrix in vitro, ex vivo on tumor implants, as well as in vivo) and found that none of these are significantly affected when ERM proteins are downregulated in macrophages. Overall, the paper is solid, the experiments are well-designed and conclusive, and the manuscript is written well.

We thank the reviewer for these encouraging comments.

I have no significant concerns with the study. My only experimental suggestion is related to a previously shown function of ERM protein in macrophages- the ERM proteins play an important role in phagosome maturation in macrophages (Defacque et al., EMBO, 2000; Lars-Peter et al., PNAS, 2006; Mylvaganam et al., Current Biology, 2021). It would be nice if authors could explore this phenotype in their perturbation system.

We thank the reviewer for this valuable suggestion. ERM proteins have indeed been proposed as important for macrophage phagocytosis. Importantly, their necessity for the early steps of this process is still debated, as conclusions differ depending on the cellular model used and the type of particle to be internalised (Erwig et al., PNAS USA 2006; Di Pietro et al., Sci. Rep. 2017; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018; Mu et al., Nat. Commun. 2018; Okazaki et al., J. Physiol. Sci. 2020). While the implication of ERMs in the early steps of phagocytosis remains controversial, there seems to be a consensus to implicate ezrin and moesin in phagosome maturation (Defacque et al. EMBO 2000 ; Erwig et al., PNAS USA 2006; Marion et al., Traffic 2011; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018).

We have already started addressing the ability of ERM-depleted macrophages to perform phagocytosis. In particular, we quantified the dynamics of phagocytosis of ovalbumin-coated or IgG-opsonized polystyrene beads, which did not reveal any difference between WT and ERM-depleted macrophages.

Proposed revision: We propose to include in the manuscript our quantification of IgG-coated and non-coated phagocytosis, and evaluate whether phago-lysosome fusion is delayed in ERM-depleted macrophages.

A minor concern with the study is, as the authors have already pointed out, that ERM proteins may still be required for some functions in macrophages under specific (environmental?) conditions. It is of course impossible to experimentally test all possible conditions that may involve ERMs, however, the authors should include a note on the hypothetical conditions that may require ERMs in macrophages. They should also discuss possible hypothetical reasons why macrophages may have evolved a cortex that does not rely on ERM proteins for specific functions. Overall, a more extended discussion on the role of ERM proteins (or the lack of them) in macrophages is required.

As suggested, in the revised version of the manuscript we will add a more extensive discussion of the role of ERM proteins in macrophages, and in particular the hypothetical conditions that might require their presence, as well as the reasons why macrophages have developed a particular cortex.

Reviewer #1 (Significance (Required)):

The manuscript is important on many accounts: The ERM proteins are considered crucial membrane-cytoskeletal linkers in many cellular systems. The study presents a surprising finding that cortical phenomena requiring membrane-cytoskeletal attachment do not essentially need ERM proteins providing a fundamental conceptual advance. The results from this study will also inform both experimental as well as theoretical studies of cortical organization and dynamics in the future. Furthermore, overexpressed mutant forms of ERMs are used as sensors as well as perturbing agents of cortical actin dynamics in many cellular systems. These utilities can now be further substantiated and if required, revised in light of the results from this study.

I am an immune cell biologist specializing in early lymphocyte activation and cytoskeleton dynamics.

We would like to thank the reviewer for pointing out the importance of our work for our understanding of the function of the cellular cortex, and for highlighting the fact that it may lead to a reinterpretation of the results obtained using ERM mutants.

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

Summary ERM proteins are known to play a central role in linking the cortical actin cytoskeleton with the plasma membrane, which is involved in regulating a diverse range of actin-rich membrane structures. The authors question the role that ERM proteins play in regulating cell shape changes and migration, specifically in macrophages. To test this, they designed an approach to systematically delete each ERM in macrophages - followed by the production of a triple-ERM ko line (tKO) using HoxB8 myeloid progenitor cells. The tKO line was subjected to a series of in vitro and in vivo experiments - all of which involve a series of imaging techniques to monitor membrane dynamics, protein subcellular organisation and cellular behaviours (e.g. rolling fraction, sticking fraction and chemotaxis). Their overall conclusion is that ERM are dispensable for macrophage membrane structures and migration.

General comments.

The experiments are very well executed. The manuscript in short demonstrates that the ERM proteins are dispensable for macrophage migration (both in 2D and 3D contexts), but there is very little beyond this work that points to what they might be doing instead. In this regard, given that the focus is exclusively on macrophage migration, the work comes across as quite specialised.

We thank the reviewer for appreciating the quality of our work.

We respectfully disagree to their assessment of the limited scope of our findings. Given the crucial importance of the migration of macrophages for so many of our body's functions, our findings will have a wide-ranging impact. Furthermore, and as acknowledged by Reviewers 1 and 3, we believe that the discovery that ERMs do not play a universal role in cortical mechanics and in cell migration, as hitherto believed, reaches a much wider scientific audience than that of the macrophage field. By proposing a unique research model (a triple KO for ERMs), our work allows to question many studies carried out with less direct molecular tools, such as the use of drugs or mutants of ERMs.

We acknowledge the fact that although our data convincingly demonstrate that the ERM proteins are dispensable for macrophage migration, they do not reveal alternative functions for these proteins. We agree that it could be interesting to search for alternative functions for ERM proteins in macrophages in future studies. However, we believe that such studies are out of the scope of the present manuscript.

The biggest concern I have is with the in vivo part. It should be noted that the work outlined in the manuscript does not actually address diapedesis, which is monitoring transmigration from the blood into tissue. Rolling and sticking do not define diapedesis. The experiments that the authors have conducted may have captured diapedesis events, but that very much depends on the length of time that the IVM was conducted. The authors would need to qualify their claims in this regard. Removing this work altogether would not lessen the impact, given that diapedesis is not shown. The work would therefore be very much in vitro/ex vivo.

We agree with the reviewer that, due to technical limitations, we only measured the rolling and sticking capacity of the +/-ERM cells and did not measure diapedesis directly. Following Reviewer's comments, we have thus modified the text of the manuscript and no longer use the term ‘diapedesis’ to describe our in vivo intravital imaging studies.

We also clarified the fact that we did not inject differentiated macrophages into the circulation, but macrophage precursors obtained by the treatment of progenitors with a 1 day treatment only (and not a 7 day treatment) with 20 ng/mL M-CSF.

Here, highlighted in yellow, are the changes to the text (in the Introduction, Results, Methods and Legends sections):

Introduction, p3:

“Surprisingly, we found that ERMs are dispensable for macrophages to migrate in diverse contexts, including in vitro 2D migration and 3D invasion of extracellular matrix, ex vivo tissue infiltration through healthy dermis and tumor tissue, and for the in vivo adhesion of macrophage precursors to an activated endothelium.”

Results, p6:

“ERM tKo cells without ezrin, radixin, and moesin exhibit no impairment in their ability to adhere to vascular endothelium in vivo and infiltrate the ear derma or fibrosarcoma.

To further investigate the migratory properties of ERM-deficient cells in vivo, we first assessed their ability to adhere to activated vascular endothelium into mice bearing a fibrosarcoma (Gui et al., 2018).”

Results, p8:

“Our study uncovered a surprising finding: ezrin, radixin and moesin are dispensable for key aspects of macrophage behavior, including the formation of lamellipodia and filopodia, the dynamics of membrane ruffles and podosomes, migration in vitro (in 2D or 3D matrices) and ex vivo (into dermis or tumor tissues) as well as for the in vivo adhesion of macrophage precursors to activated vascular endothelium).”

Methods, p14:

“In vivo analysis of adhesion to vascular endothelium with wide-field intravital microscopy”

And

“HoxB8-progenitors were directed towards monocyte/macrophage differentiation using a 1 day treatment with 20 ng/mL mouse M-CSF.”

Figure 4 legend, p33:

“Fig. 4: ERM tKO cells have no defect in adhesion to vascular endothelium in vivo and infiltrate tissues explants ex vivo

1. In vivo adhesion to vascular endothelium

Fibrosarcoma cells were injected into the flank of a mice. After a week, tumor was exposed for intravital microscopy, and the femoral artery of recipient mice was catheterized for injection of exogenous cells. Differentially labeled WT and TKO-ERM macrophage precursors were injected in the blood and their behaviour in tumor blood vessels was assessed by real-time imaging. Rolling fractions were quantified as the percentage of rolling cells in the total flux of cells in each blood vessel, and sticking fractions were quantified as the percentage of rolling cells that firmly adhered for a minimum of 30 seconds.”

Proposed revision: We propose to keep the results of the in vivo experiments in the manuscript, including the modifications proposed by the reviewer and listed above.

Specific questions

How sure are the authors that they are capturing these events in cremasteric venules?

As described in the Results and Methods section, these measurements were not captured in cremasteric venules but in fibrosarcoma tumour blood vessels, where we have previously demonstrated strong recruitment of circulating monocytes to infiltrate tumor tissue (Gui et al., Cancer Immunol. Res. 2018).

Is there any sign of cells being trapped in the microcirculation?

The diameter of the tumor blood vessels analysed is consistent with tumor post-capillary venules, and we have not seen cells trapped in these tumor blood vessels.

The reason for injecting macrophages intravenously is not explained.

We injected cells intravenously in order to compare their capacity to adhere to activated tumor blood vessels by intravital microscopy. This is now clarified in the corresponding result section (p6):

“For that purpose, one day differentiated wild-type or ERM-deficient cells were fluorescently labelled with two different cell trackers, mixed in a 1:1 ratio, and co-injected intra-arterially into recipient mice in order to analyse their behaviour in tumor blood vessels by intravital microscopy.”

Are these experiments modelling intravascular (patrolling) macrophages? Monocytes will typically differentiate into macrophages in tissue.

We again apologize for the lack of clarity. In these experiments, we did not inject fully differentiated (seven days) macrophages but progenitors directed towards monocyte/macrophage differentiation using a 1 day treatment with 20 ng/mL mouse M-CSF. We believe that these experiments model the adhesion/recruitment of monocytes by activated vascular endothelium in the tumor microenvironment.

The fact that the cells are able to "roll" and "stick" suggests that they have the complimentary cell adhesion molecules, although this is not addressed in the study.

We agree with the reviewer. Our intravital microscopy analyses indicate that the injected cells have the complementary cell adhesion molecules for firm adhesion to activated tumor blood vessels. Importantly, our data clearly demonstrate that the capacity of ERM-tKO cells to bind vascular endothelium in the tumor microenvironment is similar to that of WT cells (Fig. 4A).

Reviewer #2 (Significance (Required)):

The strength of the manuscript is based on the robust in vitro experiments, however such experiments are difficult to address in vivo - mainly because of the issue that macrophages (unless patrolling macrophages) are not a useful model to investigate for ivm experiments.

We thank the reviewer for recognizing the robustness of our in vitro experiments. We fully agree with the reviewer that the in vivo experiments are more challenging and that the behaviour of monocytes/(patrolling) macrophages is difficult to mimic in vivo. However, we believe that our intravital microscopy analyses are important because they demonstrate that ERM-tKO cells retain the capacity to bind firmly (sticking) to activated tumor blood vessels in vivo.

This would be of great interest to the macrophage field, which is quite limited in scope. An advancement in the field would be to learn what is taking over the role of ERM in macrophages. As such, this becomes a report with a series of experiments to confirm that ERM are not involved.

Again, we respectfully disagree with the reviewer, as this work goes against the dogma that ERMs are generally the most important mechanical links between the plasma membrane and the cytoskeleton. By clearly establishing that this is not the case in macrophages, cells whose importance for our immunity justifies the importance of their investigation, this study could make it possible to reconsider the functioning of the cellular cortex and the role of ERMs in other cellular systems.

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

Verdys and colleagues report an elegant study in which the authors describe that ERM proteins are dispensable for migrating monocyte-derived macrophages. The methods are adequate and the results support the conclusions.

We thank the reviewer for these very supportive comments.

Major points:

- Although the authors demonstrate, by multiple methods, the dispensability of ERM proteins in the migration of macrophages derived from monocytes, the role of these proteins must also be evaluated in the phagocytosis process (another relevant functional aspect of macrophages).

This is an excellent suggestion, which should make it possible to clarify the role of ERMs in this important function of macrophages.

ERM proteins have indeed been proposed as important for macrophage phagocytosis. Importantly, their necessity for the early steps of this process is still debated, as conclusions differ depending on the cellular model used and the type of particle to be internalised (Erwig et al., PNAS USA 2006; Di Pietro et al., Sci. Rep. 2017; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018; Mu et al., Nat. Commun. 2018; Okazaki et al., J. Physiol. Sci. 2020). While the implication of ERMs in the early steps of phagocytosis remains controversial, there seems to be a consensus to implicate ezrin and moesin in phagosome maturation (Defacque et al. EMBO 2000 ; Erwig et al., PNAS USA 2006; Marion et al., Traffic 2011; Gomez and Descoteaux, Biochem. Biophys. Res. Commun. 2018).

We have already started addressing the ability of ERM-depleted macrophages to perform phagocytosis. In particular, we quantified the dynamics of phagocytosis of ovalbumin-coated or IgG-opsonized polystyrene beads, which did not reveal any difference between WT and ERM-depleted macrophages.

Proposed revision: We propose to include in the manuscript our quantification of IgG-coated and non-coated phagocytosis, and evaluate whether phago-lysosome fusion is delayed in ERM-depleted macrophages.

• How is the activation of key downstream targets of ERM proteins involved in macrophage migration in KO models?_

This is a very pertinent question. However, while ERMs have been described as being downstream of several signalling pathways, their own downstream targets are unfortunately poorly documented and, to our knowledge, none are known in macrophages.

In different cellular contexts, it has been proposed that ERMs regulate PI3K (Gautreau et al. PNAS USA 1999), Ras (Sperka et al. Plos One 2011) or that they are involved in the initiation of protein translation (Briggs et al. Neoplasia 2012), but these results have not yet been confirmed and we believe they are outside the scope of this study.

During macrophage migration, we consider that their obvious main target is cortical actin, and demonstrate in this manuscript that the functional coupling between actin and the plasma membrane is not affected by full ERM knockout.

Reviewer #3 (Significance (Required)):

Advance: The present study fills a gap in the participation of ERM proteins in cell migration. The results obtained on the dispensability of these proteins in macrophage migration can pave avenues for identifying new processes and proteins associated with migration in this context.

Audience: The audience for this study is very broad.

We again thank the reviewer for recognising the importance of this work for the understanding of cell migration.

