- Jul 2024
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
This manuscript compiles LoF variants of M1AP and ZZS proteins (i.e., SHOC1, TEX11 and SPO16) that almost certainly underlie infertility and reports the first case of an infertile man homozygous for a variant in SPO16. The authors validated interactions between human M1AP and ZZS that were found in mice. Analyzing testicular samples from infertile men revealed that those with deficiencies in SHOC1, TEX11 or SPO16 exhibited early meiotic arrest without haploid germ cells, whereas those with M1AP variants displayed a predominant metaphase I arrest with rare haploid germ cells. Further investigations showed that disrupted SHOC1, TEX11 or SPO16 led to defective synapsis and pairing of homologous chromosomes and unpaired DNA DSBs, while M1AP mutations reduced CO events. Importantly, men with LoF variants in M1AP can father healthy children by medically assisted reproduction. Overall, the results are clear and convincing in defining likely causative variants in infertility patients.
Response: We thank reviewer #1 for the appreciation of our work. We already addressed the suggestions raised by reviewer# 1 to improve our manuscript.
I have a few minor comments for improving the manuscript:<br /> • No statistical analyses were performed. The meaning of error bars was not mentioned. It is essential to specify the minimum number of seminiferous tubules counted for each patient.
Response: We added the statistical analysis. We described now in more clarity that all round tubules in a patient's testicular section were counted (l. 646-653).
• Allele frequencies of variants are not provided.
Response: We added the allele frequencies from gnomAD v4.1.0 (SNVs) and gnomAD SVs v2.1 (CNVs) in Table 1.
• Figure 4 should clearly label the representations of each color channel.
Response: Thanks for this suggestion. We labelled each color channel accordingly.
• The authors should clearly label the bands of SPO16 in the right panel of Figure 1B.
Response: We labelled the SPO16 band in Figure 1B more clearly.
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Appendix Figure S1B and S2B, what does "rat" mean in "rat Ins2 Ex3/4/"?
Response: In the minigene assay, an artificial gene was constructed with exon 3 and 4 from the insulin 2 gene of the species rat (Rattus norvegicus). We described this in more detail in the Appendix methods section (l. 119) and in the Figure legend S1B and S2B.
Reviewer #1 (Significance):
Overall, this study significantly contributes to the understanding of some genetic causes of human infertility and offers a potential avenue to treat patients with M1AP variants/ mutants. Since no knock-in animal model was applied to mimic the subtle phenotype variations observed in patients, the functionality of truncated proteins remains unexplored. For example, it is unclear why the germ cells in patient M3260 with the SHOC1 variant can progress to round spermatids (Fig. 2C), while those in Shoc1 KO mice (10.1093/molehr/gaac015) and other patients cannot. However, this is a minor concern.
Response: Thanks for this comment. SHOC1 variant c.1939+2T>C present in M3260 is a predicted splice site variant. In vitro it results in an in-frame exon skipping as shown by the minigene assay (Appendix Figure S2) that is predicted to lead to a loss of only 4% of the protein. We assume that this does not result in a complete loss but only in an impaired protein function enabling significantly reduced progression of spermatogenesis up to the round spermatid stage in few cells (l. 354-360). We addressed this in more detail in the results section (l. 145ff and l. 189ff) and in the Appendix Figure S2 legend. Accordingly, SHOC1 variant c.1939+2T>C is not a LoF variant and we excluded it from the quantification of subsequent analyses. Immunohistological staining of this patients was excluded from Appendix Figure S6, S7, S9, S10, and S11 and incorporated into Appendix Figure S2.
In addition, the recurrent M1AP c.676dup was functionally analysed in our previous work (Wyrwoll et al., 2020, PMID: 32673564). We detected M1AP mRNA in a testicular biopsy from one patient showing that this variant leads not to degradation of the mRNA. Furthermore heterologous expression of the mutant M1AP cDNA in HEK293T cells led to the production of a truncated protein that presumably leads to loss of protein function. We added this information in l. 136. Furthermore, our preliminary experiments of co-immunoprecipitation of truncated M1AP with TEX11 hint to an abolished protein-protein interaction caused by M1AP c.676dup and thus a loss of protein function.
Our field of expertise is gametogenesis and meiosis in mice.
Reviewer #2 (Evidence, reproducibility and clarity):
Summary:<br /> This interesting manuscript provides evidence for the biological and clinical relevance in human males of mutations in genes encoding M1AP and other related proteins. In mice, M1AP, "meiosis 1 associated protein," is known to associate with several proteins (SHOC1, TEX11, and SPO16) in the ZZS complex that promotes DNA recombination and crossover formation during meiosis I prophase. Mutation of these proteins in model organisms disrupts the process of recombination and cause arrest of spermatocytes prior to the first meiotic division. Here the authors took advantage of their MERGE (Male Reproductive Genomics) cohort to screen for human loss-of-function (LoF) mutations in the relevant ZZS complex and M1AP genes and to associate these with human male reproductive phenotypes. They found that men with deficiency of ZZS proteins SHOC1, TEX11 or SPO16 genes were infertile, exhibiting arrest of germ cell development early in meiotic prophase, with aberrations of chromosome synapsis and failure to repair DNA double-strand breaks (DSBs). In interesting contrast, men with M1AP mutations exhibited metaphase arrest, and indeed, in some cases, produced haploid spermatids, which in medically assisted reproduction (ICSI), led to the birth of offspring. Because they demonstrate that M1AP interacts with the other proteins, the authors conclude that M1AP is a "catalyzer," but not essential, for the processes of synapsis, recombination, and formation of haploid gametes.
Major Comments:<br /> The work is clearly presented with detailed methods that should allow adaptation in other laboratories.<br /> Overall, this study is a tour de force with what was no doubt difficult archival samples. The histology is generally of good quality, supporting the conclusions about progress of meiotic prophase in the mutant samples. The images of H&E-stained tissue are particularly striking, especially those in supplemental figures.
Response: We thank Reviewer #2 for the appreciation of our work and the suggestions to improve our manuscript. To provide transparency of our work, we plan to upload each (immuno-) histologically stained testicular section shown in the Main and Appendix Figures in the microscopy image repository OMERO/Open Microscopy Environment (OME).
That said, and with particular reference to Fig. 3A, it is difficult to sub-stage meiotic prophase by immunocytochemistry, even in optimal samples, with only one marker (in this case gH2AX). The staging here is also at odds with the statement in the subsequent section (and Fig. 4B) on absence of pachytene cells in men with mutation of SHOC1, TEX11, or SPO16.
Because precise stages of arrest probably cannot be determined in these samples, the authors would be wiser to use phrases such as "zygotene-like"
Response: We agree with the reviewer that it is indeed difficult to sub-stage meiotic prophase based on IHC for one marker. A precise sub-staging of the meiotic prophase would require identifying the stage of the seminiferous epithelium. The cycle of the human seminiferous epithelium has been subdivided into 12 stages based on the acrosomal development made visible by immunohistochemistry for acrosin. However, in order to properly evaluate the human germ cell associations, only seminiferous tubules showing a well-preserved seminiferous epithelium with no apparent damage to the epithelium and the peritubular wall can be considered. In addition, all the different generations of germ cells have to be present as well as at least six spermatids (Muciaccia et al., 2013, PMID: 23946533). As these requirements cannot be fulfilled in the testicular tissue of men with a meiotic arrest as due to LoF variants in M1AP or the ZZS genes, we followed the reviewer's suggestion and have modified the respective phrases throughout the text, e.g. to 'zygotene-like'.
The authors should also clarify how it was confirmed that the metaphase-like cells were spermatocytes and not spermatogonia (given that gH2AX signal is weak or unclear in some such nuclei). Readers with a focus on the more regularly staged mouse or rat tubules would appreciate a few more guidelines to criteria for staging human tubules.
Response: We thank the reviewer for raising this point. In order to confirm that the metaphase-like cells were indeed spermatocytes we will perform additional IHC staining for γH2AX and MAGEA4 on sequential testis sections (distance 3 µm) on representative samples of the patient cohort as well as controls as the hosts of both antibodies are mice. For a few more guidelines on the criteria for staging human tubules, please refer to the response to the previous point.
Evidence for the birth of a (healthy) child from one individual with M1AP mutation verges on the anecdotal (N=1). It is interesting but raises multiple questions and concerns about both the frequency of chromosomal abnormalities in such individuals and the transmission of the mutant alleles.
Response: We understand very well, that the evidence based on N=1 seems to be sparse. Nevertheless, if it is in principle possible for a man affected by bi-allelic M1AP LoF variants to conceive a child by ICSI then it could be also possible for other couples with a similar genetic condition (M1AP LoF), and thus providing a proof-of-principle (l. 417f). Reviewer #2 is completely right with the concerns regarding chromosomal aberrations and the transmission of the mutant allele. Thus, it is essential for clinicians/geneticists to counsel the affected couple carefully about the small but existed chance to have a biological own child and the accompanied potential but so far unexplored risks as outlined in l. 435ff. Our future research project will address this open and highly relevant question.
The authors conclude that the M1AP protein is an essential "catalyzer" in the meiotic recombination pathway. However, it is not clear from the data presented that M1AP in fact has enzymatic catalysis activity or exactly when and how it participates. Because the word "catalyzer" is not buttressed with hard or convincing evidence, the authors should consider other ways to describe the proposed role of M1AP, perhaps as a "putative component" and/or "modifier" of the recombination pathway.
Response: We appreciate the reviewer's advice, and changed the wording to "functional enhancer".
Minor comments:<br /> Fig. 1A - these are nice illustrations, but overly simplified with respect to timing (synapsis is not completed in zygonema)
Response: We completely agree that Figure 1A is a simplified depiction that could not reflect the temporally and spatially highly complex processes of meiosis. By adding a second dotted box and describing the process in the Figure legend in more detail, we tried to reduce the simplification. Nonetheless, we believe that this simplified schematic help readers, who are less familiar with the progression of meiosis to contextualise the described processess.
Fig. 1B - greater clarity in legend would be appreciated
Response: We described Figure 1B in more detail.
Figs. 2A & 3A - colors in bar graphs are difficult to discriminate
Response: We improved the discrimination of bar graphs accordingly.
Fig. 4A - with full appreciation for the difficulty with this material, the images are of low contrast and require considerable enlargement
Response: We agree with this opinion; and we increased the contrast. In addition, we will improve the way of representation in a revised Figure 4 in the complete revision of the manuscript in accordance with the suggestions of all three Reviewers.
Reviewer #2 (Significance):
This is a very interesting paper, which I evaluated from the perspective of a reproductive geneticist with expertise in meiosis and interest in infertility. I think this report will be of interest to clinicians because it identifies a gene possibly linked to marginal fertility and establishes human protein interactions similar to those previously identified in mice. It reinforces the importance of ZZS genes in humans. The contributions of this report to the field of meiosis confirm previous evidence on M1AP, including mutant phenotypes and protein interactions, extending them to humans. We can thus appreciate the conserved function of the mammalian M1AP protein, but as yet the molecular mechanisms of M1AP are not clarified.
Response: We gratefully thank Reviewer #2 for the thorough evaluation of our work and appreciate the recognition of the significance. Indeed, it was not possible to clarify the molecular mechanisms of M1AP that, hopefully, could be identified as soon as human specific antibodies, which will function in the needed applications, will be available. Additionally, we will perform further experiments as suggested by Reviewer #3 to gain a better understanding of the processess involved. Clarifying the underlying molecular mechanism is not only one of our highest interest but will also be important for the scientific community.