My expertise: I have expertise in cellular and molecular biology with a focus on processes associated with cancer. Among the numerous research fronts of the group led by me, we recently identified the EZR gene (which encodes the ezrin protein) as a prognostic marker and molecular target in acute leukemias.

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

In the revised version of the article, we have taken into account all relevant changes proposed by the reviewers. We modified the text of the manuscript and no longer use the term ‘diapedesis’ to describe our in vivo intravital imaging studies, and clarified the fact that we did not inject differentiated macrophages into the circulation, but macrophage precursors obtained by the treatment of progenitors with a 1 day treatment only (and not a 7 day treatment) with 20 ng/mL M-CSF.

Here, highlighted in yellow, are the changes to the text (in the Introduction, Results, Methods and Legends sections):

Introduction, p3:

“Surprisingly, we found that ERMs are dispensable for macrophages to migrate in diverse contexts, including in vitro 2D migration and 3D invasion of extracellular matrix, ex vivo tissue infiltration through healthy dermis and tumor tissue, and for the in vivo adhesion of macrophage precursors to an activated endothelium.”

Results, p6:

“ERM tKo cells without ezrin, radixin, and moesin exhibit no impairment in their ability to adhere to vascular endothelium in vivo and infiltrate the ear derma or fibrosarcoma.

To further investigate the migratory properties of ERM-deficient cells in vivo, we first assessed their ability to adhere to activated vascular endothelium into mice bearing a fibrosarcoma (Gui et al., 2018). For that purpose, one day differentiated wild-type or ERM-deficient cells were fluorescently labelled with two different cell trackers, mixed in a 1:1 ratio, and co-injected intra-arterially into recipient mice in order to analyse their behaviour in tumor blood vessels by intravital microscopy.”

Results, p8:

“Our study uncovered a surprising finding: ezrin, radixin and moesin are dispensable for key aspects of macrophage behavior, including the formation of lamellipodia and filopodia, the dynamics of membrane ruffles and podosomes, migration in vitro (in 2D or 3D matrices) and ex vivo (into dermis or tumor tissues) as well as for the in vivo adhesion of macrophage precursors to activated vascular endothelium).”

Methods, p14:

“In vivo analysis of adhesion to vascular endothelium with wide-field intravital microscopy”

And

“HoxB8-progenitors were directed towards monocyte/macrophage differentiation using a 1 day treatment with 20 ng/mL mouse M-CSF.”

Figure 4 legend, p33:

“Fig. 4: ERM tKO cells have no defect in adhesion to vascular endothelium in vivo and infiltrate tissues explants ex vivo

1. In vivo adhesion to vascular endothelium

Fibrosarcoma cells were injected into the flank of a mice. After a week, tumor was exposed for intravital microscopy, and the femoral artery of recipient mice was catheterized for injection of exogenous cells. Differentially labeled WT and TKO-ERM macrophage precursors were injected in the blood and their behaviour in tumor blood vessels was assessed by real-time imaging. Rolling fractions were quantified as the percentage of rolling cells in the total flux of cells in each blood vessel, and sticking fractions were quantified as the percentage of rolling cells that firmly adhered for a minimum of 30 seconds.”

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 #3

Evidence, reproducibility and clarity

Verdys and colleagues report an elegant study in which the authors describe that ERM proteins are dispensable for migrating monocyte-derived macrophages. The methods are adequate and the results support the conclusions.

Major points:

• Although the authors demonstrate, by multiple methods, the dispensability of ERM proteins in the migration of macrophages derived from monocytes, the role of these proteins must also be evaluated in the phagocytosis process (another relevant functional aspect of macrophages).
• How is the activation of key downstream targets of ERM proteins involved in macrophage migration in KO models?

Significance

Advance: The present study fills a gap in the participation of ERM proteins in cell migration. The results obtained on the dispensability of these proteins in macrophage migration can pave avenues for identifying new processes and proteins associated with migration in this context.

Audience: The audience for this study is very broad.

My expertise: I have expertise in cellular and molecular biology with a focus on processes associated with cancer. Among the numerous research fronts of the group led by me, we recently identified the EZR gene (which encodes the ezrin protein) as a prognostic marker and molecular target in acute leukemias.

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

Summary

ERM proteins are known to play a central role in linking the cortical actin cytoskeleton with the plasma membrane, which is involved in regulating a diverse range of actin-rich membrane structures. The authors question the role that ERM proteins play in regulating cell shape changes and migration, specifically in macrophages. To test this, they designed an approach to systematically delete each ERM in macrophages - followed by the production of a triple-ERM ko line (tKO) using HoxB8 myeloid progenitor cells. The tKO line was subjected to a series of in vitro and in vivo experiments - all of which involve a series of imaging techniques to monitor membrane dynamics, protein subcellular organisation and cellular behaviours (e.g. rolling fraction, sticking fraction and chemotaxis). Their overall conclusion is that ERM are dispensable for macrophage membrane structures and migration.

General comments.

The experiments are very well executed. The manuscript in short demonstrates that the ERM proteins are dispensable for macrophage migration (both in 2D and 3D contexts), but there is very little beyond this work that points to what they might be doing instead. In this regard, given that the focus is exclusively on macrophage migration, the work comes across as quite specialised.

The biggest concern I have is with the in vivo part. It should be noted that the work outlined in the manuscript does not actually address diapedesis, which is monitoring transmigration from the blood into tissue. Rolling and sticking do not define diapedesis. The experiments that the authors have conducted may have captured diapedesis events, but that very much depends on the length of time that the IVM was conducted. The authors would need to qualify their claims in this regard. Removing this work altogether would not lessen the impact, given that diapedesis is not shown. The work would therefore be very much in vitro/ex vivo.

Specific questions

How sure are the authors that they are capturing these events in cremasteric venules? Is there any sign of cells being trapped in the microcirculation? The reason for injecting macrophages intravenously is not explained. Are these experiments modelling intravascular (patrolling) macrophages? Monocytes will typically differentiate into macrophages in tissue. The fact that the cells are able to "roll" and "stick" suggests that they have the complimentary cell adhesion molecules, although this is not addressed in the study.

Significance

The strength of the manuscript is based on the robust in vitro experiments, however such experiments are difficult to address in vivo - mainly because of the issue that macrophages (unless patrolling macrophages) are not a useful model to investigate for ivm experiments.

This would be of great interest to the macrophage field, which is quite limited in scope.

An advancement in the field would be to learn what is taking over the role of ERM in macrophages. As such, this becomes a report with a series of experiments to confirm that ERM are not involved.

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

In the manuscript, the authors systematically test the role of ERM proteins in macrophages using RNA silencing as well as the genetic knockout approaches. Previous studies have highlighted the fundamental importance of ERM proteins as structural and regulatory components of the cell cortex governing several essential functions such as the generation of surface features such as filopodia, maintenance of cortex-plasmamembrane attachment, bleb retraction, cortical mechanics, and cell migration. The authors performed a series of experiments to comprehensively test each of these functions (including cell migration in 2D surface and 3D matrix in vitro, ex vivo on tumor implants, as well as in vivo) and found that none of these are significantly affected when ERM proteins are downregulated in macrophages. Overall, the paper is solid, the experiments are well-designed and conclusive, and the manuscript is written well.

I have no significant concerns with the study. My only experimental suggestion is related to a previously shown function of ERM protein in macrophages- the ERM proteins play an important role in phagosome maturation in macrophages (Defacque et al., EMBO, 2000; Lars-Peter et al., PNAS, 2006; Mylvaganam et al., Current Biology, 2021). It would be nice if authors could explore this phenotype in their perturbation system.

A minor concern with the study is, as the authors have already pointed out, that ERM proteins may still be required for some functions in macrophages under specific (environmental?) conditions. It is of course impossible to experimentally test all possible conditions that may involve ERMs, however, the authors should include a note on the hypothetical conditions that may require ERMs in macrophages. They should also discuss possible hypothetical reasons why macrophages may have evolved a cortex that does not rely on ERM proteins for specific functions. Overall, a more extended discussion on the role of ERM proteins (or the lack of them) in macrophages is required.

Significance

The manuscript is important on many accounts: The ERM proteins are considered crucial membrane-cytoskeletal linkers in many cellular systems. The study presents a surprising finding that cortical phenomena requiring membrane-cytoskeletal attachment do not essentially need ERM proteins providing a fundamental conceptual advance. The results from this study will also inform both experimental as well as theoretical studies of cortical organization and dynamics in the future. Furthermore, overexpressed mutant forms of ERMs are used as sensors as well as perturbing agents of cortical actin dynamics in many cellular systems. These utilities can now be further substantiated and if required, revised in light of the results from this study.

I am an immune cell biologist specializing in early lymphocyte activation and cytoskeleton dynamics.

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

Learn more at Review Commons

---

Reply to the reviewers

We would like to thank all reviewers for their careful evaluation of our manuscript and their thoughtful feedback, which we could use to improve its quality significantly.

Reviewer #1 (Evidence, reproducibility and clarity):

Summary: This study addresses the problem of what is the optimal ribosome composition in terms of relative RNA and protein content, to ensure optimal growth rate and minimal energy waste. The RNA-world hypothesis suggests that primitive ribosomes were RNA-only objects, and in fact this would appear to be very advantageous from an energetic point of view, since RNA synthesis requires a much lower energy expenditure than protein synthesis. Yet a large fraction of present-day ribosome mass is protein, ranging from 30% to nearly 70% depending on the organism. The authors hypothesize that one of the main functions of ribosomal proteins is to stabilize the RNA and to protect it against degradation. According to their idea, the fast degradation of a protein-free rRNA would offset the energetic advantage given by its cheaper synthesis. To test the hypothesis, they developed a mathematical model whereby to evaluate the optimal ribosome composition under a number of different conditions.

Major comments: The paper is well-written and very readable. I am not an expert of mathematical modelling, so I cannot go into the details of the model presented. As a biologist, I can say that the conclusion arrived at are reasonable and well-justified.

We thank the reviewer for the positive evaluation.

Perhaps the point of view is rather narrow, since ribosomal proteins are known to be important not only for RNA protection and ribosome stability, but also to ensure the accuracy of decoding and, in certain contexts, to allow the ribosomes to interact with other cellular ligands. The authors make only very slight reference to these questions, so it would be worthwhile to further comment on them.

Thank you for your suggestion. To address it, we expanded the discussion as follows:<br /> "Finally, we need to consider that ribosomal proteins may play other roles in the cells, especially in eukaryotic organisms. Ribosomal proteins participate in translation processes, for example, binding of translation factors, release of tRNA, and translocation. They may also affect the fidelity of translation (Nikolay et al., 2015). Furthermore, they play roles in various cellular processes such as cell proliferation, apoptosis, DNA repair, cell migration and others (Kisly and Tamm, 2023). These additional functions might have conferred evolutionary fitness advantages. Nevertheless, the primary role of ribosomal proteins seems to be stabilization and folding of rRNA (Nikolay et al., 2015; Kisly and Tamm, 2023)."

Furthermore, their explanation of why ribosome composition should be so different in different organisms (e.g. protein-poor bacterial ribosomes versus protein-rich archaeal ones) is not entirely convincing. For instance, they suggest that archaea may have protein-richer ribosomes than bacteria because they live in extreme environments, thus needing a further aid to stabilize the organelle. While this may be a factor, one must point out that non-extremophilic archaea (e.g. methanogens) have protein-rich ribosomes, making it obvious that other factors must be at play.<br />

We appreciate the reviewer's feedback. Ribosome composition is indeed complex and influenced by various factors. While extreme environments (may) contribute to protein-rich ribosomes in archaea, it's important to note that not all archaea share this characteristic. Some, like Halobacteriales, Methanomicrobiales, and Methanobacteriales, have ribosomes with protein content similar to bacteria.

Furthermore, there are species in both archaea and bacteria with low protein content in their ribosomes despite extreme habitats. This suggests that alternative strategies, possibly involving specific sequence variants in the rRNA (Nissley et al., 2023), play a role in stabilizing ribosomes. In our model, these findings would correspond to a decreased kdegmax. However, these sequence variants are not universal.

Amils et al. (1993) suggest that protein-rich ribosomes in archaea are (more) ancient and proteins may have been lost in some species, possibly to favor higher growth rates (and in agreement with our theoretical analysis). An intriguing avenue for further research would be a phylogenetic analysis of archaeal evolution to investigate the emergence of different ribosome compositions.

To address your concerns, we added the following paragraph to the discussion:<br /> "Additionally, some extremophilic organisms, such as the bacteria Chloroflexus aurantiacus or Fervidobacterium islandicum, exhibit ribosomes with lower protein content (approximately 40%) compared to extremophilic archaea (50%). It has been suggested that protein-rich ribosomes can be traced back to the oldest phylogenetic lineages, with some ribosomal proteins being lost over time (Amils et al., 1993; Acca et al., 1993). Organisms with lower protein content in their ribosomes may have evolved alternative strategies to thrive in extreme conditions. Examples of such strategies include the presence of specific rRNA sequence variants or base modifications, as recently discussed by Nissley et al. (2023).

Moreover, certain archaeal species, such as those from Methanobacteriales or Halobacteriales, have transitioned to milder environmental conditions and subsequently shed unnecessary ribosomal proteins (Acca et al., 1993; Amils et al., 1993).

To gain a comprehensive understanding of ribosome evolution in response to changing conditions, a thorough phylogenetic analysis is warranted. This analysis should be complemented by measurements of growth rate, translation rate, RNA degradation rate, among other parameters, to delineate the order of protein loss or gain, and the emergence of sequence variations and base modifications."

Minor comments: none in particular. Referencing is adequate, text is clear and the figures are clear and well-organized.

Thank you.

Reviewer #1 (Significance):

As I stated above, the main weakness of this study may be that it concentrates overwhelmingly on a single problem, i.e. the energetic cost of adding proteins to an RNA-only ancestral ribosome. On the other hand, this is a question seldom addressed when talking about ribosome composition, which indeed makes this paper valuable and interesting. The authors expand and advance a previous study of the same kind (to which they make ample reference).

Although rather specialized, I think this paper, in its general conclusions, may be of interest to most of those working in the field of protein synthesis and ribosome evolution.

Referee's keywords: archaea, ribosome evolution, translation, translation initiation

Reviewer #2 (Evidence, reproducibility and clarity):

The authors explore a mathematical model to rationalize the variable RNA content in ribosomes across species. The mathematical model particularly considers the idea that the protein-to-RNA ratio in ribosomes emerges as a consequence of faster rRNA than r-protein synthesis coupled with a faster degradation of rRNA. This is an interesting analysis. The idea is well explained and the math of the model is overall well explained. Overall, I thus support publication of this analysis.

We thank the reviewer for the positive evaluation.

However, while reading the manuscript I was continuously wondering about two major aspects which, I suggest, should be considered more prominently in the text:

1. How clear is it that rRNA is more unstable than r-protein?
2. Why should the translation rate (the speed with which ribosomes assemble new proteins) not be highly dependent on the ribosome-to-protein ratio (with some intermediate ratio ensuring efficient synthesis and efficient translation?

Currently these points are considered briefly in the discussion part. I suggest that these points should at least be discussed more prominently in the introduction. I further appreciate any more detailed thoughts the authors have on these questions.

Finally, I think the discussion section would benefit strongly from a more detailed consideration of possible future experiments. Which data is needed to probe the idea? What types of experiments could be performed to probe the model.

We added a paragraph to the discussion with suggestions for experiments:<br /> "There are still many open questions about ribosome biogenesis and evolution. Our model could guide future experiments. There are a few studies that assessed the effect of individual rP deletions in E. coli, for example mutation in S10 increased RNA degradation (Kuwano et al., 1977), and mutation in L6 lead to disrupted ribosomal assembly (Shigeno et al., 2016). A systematic knock-out screen of all ribosomal proteins could be done (as in Shoji et al. (2011)), complemented with quantification of RNA degradation and misfolding.

In case of extremophilic organisms with protein-rich ribosomes, temperature sensitivity could also be assessed. We would expect that deletion of the extra proteins would cause growth defects only at high temperatures.

Furthermore, after removal of proteins from archaeal protein-rich ribosomes, laboratory evolution could be performed to see whether growth rate increases beyond wild-type.

Comprehensive datasets, akin to the work of Bremer and Dennis in 2008 for E. coli, should be generated for non-standard organisms by measuring various parameters such as transcription and translation rates, ribosome and RNAP activities, and other relevant factors.

Finally, as mentioned earlier, phylogenetic analysis or ribosome evolution across different species and environments could be done."

More detailed comments:

Regarding i: rRNA is pretty stable compared to other RNA types in the cell. The authors argue it is unstable. The specific question then seems to become how stable rRNA is compared to r-protein? Generally, proteins are also stable, but what data is available to support that r-proteins are more stable than rRNA?

While rRNA that is already integrated into a ribosome is stable, nascent RNA may be susceptible to degradation (Jain, 2018). It has been observed that even during exponential growth, some rRNA is degraded (Gausing, 1997; Jain 2018) and the degradation rate increases if ribosome assembly is delayed (Jain, 2018). This suggests that rRNA that is synthesized in excess cannot be stored and used later. Furthermore, when rRNA is overexpressed in excess of rPs, it is rapidly degraded (half life 15-70 min) (Siehnel and Morgan, 1985).

On the other hand, the turnover of proteins is negligible (Bremer and Dennis 2008), and most ribosomal proteins can exist in a free form without RNA. For example, under starvation/in stationary phase, rRNA is degraded, but most proteins are stable and can be reused later (Reier et al., 2022; Deutscher, 2003).

The precise mechanisms of the rRNA instability are not clear. The simplest explanation is that rRNA that is not protected by rPs is attacked by RNases. Another option is that rRNA without proteins is difficult to fold and can get trapped in misfolded states. These are then degraded as a part of quality control. The model developed in this paper allows for both of these mechanisms.

We added these references to the discussion:<br /> "In order to explain a mixed (RNA+protein) ribosome, we consider rRNA degradation in our extended model, thereby increasing the costs for RNA synthesis. While rRNA that is already integrated into a ribosome is stable, nascent RNA may be susceptible to degradation (Jain, 2018). Indeed, it has been experimentally observed that even at maximum growth rate, 10% of newly synthesized rRNA is degraded (Gausing, 1977), and the degradation rate increases if ribosome assembly is delayed (Jain, 2018). Furthermore, when rRNA is overexpressed in excess of rPs, it is rapidly degraded (Siehnel and Morgan, 1985). Due to the extremely high rates at which rRNA is synthesized, errors become inevitable, necessitating the action of quality control enzymes such as polynucleotide phosphorylase (PNPase) and RNase R to ensure ribosome integrity (Dos Santos et al., 2018). The absence of the RNases results in the accumulation of rRNA fragments, ultimately leading to cell death (Cheng and Deutscher, 2003; Jain, 2018).

In contrast, protein turnover is negligible (Bremer & Dennis, 2008), and most ribosomal proteins can exist without rRNA and can be reused (Reier et al., 2022; Deutscher, 2003). Therefore, we do not consider protein degradation in our model."

Regarding ii: Building on their model results, the authors rationalize the highly varying RNA-to-protein ratio in ribosomes across species. The model considers a non-varying rate with which ribosomes synthesize new proteins. This is briefly discussed in the discussion section. However, this appears to be a major assumption that, I think, should be stated clearly stated earlier in the text, including the abstract and introduction. Second, I wonder how the authors then rationalize variations in translation rate across species. Translation rates and the speeds with which ribosomes are varying strongly across species (indicated for example well by the change in the slope between ribosome content/rRNA and growth rate - slope in Fig. 2A). Why could the rRNA-to-protein ratio not be important in playing a role here?

We decided not to consider the effect of rRNA/protein ratio in ribosomes on translation rate mainly because it is not clear in what way it affects it. Proteins are better catalysts than rRNA. Yet, eukaryotic ribosomes which have higher protein content, have lower translation rates. For archaea and mitochondria, we were not able to find data but it is unlikely that the translation rates are faster because the growth rates are not faster.

We added a paragraph to the introduction that explains our assumption:<br /> "We focus on the primary role of ribosomal proteins, which is stabilizing rRNA (by preventing its degradation or misfolding).

Ribosome protein content might also affect other parameters, such as translation rate. Proteins are generally better catalysts than RNA (Jeffares et al., 1998), but the ribosome's catalytic core is formed by rRNA (Tirumalai et al., 2021) and operates at a relatively slow catalytic rate compared to typical enzymes. This suggests that there is little evolutionary pressure to increase the catalytic rate. Furthermore, ribosomes with the lowest protein content, like the E. coli ribosome, exhibit the highest translation rates (Bonven and Gulløv, 1979; Hartl and Hayer-Hartl, 2009; Bremer and Dennis, 2008). Therefore, we do not consider the impact on translation rate in this study."

And a sentence to the abstract:<br /> "In this study, we develop a (coarse-grained) mechanistic model of a self-fabricating cell and validate it under various growth conditions. Using resource balance analysis (RBA), we examine how the maximum growth rate varies with ribosome composition, assuming that all kinetic parameters remain independent of ribosome composition."

More minor point, but I was also not sure about the justification that ribosome mass is constant (line 111). The mass of an amino acid and a nucleotide is quite different. Why should overall mass matter, and not for example the number of amino acids and proteins. I think it also would be good here to motivate the assumption better early on instead of commenting on it in the discussion section.

Thank you for your suggestion. We agree with the reviewer that we should make our assumption of keeping the ribosome mass constant, which we used for simplicity, clearer from the beginning. Therefore, we have added the following statement to the introduction:<br /> "For simplicity, we assume a constant ribosome mass."

Reviewer #2 (Significance):

Protein synthesis by ribosomes is a major determinant of the rate with which microbes and other fast growing cells accumulate biomass. To better understand cell growth it is thus essential to better understand the makeup of ribosomes. Széliová et al present a mathematical model to entertain the idea that the varying RNA content in ribosomes across species is a consequence of RNA degradation. The model makes clear predictions which can guide future experiments.

Reviewer #4 (Evidence, reproducibility and clarity):

Summary

In this manuscript, Széliová et al. used a simple self-replicating cell model to study why the ribosome consists of both RNA and protein from an economic point of view. Their base model predicts an RNA-only ribosome, which is not surprising since the smaller RNAP has a higher turnover number compared to the larger ribosome. When rRNA instability is included, the model predicts an "RNA+Protein" ribosome. In particular, the predicted ribosome composition is comparable to the measured ribosome composition when strong cooperative binding of ribosomal proteins to rRNA is considered. The authors conclude that the maximal growth rate is achieved by the real ribosome composition when rRNA instability is taken into account.

Major comments:

1. The authors modeled the rRNA degradation rate as a function of the concentration of fully assembled ribosomes (equation 5). However, only partially assembled ribosomes are susceptible to RNase, and they make up only a small fraction of total ribosomes. The majority of ribosomes are fully assembled. In addition, the turnover number obtained from Fazal et al. (2015) and used here is the degradation rate of double-stranded RNA, not the fully assembled ribosomes, which have a stable tertiary structure. In my opinion, the rRNA degradation rate should be modeled as a function of the concentration of partially assembled ribosomes (i.e., pre-R in Figure 7) rather than the concentration of fully assembled ribosomes.

We agree with the reviewer that the way we model the process is not entirely biologically accurate. The problem is that even if we add the assembly intermediates, their concentration would be zero as they do not catalyze any reaction (similarly to the metabolites). Therefore, the degradation rate would also always be zero. Given the current modeling setup, the obvious proxy for the intracellular rRNA concentration is the rRNA concentration in the (assembled) ribosome, c_R*(1-x_rP).

1. Compared to the work by Kostinski and Reuveni (2020), the authors have made an improvement by avoiding the use of constant ribosome allocation to ribosomal protein (Φ_rP^R) and RNAP (Φ_RNAP^R), allowing these parameters to vary with predicted growth rates (by changing 𝑥_rP). This is indeed important, as bacteria are very likely to adjust these parameters in response to different growth conditions. However, certain other growth rate-dependent parameters are still treated as constants (or treated as nutrient-specific parameters) across predicted growth rates under given conditions. For example, experiments have shown that the fraction of active RNAP (f_RNAP^act) and the ribosome elongation rate (k_R^el) are growth rate-dependent (Bremer and Dennis, 1996). In contrast, when the authors predict the maximum growth rate by changing 𝑥_rP, f_RNAP^act and k_R^el are held constant regardless of the predicted growth rates.

The fraction of active RNAP (f_RNAP^act) was growth-rate dependent in all our simulations (see Table 2), only the fraction of active ribosomes (f_R^act) was kept constant according to Bremer and Dennis, 1996 & 2008.

We decided to keep the elongation rate (k_R^el) constant similar to Scott et al. 2010 (their explanation is in the supplementary material “Correlation [1] and the control of ribosome synthesis”).

We reran the simulations with variable k_R^el. It has no impact on the predictions of optimal ribosome composition. However, the linear dependence of RNA/protein ratio is less steep and predicts an offset at zero growth rate.

We added the results to the supplementary material and the following text to the results section (for the base model):<br /> "…the base model correctly recovers the well-known linear dependence of the RNA to protein ratio and growth rate (Scott et al. 2010), see Figure 2a, but not the offset at zero growth rate, since our model does not contain any non-growth associated processes and we assume constant translation elongation rate kelR as in Scott et al. (2010). At low growth rate, kelR decreases, most likely because of the lower availability of the required substrates (Bremer and Dennis, 2008; Dai et al., 2016). Interestingly, when we use variable kelR, we observe a nonzero offset (Appendix 1, Figure 2)."

and in a later section:<br /> "Using variable or constant kelR has no impact on the predicted optimal ribosome composition. As in the base model, variable kelR leads to predicted non-zero offset of RNA/protein ratio at zero growth rate (Appendix 1, Figure 6)."

1. _If amino acids or nucleotides are provided in the media, the cell does not have to synthesize all of them de novo. However, the model assumes that the cell always synthesizes all amino acids or nucleotides de novo for growth on growth on amino acid-supplemented media or on LB. This problem could in principle be solved by assuming very fast kinetics of the metabolic reactions in these media, but that should be discussed in the manuscript. Furthermore, why does the turnover number for EAA depend on the growth rate while that of ENT is constant?<br /> > _

We focused on the “enzyme” EAA because it forms a significant fraction of the proteome. However, for consistency, we now also made ENT turnover number depend on growth rate. It made no significant impact on the simulation results.

We agree with the reviewer that the model is currently very simplified and the enzymes ENT and EAA are used even in the media supplemented with AAs/NTs. However, these enzymes represent lumped pathways that aim to take into account not only AA/NT synthesis but also the different ‘nutrient efficiencies’ of the carbon sources (as in Scott et al. 2010). Therefore, to approximate these effects we increase the kcat of EAA (and now also ENT) with growth rate.

We added a paragraph to the results section to explain these simplifications:<br /> "We used parameters from E. coli grown in six different media. Three of them are rich media (Gly+AA, Glc+AA, LB) where amino acids (and nucleotides) are provided so cells only have to express the corresponding transporters instead of the synthesis pathways. In our model, the enzymes ENT and EAA represent lumped pathways for glycolysis and nucleotide / amino acid synthesis, and we only consider one type of transporter. Therefore, to model the changing `nutrient quality' of the different media (inspired by Scott et al. 2010), we assume that turnover numbers of EAA and ENT increase with growth rate."

1. All parameters related to transcription (RNAP) and translation (ribosome) used in this manuscript are adopted from Kostinski and Reuveni (2020), which are slightly modified based on Bremer and Dennis' research (1996, 2008). However, the authors changed some of the original parameters or data points, but did not provide explanations for these changes:

(a) The original data depicted a growth rate-dependent translation elongation rate, but Table 2 presents it as a constant value.

Please see the reply to point 2 above.

(b) Figure 2b displays five experimental data points, as opposed to the six data points in the original dataset and other figures in this manuscript.

The values for the transcription rate were taken from Bremer and Dennis’s paper from 1996 which only contains five growth rates. We updated the Figure 2b – it now displays data from Bremer and Dennis 2008 for six growth rates.

(c) The model does not consider the fraction of RNAP transcribing rRNA (Φ_rRNA^RNAP), except in Appendix Figure 4. In the original data (Bremer and Dennis 1996), the fraction of RNAP transcribing rRNA increases dramatically with growth rate; however, in this study, it remains constant at 1.

Our goal was to keep the model as simple as possible and keep the number of required parameters to a minimum. We only included the figure in the supplementary material because it does not change the conclusions, even though it makes the predictions quantitatively better. In the future we would like to achieve this improvement by expanding the model (with mRNA, tRNA, non-specific RNAP binding to DNA etc.). We added a sentence to the discussion to point out again how the results are affected if Φ_rRNA^RNAP is included, and how this parameter could be mechanistically included in the model in the future.