Reviewer #3 (Evidence, reproducibility and clarity):
In this manuscript, Rotte et al. investigate the meiotic molecular function in human of the M1AP protein and of the ZZS complex (SCHOC1, TEX11 and SPO16 proteins). The ZSS complex is a key player of meiotic recombination. It is a sub-complex of the conserved family of the ZMM proteins, essential for the formation of class I crossovers, a proper chromosomes segregation and fertility. Understanding its mode of action, regulation and conservation in human is thus a crucial issue in the fields of meiosis and human reproduction, with potential implications for patients. In that context, the recent identification of the protein M1AP as a partner of the ZSS proteins raise the question of its role, function and conservation. The aim of this study is thus of primary importance.<br /> To perform this molecular characterization, the authors made a cohort (24 total) of men carrying LoF variants in M1AP and ZSS genes. They performed a molecular biology analysis to assess the physical interaction between the human M1AP protein and the three components of the ZSS complex. Their results confirm a previous work performed in mice, mentioned by the authors.<br /> Then, they took advantage of available biopsies from different mutant men to perform a histological and cytological analysis of the impact of the different mutations on meiosis. The main conclusions are that in human, similarly to what is known in different organisms (ranging from yeast to mice), the ZSS complex is essential for crossover formation, synapsis and spermatogenesis, and that defect in the genes is associated with a premature prophase I arrest and no sperm formation. The authors also showed that M1AP protein plays a role in meiotic progression, but to a lesser extend compare to the ZSS proteins, with a metaphase I arrest, an undetectable recombination phenotype, apart of a reduced crossover number and, spermatozoa can form in its absence.
Major points:<br /> The authors investigate the physical interaction between M1AP and the ZSS members through a single approach: Co-IP of tagged proteins after expression in human HEK293T cells. This approach is informative, but to reinforce the conclusions the authors should provide data from independent approaches: yeast two hybrid, expression of recombinant proteins followed by pull down, co-immunostaining (TEX11 antibodies were used in the study and M1AP antibody is present in the literature) are possible non-exclusive approaches to decipher, more in details, the interaction. Moreover, understanding the hierarchy of interactions appears important to understand its rational, regulation and function. What is the meaning of a M1AP interaction with all the members of the complex? Remains an open question.
Response: We thank Reviewer #3 for this comment. In an independent approach we aimed to specify the interaction of M1AP to the ZZS proteins. Thus, we already cloned truncated versions of M1AP to refine the binding site of M1AP to the ZZS proteins (Figure R1). In a preliminary experiment, we co-transfected full-length as well as truncated forms of M1AP with TEX11 and showed via Co-IP that the interaction is only possible with full-length M1AP. Within the full-revision, we plan to finalise these experiments and thus validate the specifity of the interaction between M1AP and TEX11 and thereby gain more insight into the interaction/hierarchy of the interaction of M1AP with the ZZS complex.
Figure R1 Tolerance landscape of M1AP NM_001321739.2 illustrating the respective regions selected for mutagenesis of truncated M1AP constructs. Adapted from MetaDome.
Moreover, in the last couple of years, we spent enormous resources (personnel, time, financial) to get a functional antibody against human M1AP, including testing of different commercial (and already published) antibodies, creating three customised antibodies against different M1AP polypeptides, a nanobody raised against the complete M1AP protein (failed because of the impossibility to purify the protein), and contacting the authors of previously published customised M1AP antibodies (Arango et al., 2013/PMID 23269666 and Li et al. 2023/PMID 36440627). Figure R2 recapitulates some of our attempts. Moreover, we published the initial attempts of establishing an M1AP antibody in Wyrwoll et al., 2020/PMID 32673564. Unfortunately, no human M1AP-specific antibody is available.
Additionally, we tested different TEX11, SHOC1 and SPO16 antibodies in immunohistochemistry and SHOC1 and SPO16 antibodies in immunofluorescence of spermatocyte spreads, which did not result in a specific staining (Figure R3). Due to the lack of a human specific antibody against M1AP as well as antibodies against SHOC1 and SPO16, we are not able to localise these proteins in patient testicular sections to address this highly interesting research question that remains of great interest within our work on M1AP.
Figure R2. Attempts to locate M1AP in the human testis. Previous attempts to identify a commercially available antibody that reliably detects M1AP in the human testis have not been successful (Wyrwoll et al., 2020/ PMID 32673564). Accordingly, we tried to produce a human-specific antibody in cooperation with companies specified in antibody customisation (Eurogentec, Biotem). The last attempt, conducted with Biotem, is exemplarily shown in this figure. A. Human M1AP protein sequence (NP_620159.2) highlighting the antibody epitopes (orange) that were selected so that in men carrying the M1AP LoF variant c.676dup p.Trp226Leufs*4 in a homozygous state, the respective antibody should not be able to bind due to the protein truncation. For rabbit immunisation, both epitopes were pooled. B. HEK293T cells were transfected with DYK-tagged M1AP plasmids, either expressing the wildtype (WT) or the truncated protein (W226L). Sera of day (D) 28 and 42 of the immunised rabbit as well as the purified antibody product, a commercially available anti-M1AP antibody (HPA), and anti-DYK control antibody specificity was confirmed by Western blotting. C. Customised anti-M1AP antibody validation in human testicular control and D. M1AP-deficient tissue did not yield in a reliable staining. Various protocol optimisations were tested (different antigen retrieval, adapted blocking and antibody dilution solution, various primary and secondary antibody concentrations). Date shown represents the best result, respectively. The application of both sera and the purified antibody for spermatocyte spreading was tested in parallel and has not been successful either (data not shown). SC: Sertoli cells, SPC: spermatocytes, M-I: metaphase I cells, RS: round spermatids, ES: elongated spermatids. The scale bar represents 100 µm and 10 µm.
Figure R3. Efforts to identify human-specific antibodies for ZZS localisation. A. Commercially available antibodies for ZZS were tested via Western blotting, aiming to reliably detect SHOC1, SPO16, and TEX11 in human testicular biopsies. HA-tagged wildtype plasmid DNA (WT) was transfected in HEK293T cells and the anti-HA antibody was used as a positive control. Only one antibody detected TEX11 reliable in the purified lysates (anti-TEX11: HPA002950). B.-D. Immunohistochemical staining was performed with all antibodies on human testicular and is representatively shown for anti-SHOC1: #BS155344-R, anti-SPO16: #BS15024-R, and anti-TEX11: HPA002950. Only the anti-TEX11 (#HPA002950) was found to be specific. However, presumably due to the fixation with Bouin's solution, staining could not reliably be repeated in all samples and was not implied in this study. Various protocol optimisations were tested (different antigen retrieval, adapted blocking and antibody dilution solution, various primary and secondary antibody concentrations). Date shown represents the best result, respectively. The application of all antibodies for spermatocyte spreading was tested in parallel and have not been successful (data not shown), except for anti-TEX11 (#HPA002950, Appendix Figure S13). SC: Sertoli cells, SPC: spermatocytes, RS: round spermatids, ES: elongated spermatids. The scale bar represents 100 µm and 10 µm.
The ZZS mutants have a defect in gH2AX pattern, a defect in synapsis and no MLH1 foci, associated to apoptosis and prophase I arrest. M1AP mutation has a minor impact. The characterization of the effect of the different mutations (in particular M1AP) on the recombination process should be addressed further, by cytological means. For example, effect on strand invasion and ssDNA production should be monitored using RPA, DMC1 and RAD51 antibodies. The impact on alternative resolution pathway (e.g. BLOOM dependent) should be tested as well as the effect on other ZMM proteins, in particular MSH4-5, should be investigated. These experiments are essential to characterize, at the molecular level, the function of the different proteins during recombination.
Response: We thank the reviewer for this suggestion and highly appreciate to investigate the different pathways in more depth. We plan to perform additional immunofluorescence staining of spermatocyte spreads of identified patients compared to the control in the planned revision for a better understanding of M1AP within human recombination. We already ordered the antibodies against meiotic marker proteins as suggested by the Reviewer.
We would like to take the opportunity to refer to the extremely limited access to cryopreserved testicular material of the patients presented in this manuscript: for each gene (M1AP, SHOC1, TEX11, SPO16) we were lucky to get one testicular biopsy specimen from one man for only one preparation of spermatocyte spreads. We hope for the Reviewer's understanding that we cannot address each requested staining albeit this would be of highest interest. However, we are very confident that we will provide additional staining added to the yet shown to improve the understanding of M1AP's function on human male meiotic recombination.
In the same line, TEX11 staining in M1AP mutant should be more documented and in particular the different stages shown, as well as the foci counting, to have a quantitative result, that can be compared to MLH1. Moreover, co-immunostaining of different markers with TEX11: RPA, DMC1, MSH and MLH1 are also important to understand how the pathway is perturbed and the recruitment delayed/affected.
Response: In the planned revision, we will include the TEX11 foci counting using the acquired images that will be compared to MLH1 foci quantification. In addition, we plan additional co-immunostaining of TEX11 with different markers dependent on the availability of testicular material. Due to the limited resources of cryopreserved material, we cannot repeat the TEX11 staining in the patients with M1AP LoF variant for documentation of different stages. Slides that have already been stained are unfortunately bleached and cannot be re-analysed.
The published M1AP antibody should be tested to investigate its perturbation in the absence of the ZZS proteins and the hierarchy of event.
Response: As already outlined above, we tried to get any functional M1AP antibody for several years, which was not possible (Figure R2). Thus, we unfortunately cannot address this comment via this approach albeit this research question remains of great interest within our work on M1AP.
OPTIONAL: the obligatory crossover was measured, a comment or calculation of interference would be very interesting, and it seems doable using the MLH1 counting, to test whether thses mutants have an effect on this process.
Response: We thank Reviewer #3 for the suggestion of this interesting question that was not within our focus so far. Due to the limited material and the small number of cells from which we could digitally separate the chromosomes, we believe that the sample size is insufficient to obtain a statistically significant result.
Minor comments<br /> As written, the title is misleading, the paper does not investigate the impact of M1AP in ZSS recombination. Such study implies to study genetic interactions or the genetic dependency between the different proteins, which is not the case here.
Response: Thanks for this comment. We changed the title to "Genotype-specific differences in infertile men due to loss-of-function variants in M1AP or ZZS genes".
Labelled on histological images is not clear. The authors should clearly explain to what marker each staining correspond.
Response: We changed the labelling accordingly.
L67 to 72: the authors should update and use more accurate citations for meiotic recombination.
Response: Thanks for this suggestion. In this section, we have described the fundamental processes of meiosis, which have been repeatedly reviewed by renowned scientists. We have therefore chosen four well-cited expert reviews from different groups as references (PMID: 29385397, 24050176, 27648641, 35613017).
L76: the ZMM are specifically involved in the resolution of class I crossover. Please rephrase.
Response: We rephrased the sentence and changed it throughout the manuscript.
L94: Strictly, the author identified an interaction, they didn't establish how the interaction takes place.
Response: We rephrased the sentence.
FigS13: TEX11 staining should be presented with foci counting as a main figure.
Response: We plan to restructure Figure 4 along with the new meiosis specific markers and will consider this comment.
L255: MLH1 does not quantify homologous recombination but, class I crossovers.
Response: We rephrased the sentence.
L352: The sentence is hard to understand, rephrase please.
Response: We rephrased the sentence.
Reviewer #3 (Significance):
In general, the paper is well written and easy to follow. However, in light of the importance of the questions for the field of meiosis, it currently seems a little superficial, in particular if the authors aim at addressing the molecular function of the different proteins. The role of the ZSS proteins and M1AP in the control of meiotic recombination, at the molecular level is very important to decipher and additional experiments might help to better address this question. In addition, the functional links between M1AP and ZSS remains unclear and to investigate further.<br /> This study gives information for human process, and can be compared to more advanced work done with mice.<br /> This study will be important for the community working on meiosis in mammals, but also for people interested in reproduction.
Response: We thank Reviewer #3 for the thorough evaluation and acknowledgment of the significance of our work. We appreciate the suggestion of performing additional experiments to gain a better and more in depth understanding of the molecular pathways involved. We hope for the Reviewer's understanding that we cannot address all raised comments due to the limited material and the difficulty to get human specific antibodies in a research field that primarily works with highly valuable mouse models.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, Rotte et al. investigate the meiotic molecular function in human of the M1AP protein and of the ZZS complex (SCHOC1, TEX11 and SPO16 proteins). The ZSS complex is a key player of meiotic recombination. It is a sub-complex of the conserved family of the ZMM proteins, essential for the formation of class I crossovers, a proper chromosomes segregation and fertility. Understanding its mode of action, regulation and conservation in human is thus a crucial issue in the fields of meiosis and human reproduction, with potential implications for patients. In that context, the recent identification of the protein M1AP as a partner of the ZSS proteins raise the question of its role, function and conservation. The aim of this study is thus of primary importance.
To perform this molecular characterization, the authors made a cohort (24 total) of men carrying LoF variants in M1AP and ZSS genes. They performed a molecular biology analysis to assess the physical interaction between the human M1AP protein and the three components of the ZSS complex. Their results confirm a previous work performed in mice, mentioned by the authors.
Then, they took advantage of available biopsies from different mutant men to perform a histological and cytological analysis of the impact of the different mutations on meiosis. The main conclusions are that in human, similarly to what is known in different organisms (ranging from yeast to mice), the ZSS complex is essential for crossover formation, synapsis and spermatogenesis, and that defect in the genes is associated with a premature prophase I arrest and no sperm formation. The authors also showed that M1AP protein plays a role in meiotic progression, but to a lesser extend compare to the ZSS proteins, with a metaphase I arrest, an undetectable recombination phenotype, apart of a reduced crossover number and, spermatozoa can form in its absence.
Major points:
The authors investigate the physical interaction between M1AP and the ZSS members through a single approach: Co-IP of tagged proteins after expression in human HEK293T cells. This approach is informative, but to reinforce the conclusions the authors should provide data from independent approaches: yeast two hybrid, expression of recombinant proteins followed by pull down, co-immunostaining (TEX11 antibodies were used in the study and M1AP antibody is present in the literature) are possible non-exclusive approaches to decipher, more in details, the interaction. Moreover, understanding the hierarchy of interactions appears important to understand its rational, regulation and function. What is the meaning of a M1AP interaction with all the members of the complex? Remains an open question.
The ZZS mutants have a defect in H2AX pattern, a defect in synapsis and no MLH1 foci, associated to apoptosis and prophase I arrest. M1AP mutation has a minor impact. The characterization of the effect of the different mutations (in particular M1AP) on the recombination process should be addressed further, by cytological means. For example, effect on strand invasion and ssDNA production should be monitored using RPA, DMC1 and RAD51 antibodies. The impact on alternative resolution pathway (e.g. BLOOM dependent) should be tested as well as the effect on other ZMM proteins, in particular MSH4-5, should be investigated. These experiments are essential to characterize, at the molecular level, the function of the different proteins during recombination.
In the same line, TEX11 staining in M1AP mutant should be more documented and in particular the different stages shown, as well as the foci counting, to have a quantitative result, that can be compared to MLH1. Moreover, co-immunostaining of different markers with TEX11: RPA, DMC1, MSH and MLH1 are also important to understand how the pathway is perturbed and the recruitment delayed/affected.
The published M1AP antibody should be tested to investigate its perturbation in the absence of the ZZS proteins and the hierarchy of event.
OPTIONAL: the obligatory crossover was measured, a comment or calculation of interference would be very interesting, and it seems doable using the MLH1 counting, to test whether thses mutants have an effect on this process.
Minor comments
As written, the title is misleading, the paper does not investigate the impact of M1AP in ZSS recombination. Such study implies to study genetic interactions or the genetic dependency between the different proteins, which is not the case here.
Labelled on histological images is not clear. The authors should clearly explain to what marker each staining correspond.<br /> L67 to 72: the authors should update and use more accurate citations for meiotic recombination.
L76: the ZMM are specifically involved in the resolution of class I crossover. Please rephrase.
L94: Strictly, the author identified an interaction, they didn't establish how the interaction takes place.
FigS13: TEX11 staining should be presented with foci counting as a main figure.
L255: MLH1 does not quantify homologous recombination but, class I crossovers.
L352: The sentence is hard to understand, rephrase please.
Significance
In general, the paper is well written and easy to follow. However, in light of the importance of the questions for the field of meiosis, it currently seems a little superficial, in particular if the authors aim at addressing the molecular function of the different proteins. The role of the ZSS proteins and M1AP in the control of meiotic recombination, at the molecular level is very important to decipher and additional experiments might help to better address this question. In addition, the functional links between M1AP and ZSS remains unclear and to investigate further.
This study gives information for human process, and can be compared to more advanced work done with mice.<br /> This study will be important for the community working on meiosis in mammals, but also for people interested in reproduction.
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #2
Evidence, reproducibility and clarity
Summary:
This interesting manuscript provides evidence for the biological and clinical relevance in human males of mutations in genes encoding M1AP and other related proteins. In mice, M1AP, "meiosis 1 associated protein," is known to associate with several proteins (SHOC1, TEX11, and SPO16) in the ZZS complex that promotes DNA recombination and crossover formation during meiosis I prophase. Mutation of these proteins in model organisms disrupts the process of recombination and cause arrest of spermatocytes prior to the first meiotic division. Here the authors took advantage of their MERGE (Male Reproductive Genomics) cohort to screen for human loss-of-function (LoF) mutations in the relevant ZZS complex and M1AP genes and to associate these with human male reproductive phenotypes. They found that men with deficiency of ZZS proteins SHOC1, TEX11 or SPO16 genes were infertile, exhibiting arrest of germ cell development early in meiotic prophase, with aberrations of chromosome synapsis and failure to repair DNA double-strand breaks (DSBs). In interesting contrast, men with M1AP mutations exhibited metaphase arrest, and indeed, in some cases, produced haploid spermatids, which in medically assisted reproduction (ICSI), led to the birth of offspring. Because they demonstrate that M1AP interacts with the other proteins, the authors conclude that M1AP is a "catalyzer," but not essential, for the processes of synapsis, recombination, and formation of haploid gametes.
Major Comments:
The work is clearly presented with detailed methods that should allow adaptation in other laboratories.<br /> Overall, this study is a tour de force with what was no doubt difficult archival samples. The histology is generally of good quality, supporting the conclusions about progress of meiotic prophase in the mutant samples. The images of H&E-stained tissue are particularly striking, especially those in supplemental figures. That said, and with particular reference to Fig. 3A, it is difficult to sub-stage meiotic prophase by immunocytochemistry, even in optimal samples, with only one marker (in this case gH2AX). The staging here is also at odds with the statement in the subsequent section (and Fig. 4B) on absence of pachytene cells in men with mutation of SHOC1, TEX11, or SPO16. Because precise stages of arrest probably cannot be determined in these samples, the authors would be wiser to use phrases such as "zygotene-like." The authors should also clarify how it was confirmed that the metaphase-like cells were spermatocytes and not spermatogonia (given that gH2AX signal is weak or unclear in some such nuclei). Readers with a focus on the more regularly staged mouse or rat tubules would appreciate a few more guidelines to criteria for staging human tubules.<br /> Evidence for the birth of a (healthy) child from one individual with M1AP mutation verges on the anecdotal (N=1). It is interesting but raises multiple questions and concerns about both the frequency of chromosomal abnormalities in such individuals and the transmission of the mutant alleles.<br /> The authors conclude that the M1AP protein is an essential "catalyzer" in the meiotic recombination pathway. However, it is not clear from the data presented that M1AP in fact has enzymatic catalysis activity or exactly when and how it participates. Because the word "catalyzer" is not buttressed with hard or convincing evidence, the authors should consider other ways to describe the proposed role of M1AP, perhaps as a "putative component" and/or "modifier" of the recombination pathway.
Minor comments:
Fig. 1A - these are nice illustrations, but overly simplified with respect to timing (synapsis is not completed in zygonema)
Fig. 1B - greater clarity in legend would be appreciated
Figs. 2A & 3A - colors in bar graphs are difficult to discriminate
Fig. 4A - with full appreciation for the difficulty with this material, the images are of low contrast and require considerable enlargement
Significance
This is a very interesting paper, which I evaluated from the perspective of a reproductive geneticist with expertise in meiosis and interest in infertility. I think this report will be of interest to clinicians because it identifies a gene possibly linked to marginal fertility and establishes human protein interactions similar to those previously identified in mice. It reinforces the importance of ZZS genes in humans. The contributions of this report to the field of meiosis confirm previous evidence on M1AP, including mutant phenotypes and protein interactions, extending them to humans. We can thus appreciate the conserved function of the mammalian M1AP protein, but as yet the molecular mechanisms of M1AP are not clarified.
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Referee #1
Evidence, reproducibility and clarity
This manuscript compiles LoF variants of M1AP and ZZS proteins (i.e., SHOC1, TEX11 and SPO16) that almost certainly underlie infertility and reports the first case of an infertile man homozygous for a variant in SPO16. The authors validated interactions between human M1AP and ZZS that were found in mice. Analyzing testicular samples from infertile men revealed that those with deficiencies in SHOC1, TEX11 or SPO16 exhibited early meiotic arrest without haploid germ cells, whereas those with M1AP variants displayed a predominant metaphase I arrest with rare haploid germ cells. Further investigations showed that disrupted SHOC1, TEX11 or SPO16 led to defective synapsis and pairing of homologous chromosomes and unpaired DNA DSBs, while M1AP mutations reduced CO events. Importantly, men with LoF variants in M1AP can father healthy children by medically assisted reproduction. Overall, the results are clear and convincing in defining likely causative variants in infertility patients.
I have a few minor comments for improving the manuscript:
- No statistical analyses were performed. The meaning of error bars was not mentioned. It is essential to specify the minimum number of seminiferous tubules counted for each patient.
- Allele frequencies of variants are not provided.
- Figure 4 should clearly label the representations of each color channel.
- The authors should clearly label the bands of SPO16 in the right panel of Figure 1B.
- Appendix Figure S1B and S2B, what does "rat" mean in "rat Ins2 Ex3/4/"?
Significance
Overall, this study significantly contributes to the understanding of some genetic causes of human infertility and offers a potential avenue to treat patients with M1AP variants/ mutants. Since no knock-in animal model was applied to mimic the subtle phenotype variations observed in patients, the functionality of truncated proteins remains unexplored. For example, it is unclear why the germ cells in patient M3260 with the SHOC1 variant can progress to round spermatids (Fig. 2C), while those in Shoc1 KO mice (10.1093/molehr/gaac015) and other patients cannot. However, this is a minor concern.
Our field of expertise is gametogenesis and meiosis in mice.
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Reply to the reviewers
Author responses
__Reviewer #1 (Evidence, reproducibility and clarity (Required)): __
In their manuscript, Dutta and colleagues compared the meiotic recombination landscapes between five budding yeast species. In the first part of the work, the authors constructed a high-resolution map of meiotic recombination events in Kluyveromyces lactis supported by high-quality genome assemblies for two strains of this yeast. Then, partially repeating their CO and NCO mapping strategy, they compared a number of meiotic recombination parameters between the five species (sometimes three, depending on the quality of the data for each species). They particularly focused on key parameters for meiotic recombination, such as crossover interference and homeostasis and obligate crossover. Although the analysis is interesting, it is underdeveloped in many places and lacks the general conclusions regarding the evolution of recombination and the broader perspective that would be expected from a comparison of these phenomena in budding yeasts.