"Furthermore, incorporating other types of RNA (mRNA, tRNA) and energy metabolism, or even constructing a genome-scale RBA model (Hu et al., 2020), will likely lead to more quantitative predictions of fluxes and growth rate. A strong indication of this is that including a variable RNAP allocation into the model leads to quantitatively better predictions (see Appendix 1, Figure 5). Therefore, in the future, we aim to model RNAP allocation mechanistically. This will involve for example adding other RNA species (mRNA, tRNA), and considering non-specifically bound RNAP which is a significant fraction of RNAP (Klumpp and Hwa, 2008)."

Furthermore, Φ_rRNA^RNAP was first introduced in line 205 but was not explained until line 337.

We added an explanation to the sentence in line 205:<br /> "If we consider RNAP allocation to rRNA (k_RNAP^el^bar = k_RNAP^el f_act^RNAP Φ_rRNA^RNAP, where Φ_rRNA^RNAP is the fraction of RNAP allocated to the synthesis of rRNA), the results get closer to the experimental data (Appendix 1, Figure 5)."

The value(s) of Φ_rRNA^RNAP for Appendix Figure 4 are also missing from this manuscript.

We added the missing values to the figure caption.

1. How, exactly, is the unit of flux converted to mmol g-1 h-1?

We are not exactly sure what the reviewer means by this question. As an example of unit conversion, we provide an explanation for the conversion of literature RNAP fluxes. The RNAP fluxes predicted by the model are in mmol g^-1 h^-1. The RNAP fluxes in Bremer and Dennis (2008) were in nt min^-1 cell^-1. To convert them to mmol g^-1 h^-1, we used the values of dry mass/cell from Bremer and Dennis (2008) and the number of nucleotides in rRNA (the stoichiometric coefficient n_rRNA). The code for the conversion is available on GitHub (https://github.com/diana-sz/RiboComp) in the script fluxes_vs_growth_rate.py.

1. What is the (dry) mass constraint and how is it defined? In the manuscript, both the second equation in line 101 and the bottom row of Table 1 are dry mass constraint(s). Why are they different? Furthermore, why is the right-hand side of the second equation in line 101 a dimensionless 1, and how does the last row of Table 1 result in the unit of growth rate, time^(-1)?

These are two forms of the same constraint. We added a paragraph to the methods section that explains how to convert the equations (capacity constraints, dry mass constraint) into the form in Table 1.

In the first form of the equation, ⍵Tc = 1, the units of ⍵ are g/mmol, and the units of c are mmol/g, so they cancel out.

The rows in Table 1 are multiplied by the vector of fluxes, so we get ⍵C [g/mmol] * vIC [mmol/gh] = μ [1/h].

1. The concentrations of all components that serve as "substrates" will be zero when growth rate is maximized, as these molecules do not catalyze any reactions, nor do they influence reaction kinetics in the model. These "0" concentration components are C, AA, NT, rP, and rRNA. Why are these concentrations even included in the model?

The reviewer is correct in pointing out that these species have zero concentrations at maximum growth, and it would be possible to simplify the model accordingly. However, we have chosen not to merge these reactions to maintain clarity in distinguishing between metabolic and macromolecular synthesis processes. Additionally, while we currently use the model to predict optimal behavior, it is not inherently limited to this purpose, as it can equally describe sub-optimal states (as in Figure 2b). Finally, if needed, we can easily introduce minimum concentration constraints (e.g. obtained from measurements) for any of these species without affecting our overall arguments.

Minor comments:

1. Questions regarding Figure 2:

(a) The explanation of Figure 2a is unclear. Intuitively, I assumed that it was a comparison between model predictions and experimental data, with the points representing experimental data and the line representing predictions; and the authors wrote in the figure legend "The points represent maximum growth rates in six experimental conditions". However, the growth rates shown in the figure do not match the original experimental data. Are all the data in the figure predictions?

Yes, the points are predictions and the line is a linear fit. We changed the figure caption as follows:<br /> "The model predicts a linear relationship between RNA to protein ratio and growth rate. The points represent the predicted maximum growth rates in six experimental conditions (Table 2). The line is a linear fit."

(b) Figure 2b is difficult to understand. This figure shows the non-optimal solutions of the model. It is unclear how these solutions are achieved and why there are three lines in the figure.

We expanded the figure caption to make it clearer:<br /> "Alternative RNAP fluxes at different non-optimal growth rates in glucose minimal medium. These are obtained by varying the growth rate step by step from zero to maximum and enumerating all solutions (elementary growth vectors as defined in Müller et al. (2022)) for each growth rate. The grey and blue lines are the alternative solutions. The blue line corresponds to solutions, where rRNA and ribosomes do not accumulate (constraints rRNA' andcap R' in Table 1 are limiting)."

1. Table 1 is also difficult to understand. While the stoichiometric constraints can be easily derived, the capacity constraints and the dry mass constraint cannot be easily derived from related equations from the text.

We added a paragraph into the methods section that explains how to convert the equations (capacity constraints, dry mass constraint) into matrix form.

1. As the authors ask a question in the title, they should provide an explicit answer in the abstract.

We added a sentence to the abstract:<br /> "Our model highlights the importance of RNA instability. If we neglect it, RNA synthesis is always ``cheaper' than protein synthesis, leading to an RNA-only ribosome at maximum growth rate. However, when we account for RNA turnover, we find that a mixed ribosome composed of RNA and proteins maximizes growth rate."

1. The authors should cite a seminal modeling paper, which was the first to examine resource allocation in simplified self-replicating cell systems (Molenaar et al. 2009, Molecular Systems Biology 5:323).

The citation was added.

1. The meaning of v is not consistently defined throughout the manuscript. It refers to the fluxes of enzymatic reactions in some instances, but in other contexts, it refers to the fluxes of the entire network of enzymatic reactions and protein synthesis reactions (Figure 1, Equation 1, and Line 386).

We have made the notation more consistent. When we refer to the fluxes of the entire network we now use v_tot instead of v.

1. Line 85, it might be difficult to interpret "RNAP fluxes" as the flux of rRNA synthesis without reading the subsequent text.

_We added the explanation in brackets.<br /> "_We validate the model by predicting RNAP fluxes (rRNA synthesis fluxes)."

1. Typo in line 102-103. "...protein fluxes 𝒘" → "...protein synthesis fluxes 𝒘".

Thank you for spotting that, we added the missing word.

1. Line 104, f_RNAP^act and f_R^act are not explained in the text; and their biological significance cannot be understood from their names in Table 2 ("RNAP activity" and "Ribosome activity").

We added a sentence that explains these parameters:<br /> "f_RNAP^act is the fraction of actively transcribing RNAPs, and f_R^act is the fraction of actively translating ribosomes."

1. Notion "**" in Table 2. The coupling between transcription and translation means the coupling of "mRNA" transcription and translation, not rRNA. At least in E. coli, the transcription rate of rRNA is faster than that of mRNA.

The transcription rate of the archaeal RNAP was determined in vitro. To our knowledge, data for transcription rates of rRNA vs. mRNA in vivo are not available. Therefore, the translation rate is only a very rough estimate.

1. Is the citation correct in line 136? I didn't find related information in Bremer and Dennis' paper after a quick scan.

We corrected the citation. Additionally, we added references that indicate that if rRNA is transcribed in excess of available r-proteins, it gets rapidly degraded:<br /> "In fact, the accumulation of free rRNA in a cell is biologically not realistic as it is bound by rPs already during transcription (Rodgers and Woodson, 2021). Furthermore, if rRNA is expressed in excess of rPs, it is rapidly degraded (Siehnel and Morgan, 1985)."

1. Lines 136-138. The statement is not accurate, as the fraction of inactive ribosomes increases with decreasing growth rate in E. coli (Dai et al. 2016, Nat Microbiol 2, 16231). If the studied growth rates are relatively high, it is acceptable to use a constant active ribosome fraction as an approximation, but this approximation should be made explicit.

We used the fractions of active ribosomes as reported in Bremer and Dennis, 2008 which are constant between growth rates of 0.4-2.1 1/h. In Dai et al. 2016, it was similarly observed that above the growth rate of ~0.5 1/h, the active fraction is quite constant. We rephrase the text to make it more accurate:<br /> "For the growth rates studied here (0.4-2.1 1/h), the fraction of inactive ribosomes stays roughly constant at 15-20% (Bremer and Dennis, 1996, 2008; Dai et al., 2016). In our model, we have already incorporated this fraction using the effective translation elongation rate (k_R^el^bar = k_R^el*f_R^act). However, below the growth rate of ~0.5 1/h, the fraction of active ribosomes rapidly decreases (Dai et al. 2016)."

1. The citation in line 142 is not accurate. It should be (Bremer and Dennis, 1996).

We corrected the citation.

1. Lines 192-193: "six" different growth media, not five.

Thank you for pointing that out, we corrected it.

1. Line 287: The statement "... translation rate does not increase in ribosomes with a higher protein content" could be misinterpreted as discussing translation elongation rate changes with different protein content in ribosomal protein mutant strains in a given species. It should be rephrased to remove ambiguity.

We rephrased the sentence as follows:<br /> "…translation rate does not increase in ribosomes from different species which have higher protein content."

1. Parameters for the three panels in Figure 8 are missing.

The parameters used for mitochondria are the same as for E. coli in glucose minimal media. The only difference is that a fraction of rPs can be imported. We added a sentence to the figure caption to clarify this:<br /> "The model can be adjusted to predict mitochondrial protein-rich ribosome composition. All parameters used for the simulation of mitochondria are the same as for E. coli in glucose minimal media, except a fraction of rPs can be imported for free from the cytoplasm and does not have to be synthesized. For simplicity, we assumed that 1/3 of rPs are imported. (In reality, almost all rPs are imported, but mitochondria make additional proteins to provide energy for the whole cell.)"

Reviewer #4 (Significance):

Strengths: Why the ribosome is composed of RNA and protein parts is a fundamental biological question. This manuscript proposes a very interesting hypothesis, arguing that the mixed ribosome composition results from rRNA instability. To test their hypothesis, the authors parameterize a simplified self-replicating cell model with realistic parameters. The model is first developed/parameterized for E. coli, and it could be easily adapted to other organisms with higher ribosomal protein content.

Limitations: The main limitations of this manuscript lie in the development of the model, especially the modeling of rRNA degradation and the use of constant values for growth rate-dependent parameters.

Advances: (1) This manuscript proposes a new hypothesis that rRNA instability is a universal factor that influences the ribosome composition across living organisms. (2) Compared to Kostinski and Reuveni's work, the authors have made certain improvements by including adjustable ribosome allocation to RNA and ribosomal protein when maximizing growth rate, which may lead to more realistic conclusions.

Audience: This work will be of interest to people in the field of theoretical biology, computational biology, and evolution, as well as to researchers studying ribosome structure and function.

Areas of expertise: Microbial systems biology, computational biology, and prokaryotic genomics.

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 #4

Evidence, reproducibility and clarity

Summary

In this manuscript, Széliová et al. used a simple self-replicating cell model to study why the ribosome consists of both RNA and protein from an economic point of view. Their base model predicts an RNA-only ribosome, which is not surprising since the smaller RNAP has a higher turnover number compared to the larger ribosome. When rRNA instability is included, the model predicts an "RNA+Protein" ribosome. In particular, the predicted ribosome composition is comparable to the measured ribosome composition when strong cooperative binding of ribosomal proteins to rRNA is considered. The authors conclude that the maximal growth rate is achieved by the real ribosome composition when rRNA instability is taken into account.

Major comments:

1. The authors modeled the rRNA degradation rate as a function of the concentration of fully assembled ribosomes (equation 5). However, only partially assembled ribosomes are susceptible to RNase, and they make up only a small fraction of total ribosomes. The majority of ribosomes are fully assembled. In addition, the turnover number obtained from Fazal et al. (2015) and used here is the degradation rate of double-stranded RNA, not the fully assembled ribosomes, which have a stable tertiary structure. In my opinion, the rRNA degradation rate should be modeled as a function of the concentration of partially assembled ribosomes (i.e., pre-R in Figure 7) rather than the concentration of fully assembled ribosomes.
2. Compared to the work by Kostinski and Reuveni (2020), the authors have made an improvement by avoiding the use of constant ribosome allocation to ribosomal protein (Φ_rP^R) and RNAP (Φ_RNAP^R), allowing these parameters to vary with predicted growth rates (by changing 𝑥_rP). This is indeed important, as bacteria are very likely to adjust these parameters in response to different growth conditions. However, certain other growth rate-dependent parameters are still treated as constants (or treated as nutrient-specific parameters) across predicted growth rates under given conditions. For example, experiments have shown that the fraction of active RNAP (f_RNAP^act) and the ribosome elongation rate (k_R^el) are growth rate-dependent (Bremer and Dennis, 1996). In contrast, when the authors predict the maximum growth rate by changing 𝑥_rP, f_RNAP^act and k_R^el are held constant regardless of the predicted growth rates.
3. If amino acids or nucleotides are provided in the media, the cell does not have to synthesize all of them de novo. However, the model assumes that the cell always synthesizes all amino acids or nucleotides de novo for growth on growth on amino acid-supplemented media or on LB. This problem could in principle be solved by assuming very fast kinetics of the metabolic reactions in these media, but that should be discussed in the manuscript. Furthermore, why does the turnover number for EAA depend on the growth rate while that of ENT is constant?
4. All parameters related to transcription (RNAP) and translation (ribosome) used in this manuscript are adopted from Kostinski and Reuveni (2020), which are slightly modified based on Bremer and Dennis' research (1996, 2008). However, the authors changed some of the original parameters or data points, but did not provide explanations for these changes:

(a) The original data depicted a growth rate-dependent translation elongation rate, but Table 2 presents it as a constant value.

(b) Figure 2b displays five experimental data points, as opposed to the six data points in the original dataset and other figures in this manuscript.

(c) The model does not consider the fraction of RNAP transcribing rRNA (Φ_rRNA^RNAP), except in Appendix Figure 4. In the original data (Bremer and Dennis 1996), the fraction of RNAP transcribing rRNA increases dramatically with growth rate; however, in this study, it remains constant at 1. Furthermore, Φ_rRNA^RNAP was first introduced in line 205 but was not explained until line 337. The value(s) of Φ_rRNA^RNAP for Appendix Figure 4 are also missing from this manuscript.<br /> 5. How, exactly, is the unit of flux converted to mmol g-1 h-1?<br /> 6. What is the (dry) mass constraint and how is it defined? In the manuscript, both the second equation in line 101 and the bottom row of Table 1 are dry mass constraint(s). Why are they different? Furthermore, why is the right-hand side of the second equation in line 101 a dimensionless 1, and how does the last row of Table 1 result in the unit of growth rate, time^(-1)?<br /> 7. The concentrations of all components that serve as "substrates" will be zero when growth rate is maximized, as these molecules do not catalyze any reactions, nor do they influence reaction kinetics in the model. These "0" concentration components are C, AA, NT, rP, and rRNA. Why are these concentrations even included in the model?