[R] Tackling the evolution of recombination is ambitious. Here, with a dataset of five species, it is hard to draw strong evolutionary conclusions besides the variations in the crossover (CO) landscapes and the control of CO formation that we observed, which is already significant. The multiple losses of CO interference that we describe here may constitute our strongest evolutionary conclusion. It potentially underscores the minor evolutionary advantage associated to CO interference at least in budding yeasts. In this context, we changed the title to be more factual and updated the text to better highlight the significance and implications of our findings.
Major comments:
The authors indicate that the distribution of hotspots and coldspots is not preserved between species, but this finding is not properly documented. I think it would be useful to include recombination maps in a main figure for all species (or at least for S. cerevisiae, K. lactis and L. waltii) with the elements highlighted. This will allow for a visual illustration of the variability in the recombination landscape between the studied species. [R] The genomes of the species show blocks of synteny but overall, they are not collinear and therefore, it is not possible to have a direct comparison of the recombination maps. In our previous work, we have highlighted the non-conservation of CO hotspots between S. cerevisiae, L. kluyveri and L. waltii (Brion et al. 2017; Dutreux et al. 2023). Briefly, we retrieved conserved syntenic blocks in L. kluyveri and L. waltii genomes containing at least two S. cerevisiae orthologs associated with one hotspot. L. waltii shares only five out of the 92 S. cerevisiae crossover hotspots (RHO5, SLS1, GYP6, OLE1 and MRPL8), while L. kluyveri shares only one. L. waltii and L. kluyveri share no crossover hotspots. In addition, our current study shows that none of the K. lactis hotspot is conserved in any of the four other species (response figure 1 and new supplementary figure S11).
Response Figure 1. Density of crossovers along the genome using a 5 kb window in the S. cerevisiae genome (Mancera et al. 2008; Oke et al. 2014; Krishnaprasad et al. 2015 combined dataset). Horizontal dotted green line represents crossover hotspot significance threshold. Solid spheres represent the conserved CO hotspots with either L. kluyveri (red) or L. waltii (blue). None of the 92 S. cerevisiae crossover hotspot is conserved in L. lactis.
Although analyses analogous to those presented in Fig. S5 had already been published in other comparisons of the recombination landscape in yeast (e.g. Dutreux et al., 2023), I think that Figs. S5A and S5B are worth to be presented in the main figures (not supplementary data). In many species of eukaryotes, the detection of NCOs is practically impossible, therefore only results for COs are presented. Therefore, it is perhaps also worth discussing the fact that the relationship applies to all recombination events and not only COs, and therefore is related to the regulation of DSBs frequency and not individual DSBs repair pathways.
[R] Figures S5A-B are now included in the main figure, Figure 2B. The association holds true for all total recombination (CO+NCO) events as well, new supplementary figure S6A.
The authors find that CO coldspots were associated with DNA repair genes. Unfortunately, an equivalent analysis was not performed for all recombination events (CO + NCO). I presume this approach is based on the belief that COs are more mutagenic than NCOs. However, recent studies in humans suggest that, at least in mammals, meiotic DSBs themselves are mutagenic, regardless of the pathway used for their repair (Hinch et al., Science 2023). Therefore, I would suggest repeating the analysis also considering NCOs (although I am aware that the picture of NCOs may be incomplete). I would also like to see some graphical representation of the analysis. Is it possible to perform a classic analysis of coldspots/hotspot enrichment in relation to gene ontology?
[R] As suggested, we performed the analysis to independently detect coldspots for all recombination events (CO+NCO). Based on a threshold of
In relation to the previous point - it may be worth repeating this type of analysis also for other yeasts used in this study, or at least for S. cerevisiae, to be able to consider the extent to which this relationship is universal and dependent on the meiotic DSB repair pathway.
[R] The analysis regarding the CO coldspots has been performed in the other species as well. As mentioned in the main text, although some overlap between CO coldspots and DNA repair genes has been observed in the other species as well, we observed a significant enrichment in K. lactis only, maybe because the dataset is larger than in the other species.
In Fig. S7, the point where WGD occurred is marked in the wrong place, or at least that is what the sentence in the text says ("The Lachancea and Kluyveromyces species branched from the Saccharomyces lineage more than 100 million years ago, before to the ancestral whole-genome duplication (WGD) event specific of the S. cerevisiae lineage").
[R] We regret the oversight and have corrected the figure.
The result presented in Fig. S8 is interesting and should be shown in the main figures. Perhaps it would be worth adding an illustration illustrating simple versus complex COs.
[R] The old Figure S8 is now a part of main Figure 2C-D with the illustrations describing the CO types.
The last part of the results includes an analysis of the evolutionary rates of the ZMM genes. In the discussion, the authors should also refer the results of this analysis to the previous analysis of the overrepresentation of DNA repair genes in recombination coldspots. I understand that ZMM are not DNA repair proteins in the strict sense, but I think it is worth familiarizing readers with the authors' view on this matter. Moreover, I would suggest showing where MLH1 and MLH3 are located on the plot in Fig. 6 (especially the meiosis-specific MLH3), whether the selection pressure acts on them as on ZMM proteins, or rather as on DNA repair proteins. Showing the SLX4 and MUS81 would also be interesting.
[R] Figure 6 has been updated as suggested and now shows the Mlh1, Mlh3, Slx4 and Mus81 dN/dS values for the three species.
I feel like the discussion is underdeveloped. I missed a deeper summary of the comparison between meiotic recombination among the tested budding yeasts in the context of the presence and absence of functional ZMM. Even the title of the work is not properly developed in the manuscript text. The analysis shows that it is not the presence of a functional ZMM pathway or its lack that introduces differences between the individual recombination landscapes, although ZMM determines the presence of proper CO interference. With the caveat that for L. kluyveri it is basically unknown whether it has a functional ZMM or not. Maybe confirming the lack of expression of some ZMM genes in meiosis of this species would answer the question of how it should be treated?
[R] We agree with this reviewer that our original title was imprecise, so we changed it to be more factual, emphasizing on the multiple losses of crossover interference in budding yeasts. As stated above, it potentially underscores the minor/negligible evolutionary advantage associated to CO interference at least in budding yeasts. From there, it is hard to draw deeper conclusions since the actual roles/functions of CO interference are still under debate, notably in yeasts where the CO frequency tends to be high. We improved the discussion to better highlight these points.
We also agree that a deeper characterization of the ZMM factors persisting in the non-Saccharomyces yeasts would be informative, but we believe it is beyond the scope of the current manuscript and more suitable for a follow up work. However, our recent publication about L. kluyveri (Legrand et al 2024) shows that Zip3 is properly expressed in meiosis and behaves as in S. cerevisiaesince it is located at DSB sites. Furthermore, we have unpublished transcriptomic data (Response Figure 2) showing that all the ZMM genes from L. kluyveri are specifically induced in meiosis (fold increase >16 at least compared to pre-sporulation conditions). Therefore, so far, although the level of CO interference in L. kluyveri is minimal, there is no indication that the ZMM genes are mis regulated.
Response Figure 2. Transcriptomic data showing that all the ZMM genes from L. kluyveri are specifically induced in meiosis (Unpublished data from Llorente Lab, CRCM, Marseille).
Minor comments:
In general, Figure captions are imprecise, many of them lack clear information explaining what is depicted. Authors should remember that figure legends should be self-sufficient. [R] The figure legends have been updated and are now self-sufficient.
In the revised manuscript, I would suggest placing figure numbers on the figures and using line numbering, which would facilitate the reception of the work and possible reference to its individual elements in the review.
[R] We regret the omission. Figure numbers, Line numbers and Page numbers have been added.
Reviewer #1 (Significance (Required)):
The study provides a new insight into the variation in recombination landscape within budding yeast species with a special emphasis on crossover control. This includes also de novo assemblies of Kluyveromyces lactis genome and high-resolution tetrad-based maps of meiotic recombination events. Previously, recombination maps of different yeast species were compared, however this study focuses on budding yeasts, some of which lost ZMM pathway and differ in some crossover parameters, like interference and homeostasis. Although the analysis is interesting, it lacks the general conclusions regarding the evolution of recombination and the broader perspective that would be expected from a comparison of these phenomena in budding yeasts.
__Reviewer #2 (Evidence, reproducibility and clarity (Required)): __
This paper describes the genome-wide mapping of meiotic recombination in non-Saccharomyces yeast, Kluyveromyces lactis. By using heterologous parental strains, the authors mapped crossovers (COs) and noncrossovers (NCOs) on the genome of K. lactis which lacks proteins necessary for CO formation such as S. cerevisiae, mammals and plants. This is an extension of previous works by the authors' group which mapped CO and NCO in different yeast, Lachancea kluyveri and L. waltii by a similar approach. The authors found that CO frequencies in K. lactis are much lower than those in S. cerevisiae and COs showed weaker interference, which facilitates the non-random distribution of COs along a chromosome. Overall, the experiments and informatic analyses have been done in good quality and the results are convincing. The paper provides additional new information on the landscape of meiotic recombination in different yeast species. These results are of great interest to researchers in the field of meiotic recombination and evolution of meiosis. There are some issues that the authors may be able to address before the publication.
Major points: While the authors noted that K. lactic shows the loss of a pro-CO factors (ZMM protein), Spo16, and Msh5 (due to the introduction of an in-frame stop codon), it still possesses other proteins such as Zip1, Zip2, Zip3, Zip4/Spo22, Mer3, and Msh4. It is still likely that these pro-CO factors control CO formation (and interference) in this yeast. It would be nice for the authors to study whether the knockout of these genes is dispensable for CO formation and interference in meiosis. A similar analysis should be done for L. kluyveri which retains all ZMM genes, but this is clearly out of the scope of this paper.
[R] The question of the functions of the remaining ZMM factors is indeed interesting and related to point #8 from reviewer 1 (please see above). Although this is beyond the scope of our work, we would like to refer here to work from Amy McQueen's lab using L. lactis Zip1 in S. cerevisiae (Voelkel-Meiman 2015). This study shows that L. lactis Zip1 does not allow synaptonemal complex assembly in S. cerevisiae but allows CO formation independently of the Msh4/5 complex but that depend on Zip2/4/Spo16 and Mlh1/3 for their resolution. Overall, these results suggests that L. lactis Zip1 at least retained ancestral functions shared with S. cerevisiae Zip1. However, it is not possible to conclude if the lack of full complementation of L. lactis Zip1 in S. cerevisiae comes from functional divergence or simply by the inability of L. lactis Zip1 to function properly in a heterologous context.
Minor points:
No page number, no main Figure number. It is hard to review this paper. [R] We regret the oversight. Figure numbers, Line numbers and Page numbers have been added.
References: In some cases, in the Introduction, the authors referred to review papers such as Pyatnitskaya et al. (2019) for ZMM proteins while in the other parts, they referred to original papers; for example, three papers for Mlh1-Mlh3. If the number of references is not limited, original papers should be cited in the text.
[R] We regret this omission. Original papers have now been included in the citations.
Figure 3A, page 9, second paragraph: When the authors compared CO and NCO densities, it would be nice to show P-values for the comparison.
[R] p-values have now been added to the updated figure.
Please show a ratio of CO to NCO in each yeast in Figure 3B in the second paragraph of page 9 in the main text.
[R] The ratios have now been included in the figure for both the CO:NCO ratios and CO:corrected_NCO ratios, in the main text and figure legends.