Minor comments:

1. Questions regarding Figure 2:

(a) The explanation of Figure 2a is unclear. Intuitively, I assumed that it was a comparison between model predictions and experimental data, with the points representing experimental data and the line representing predictions; and the authors wrote in the figure legend "The points represent maximum growth rates in six experimental conditions". However, the growth rates shown in the figure do not match the original experimental data. Are all the data in the figure predictions?

(b) Figure 2b is difficult to understand. This figure shows the non-optimal solutions of the model. It is unclear how these solutions are achieved and why there are three lines in the figure.<br /> 2. Table 1 is also difficult to understand. While the stoichiometric constraints can be easily derived, the capacity constraints and the dry mass constraint cannot be easily derived from related equations from the text.<br /> 3. As the authors ask a question in the title, they should provide an explicit answer in the abstract.<br /> 4. The authors should cite a seminal modeling paper, which was the first to examine resource allocation in simplified self-replicating cell systems (Molenaar et al. 2009, Molecular Systems Biology 5:323).<br /> 5. The meaning of v is not consistently defined throughout the manuscript. It refers to the fluxes of enzymatic reactions in some instances, but in other contexts, it refers to the fluxes of the entire network of enzymatic reactions and protein synthesis reactions (Figure 1, Equation 1, and Line 386).<br /> 6. Line 85, it might be difficult to interpret "RNAP fluxes" as the flux of rRNA synthesis without reading the subsequent text.<br /> 7. Typo in line 102-103. "...protein fluxes 𝒘" → "...protein synthesis fluxes 𝒘".<br /> 8. Line 104, f_RNAP^act and f_R^act are not explained in the text; and their biological significance cannot be understood from their names in Table 2 ("RNAP activity" and "Ribosome activity").<br /> 9. Notion "**" in Table 2. The coupling between transcription and translation means the coupling of "mRNA" transcription and translation, not rRNA. At least in E. coli, the transcription rate of rRNA is faster than that of mRNA.<br /> 10. Is the citation correct in line 136? I didn't find related information in Bremer and Dennis' paper after a quick scan.<br /> 11. Lines 136-138. The statement is not accurate, as the fraction of inactive ribosomes increases with decreasing growth rate in E. coli (Dai et al. 2016, Nat Microbiol 2, 16231). If the studied growth rates are relatively high, it is acceptable to use a constant active ribosome fraction as an approximation, but this approximation should be made explicit.<br /> 12. The citation in line 142 is not accurate. It should be (Bremer and Dennis, 1996).<br /> 13. Lines 192-193: "six" different growth media, not five.<br /> 14. Line 287: The statement "... translation rate does not increase in ribosomes with a higher protein content" could be misinterpreted as discussing translation elongation rate changes with different protein content in ribosomal protein mutant strains in a given species. It should be rephrased to remove ambiguity.<br /> 15. Parameters for the three panels in Figure 8 are missing.

Significance

Strengths: Why the ribosome is composed of RNA and protein parts is a fundamental biological question. This manuscript proposes a very interesting hypothesis, arguing that the mixed ribosome composition results from rRNA instability. To test their hypothesis, the authors parameterize a simplified self-replicating cell model with realistic parameters. The model is first developed/parameterized for E. coli, and it could be easily adapted to other organisms with higher ribosomal protein content.

Limitations: The main limitations of this manuscript lie in the development of the model, especially the modeling of rRNA degradation and the use of constant values for growth rate-dependent parameters.

Advances: (1) This manuscript proposes a new hypothesis that rRNA instability is a universal factor that influences the ribosome composition across living organisms. (2) Compared to Kostinski and Reuveni's work, the authors have made certain improvements by including adjustable ribosome allocation to RNA and ribosomal protein when maximizing growth rate, which may lead to more realistic conclusions.

Audience: This work will be of interest to people in the field of theoretical biology, computational biology, and evolution, as well as to researchers studying ribosome structure and function.

Areas of expertise: Microbial systems biology, computational biology, and prokaryotic genomics.

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

The authors explore a mathematical model to rationalize the variable RNA content in ribosomes across species. The mathematical model particularly considers the idea that the protein-to-RNA ratio in ribosomes emerges as a consequence of faster rRNA than r-protein synthesis coupled with a faster degradation of rRNA. This is an interesting analysis. The idea is well explained and the math of the model is overall well explained. Overall, I thus support publication of this analysis. However, while reading the manuscript I was continuously wondering about two major aspects which, I suggest, should be considered more prominently in the text:

i. How clear is it that rRNA is more unstable than r-protein?

ii. Why should the translation rate (the speed with which ribosomes assemble new proteins) not be highly dependent on the ribosome-to-protein ratio (with some intermediate ratio ensuring efficient synthesis and efficient translation?

Currently these points are considered briefly in the discussion part. I suggest that these points should at least be discussed more prominently in the introduction. I further appreciate any more detailed thoughts the authors have on these questions. Finally, I think the discussion section would benefit strongly from a more detailed consideration of possible future experiments. Which data is needed to probe the idea? What types of experiments could be performed to probe the model.

More detailed comments:

Regarding i: rRNA is pretty stable compared to other RNA types in the cell. The authors argue it is unstable. The specific question then seems to become how stable rRNA is compared to r-protein? Generally, proteins are also stable, but what data is available to support that r-proteins are more stable than rRNA?

Regarding ii: Building on their model results, the authors rationalize the highly varying RNA-to-protein ratio in ribosomes across species. The model considers a non-varying rate with which ribosomes synthesize new proteins. This is briefly discussed in the discussion section. However, this appears to be a major assumption that, I think, should be stated clearly stated earlier in the text, including the abstract and introduction. Second, I wonder how the authors then rationalize variations in translation rate across species. Translation rates and the speeds with which ribosomes are varying strongly across species (indicated for example well by the change in the slope between ribosome content/rRNA and growth rate - slope in Fig. 2A). Why could the rRNA-to-protein ratio not be important in playing a role here?

More minor point, but I was also not sure about the justification that ribosome mass is constant (line 111). The mass of an amino acid and a nucleotide is quite different. Why should overall mass matter, and not for example the number of amino acids and proteins. I think it also would be good here to motivate the assumption better early on instead of commenting on it in the discussion section.

Significance

Protein synthesis by ribosomes is a major determinant of the rate with which microbes and other fast growing cells accumulate biomass. To better understand cell growth it is thus essential to better understand the makeup of ribosomes. Széliová et al present a mathematical model to entertain the idea that the varying RNA content in ribosomes across species is a consequence of RNA degradation. The model makes clear predictions which can guide future experiments.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

Summary: This study addresses the problem of what is the optimal ribosome composition in terms of relative RNA and protein content, to ensure optimal growth rate and minimal energy waste. The RNA-world hypothesis suggests that primitive ribosomes were RNA-only objects, and in fact this would appear to be very advantageous from an energetic point of view, since RNA synthesis requires a much lower energy expenditure than protein synthesis. Yet a large fraction of present-day ribosome mass is protein, ranging from 30% to nearly 70% depending on the organism. The authors hypothesize that one of the main functions of ribosomal proteins is to stabilize the RNA and to protect it against degradation. According to their idea, the fast degradation of a protein-free rRNA would offset the energetic advantage given by its cheaper synthesis. To test the hypothesis, they developed a mathematical model whereby to evaluate the optimal ribosome composition under a number of different conditions.

Major comments: The paper is well-written and very readable. I am not an expert of mathematical modelling, so I cannot go into the details of the model presented. As a biologist, I can say that the conclusion arrived at are reasonable and well-justified. Perhaps the point of view is rather narrow, since ribosomal proteins are known to be important not only for RNA protection and ribosome stability, but also to ensure the accuracy of decoding and, in certain contexts, to allow the ribosomes to interact with other cellular ligands. The authors make only very slight reference to these questions, so it would be worthwhile to further comment on them.<br /> Furthermore, their explanation of why ribosome composition should be so different in different organisms (e.g. protein-poor bacterial ribosomes versus protein-rich archaeal ones) is not entirely convincing. For instance, they suggest that archaea may have protein-richer ribosomes than bacteria because they live in extreme environments, thus needing a further aid to stabilize the organelle. While this may be a factor, one must point out that non-extremophilic archaea (e.g. methanogens) have protein-rich ribosomes, making it obvious that other factors must be at play.

Minor comments: none in particular. Referencing is adequate, text is clear and the figures are clear and well-organized.

Significance

As I stated above, the main weakness of this study may be that it concentrates overwhelmingly on a single problem, i.e. the energetic cost of adding proteins to an RNA-only ancestral ribosome. On the other hand, this is a question seldom addressed when talking about ribosome composition, which indeed makes this paper valuable and interesting. The authors expand and advance a previous study of the same kind (to which they make ample reference).

Although rather specialized, I think this paper, in its general conclusions, may be of interest to most of those working in the field of protein synthesis and ribosome evolution.

Referee's keywords: archaea, ribosome evolution, translation, translation initiation

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

Learn more at Review Commons

---

1. General Statements

We thank the reviewers for their comments, and their appreciation of the value and thoroughness of our work in identifying a novel and clinically relevant consequence of centrosome amplification in favoring cell death. The reviewers accurately identify weaknesses of our work which we had also pointed out in the discussion of our manuscript. In particular, we agree as pointed out by Reviewer 1 that direct links between our cellular and clinical observations are difficult to establish given the low level of centrosome amplification observed in the tumor samples. Although multiple hypothesis might explain why preferentially eliminating a small population of cells is beneficial for the patients, we consider that this is out of the scope of this manuscript. However, given that our cellular and clinical observations point in the same direction, we remain confident in the value of presenting them together in this manuscript. We have made it clearer both in the results section and discussion that further work is required to better understand the influence of low levels of centrosome amplification on chemotherapy responses in patients.

We also thank the reviewers for their suggestions to improve the in vitro work we have performed. Our point by point response below lists the experiments we plan to perform, and corrections we have already included in this submitted version. Although Reviewer 3 points out that the molecular mechanism underlying apoptotic priming in cells with centrosome amplification remains a mystery, we argue that the identification of this priming already provides a mechanistic explanation for enhanced chemotherapy responses. Our careful and thorough analysis of these responses, using a diversity of advanced technical approaches was key to achieve this. We were also able to clearly rule out that priming is caused by a previously characterized centrosome amplification consequence, demonstrating its novelty. Reviewer 3’s characterization of our work as “archival” is diminishing to say the least, and we believe multiple aspects of this work will be built upon, even beyond the identification of a molecular mechanism which will of course also be important. Indeed, centrosome amplification is observed in a diversity of healthy cell types (megakaryocytes, B cells, hepatocytes…) and could contribute to the homeostasis of these tissues via apoptotic priming. We predict the translational perspectives of this work also to be important, from the point of view of centrosome amplification in disease, but also in understanding apoptotic priming and responses to BH3-mimetics.

2. Description of the planned revisions

Reviewer 1, Major comments:

1. The conclusion that centrosome amplification primes to apoptosis irrespective of mitotic defects is largely based on low resolution timelapse analysis (20x magnification, 10 minute imaging intervals, no tubulin). Imaging at this resolution is likely to miss mitotic defects, reducing the confidence with which this conclusion can be drawn.

We were unsure of the exact point brought up by Reviewer 1 here and we have consulted Reviewer 1 (through Reviews commons) to confirm the revision plan. In Figure 5A, we show that the level of heterogeneity stemming from chromosome instability is lower for PLK4OE than for MPS1 inhibition, and Figure 5D then shows that apoptotic priming only occurs in PLK4OE, and not in response to MPS1 inhibition. Combined, these experiments allow us to conclude that apoptotic priming occurs independently of mitotic defects. Nevertheless, we propose to reproduce the live-imaging of mitosis, increasing the resolution and including tubulin, to better visualize mitotic defects in response to the different mitotic perturbations induced.

1. Data from timelapse analysis of DNA content in Fig. 2 are used to conclude that Plk4OE cells are more sensitive to carboplatin due to mitotic defects that occurred without multipolar spindles. However, it is premature to conclude that multipolar spindles were not involved in DNA mis-segregation without visualizing the spindles themselves. While DNA positioning can be used as a proxy for spindle morphology, as performed here, it only reliably detects multipolar spindles when all poles are relatively equal in size and the multipolar spindle is maintained throughout mitosis. However, the poles in multipolar spindles often differ in size and ability to recruit DNA. Additionally, they often cluster over time, which can preclude their identification when only visualizing DNA, especially at 20x magnification. Compelling evidence that high mis-segregation is occurring without multipolar spindles would require visualizing the spindles and also demonstrating the cause of the increased chromosome mis-segregation. (Are acentric fragments being mis-segregated as lagging chromosomes?)

We agree with Reviewer 1 that the “High mis-segregation” mitotic phenotype we describe is poorly characterized in our original manuscript, and that we cannot formally exclude that multipolar spindles are involved, although we observe this phenotype in similar proportions in PLK4Ctl and PLK4OE. We also agree that identifying the origin of increased chromosome mis-segregation is relevant here. We therefore propose to characterize spindles and mis-segregating chromosomes by imaging fixed mitotic figures upon Carboplatin treatment, staining for Tubulin, centrosomes, and centromeres. This will allow us to better determine if Carboplatin induces the same mitotic phenotypes in PLK4OE and PLK4Ctl cells.

1. The images in Fig. 3D and 4D are not of sufficient resolution to support the central conclusion that centrosome amplification primes cells for MOMP. This conclusion is further weakened by the facts that 1) Plk4OE was the only source of centrosome amplification tested and 2) Plk4OE was reported to prime for MOMP in only 2 of 3 cell lines. Potential explanations for the lack of priming in SKOV3 cells should be discussed. Additionally, the sensitization in Fig. S4H-J appears quite modest. (These data are also difficult to see, perhaps because the Plk4Ctl -/+ chemo conditions are overlapping.)

We have made images in Fig. 3D and 4D larger. We hope that this makes our observations of Cytochrome C release (quantified in Fig. 3E and 4E) easier to visualize for readers. We would like to point out that our conclusion that centrosome amplification primes for MOMP in OVCAR8 does not only arise from the assays presented in these figures. In Figures 4A and 4C we show by MTT assays and detection of apoptotic cells by cytometry respectively, that PLK4OE OVCAR8 are sensitized to BH3-mimetic WEHI-539 compared to PLK4Ctl cells. We also show this priming using BH3-mimetic Navitoclax in Fig S4F. Regarding the source of centrosome amplification, we use OVCAR8 cells devoid of the inducible PLK4OE transgene and show that “natural” centrosome amplification also makes OVCAR8 cells more sensitive to WEHI-539 (Fig. S4C). This strongly suggests that priming does indeed stem from centrosome amplification and not from other consequences of PLK4 over-expression. Nevertheless we are currently generating cells to induce centrosome amplification via SAS-6 over-expression, to test an alternative source of centrosome amplification.

We do not claim that this apoptotic priming is a universal consequence of centrosome amplification, as indeed we show that it is not observed for the cell line SKOV3 (Fig. S4F). For now, we do not have a clear hypothesis of the reason for the cell line differences. Initially we hypothesized that p53 status could be in cause because OVCAR8 and COV504 both express mutant p53 whereas p53 protein is completely absent in SKOV3. However we observe that p53 does not seem to affect cell death signaling in OVCAR8 (Fig. S3C-D), making this hypothesis less likely. The 3 cell lines are indeed very different in terms of origin and genomic alterations, making it difficult at this stage to propose an evidence-based explanation of differences in terms of apoptotic priming.

Finally, regarding the sensitization to chemotherapy associated priming presented in Fig. S4H-J, we have made the figures bigger and non-overlapping hoping that this makes them easier to read. Additionally, we agree that the effect of centrosome amplification appears modest. The trypan-blue assays we used have the advantage of being relatively high-throughput, but they only detect a fraction of the killed cells: only cells that are late in the apoptotic process but that haven’t yet been degraded. This makes the assay less sensitive. The general tendency we observe nevertheless proposes an association between apoptotic priming induced by centrosome amplification, and an enhanced sensitivity to a diversity of chemotherapy agents. We propose to confirm this for OVCAR8 cells treated with Olaparib, by performing cytometry experiments staining for AnnexinV and Propidium Iodide in order to increase sensitivity by detecting earlier signs of apoptosis.

Reviewer 2, Major comments:

1. The authors state that (Line 133) "the increased multipolarity we observe in presence of the combined chemotherapy is caused by the effect of Paclitaxel on the capacity of cells to cluster centrosomes." Could the authors to back up this claim by reanalyzing the imaging data to look for clustering as a survival mechanism versus inhibition of clustering in Paclitaxel-treated cells? Or indeed test any of the range of available clustering inhibitors directly on PLK4OE and thus prove the contribution of clustering to survival?

We agree that the conclusion that Paclitaxel suppresses centrosome clustering is not sufficiently backed by experimental data. We cannot directly view clustering in the live-imaging experiments we performed, because we are visualizing neither tubulin nor centrosomes. To clarify this point, we will:

1. Image fixed mitotic figures of Plk4Ctl and PLK4OE cells untreated or treated with Paclitaxel, staining for tubulin and centrosomes to identify if indeed Paclitaxel increases the proportion of anaphases with multiple poles characterized by the presence of centrosomes. We will use this type of assay as live imaging approaches to film both microtubules and centrosomes will not be feasible within the timing of this revision, but also because paclitaxel responses maybe modified if tubulin dyes are used such as Sir-tubulin.
2. We will use HSET inhibitor CW069, to test if this also prevents centrosome clustering.
3. If this is the case, we will then test if CW069 also preferentially kills PLK4OE compared to PLK4Ctl by Trypan-blue viability assays.

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

Reviewer 1, Major comments:

1. In its current form, the title suggests that the major role of centrosome amplification in sensitizing to chemotherapy is independent of multipolar divisions. Based on Figure 1, this is misleading. Figure 1D shows that in centrosome amplified cells treated with combination chemotherapy, the most common cause of death is high mis-segregation on multipolar spindles. Modifying the title to "Centrosome amplification favors the response to chemotherapy in ovarian cancer by priming for apoptosis in addition to promoting multipolar division" would more accurately reflect the data.

We agree with Reviewer 1 that our title should include the promotion of multipolar divisions, and have modified the title accordingly.

1. Line 191 points out that more Plk4OE cells that were in G1 at the beginning of carboplatin died than Plk4Ctl cells. However, in Fig. 2H-I, it looks like longer G1 durations in the presence of carboplatin led to increased cell death and that the Plk4OE cells happened to spend more time in G1 at the beginning of carboplatin treatment than Plk4Ctl cells did. Is this the case? Quantification of the average time spent in G1 for each group would be helpful.

Upon Carboplatin exposure, G1 length is indeed longer for PLK4OE cells compared to PLK4Ctl cells, as shown in Fig. S2D for complete G1 phases observed after the first mitosis (although the induced lengthening is mild compared to the observed extension of G2 induced upon carboplatin exposure shown in Fig. S2D). The same tendency, although not significant, is observable for cells in G1 at the beginning of carboplatin treatment, although it is harder to conclude because these are not complete G1 phases.

To assess links between G1 phase length and cell fate, we have plotted the length of G1 depending on whether cells live or die, focusing on cells of the second generation for which G1 length is complete. We observed no link between G1 length and cell fate, and have added this figure as Fig. S1E.

Reviewer 1, minor comments:

1. The authors cite Fig. 1B when drawing the conclusion that "combined chemotherapy induced a stronger reduction of viable cells produced per lineage in PLK4OE compared to PLK4Ctl". But Fig. 1B shows that combination chemotherapy produced a similar decrease in viable cells per lineage +/- Plk4OE. If anything, the Plk4OE+ cells showed slightly less sensitivity because they proliferated more poorly in the absence of chemotherapy. This is also true for carboplatin sensitivity in Fig. 2D (line 156).

Here our focus is actually more on the proportion of cell death that on the number of viable cells. We agree that the way we wrote this makes it confusing so we have re-written this paragraph to make it clearer.

1. Line 202 concludes that Fig. S2H-I shows that Plk4OE doesn't affect recruitment of DNA damage repair factors. The dotted outlines around the nuclei in Fig. S2H-1 make it very difficult to see, but it appears that gH2AX, FancD2, and 53BP1 signals are lower in Plk4OE cells.

We have made images in Fig. S2H bigger and the dotted outlines around nuclear less strong, and we hope this makes the signal easier to see. Strong cell to cell variations in signal make it hard to draw conclusions from images, although we have aimed to present this heterogeneity. The quantifications shown in Fig. S2I however show that there are no differences in Rad51 or FANCD2 recruitment in PLK4OE cells. For 53BP1 however, we observed less recruitment in PLK4OE for one biological replicate (squares in quantification shown in Fig. S2I) but not in the two other replicates. Although there may be some interesting observation here, this difference does not appear sufficiently robust to consider it as relevant in the context of this study.

1. The images for "dies in interphase" and "dies in mitosis" in Fig. 1B are suboptimal. Alternative images would be beneficial.

We have modified the images and added timepoints to make the phenotypes clearer. We have also added supplementary movies to better visualize the events (See Movie S1A for cell death events).

1. It would be helpful to discuss the clinical relevance of WEHI-539 and Navitoclax.

We have further developed the section of our discussion about the clinical relevance of BH3-mimetics and these drugs.

1. The discussion states that "mitotic drugs that limit centrosome clustering have had limited success in the clinic". I am not aware of any drugs that limit centrosome clustering that are suitable for in vivo use and the citation provided does not mention centrosome clustering.

We thank Reviewer 1 for this comment. Indeed, we oversimplified things a bit here, and have rewritten this paragraph. We have however kept the citation because although this reference does not directly mention centrosome clustering, some of the discussed drugs have been shown to kill cancer cells via centrosome unclustering in vitro in other studies.

1. The dark purple and black are very difficult to discriminate (Figure 1,2 and S1), as are the light green and light turquoise (Fig. 4A,S4A-B, S4F, S4H).

We changed these colors in the indicated graphs, and also in other figures where they were used. We hope these changes make the figures easier to read.

1. Line 246 claims that Fig. S3B shows that p21 and PUMA mildly increase upon carboplatin exposure, but it isn't clear that these increase in a biologically or statistically significant manner.

We have modified this paragraph because indeed it seems unlikely that the differences are statistically or biologically significant.

1. The green used to indicate S/G2 in Fig. S2A-B is different in Plk4 Ctrl vs Plk4OE cells.

We thank Reviewer 1 for spotting this and have changed the colors.

1. I do not believe that carboplatin + paclitaxel is standard of care treatment for breast or lung cancer, as stated on line 48-49.

Based on the guidelines of the NIH, we believe that these two drugs are indeed used in combination for the treatment of Stage IV non small cell lung cancer, as well as triple-negative breast cancer. (https://www.cancer.gov/types/lung/hp/non-small-cell-lung-treatment-pdq#_48414_toc, https://www.cancer.gov/types/breast/hp/breast-treatment-pdq#_1049).

However, given the complexity and diversity of treatment protocols, we have modified the text so as not to convey the idea that these are the only drugs used.

1. This study advances, but does not complete our understanding of centrosome amplification in breast cancer, as stated on line 75.

Agreed and changed.

1. Line 297 describes Navitoclax as an "inhibitor of BCL2, BCL-XL and BCL2". (ie BCL2 is listed twice).

Thank you for noticing this, we have corrected this.

1. It's not clear why line 120, which refers to effects of combined chemotherapy, cites Fig. S1G-I, which apparently show data from untreated (even without dox?) Plk4Ctrl and Plk4OE cells.

We indeed meant to refer to Fig. 1E-F and have therefore made this change.

1. In Fig. S6A, how can the mitotic index be 200%?

We thank Reviewer 1 for noticing the poor labelling of this figure. It is not a percentage of cells we are presenting, but the number of mitotic figures identified in 10 analyzed fields. We have corrected the figure.

Reviewer 2 major comments:

1. Fig 3: Results line 229-236 refer to quantification of fragmented nuclei which the authors interpret as poised for apoptosis. Micronuclei are also quantified- do the authors interpret this phenotype as advanced apoptosis? There is no mention of apoptotic bodies in the analysis. I would ask the authors to provide a bank of representative images with explanations to illustrate their interpretation of the range of morphologies - differences between nuclear fragmentation, versus micronuclei versus DNA contained in apoptotic bodies.

The cells we defined as “poised for apoptosis” are cells that release cytochrome C in presence of a pan-caspase inhibitor. These cells are therefore activating mitochondrial outer membrane permeabilization without executing apoptosis. It is then within these cells that we observe different nuclear morphologies, reflecting different behaviors in mitosis rather than apoptosis advancement. Apoptotic bodies are not observed in these cells, because they are actually not executing apoptosis owing to the presence of the pan-caspase inhibitor. We visualized apoptotic bodies only in absence of pan-caspase inhibitor. These are indicated by white arrow-heads, in Fig. 3D which was made bigger for more clarity. We have also added images of nuclei to present the different morphologies we describe in Fig. 3F.

1. Although this patient cohort is described in a previous publication, authors should include a cohort description in a table within supplemental for this manuscript: age range of patients, number of patients in each stage, size of tumours, and most relevant to this study, treatment regimens- adjuvant versus neoadjuvant, surgery vs no surgery? How is the cohort selected- sequentially selected? inclusion/exclusion criteria? Statement in abstract "we show that high centrosome numbers associate with improved chemo responses" is too specific as we have no information on the treatment regimens received by the patients (neo or adjuvant chemo versus surgical/radiological interventions?). Maybe treatment response would be more appropriate? Were there any cases of Pathological complete (or even near complete) response in this cohort and if so, what was the CNR in those cases?

We have included the cohort description in Supplementary Table 1. This is a retrospective cohort, so no specific inclusion criteria were applied. Treatment mainly consisted of surgery (100% patients) followed by adjuvant chemotherapy consisting of platinium salts and/or taxanes (84% of patients, 67% treated with both). Despite the wide common ground of treatment (surgery followed by taxanes and/or platinium salts for 84% of patients), we have nevertheless modified the abstract as suggested by Reviewer 2. Regarding complete or near-complete response, there were no such cases in this cohort.

Reviewer 2, minor comments:

Just some minor points on language:<br /> Line 54: Suggest rephrasing of the statement "and this can be favored by centrosome amplification (29) "<br /> Perhaps a word like potentiated instead of favored?<br /> Line 67: Again consider using an alternative to favored "We show that centrosome amplification favors the response to combined Carboplatin and Paclitaxel via multiple mechanisms."<br /> Favored is in fact used throughout the manuscript text- in my opinion this is not a scientific enough term and would consider replacing with alternative.

We have replaced the term favor with more appropriate terms, depending on the context.

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

Reviewer 1, major comments:

1. In a previous technical tour de force (Morretton et al, EMBO Mol Med 2022), the Basto lab quantified centriole numbers in the ovarian patient cancer samples analyzed here, and found that the percentage of cells with centrosome amplification in a given ovarian tumor is quite small, only reaching a maximum of 3.2%. It is critical background information to cite that quantification here. This information also begs the question of whether introducing this low rate of centrosome amplification is sufficient to cause a more global apoptotic priming in the sample, as suggested.

We have now included this important background information in our results section. We agree with Reviewer 1 that the low levels of centrosome amplification in tumors may not cause a more global apoptotic priming in the whole tumor. However, based on our findings, this low proportion of cells will most likely be more sensitive to chemotherapy. We cannot affirm for now what will be the consequences of the preferential elimination of these cells. However, given centrosome amplification’s potential to promote malignant behaviors such as genetic instability or invasiveness, we hypothesize that the elimination of these cells may have effects on tumor survival that are not proportional to their numbers. Testing this hypothesis would require many more experiments using in vivo models, which we cannot carry out within the scope of this study.

Reviewer 2, major comments:

1. Fig 6: While the authors have already acknowledged this as a weakness of the study, can the patient data really be compared to cell line data on CA because inclusion of CNRs between 1.4 and 2 as "high CNR" is questionable given that this ratio represents a completely normal centrosome complement? Are the authors confident enough in the imaging technique that all centrosomes are being detected? Can the authors justify the inclusion of the 1.4-2 CNR tumours by breaking down individual patient data on response to various treatments? Have the authors tried to analyse the cohort for OS and RFS using only those 9% of tumours exhibiting CA? What does the analyses of Fig 6 and S6 look like with a CNR cut-off of 2 instead of 1.45? Does the re-analysis show a better correlation between CNR and FIGO stage?