Figure S5 and page 7, the first paragraph and page 9, third paragraph: CO/NCO densities (negative correlation to chromosome sizes) in S. cerevisiae should be checked with or without short chromosomes (I, III, and VI), which show very unique regulation of meiotic DSB formation (see Murakami et al. Nature 2020).
[R] Even excluding the small chromosomes, the size dependent trend persists for S. cerevisiae and S. paradoxus.
Table S7: Please add the S. cerevisiae gene name such as ZIP1 next to S. cerevisiae orthologs such as YDR285W. Moreover, please explain the column in detail or clarify the data. What does "meiosis" mean here? For example, YJL074C is SMC3, which is expressed in mitosis as well as in meiosis. The same is true for YGL163C, which is RAD54, which plays a minor role in meiosis, but plays a critical in mitotic DSB repair.
[R] We corrected Table S7 as desired by systematically including the standardized gene names.
The Gene Ontology (GO) annotation is a statement about the function of a particular gene. It offers a structured framework and a comprehensive set of concepts to describe the functions of gene products across all organisms. It is specifically crafted to support the computational representation of biological systems. In our specific case, we only looked at genes with the gene ontology annotation "meiosis". Together, these statements comprise a "snapshot" of current biological knowledge and is by no means absolute. This has been detailed in the supplementary Table S7.
Reviewer #2 (Significance (Required)):
This study provides the landscape of meiotic recombination in non-Saccharomyces yeast, Kluyveromyces lactis. The genome-wide recombination map in K. lactis shows lower crossover frequencies with weaker crossover interference than those in S. cerevisiae. Overall, the experiments and informatic analyses have been done in good quality and the results are convincing. The paper provides additional new information on the landscape of meiotic recombination in different yeast species, particularly in terms of the evolution of meiotic recombination. These results are of great interest to researchers in the field of meiotic recombination and evolution of meiosis.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)): __
Dutta et al. have compiled a genome-wide meiotic recombination map for Kluyveromyces lactis and compared it to a compilation of meiotic recombination maps for four other species, two of which (Lachancea kluyveri and Lachancea waltii), like K. lactis, predate the genome duplication event that produced the other two (Saccharomyces cerevisiae and S. paradoxus). Meiosis in many species studied (including metazoans and plants) shows control over the number and distribution of crossovers, which are critical for faithful chromosome segregation during meiosis. This takes the form of crossover interference, where crossovers are spaced more evenly than expected by chance, and crossover homeostasis, where many fewer chromosomes lack a crossover than is expected by chance. While both of the post-duplication species show both crossover interference and homeostasis, none of the pre-duplication species show crossover homeostasis, and crossover interference is very weak. In two cases (K. lactis and L. waltii), this can be explained by mutational loss of a few of the genes (called the ZMM genes) that promote meiotic crossovers in many species. However, L. kluyveribehavior cannot be explained in this way. Recombination hotspots are present but are not shared between the pre-duplication species or between the pre- and post-duplication species, perhaps not surprising for species that diverged more that 100 million years ago. Overall, this work will be a useful contribution to our understanding of the different possible flavors of meiotic recombination mechanisms and control that are possible (and, one might add, promote long-term species viability). A) Evaluation, reproducibility and clarity The work presented in this paper is straightforward and unimpeachable and will largely be of interest to those studying meiotic recombination, be it mechanistic studies or studies of the implications for population genetics. The analysis is technically correct, although there are some aspects where a slightly different emphasis should be considered (see comments below). However, the data and the analysis could stand as they currently are, without further revision.
Suggestions are below. 1. (trivial) it would have been useful if pages and lines were numbered.
[R] We regret the oversight. Figure numbers, Line numbers and Page numbers have been added.
"Across the 205 meioses...". In general, it would be desirable to apply compensation for the fact that NCOs and COs are differently detected. Since, in K. lactis, 35% of COs are not accompanied by detectable gene conversion, it seems reasonable to apply a correction to measured NCOs here and throughout the paper, regardless of the species. For example, if one assumes that 35% of NCOs are not detected, how does this affect estimates of chromosomes that do not appear to have undergone interhomolog recombination? Estimates of CO/NCO bias? In a similar vein, if the CO event is not considered (just the conversion events associated with it), how does this affect measures of conversion tract lengths in COs and NCOs?
[R] We thank the reviewer for this suggestion. We have performed the correction for the NCO estimates as described in Mancera et al. 2008, on a per tetrad basis across all the species. The fraction of missed NCOs were 7%, 34%, 30%, 23% and 25% respectively for S. paradoxus, S. cerevisiae, K. lactis, L. waltii and L. kluyveri. The fraction of missed NCOs depend upon the parental marker density. In addition, we performed the CO:NCO bias analysis both with the detected and the corrected NCO frequencies and the trends remain unchanged (Now included in figure 3). Finally, we refrain from using the corrected NCO frequencies while reporting the NCO frequencies (Table 1, main text) to maintain uniformity with our previous work and since, these corrections do not alter any results.
It might be useful to report recombination event frequencies in terms of events/chromosome, as this, rather than event/unit distance, is functionally more relevant. In the same vein, it might be useful to consider total event homeostasis, in addition to just crossover homeostasis.
[R] This has been updated as suggested. .
An interesting observation is that two of the three pre-duplication species clearly at one time had a full complement of ZMM genes but lost some due to mutation. Have there ever been attempts to detect either synaptonemal complex or axial elements in these species?
[R] This is related to point #8 from reviewer 1 and to the major point of reviewer 2 (please see above).
To our knowledge, cytological observations of synaptonemal complex (SC) or axial elements have been performed in L. kluyverionly by us and the SC is clearly visible (Legrand et al 2024).
However, it is key to remind here that K. lactis axis protein encoding genes HOP1 and RED1 have been cloned by the Roeder's lab by functional complementation of S. cerevisiae corresponding mutants, supporting the functional conservation of these genes (Smith and Roeder 2000). Finally, as mentioned above, K. lactis Zip1 retained at least some function of the ancestral Zip1 protein that are also shared by the S. cerevisiae protein (Voelkel-Meiman 2015).
The observation of elevated evolutionary rates in ZMM genes is also intriguing, but it would help if "dN/dS ratio" was defined.
[R] It is now defined in the text.
The observation of frequent E0 chromosomes is taken to suggest efficient achiasmate segregation; has the "corrected" NCO frequency been considered? Do the different frequencies of E0 chromosomes predict the different spore viabilities seen between species?
[R] E0 is not predictive at all of the spore viability as we have shown in previous studies (see L. kluyveri - Brion et al. 2017, L. waltii-Dutreux et al. 2023). In addition, this has been shown is S. cerevisiae as well (Nishant et al. 2009).
Figure 3A-what would this look like if it were plotted as "Events per chromosome" rather than per megabase?
[R] We changed the figure (now figure 2A) and plotted as events per chromosome to show the variability of events at the chromosome level.
Figure legends tend to be unreasonably terse, which makes figures more difficult to interpret.
[R] This has been updated as suggested.
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Referee #3
Evidence, reproducibility and clarity
Dutta et al. have compiled a genome-wide meiotic recombination map for Kluyveromyces lactis and compared it to a compilation of meiotic recombination maps for four other species, two of which (Lachancea kluveri and Lachancea waltii), like K. lactis, predate the genome duplication event that produced the other two (Saccharomyces cerevisiae and S. paradoxus). Meiosis in many species studied (including metazoans and plants) shows control over the number and distribution of crossovers, which are critical for faithful chromosome segregation during meiosis. This takes the form of crossover interference, where crossovers are spaced more evenly than expected by chance, and crossover homeostasis, where many fewer chromosomes lack a crossover than is expected by chance. While both of the post-duplication species show both crossover interference and homeostasis, none of the pre-duplication species show crossover homeostasis, and crossover interference is very weak. In two cases (K. lactis and L. waltii), this can be explained by mutational loss of a few of the genes (called the ZMM genes) that promote meiotic crossovers in many species. However, L. kluyveri's behavior cannot be explained in this way. Recombination hotspots are present but are not shared between the pre-duplication species or between the pre- and post-duplication species, perhaps not surprising for species that diverged more that 100 million years ago. Overall, this work will be a useful contribution to our understanding of the different possible flavors of meotic recombination mechanisms and control that are possible (and, one might add, promote long-term species viability).
A) Evaluation, reproducibility and clarity
The work presented in this paper is straightforward and unimpeachable, and will largely be of interest to those studying meiotic recombination, be it mechanistic studies or studies of the implications for population genetics. The analysis is technically correct, although there are some aspects where a slightly different emphasis should be considered (see comments below). However, the data and the analysis could stand as they currently are, without further revision. Suggestions are below.
- (trivial) it would have been useful if pages and lines were numbered.
- "Across the 205 meioses...". In general, it would be desirable to apply compensation for the fact that NCOs and COs are differently detected. Since, in K. lactis, 35% of COs are not accompanied by detectable gene conversion, it seems reasonable to apply a correction to measured NCOs here and throughout the paper, regardless of the species. For example, if one assumes that 35% of NCOs are not detected, how does this affect estimates of chromosomes that do not appear to have undergone interhomolog recombination? Estimates of CO/NCO bias? In a similar vein, if the CO event is not considered (just the conversion events associated with it), how does this affect measures of conversion tract lengths in COs and NCOs?
- It might be useful to report recombination event frequencies in terms of events/chromosome, as this, rather than event/unit distance, is functionally more relevant. In the same vein, it might be useful to consider total event homeostasis, in addition to just crossover homeostasis.
- An interesting observation is that two of the three pre-duplication species clearly at one time had a full complement of ZMM genes, but lost some due to mutation. Have there ever been attempts to detect either synaptonemal complex or axial elements in these species?
- The observation of elevated evolutionary rates in ZMM genes is also intriguing, but it would help if "dN/dS ratio" was defined.
- The observation of frequent E0 chromosomes is taken to suggest efficient achiasmate segregation; has the "corrected" NCO frequency been taken into account? Do the different frequencies of E0 chromosomes predict the different spore viabilities seen between species?
- Figure 3A-what would this look like if it were plotted as "Events per chromosome" rather than per megabase?
- Figure legends tend to be unreasonably terse, which makes figures more difficult to interpret.
Significance
This paper adds to our understanding of the spectrum of meiotic recombination behaviors that are possible, and thus is of interest primarily to those who study meiotic recombination. It expands significantly the number of species for which meiotic recombination has been analyzed, and in particular has the surprising finding that loss of crossover control by mutation of the existing crossover machinery is remarkably common, with four of the six yeast species (I include here Schizzosaccharomyces pombe) lacking crossover interference. It will be a substantial, solid contribution to the field.
My expertise: meiosis, recombination, yeast, chromatin, chromosomes
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Referee #2
Evidence, reproducibility and clarity
This paper describes the genome-wide mapping of meiotic recombination in non-Saccharomyces yeast, Kluyveromyces lactics. By using heterologous parental strains, the authors mapped crossovers (COs) and noncrossovers (NCOs) on the genome of K. lactics which lacks proteins necessary for CO formation such as S. cerevisiae, mammals and plants. This is an extension of previous works by the authors's group which mapped CO and NCO in different yeast, Lachancea kluyveri and L. waltii by a similar approach. The authors found that CO frequencies in K. lactics are much lower than those in S. cerevisiae and COs showed weaker interference, which facilitates the non-random distribution of COs along a chromosome. Overall, the experiments and informatic analyses have been done in good quality and the results are convincing. The paper provides additional new information on the landscape of meiotic recombination in different yeast species. These results are of great interest to researchers in the field of meiotic recombination and evolution of meiosis. There are some issues that the authors may be able to address before the publication.