At the single-cell level, centrosome amplification is indeed defined as the presence of more than 2 centrosomes per cell. Tumor samples are characterized by heterogeneous centrosome numbers, with some regions showing extensive centrosome loss, and some others showing nuclei associated with either one, two or various levels of centrosome amplification. In such a heterogeneous population of cells, it is therefore not straight-forward to use the cut-off CNR=2 to define tumors with centrosome amplification. We have nevertheless analyzed the clinical parameters using the cut-off for CNR at 2 as proposed. Using this cut-off, High CNR patients still show improved overall survival, but a non-statistically significant extension of time to relapse. There are very few patients with CNR>2 (6 for overall survival and 5 for time to relapse), and therefore we remain unconvinced by the statistical value of such an analysis.

The definition of a cut-off at 1.45 was not arbitrary. We dichotomized the population into two groups using the Classification And Regression Trees (CART) method. Taking into consideration the binary outcome “relapse within 6 months or no relapse within 6 months” this method resulted in the categorization of the cohort into low CNR (£ 1.45) and high CNR (> 1.45). Independently, we also used predictiveness curves to estimate an optimal cut-off parameter for a continuous biomarker such as the CNR. The threshold obtained by this robust methodology was in agreement with CART approach with a cut-off of 1.40.

Dichotomizing the population does not guarantee the identification of significant clinical differences between the generated groups. We therefore analyzed overall survival and time to relapse ex post, and observed that high CNR and low CNR populations differed significantly for both these parameters.

Regarding FIGO stage, given frequent late detection of ovarian cancer, 59% of the cohort is considered at stage III (See Fig. S6C). All patients with CNR>2 are in the group of Stage III, except one which is Stage II. However, no patient with CNR>2 is in Stage IV, arguing that even with a higher CNR cut-off, there is no association between CNR and Figo stage.

1. The experimental PLK4 overexpression system is an accepted and clean method to induce CA in vitro. Could the authors comment in the discussion on how they envision CA being induced as a sensitizing agent in the clinical setting to support the translational aspects of their work?

In the clinical setting, we do not suggest to induce centrosome amplification as a sensitization agent. Indeed, centrosome amplification induces multiple phenotypes associated with malignancy (genomic instability, invasiveness). The translational aspects of our work relate more to the detection of centrosome amplification as a potential biomarker of chemotherapy responses, from conventional chemotherapies to BH3-mimetics for which biomarkers are absent. This aspect we have commented on in our discussion.

Reviewer 2, minor comments:

Line 263: "Centrosome amplification primes for MOMP and sensitizes cells to a diversity of chemotherapies." CA primes to one very specific BCL-XL inhibitor in this section so consider modifying the title of the section.

We agree that centrosome amplification makes cells sensitive to a specific BCL-XL inhibitor. However, we nevertheless claim that this very specific priming, can potentiate these cells responses to a diverse range of chemotherapies with different targets (paclitaxel, carboplatin, and olaparib).

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 #3

Evidence, reproducibility and clarity

This is a valuable archival paper that catalogues the effects of combined treatment with paclitaxel and carboplatin predominantly on one ovarian cancer cell line, OVCAR8, in which extra centrosomes can be induced by induced overexpression of Plk4. It systematically examines cellular responses to this drug treatment regimen in control and Plk4 overexpressing cells. Together the experiments show that Plk4-mediated formation of extra-centrosomes sensitizes cells to cell death independently of any effect upon spindle multipolarity and chromosome segregation, irregular spindle formation and mitotic errors, and of the DNA damage response. The authors then go on to show that Plk4 over expression results in premature cleavage of Caspase 3 and so favors the apoptotic response. \This appears to be mediate through increased mitochondrial outer membrane permeabilization. The PIDDosome is believed to contribute to apoptosis in the presence of extra centrosomes through a p%£ mediated pathway. However, in this case, apoptosis appears to be independent of p53 and also of the PIDDosome, as show by deleting a key PIDDosome component. The authors are therefore left with a bit of a mystery in terms of providing a mechanistic explanation of their findings.<br /> I do recommend publication of this paper in its present form as the study has been carried out very carefully and it is very important for workers in the field to know what has been tried in attempt to explain the phenomenon of increased cell death following Plk4 overexpression. It does not lead to a new mechanistic discovery but highlights an important phenomenon that we still have to explain.

Significance

valuable archival information

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

Summary

The authors' findings suggest that induction of centrosome amplification synergises with and potentiates the cytotoxic effects of standard chemo in epithelial ovarian cancer cell lines via a mechanism involving mitochondrial membrane priming and Cyt C release. CA has differential apoptotic priming effects depending on the cell line context. The authors use single cell analysis to characterise the range of mitotic defects through to cell fate following PLK4 OE and the combination treatments. The studies are extended to an ovarian cancer patient cohort where elevated centrosome numbers are associated with better OS and RFS. These findings have the potential to improve future patient stratification and treatment in EOC in addition to prognosis of treatment response.

Major comments

1. The authors state that (Line 133) "the increased multipolarity we observe in presence of the combined chemotherapy is caused by the effect of Paclitaxel on the capacity of cells to cluster centrosomes." Could the authors to back up this claim by reanalysing the imaging data to look for clustering as a survival mechanism versus inhibition of clustering in Paclitaxel-treated cells? Or indeed test any of the range of available clustering inhibitors directly on PLK4OE and thus prove the contribution of clustering to survival?
2. Fig 3: Results line 229-236 refer to quantification of fragmented nuclei which the authors interpret as poised for apoptosis. Micronuclei are also quantified- do the authors interpret this phenotype as advanced apoptosis? There is no mention of apoptotic bodies in the analysis. I would ask the authors to provide a bank of representative images with explanations to illustrate their interpretation of the range of morphologies - differences between nuclear fragmentation, versus micronuclei versus DNA contained in apoptotic bodies.
3. Fig 6: While the authors have already acknowledged this as a weakness of the study, can the patient data really be compared to cell line data on CA because inclusion of CNRs between 1.4 and 2 as "high CNR" is questionable given that this ratio represents a completely normal centrosome complement? Are the authors confident enough in the imaging technique that all centrosomes are being detected? Can the authors justify the inclusion of the 1.4-2 CNR tumours by breaking down individual patient data on response to various treatments? Have the authors tried to analyse the cohort for OS and RFS using only those 9% of tumours exhibiting CA? What does the analyses of Fig 6 and S6 look like with a CNR cut-off of 2 instead of 1.45? Does the re-analysis show a better correlation between CNR and FIGO stage?
4. Although this patient cohort is described in a previous publication, authors should include a cohort description in a table within supplemental for this manuscript: age range of patients, number of patients in each stage, size of tumours, and most relevant to this study, treatment regimens- adjuvant versus neoadjuvant, surgery vs no surgery? How is the cohort selected- sequentially selected? inclusion/exclusion criteria?<br /> Statement in abstract "we show that high centrosome numbers associate with improved chemo responses" is too specific as we have no information on the treatment regimens received by the patients (neo or adjuvant chemo versus surgical/radiological interventions?). Maybe treatment response would be more appropriate? Were there any cases of Pathological complete (or even near complete) response in this cohort and if so, what was the CNR in those cases?
5. The experimental PLK4 overexpression system is an accepted and clean method to induce CA in vitro. Could the authors comment in the discussion on how they envision CA being induced as a sensitizing agent in the clinical setting to support the translational aspects of their work?

Minor comments

The manuscript is well written and all data clearly and thoroughly presented.

Just some minor points on language:

Line 54: Suggest rephrasing of the statement "and this can be favored by centrosome amplification (29)"<br /> Perhaps a word like potentiated instead of favored?<br /> Line 67: Again consider using an alternative to favored "We show that centrosome amplification favors the response to combined Carboplatin and Paclitaxel via multiple mechanisms."<br /> Favored is in fact used throughout the manuscript text- in my opinion this is not a scientific enough term and would consider replacing with alternative.<br /> Line 263: "Centrosome amplification primes for MOMP and sensitizes cells to a diversity of chemotherapies." CA primes to one very specific BCL-XL inhibitor in this section so consider modifying the title of the section.

Significance

Overall, this well-written work extends and provides mechanistic detail to understand the role of CA in priming cells for cytotoxicity in response to commonly used chemo agents in the EOC context. It is a thorough study with sound conclusions drawn from the data provided. It also employs a broad range of assays and techniques to explore the hypotheses from every angle. In view of this, this manuscript is a valuable contribution to the literature on the role of CA in ovarian cancer and its treatment.

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

In this manuscript, Edwards et al analyze OVCAR8 cells with dox inducible expression of Plk4. Doxycycline treatment induces centrosome amplification in ~80% of cells. 72 hour timelapse analysis of cells with fluorescent chromosomes revealed that cell death after carboplatin+paclitaxel was more common in Plk4OE than Plk4Ctl cells. Cell death was most common after high chromosome mis-segregation/multipolar division, which resulted in death of ~80% of the daughter cells. However, death was also elevated in cells with no or slight mis-segregation when comparing Plk4OE to PlkrCtl (40% vs 12%), suggesting an additional sensitization effect. Plk4OE also increased cell death in carboplatin alone, most notably after high mis-segregation but also to a lesser extent in cells with no or slight mis-segregation. 17% of Plk4OE cells exposed to carboplatin in G1 died in S/G2 vs 6% of Plk4Ctl. This difference did not appear to be due to DNA damage response or PIDDosome activity. Carboplatin caused caspase 3 cleavage and cytochrome C release to a greater extent in Plk4OE than Pl4Ctl cells, suggesting MOMP priming. Plk4OE sensitizes OVCAR8 cells to the BCL-XL inhibitor WEHI-539, and Plk4OE sensitized COV504 but not SKOV3 cells to the less specific inhibitor Navitoclax. In 88 patients with high grade serous ovarian carcinoma, a high (>1.45) centrosome-to-nucleus ratio was associated with increased relapse-free and overall survival. The authors conclude that centrosome amplification primes ovarian cancer cells to chemotherapy independent of mitotic defects.

Major comments

1. In its current form, the title suggests that the major role of centrosome amplification in sensitizing to chemotherapy is independent of multipolar divisions. Based on Figure 1, this is misleading. Figure 1D shows that in centrosome amplified cells treated with combination chemotherapy, the most common cause of death is high mis-segregation on multipolar spindles. Modifying the title to "Centrosome amplification favors the response to chemotherapy in ovarian cancer by priming for apoptosis in addition to promoting multipolar division" would more accurately reflect the data.
2. In a previous technical tour de force (Morretton et al, EMBO Mol Med 2022), the Basto lab quantified centriole numbers in the ovarian patient cancer samples analyzed here, and found that the percentage of cells with centrosome amplification in a given ovarian tumor is quite small, only reaching a maximum of 3.2%. It is critical background information to cite that quantification here. This information also begs the question of whether introducing this low rate of centrosome amplification is sufficient to cause a more global apoptotic priming in the sample, as suggested.
3. The conclusion that centrosome amplification primes to apoptosis irrespective of mitotic defects is largely based on low resolution timelapse analysis (20x magnification, 10 minute imaging intervals, no tubulin). Imaging at this resolution is likely to miss mitotic defects, reducing the confidence with which this conclusion can be drawn.
4. Data from timelapse analysis of DNA content in Fig. 2 are used to conclude that Plk4OE cells are more sensitive to carboplatin due to mitotic defects that occurred without multipolar spindles. However, it is premature to conclude that multipolar spindles were not involved in DNA mis-segregation without visualizing the spindles themselves. While DNA positioning can be used as a proxy for spindle morphology, as performed here, it only reliably detects multipolar spindles when all poles are relatively equal in size and the multipolar spindle is maintained throughout mitosis. However, the poles in multipolar spindles often differ in size and ability to recruit DNA. Additionally, they often cluster over time, which can preclude their identification when only visualizing DNA, especially at 20x magnification. Compelling evidence that high mis-segregation is occurring without multipolar spindles would require visualizing the spindles and also demonstrating the cause of the increased chromosome mis-segregation. (Are acentric fragments being mis-segregated as lagging chromosomes?)
5. The images in Fig. 3D and 4D are not of sufficient resolution to support the central conclusion that centrosome amplification primes cells for MOMP. This conclusion is further weakened by the facts that 1) Plk4OE was the only source of centrosome amplification tested and 2) Plk4OE was reported to prime for MOMP in only 2 of 3 cell lines. Potential explanations for the lack of priming in SKOV3 cells should be discussed. Additionally, the sensitization in Fig. S4H-J appears quite modest. (These data are also difficult to see, perhaps because the Plk4Ctl -/+ chemo conditions are overlapping.)
6. Line 191 points out that more Plk4OE cells that were in G1 at the beginning of carboplatin died than Plk4Ctl cells. However, in Fig. 2H-I, it looks like longer G1 durations in the presence of carboplatin led to increased cell death and that the Plk4OE cells happened to spend more time in G1 at the beginning of carboplatin treatment than Plk4Ctl cells did. Is this the case? Quantification of the average time spent in G1 for each group would be helpful.

Minor comments

1. The authors cite Fig. 1B when drawing the conclusion that "combined chemotherapy induced a stronger reduction of viable cells produced per lineage in PLK4OE compared to PLK4Ctl". But Fig. 1B shows that combination chemotherapy produced a similar decrease in viable cells per lineage +/- Plk4OE. If anything, the Plk4OE+ cells showed slightly less sensitivity because they proliferated more poorly in the absence of chemotherapy. This is also true for carboplatin sensitivity in Fig. 2D (line 156).
2. Line 202 concludes that Fig. S2H-I shows that Plk4OE doesn't affect recruitment of DNA damage repair factors. The dotted outlines around the nuclei in Fig. S2H-1 make it very difficult to see, but it appears that gH2AX, FancD2, and 53BP1 signals are lower in Plk4OE cells.
3. The images for "dies in interphase" and "dies in mitosis" in Fig. 1B are suboptimal. Alternative images would be beneficial.
4. It would be helpful to discuss the clinical relevance of WEHI-539 and Navitoclax.
5. The discussion states that "mitotic drugs that limit centrosome clustering have had limited success in the clinic". I am not aware of any drugs that limit centrosome clustering that are suitable for in vivo use and the citation provided does not mention centrosome clustering.
6. The dark purple and black are very difficult to discriminate (Figure 1,2 and S1), as are the light green and light turquoise (Fig. 4A,S4A-B, S4F, S4H).
7. Line 246 claims that Fig. S3B shows that p21 and PUMA mildy increase upon carboplatin exposure, but it isn't clear that these increase in a biologically or statistically significant manner.
8. The green used to indicate S/G2 in Fig. S2A-B is different in Plk4 Ctrl vs Plk4OE cells.
9. I do not believe that carboplatin + paclitaxel is standard of care treatment for breast or lung cancer, as stated on line 48-49.
10. This study advances, but does not complete our understanding of centrosome amplification in breast cancer, as stated on line 75.
11. Line 297 describes Navitoclax as an "inhibitor of BCL2, BCL-XL and BCL2". (ie BCL2 is listed twice).
12. It's not clear why line 120, which refers to effects of combined chemotherapy, cites Fig. S1G-I, which apparently show data from untreated (even without dox?) Plk4Ctrl and Plk4OE cells.
13. In Fig. S6A, how can the mitotic index be 200%?