Major points:
While the authors noted that K. lactics shows the loss of a pro-CO factors (ZMM protein), Spo16, and Msh5 (due to the introduction of an in-frame stop codon), it still possesses other proteins such as Zip1, Zip2, Zip3, Zip4/Spo22, Mer3, and Msh4. It is still likely that these pro-CO factors control CO formation (and interference) in this yeast. It would be nice for the authors to study whether the knockout of these genes is dispensable for CO formation and interference in meiosis. A similar analysis should be done for L. klyuveri which retains all ZMM genes, but this is clearly out of the scope of this paper.
Minor points:
- No page number, no main Figure number. It is hard to review this paper.
- References: In some cases in the Introduction, the authors referred to review papers such as Pyatnitskaya et al. (2019) for ZMM proteins while in the other parts, they referred to original papers; for example, three papers for Mlh1-Mlh3. If the number of references is not limited, original papers should be cited in the text.
- Figure 3A, page 9, second paragraph: When the authors compared CO and NCO densities, it would be nice to show P-values for the comparison.
- Please show a ratio of CO to NCO in each yeast in Figure 3B in the second paragraph of page 9 in the main text.
- Figure S5 and page 7, the first paragraph and page 9, third paragraph: CO/NCO densities (negative correlation to chromosome sizes) in S. cerevisiae should be checked with or without short chromosomes (I, III, and VI), which show very unique regulation of meiotic DSB formation (see Murakami et al. Nature 2020).
- Table S7: Please add the S. cerevisiae gene name such as ZIP1 next to S. cerevisiae orthologs such as YDR285W. Moreover, please explain the column in detail or clarify the data. What does "meiosis" mean here? For example, YJL074C is SMC3, which is expressed in mitosis as well as in meiosis. The same is true for YGL163C, which is RAD54, which plays a minor role in meiosis, but plays a critical in mitotic DSB repair.
Significance
This study provides the landscape of meiotic recombination in non-Saccharomyces yeast, Kluyveromyces lactics. The genome-wide recombination map in K. lactis shows lower crossover frequencies with weaker crossover interference than those in S. cerevisiae. Overall, the experiments and informatic analyses have been done in good quality and the results are convincing. The paper provides additional new information on the landscape of meiotic recombination in different yeast species, particularly in terms of the evolution of meiotic recombination. These results are of great interest to researchers in the field of meiotic recombination and evolution of meiosis.
I have been studying meiotic recombination. On the other hand, because of my limited experience, I can not evaluate bioinformatics parts in this paper.
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Referee #1
Evidence, reproducibility and clarity
In their manuscript, Dutta and colleagues compared the meiotic recombination landscapes between five budding yeast species. In the first part of the work, the authors constructed a high-resolution map of meiotic recombination events in Kluyveromyces lactis supported by high-quality genome assemblies for two strains of this yeast. Then, partially repeating their CO and NCO mapping strategy, they compared a number of meiotic recombination parameters between the five species (sometimes three, depending on the quality of the data for each species). They particularly focused on key parameters for meiotic recombination, such as crossover interference and homeostasis and obligate crossover. Although the analysis is interesting, it is underdeveloped in many places and lacks the general conclusions regarding the evolution of recombination and the broader perspective that would be expected from a comparison of these phenomena in budding yeasts.
Major comments:
- The authors indicate that the distribution of hotspots and coldspots is not preserved between species, but this finding is not properly documented. I think it would be useful to include recombination maps in a main figure for all species (or at least for S. cerevisiae, K. lactis and L. waltii) with the elements highlighted. This will allow for a visual illustration of the variability in the recombination landscape between the studied species.
- Although analyzes analogous to those presented in Fig. S5 had already been published in other comparisons of the recombination landscape in yeast (eg, Dutreux et al., 2023), I think that Figs. S5A and S5B are worth to be presented in the main figures (not supplementary data). In many species of eukaryotes, the detection of NCOs is practically impossible, therefore only results for COs are presented. Therefore, it is perhaps also worth discussing the fact that the relationship applies to all recombination events and not only COs, and therefore is related to the regulation of DSBs frequency and not individual DSBs repair pathways.
- The authors find that CO coldspots were associated with DNA repair genes. Unfortunately, an equivalent analysis was not performed for all recombination events (CO + NCO). I presume this approach is based on the belief that COs are more mutagenic than NCOs. However, recent studies in humans suggest that, at least in mammals, meiotic DSBs themselves are mutagenic, regardless of the pathway used for their repair (Hinch et al., Science 2023). Therefore, I would suggest repeating the analysis also taking into account NCOs (although I am aware that the picture of NCOs may be incomplete). I would also like to see some graphical representation of the analysis. Is it possible to perform a classic analysis of coldspot/hotspot enrichment in relation to gene ontology?
- In relation to the previous point - it may be worth repeating this type of analysis also for other yeasts used in this study, or at least for S. cerevisiae, to be able to consider the extent to which this relationship is universal and dependent on the meiotic DSB repair pathway.
- In Fig. S7, the point where WGD occurred is marked in the wrong place, or at least that is what the sentence in the text says ("The Lachancea and Kluyveromyces species branched from the Saccharomyces lineage more than 100 million years ago, before to the ancestral whole-genome duplication (WGD) event specific of the S. cerevisiae lineage").
- The result presented in Fig. S8 is interesting and should be shown in the main figures. Perhaps it would be worth adding an illustration illustrating simple versus complex COs.
- The last part of the results includes an analysis of the evolutionary rates of the ZMM genes. In the discussion, the authors should also refer the results of this analysis to the previous analysis of the overrepresentation of DNA repair genes in recombination coldspots. I understand that ZMM are not DNA repair proteins in the strict sense, but I think it is worth familiarizing readers with the authors' view on this matter. Moreover, I would suggest showing where MLH1 and MLH3 are located on the plot in Fig. 6 (especially the meiosis-specific MLH3), whether the selection pressure acts on them as on ZMM proteins, or rather as on DNA repair proteins. Showing the SLX4 and MUS81 would also be interesting.
- I feel like the discussion is underdeveloped. I missed a deeper summary of the comparison between meiotic recombination among the tested budding yeasts in the context of the presence and absence of functional ZMM. Even the title of the work is not properly developed in the manuscript text. The analysis shows that it is not the presence of a functional ZMM pathway or its lack that introduces differences between the individual recombination landscapes, although ZMM determines the presence of proper CO interference. With the caveat that for L. kluyveri it is basically unknown whether it has a functional ZMM or not. Maybe confirming the lack of expression of some ZMM genes in meiosis of this species would answer the question of how it should be treated?
Minor comments:
- In general, Figure captions are imprecise, many of them lack clear information explaining what is depicted. Authors should remember that figure legends should be self-sufficient.
- In the revised manuscript, I would suggest placing figure numbers on the figures and using line numbering, which would facilitate the reception of the work and possible reference to its individual elements in the review.
Significance
The study provides a new insight into the variation in recombination landscape within budding yeast species with a special emphasis on crossover control. This includes also de novo assemblies of Kluyveromyces lactis genome and high-resolution tetrad-based maps of meiotic recombination events. Previously, recombination maps of different yeast species were compared, however this study focuses on budding yeasts, some of which lost ZMM pathway and differ in some crossover parameters, like interference and homeostasis.
Although the analysis is interesting, it lacks the general conclusions regarding the evolution of recombination and the broader perspective that would be expected from a comparison of these phenomena in budding yeasts.
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I thank the Referees for their...
Referee #1
- The authors should provide more information when...
Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...
Response: We expanded the comparison
Minor comments:
- The text contains several...
Response: We added...
Referee #2
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Referee #3
Evidence, reproducibility and clarity
In this manuscript Ochner et al. report the 3.2 Å cryo-EM structure of the type IV pilus (minus PilY1 adhesin) from P. aeruginosa PAO1. The authors demonstrate that the conserved N-terminal helix of pilin subunits (PilA) form a tubular arrangement within the hydrophobic core of the pilus whereas the divergent C-terminal pilin globular domain decorates the periphery of the pilus. Comparisons are then made against T4P structures from other organisms, highlighting interesting differences including a shorter rod diameter and lack of solvent-accessible loops for which the authors propose reduces proteolysis of the T4P compared to other organisms.
Major comments:
The results of this manuscript are convincing. The models and cryo-EM volumes, which are already accessible from the PDB and EMDB, are of good quality with no obvious issues. The conclusions drawn from the model are not speculative. While extensive mutagenesis experiments could help delineate critical residues involved in T4P assembly and clarify involvement in adhesion/biofilm formation, these would have to be done in the native organism, would require a significant amount of time and effort, and would be beyond the scope of the current manuscript.
Minor comments:
The figures are excellent and clear, and the text is well-written, with results easy to interpret.
One of the strengths of this paper is the comparative analyses across current bacterial T4P structures. In this respect, I would have liked a more thorough analysis here: - While differences in helical parameters, rod diameter, and rod length are presented, a figure showing comparison of surface electrostatics and/or hydrophobicity could help delineate differences (if any) across these species, which may reflect the different environments these bacteria inhabit.<br /> - A consurf representation of PilA is shown in Fig. 3h. It would be helpful to include either the sequence alignment used for this analysis or a sequence alignment for all the species presented in the manuscript, to show precisely which residues are absolutely conserved across these species.<br /> - A panel showing their full T4P as a surface with Consurf coloring would be informative to show conservation across the entire pilus and not just a PilA subunit. - The authors state that the models in Fig. 3 were aligned based on the matchmaker function in Chimera. Wouldn't the poor sequence conservation of the C-terminal globular domain of PilA drive the alignment towards the N-terminal helix? In that case, wouldn't using a comparative alignment strategy that focuses on the model itself (LSQ) or secondary structure elements (SSM) which would drive the alignment more towards the globular domain be more reflective of the full pilin subunit? - Related to the point above, it would be useful to include a table highlighting pairwise RMSDs across all models presented in this manuscript.
Significance
The authors rightfully highlight the importance of P. aeruginosa T4P in the development of biofilms; structural analyses of these pili are of clinical importance and of interest to researchers involved in bacterial motility.
To date, various structures of T4P and T4P subunits across a variety of bacterial have been solved by X-ray crystallography and cryo-EM (PDB: 9EWX (this study), 6GV9, 5VXX, 6XXD, 6VK9, 8TJ2). It appears that another group has recently published a slightly lower resolution (3.6 Å vs 3.2 Å) cryo-EM structure (PDB: 8TUM) of the T4P of P. aeruginosa PAO1 (Thongchol et al., Science, 2024). The model from this latter publication appears to be identical to the model presented in this manuscript. Since this work has now been published (with models being released mid-March 2024), and since Ochner et al.'s manuscript only appeared on Biorxiv on April 9th 2024, I feel it would have been appropriate and necessary to cite this paper. And while the Thongchol publication reduces the novelty of Ochner et al.'s manuscript, there is some merit in the comparative analyses performed, which if expanded upon, could further strengthen this manuscript enough to stand on its own.
Field of expertise: cryo-EM, bacterial secretion, membrane proteins
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Ochner et al. reports the cryo-EM structure of the Type 4 pili of Pseudomonas aeruginosa at decent 3.2 resolution with fully resolved pilin fold. It is a straightforward report, using state-of-the-art microscopy and data processing approaches as usual for the group and the figures and data representation are clear. The main findings of the work is that it visualizes the assembled pilus of an important pathogen (Pseudomonas aeruginosa is one of the ESKAPE pathogens with particularly impressive adaptability and Type IV pili are important for substrate colonization and biofilm formation). The PilA pilin fold is not far from that of a previously crystallized isolated homolog from the PAK strain (core hydrophobic N-helix, globular b-sheet-containing exposed CTD) and presents a central melting of the core helix also observed among multiple other PilA homologs from solved G- pilin structures. The main difference for the PAO1 pilus is the tight packing in a significantly thinner filament, which lacks protruding loops or other secondary structure insertions in the core pilin fold. The authors propose that this could lead to increased stability such as to proteases.
Again, the study is quite straightforward and besides the standard and well-executed EM workflow, it uses the classical approaches for pilus overexpression and purification (a PilT mutant that cannot retract and presents more T4P; well established mechanical shearing protocol for surface release, etc.). The structure is at decent resolution allowing full backbone tracing and side-chain resolution for confident model building, etc. The figures are clear, even if I would encourage some more vivid or at least contrasting colors for the cartoon model in Fig. 2 and some more detailed surface and conservation analyses, especially in terms of packing and surface exposure.
Minor comment: The electron density maps, atomic models and validation reports should be available for the review process. The refinement statistics in the table are very good and the figures and supplementary movie present clear densities but this should be standard protocol and could help with constructive suggestions from the reviewers. Large map files, etc can be provided via a link if too big for upload directly through the manuscript tracking system.
Significance
My main concern with this work is that it is quite minimalistic in terms of biology/physiology, especially in light of the many G- pili structures available, some of which the authors nicely review in terms of specific structural parameters. The hypothesis of increased protease or perhaps mechanical resistance is tantalizing but how the compact pilus fold actually affects Pseudomonas aeruginosa in its physiology is unclear. Are there any other differences in the surface properties relative to other pili (charge, surface motif conservation, etc.) and could they have relevance in terms of interactions with the substrate, another matrix component or a peculiar niche within the host? As a PilA mutant should be easy to get from a number of laboratories, how would a Pseudomonas delta-PilA mutant behave in terms of twitching motility, surface attachment and biofilm formation if complemented with PaO1 PilA vs. other pilins from Table S2 or a pilin with an engineered hybrid architecture (e.g. a loop or b-hairpin insertion)? If such heterologous/engineered pili are incubated with a mild protease mix, would they indeed exhibit increased fragmentation relative to the wt PAO1 pili? To me, most of these assays are relatively easy to be attempted in terms of molecular biology, phenotypic assays and in vitro biochemistry (e.g. plasmid-based complementation if full genetics are judged beyond the scope/time available, twitching, pellicle formation, SDS-PAGE of protease-treated sheared pili) and could really shed more specific insights light into the peculiarities of Pseudomonas T4P function, rather than just present the next resolved filament among the multiple other T4P already out there.
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Referee #1
Evidence, reproducibility and clarity
Summary
The study titled "Structure of the Pseudomonas aeruginosa PAO1 Type IV pilus" by Ochner and colleagues utilised cryo-electron microscopy (cryo-EM) to determine and describe the atomic structure of a complete type IV pilus (T4P) filament from Pseudomonas aeruginosa in its native state at an impressive resolution of 3.2 Å. The authors use state-of-the-art cryo-EM methodology, and the detailed description of their procedures allows for an adequate replication of the results. The T4P are essential for the virulence of P. aeruginosa, which is a clinically important human pathogen, as they play a crucial role in biofilm formation, a major factor in its resistance to antibiotics and ability to cause infections. Therefore, understanding the molecular mechanisms behind the ability of these bacteria to establish infections is vital, and this high-resolution structure of T4P provides valuable insight into this process.
Overall, this reviewer acknowledges the importance of the structure presented here and its significance to our understanding of P. aeruginosa infection and persistence, and hence is positive about the publication of the results, however, at its current state the manuscript raises the major questions outlined below, which must be addressed and corrected.
Major comments
The authors propose a model where T4P interact with the type IV secretion systems (T4SS) in the P. aeruginosa membrane. However, there is no current evidence in the literature to support a direct interaction between T4P and T4SS as these are functional and structural distinct secretion systems. T4P biogenesis is mediated by a specialised secretion complex (homologous to type II secretion systems), spanning both bacterial membranes and consisting of the outer membrane secretin subcomplex, the alignment subcomplex, and the inner membrane motor complex. This reviewer recommends that authors refer to the comprehensive review by Hospenthal and colleagues [PMID: 28496159] that details the T4P biogenesis and Craig and colleagues [PMID: 30988511] that provides an in-depth analysis of T4P secretin architecture. This reviewer recommends the authors to remove any misleading claims regarding a T4P-T4SS interaction. Furthermore, the introduction would benefit from a brief overview of the T4P biogenesis and secretin architecture to prevent any further confusion. While this study offers a higher resolution structure of P. aeruginosa T4P (3.2 Å) compared to the previously described work on the P. aeruginosa T4P (8 Å) described by Wang and colleagues [PMID: 28877506], the manuscript fails to convey the significance of this improvement. The authors should directly compare the new structure with the previously obtained cryo-EM structure, similarly to how they tackled the comparison to the X-ray crystallography structure (Figure S6). A dedicated figure visualising the key differences and benefits associated with the higher resolution is necessary to highlight the manuscript's significance. Furthermore, the authors should specify that the reported T4P belongs to the type IVa category and the "globular domain" of PilA should be further differentiated into the αβ loop and D region - widely accepted motifs present in the structures of type IV pilins [PMID: 31784891]. Highlighting them is crucial due to their roles in receptor binding, microcolony formation, and antigenic variation, warranting their inclusion in the manuscript. A more detailed display of intersubunit interactions, including the types and numbers of interactions is also recommended, however optional. Previous studies [PMID: 27698424, PMID: 28609682] hypothesize that disordered loops might be involved in significant T4P stretching, the authors should address how the lack of these structures in their model might affect the filament dynamics. Lastly, the study lacks experimental validation of the structure, either within the study or referenced from the existing literature and very weakly connects the structure to T4P's biological functions, such as twitching motility or DNA acquisition. For instance, a comparison could be drawn between the surface charge of the pili and its DNA binding capacity. Additionally, the T4P secretin complex of P. aeruginosa documented in [PMID: 27705815] should be modelled alongside the obtained T4P structure to compare the structure diameter with the PilQ secretin lumen. These revisions will strengthen the manuscript by addressing crucial points and highlighting the significance of the high-resolution T4P structure.
Minor comments
Figure 2 For consistency, the colours of PilA subunits between panels (a) and (d) should match.
Figure 3 For clarity, pilins should be coloured by domain.
L41 The word "surfaces" or "target receptors" rather than just "substrates" would be more accurate.
L87 Rather than "other bacteria" consider using "wild-type strains".
L145-147 For clarity, residues C134 and C147 that form a disulfide bond in the C-terminal loop should be displayed in the figure.
L371 For consistency, "h" should be in brackets, following the authors' style.
Significance
General assessment:
The significance of the study stems from a resolution improvement from the previously reported type IV pilus of P. aeruginosa by Wang and colleagues [PMID: 28877506] and complements well the X-ray crystallography data obtained previously by Craig and colleagues [PMID: 12769840]. Due to the role of T4P in the virulence of P. aeruginosa, the structure provides important biological information about the molecular mechanism of its niche establishment. Moreover, the structure can be used in subsequent drug design against P. aeruginosa infections.
Nature of advance:
The nature of the advance provided by this study is in the added structural detail of the T4P due to the obtained higher resolution of the map. Usually, the highest resolution structure is used to derive the conclusions about the biological functions of the filament, hence the structure provided here will be referenced as the final P. aeruginosa T4P structure in further studies.
Audience:
The higher resolution structure compared to the previously described will be interesting to the translational/clinical drug discovery audiences, which require a high-resolution structure for accurate drug design.
Field of expertise:
Type IV secretion systems, bacterial conjugation, conjugative pili.
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Referee #2
Evidence, reproducibility and clarity
Summary
Lavigna et al. described the effect of Trazodone in Marinesco-Sjögren syndrome model mice. Although the results are somewhat disappointing, this research has provided fundamental evidence for the future development of MSS therapeutics. There are few minor comments to further improve the manuscrip
Major comment
P14 "Trazodone metabolism to m-CPP was slightly impaired in woozy mice compared to C57BL/6J mice. This was evident from the m-CPP/trazodone ratio, calculated on the AUC0-t in the plasma, which was 0.34 in woozy and 0.67 in C57BL/6J mice."
Why was the concentration different between WT and woozy mice? Which organ mainly contributes to the metabolism of trazodone? Is the function of this target organ different between WT and woozy mice? Similar to trazodone, m-CPP clearance from plasma was slightly faster in woozy than in C57BL/6J mice. Is m-CPP eliminated via the kidney? Or liver? Why is there a difference? Does SIL1 functions in liver or kidney? Needs discussion. This is the same for brain m-CPP levels.
Minor comments
P3 L5 mutation should be variant. P4 L1 eIF2a-P should be phosphorylated eIF2α (p- eIF2α). The reviewer prefers (p- eIF2α) than (eIF2α-p) throughout the manuscript.
P9 L11 M-CPP should be fully spelled out the first time it appears. m-Chlorophenylpiperazine (m-CPP) Please explain the difference between the expected function of trazodone and its metabolite m-CPP. Why m-CPP is not effective.
P11 L3 Fig. 3 Fig. 3A and B? P11 L6 at 7 weeks of age? P12 L17 ~4 times, 4 times? Please state the exact value.
Figure 7 Why are brain m-CPP levels higher than plasma levels? Is trazodone metabolized in brain tissue?
P19 L7 ISRIB; please fully spell out the first time it appears.
Referees cross-commenting
Since the viewpoints of Reviewer 1 and Reviewer 2 are different, it would be a good report if both comments are satisfied.
Significance
What are the strongest and most important aspects?
The author tested three potential drug candidates for MSS in MSS model mice in vivo. In addition, the author performed a PK study.
What aspects of the study should be improved or could be developed?
The description of the PK study is not sufficient to explain why the PK is different between the WT and the woozy mice is different.
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Referee #1
Evidence, reproducibility and clarity
This study provides negative in vivo evidence for the use of two PERK inhibitors and of TUDCA for the treatment of Sli1-related Marinesco-Sjögren syndrome (MSS).
Overall, the manuscript reports a substantial amount of work and the study could be published in its present format. The experiments are well described in terms of methodology and appropriate analysis has been applied. Claims are proportionate and not overstated
I would have only minor comments related to some clarifications that the authors could make in the present manuscript and a suggestion for experiments that could improve the manuscript.
First, although this is not my expertise, the in vitro analysis of CHOP luciferase assays suggests that very high concentrations, in particular of TUDCA, are needed to observe an effect. The authors may wish to clarify their opinion and whether this could be the reason why in vivo they have been unable to obtain any inhibition of the PERK pathway. Second, it seems to me that when measuring the Trazodone metabolism there is a difference between acute and chronic treatment. It would be worth discussing what the authors make of that and what is more relevant (I assume chronic) to the disease model outcome.
With respect to the experiments a simple and informative addition would be the evaluation of the PERK pathway in mice treated with TUDCA, as this is missing.
A more difficult but potentially more interesting line of investigation is that of searching for potential actions of Trazodone that are PERK independent and might be responsible for the partial rescue observed in the beam walking test, which is much more sensitive and specific than rotarod, so worth considering. Assuming authors want to go down this route and add significance to their study my suggestion would be an unbiased RNA seq from the brain samples they already have. However, this is a suggestion to steer the study towards a more positive outcome and it is not necessary to support their current conclusions.
Significance
My expertise is in the characterization of preclinical models of neurodegenerative diseases. This study is significant in the field because it reveals the complications arising when searching for non toxic PERK inhibitors for MSS. There is no current treatment for MSS and this study can help directing future studies towards more promising alternatives. Of course, providing only negative results is a limitation and the study would greatly increase its overall impact and significance if the effect of Trazodone would be further investigated.