Significance

The importance of centrosome amplification in cancer has long been debated. The possible effects of extra centrosomes on multipolar divisions are well known. An independent apoptosis-priming effect of additional centrosomes is novel and of interest. However, in their previous manuscript (Morretton et al, EMBO Mol Med 2022), the Basto lab showed that centrosome amplification only occurs in a maximum of 3.2% of cells in a given ovarian cancer. Given the large discrepancy between the rate of centrosome amplification in the models here and in ovarian cancers ({greater than or equal to}80% vs {less than or equal to}3%), it is unclear whether the mechanism of apoptosis priming reported here is at play in a clinical setting. It is unclear whether the low rate of centrosome amplification observed in cancers can predispose response to a particular inhibitor, as suggested, particularly when centrosome amplification in {greater than or equal to}80% of cells 1) only induced apoptosis priming in 2 of 3 cell lines (Fig. S4F) and 2) induced relatively modest drug sensitivity (Fig. S4J). If it were shown in an additional experiment that induction of centrosome amplification in a small minority of cells, as occurs in patient tumors, increases MOMP priming and drug response, this would substantially increase the significance of the study.

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

Learn more at Review Commons

---

1. General Statements [optional]

The findings presented in this manuscript are original and have not been previously published, nor is the manuscript under consideration for publication by another journal. The authors of this manuscript declare to have no conflicts of interest.

1. Description of the planned revisions

We believe that incorporating the suggested corrections and conducting the additional experiments recommended by the reviewers will significantly enhance the quality of this study. These revisions will not only bolster the current conclusions but also broaden the relevance and applicability of our work to a wider scientific audience, extending beyond the field of virology.

As outlined in the following sections, we are fully committed to implementing the experiments proposed by the reviewers and making the necessary modifications to the manuscript in line with their suggestions. Our responses to each specific comment are provided below.

Reviewer #1

Evidence, reproducibility and clarity

Summary: Several target cell entry pathways have been described for different viruses, including endocytic/ fusion pathways, some which are dynamin-dependent.

Here the authors exploited cell lines with multiple dynamin gene disruptions and other cell biological tools, as well as a phenotypic range of previously characterized viruses, to evaluate the relative importance of dynamin and actin for entry of viruses, including SARS-CoV-2.

In cells that lack the serine protease TMPRSS2, dynamin depletion blocked uptake and infection by SARS-CoV-2. Increasing the input virus partially rescued SARS-CoV-2 infection in the absence of dynamin, and both dynamin-dependent and dynamin-independent entry pathways were inhibited by drugs that disrupt actin polymerization.

Examination by electron microscopy indicated that the dynamin-independent endocytic process was clathrin-independent, in that, in the absence of dynamin, the majority of Semliki Forrest Virions were detected in bulb-shaped, non-coated pits. When TMPRSS2 was expressed, SARS-CoV-2 infection was rendered dynamin-independent.

Significance

Overall, the experiments are expertly performed, the results and conclusions are convincing, the text is clearly written and accurately describes the data, and the manuscript makes an important contribution to a complex and important topic in the cell biology of virus infection. It would be reasonable for the authors to publish the manuscript with the current data.

That being said, we have two main questions/comments:

1. The authors point out that SFV differs from SARS-CoV-2 in that it required actin only for the dynamin-independent entry. The EM experiments were done with SFV, not with SARS-CoV-2. This raises the question of the relevance for SARS-CoV-2 of the interesting finding that, in the absence of dynamin, SFV associated with non-coated pits.

If the authors had the tools to do similar EM experiments with SARS-CoV-2, it would be nice to include those results. Otherwise, it is fine to discuss/speculate about SARS-CoV-2 regarding this issue.

RESPONSE:As requested by the reviewer, we are currently perform the suggested EM analysis of SARS-CoV-2 entry in the presence and absence of dynamins.

1. The authors show that TMPRSS2 allows the original Wuhan strain and Delta Variant of SARS-CoV-2 to bypass the need for dynamin. This is presumably because TMPRSS2 allows SARS-CoV-2 to fuse at the plasma membrane, precluding need for endocytosis altogether. The authors also mention literature claiming that Omicron is more dependent upon endocytosis than the Wuhan and Delta variants. If the authors had data with Omicron it would be really nice to include it.

RESPONSE: We have already conducted this experiment and have incorporated the quantitative results into the updated version of the manuscript, now presented as Figure 8.

There were some minor typos/grammar/other quoted here:

• Ultrastructural analysis by electron microscopy showed that this dynamin-independent endocytic processes - cell injests particles and nutrients by encoulfing them - some viruses have been show

RESPONSE: Thank you for noticing the error. We have modified the text as: “Ultrastructural analysis by electron microscopy showed that this dynamin-independent endocytic processes appeared as 150-200 nm non-coated invaginations that have been shown to be efficiently used by numerous mammalian viruses, including alphaviruses, influenza, vesicular stomatitis, bunya, adeno, vaccinia, and rhinovirus.”.

• The final step of an endocytic vesicle formation culminates with the pinching of vesicle off from the PM into the cytoplasm

RESPONSE: We have modified the sentence as: “The concluding stage of endocytic vesicle formation is marked by the vesicle being pinched off from the plasma membrane and released into the cytoplasm.”

• For other viruses, such as respiratory viruses (This word is a little strange here since influenza was mentioned in the last sentence.)

RESPONSE: Thank you for noticing the error, we have removed the mention to respiratory viruses: “ For other viruses (including coronaviruses), the fusion is triggered by proteolytic cleavage of the spike proteins that, once cleaved, undergo conformational changes leading first to the insertion of the viral spike into the host membrane and, upon retraction, the fusion of viral and cellular membranes9,10.”.

• Viruses that use a receptor that is internalized by dynamin-dependent endocytosis (e.g. CPV and the TfR) (just reminding that TfR is not a virus)

RESPONSE: We have amended the sentence to avoid misunderstandings: “Viruses (e.g. CPV) that use a receptor (e.g. TfR) that are internalized by dynamin-dependent endocytosis cannot efficiently infect cells in the absence of dynamins.”.

• that appeared surrounded by an electron dense coated

RESPONSE: We have corrected the typo: “In MEFDNM1,2 DKO cells treated with vehicle control, TEM analysis revealed numerous viruses at the outer surface of the cells (Figure 4 A), as well as inside endocytic invaginations that were surrounded by an electron dense coat, consistent with the appearance of clathrin coated pits47,48 (CCP) (Figure 4 B).”

• The main virial receptor could be internalized using two endocytic

RESPONSE: We have corrected the typo: “The main viral receptor could be internalized using two endocytic mechanisms, one mainly available in unperturbed cells (e.g. dynamin-dependent), the other activated upon dynamin depletion (i.e. dynamin independent).”

• Virus infection was determined by FACS analysis of virial induced EGFP

RESPONSE: We have corrected the typo: ‘Virus infection was determined by FACS analysis of EGFP (VAVC and VSV), mCherry (SINV) or after immunofluorescence of viral antigens using virus-specific antibodies (IAV X31 and UUKV).”.

Reviewer #2

Evidence, reproducibility and clarity

Summary: Ohja et al. present an interesting study investigating dynamin independent endocytic entry mechanism of viral infection. Using a genetic KO of 2 dynamin isoforms they show impacts on the infection of a range of large and small DNA and RNA viruses.

They go onto show that SARS-CoV-2 may utilise a dynamin independent mechanism of infection that requires an intact actin cytoskeleton.

Significance

This work is of interest to the field of virology and has the potential to answer previously understudied entry mechanisms important for a wide range of viruses. It is a well presented piece of work overall.

Major Comments:

• The abstract does not in my opinion reflect the content of the paper and is too 'SARS-CoV-2' centric. The work involves the use of a range of viruses to first define a mechanism that is applicable to SARS-CoV-2 and I think the abstract and title should reflect this.

RESPONSE: As per the reviewer's request, we will make revisions to the Title and Abstract. As a ‘non SARS-CoV-2-centric’ title we have amended the title to: Multiple animal viruses, including SARS-CoV-2, can infect cells using alternative entry mechanisms.

• In figure 1H the authors postulate that the reduced impact of dyn1,2 KO on SFV infection may be due to the interaction with heparin sulphate proteoglycans. Have the authors considered performing experiments using Heparin to block infection in their KO cells -/+ tamoxifen treatment?

RESPONSE: As per the reviewer's request, we will perform the proposed heparin experiments for SFV.

• In Figure 2 the authors assess infection of a range of viruses in dyn1,2 KO cells showing differential effects in some viruses but not all.

Have the authors confirmed whether tamoxifen treatment and the subsequent KD of dyn1,2 effect surface expression of the entry receptors for the viruses tested?

RESPONSE: Although in general blocking receptor endocytosis results in an increase in its cell surface levels, we agree with the Reviewer that the effect of dynamin depletion on receptors levels should be monitored at least for some of the viruses. To address the question raised by the reviewer, we will monitor the surface expression of SFV receptors VLDLR and ApoER2, and of the CPV receptor TfR in the presence and absence of dynamins.

We have already confirmed that there are no changes in the surface expression of SARS-CoV-2 receptor ACE2 in the absence of dynamin and this new data will be added to Figure 7.

• Additionally in this setting, dyn1,2 KD may impact on post entry steps in the virus life cycle such as the initial establishment of viral replication.

Can the authors either provide evidence as to how they have delineated measurement entry over replication or support their findings with psuedotyped virus-like-particles?

RESPONSE: This is an important point. As suggested by the reviewer, we will perform infection experiments in the presence or absence of Dynamins using VLPs pseudotyped with SFV and VSV spikes.

In addition, several of our experiments already indicate that upon dynamin depletion, the main block in virus infection is at the step of cell entry: 1) Upon DNM-depletion, the decrease in SARS-CoV-2 infection strongly correlates with a proportional block in spike (Figure 5) and virions (Figure 7) endocytosis; 2) exogenous expression of even low levels of the cell surface protease TMPRSS2 rescued SARS-CoV-2 infection in cells devoid of dynamins, indicating that merely by-passing endocytosis restores virus infection; 3) as shown in Figure 1 H for SFV, and in Figure 2 for multiple viruses, increasing the multiplicity of infection increases the number of infected cells, indicating that when virions access the dynamin-independent entry route, cells can be efficiently infected; 4) the infection of both negative strand (i.e. Uukuniemi virus, UUKV, Figure 2 ) and positive strand (i.e. human Rhino virus, HRVA1, Figure S3 D-E) RNA viruses, as well as DNA viruses (i.e. Vaccinia, Figure 2, and Adenovirus-5, Figure S3 B-C) are not affected by dynamin depletion, arguing against a general negative impact of dynamin depletion on cellular protein synthesis or other basic cell functions required for virus replication.

• Figure 3, given the unexpected results with the dynamin inhibitors, could this experiment be repeated with the dyn1-3 triple KD presented in figures 5-8?

RESPONSE: As requested by the reviewer, we will repeat the main inhibitor experiments presented in Figure 3 for SFV also in DNM TKO cells.

• Statistical analysis of imaging data in figure 4 would help with the conclusions.

RESPONSE: We have already performed the requested statistical analysis and modified Figure 4 accordingly.

• Additionally, the authors comment that in the KD cells the viruses were trapped in 'stalled CCPs'. What morphological changes determine this classification?

RESPONSE: As previously reported by Ferguson et al. (Developmental Cell, 2009), who developed the conditional MEF DNM knock out cell models, all CCPs are stalled at 6 days post induction of dynamin depletion. When observed by electron microscopy, stalled CCPs are readily identified by the presence of elongated, membranous narrow neck structures that connects the vesicle to the plasma membrane. We have clarified this description in the manuscript text and indicated the morphological features typical for a ‘stalled’ clathrin coated pit in Figure 4 F (black asterisk and white arrowheads).

• Concerning the SARS-CoV-2 work presented in figures 6-8, the use of exogenous expression of the viral entry receptors ACE2 and TMPRSS2 is a concern.

RESPONSE: While the reviewer appreciates that this is a necessary step to allow entry into their MEF-dyn1-3 KD cells, exogenous receptor expression can result in artificial entry of the virus.

• To support their findings, can the authors perform experiments in either cell lines endogenously expressing ACE-2/TMPRSS2 such as Calu3 or Caco2 and KD dyn1-3 using transient siRNA?

RESPONSE: This experiment poses a challenge due to the inherent difficulty of transfecting Caco2 and Calu3 cells and the potential difficulty of achieving a robust (>80%) simultaneous knockdown of all three dynamin isoforms. This is one of the reasons why we chose the conditional knock out approach. Nevertheless, we are committed to attempting this experiment.

• This approach would also provide more evidence for the role of TMPRSS2 presented in SF5 as the limited expression of this protease limits the robustness of the conclusions one can draw from the data presented.

RESPONSE: We appreciate the reviewer's observation, and to address this concern, we plan to not only perform siRNA knockdown of dynamins in cells with endogenous ACE2 and TMPRSS2 but also endeavor to elevate the expression levels of TMPRSS2 in our MEF DNM1,2,3 TKO ACE2 cells. It's worth noting, however, that this task presents a unique challenge since expression of TMPRSS2, a trypsin-like cell surface protease, leads to cell detachment even when expressed at moderate levels.

Minor comments & typo:

• Introduction paragraph 1 engulfing

RESPONSE: The sentence has been amended: “To gain access into the host cell's cytoplasm where viral protein synthesis and genome replication take place, most animal viruses hijack cell’s endocytic pathways1 by which the cell engulfs particles and nutrients into vesicular compartments. “.

• Pg 13 - typo in 'Figurre 6B'

RESPONSE: The typo has been corrected.

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

• Regarding the Reviewer 1 request on the use of Omicron variants, we have already conducted the requested experiments and have incorporated the quantitative results into the updated version of the manuscript, now presented as Figure 8.
• Regarding the Reviewer 2 request on the EM data, we have already performed the requested statistical analysis and modified Figure 4 accordingly. We have also clarified the EM descriptions in the manuscript text and indicated the morphological features typical for a ‘stalled’ clathrin coated pit in Figure 4 F (black asterisk and white arrowheads).

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

none