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Reply to the reviewers
We thank the reviewers for their positive assessment of our manuscript. We agree that there are some further experiments suggested by the reviewers that would enhance our study. We have highlighted further proposed experimental work in bold for clarity.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
- EVIDENCE, REPRODUCIBILITY AND CLARITY Summary: The Matrix 2 (M2) protein of influenza A virus (IAV) is a single pass transmembrane protein known to act as a tetrameric ion channel that is important for both viral entry and egress. The paper by Figueras-Nova et al. entitled "Caspase cleavage of Influenza A virus M2 disrupts M2-LC3 interaction and regulates virion production" reports on the regulation of IAV virion production through a regulatory interplay between a caspase cleavage site and a LC3 interacting region (LIR) motif in M2. In its C-terminal cytoplasmic tail the IAV M2 protein contains a C-terminal LIR motif interacting with LC3. The authors show that this LIR motif is preceded by a functional caspase cleavage motif cleaved predominantly by caspase-6, with some contribution from caspase-3: The motif 82-SAVD-85 directs cleavage after the aspartate (D) at position 85. The cleavage leads to loss of the remaining C terminal sequence from amino acid 86 to 97. The core LIR motif 91-FVSI-94 LIR motif is then lost from M2 which can no longer bind LC3. As previously described by the same group using point mutations in the LIR motif (Ref 12.), loss of a functional LIR., here by caspase- mediated deletion of the LIR, affects the virion production and inhibits filamentous budding. LC3B lipidation is increased upon treatment with a caspase inhibitor. The authors show for the first time that LC3 is included into IAV virions via binding to M2. Furthermore, they also report a co-crystal structure of the M2 C terminus (aa 70-97), containing the caspase cleavage site and LIR, and LC3B (aa 3-125) adding new insights into this interaction and showing that the caspase cleavage site is in a flexible region N-terminal to the LIR. This work shows how caspase cleavage may modulate LC3B lipidation, trafficking to the plasma membrane, incorporation of LC3B in the virions, filamentous budding and virion production (viral titer).
Major comments: The findings reported here are very well supported by the data shown. This is a very clearly written paper with well described and nicely visualized results that are accompanied by adequate statistical analyses.
We thank the reviewer for their assessment of our manuscript.
The authors report a new way the LC3B binding to the C-terminal tail of the M2 proteins is regulated and suggest that this is an adaptation the virus has made to adjust virion production to host cell status by hijacking the function of host caspases. They show that the caspase cleavage motif is evolutionary conserved and use that as an argument. Perhaps it could be discussed if it also could be an argument that the host protects itself against a too massive virion production as this could be too detrimental to the host? Would it not also be an evolutionary advantage to the virus in the long run by avoiding killing the host?
This is an interesting point. We agree there could be advantage for the virus not to overproduce virions under certain circumstances. Consistent with this caspase-6 deficient mice had increased mortality in response to IAV PR8 infection, and presented and increase in viral spread in the lungs (Zheng, 2021; doi: 10.1016/j.cell.2020.03.040). This is also relevant for the comments made by Reviewer 2. The manuscript will be updated to include a discussion of this point.
A question I may raise which is optional as it may be too much work to address as part of this study is if the reported regulation of LC3B binding has any role in regulating the ion channel function of the M2 tetramer?
It is well established that there is no impact of distal C-terminal truncations on M2 ion channel activity (Cady et al., 2009, doi: 10.1021/bi9008837 Schnell and Chou, doi: 10.1038/nature06531; Nguyen et al., 2008, doi: 10.1021/bi801315m; Tobler et al., 1999, doi: 10.1128/jvi.73.12.9695-9701.1999). This is also consistent with data from our lab (Ulferts et al., 2021, doi: 10.1016/j.celrep.2021.109899, Beale et al., 2014, doi: 10.1016/j.chom.2014.01.006) as well as others (Ren et al., 2015, doi: 10.1128/JVI.00576-15) showing the effects of the LIR motif and the proton channel are distinct. We appreciate the reviewer suggesting further work here as optional, but there is already compelling evidence to show there is no substantial effect of the LIR motif on ion channel activity. (See also Reviewer 2 points 4 and 5).
Minor comments: Delete "with" in line 145.
This will be changed in the updated manuscript.
Line 217: It should be written more specifically how "cells were surface stained with M2"
The protocol for surface staining of M2 will be explained in more detail in the updated manuscript.
- SIGNIFICANCE
This is a very well performed study with a sound experimental strategy and well performed assays with clear results increasing our insight into the interplay between the Influenza A virus and host cells. Although caspase mediated cleavage of the autophagy receptor and signaling scaffold protein p62 (Ref. 25), removing the LIR and LC3-binding, has been reported before I consider this study as novel in reporting this type of regulation of LC3 binding. The cleavage of p62 deletes a large part of the protein while here it is a "clean" deletion of the LIR sequence representing a conceptual advance of regulation of LC3 binding. The study also reports for the first time on LC3B incorporated into virions. The effects on trafficking to the plasma membrane and viral budding and virion production are similar to those reported before (Ref. 12) using viruses with point mutations crippling the LIR motif. This research will be of interested to all studying virus- host interaction and to the autophagy field both as a non autophagic role of LC3B, and as a regulatory mechanism of LIR-LC3B interactions involving the irreversible caspase cleavage-mediated deletion of the LIR motif.
We thank the reviewer for this assessment of our manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The influenza A virus (IAV) M2 protein is small transmembrane protein which plays a role in virus entry and egress. In a previous study, Beale et al. (2014) identified an LC3-interacting region (LIR) in the M2 cytoplasmic domain that was found to recruit the LC3B protein to the plasma membrane. Recombinant IAV harboring mutations in the LIR motif showed reduced particle stability and lost filamentous morphology.
In the present study, Figueras-Novoa et al. show that the LIR motif is removed in response to activation of cellular caspases. The authors demonstrate that in in IAV-infected THP-1 cells M2 is partially cleaved at the motif (82)SAVD(85)¯A by caspase 6. Caspase inhibitors abolished cleavage, and a mutant virus harboring the D85A substitution was found to be resistant to caspase action. A crystal structure of purified M2 C- terminus and LC3B revealed that the caspase cleavage site lies in a flexible region that is accessible to caspases.
Mutant virus encoding a truncated M2 protein (M2D86-97) was unable to interact with LC3, in accordance with the absence of the LIR motif. The M2D86-97 mutant showed reduced lipidation of LC3, while enhanced lipidation of LC3 was observed when wild-type virus-infected cells were treated with caspase inhibitors. The authors also observed that cell surface transport of M2D86-97 but not M2-D85A was impaired. However, in purified virus particles a mix of cleaved and uncleaved M2 was detected. The authors also demonstrated that lipidated LC3B was present in purified virions of wild-type virus particles but even more abundant in M2-D85A virions. Finally, M2D86-97 mutants produced significantly less infectious particles compared to wild-type virus while the D85A cleavage mutant replicated to similar titers than wt virus.
Based on these findings the authors concluded that caspases regulate the interaction of M2 protein with LC3 which impacts virion production. Specifically, they propose that caspase-mediated removal of the LIR motif may enable a switch between filamentous and non-filamentous budding in response to depletion of cellular resources. However, the authors were unable to rescue a filamentous IAV with a truncated M2 protein and therefore could not provide direct proof for their guess.
While the data are sound and presented well, they do not support the conclusions of the authors.
- To the authors opinion, the conserved caspase cleavage site in the M2 protein might provide an evolutionary advantage for the virus. However, the M2-D85A mutation has no effect on viral replication, so the biological significance of why M2 needs to be cleaved at all is unclear. The conclusion that caspase-induced M2 cleavage is a fine-tuning mechanism of IAV has not been supported by experiments.
We thank the reviewer for the assessment of our data. We think the reviewer is specifically objecting to the phrase “We conclude that this highly conserved interaction and cleavage act as a regulatory mechanism exploited by IAV to fine-tune virion production in different cellular contexts.” This is a reasonable inference from our results, but we accept that it is not proven. We will change the wording to make it clear this has not been definitively demonstrated.
- The finding that the permanently truncated IAV M2 mutant virus was substantially attenuated does not necessarily mean that abrogation of M2-LC3 interaction was responsible for this attenuation. As the M2 protein plays a role in virus budding at the plasma membrane (recruitment of M1 protein, induction of membrane curvature, membrane scission), the impaired transport of the truncated M2 protein might already explain that the virus was attenuated and that incorporation of the protein into the viral envelope was reduced.
We will confirm this further with additional experiments using LIR mutants. Recapitulating the plasma membrane transport defect of truncated M2 with LIR mutants including the newly characterised M2D87A and M2D88A mutants and a more severe mutant with a FVSI_AAAA substitution would strongly imply this truncation mutant phenotype is due to the lack of LIR motif.
- It is also not clear whether the loss of the C-terminal 11 amino acids may have affected the interaction of the M2 protein with other proteins such as TRAPPC6A-delta (Zhu et al., 2017).
This is a reasonable point, however Zhu et al., 2017 (https://doi.org/10.1128/jvi.01757-16) reported that the interaction with TRAPPC6A retains M2 intracellularly. If the phenotype observed with our truncation was due to the loss of interaction with TRAPPC6A, the opposite phenotype would be observed (more M2 in the plasma membrane with the truncated M2∆86-97 mutant). To address this point directly we will attempt to rescue an M2 mutant virus that has disrupted the reported TRAPPC6A binding site and assess M2 plasma membrane localization.
The authors did not rule out whether the truncation of the M2 protein by 11 amino acids would have an effect on proton channel activity. Proton channel activity, however, might be important to preserve the metastable conformation of HA in the secretory pathway and might be also important for virus uncoating.
M2D86-97 induced less LC3 lipidation than wild-type M2 or the D85A mutant. The remaining lipidation was attributed to the ion channel activity of the M2 protein. Can the authors rule out that the truncation of the M2 protein led to reduced ion channel activity which in turn led to reduced LC3B lipidation?
We have addressed points 4 and 5 in response to Reviewer 1.
The suggested role of caspase cleavage as a regulatory switch between filamentous and spherical virions (lines 304- 313) is highly speculative as long as the authors do not provide any experimental proof for it. The authors indicated that they were unable to rescue filamentous IAV with M2D86-97. However, would it be possible to use caspase inhibitors to test their hypothesis?
We acknowledge that M2∆86-97 could not be rescued in a filamentous background. The use of caspase inhibitors would only increase the amount of full length M2 present, and does not provide an alternative strategy for increasing the proportion of truncated M2. However, since M2∆86-97 mutant could not be rescued, we will attempt to rescue additional LIR motif mutants to address this point. In particular, D87A and D88A mutants could be generated in a MUd background, as well as the F91S mutant.
The authors used only the PR8 strain for their studies, a highly cell culture-adapted strain with spherical morphology. Are the findings obtained with this strain are also valid for others IAV strains?
As we highlight in Figure 2I, both the caspase cleavage motif and LIR motif are highly conserved in human IAV strains. PR8 was used as it is the reverse genetic system in use and approved for use in the lab. We will attempt to address this by testing whether other IAV strains we are able to obtain also undergo caspase mediated cleavage of M2. If possible, we will obtain recent clinical isolates to show cleavage of M2 in a strain that has not adapted to cell culture.
- The authors mainly used the THP-1 cells for their studies, a human macrophage-like cell line. However, human IAV mostly replicate in epithelial cells of the respiratory tract and cause only abortive infections of macrophages. Why did the authors choose this cell line? Can the findings obtained with this cell line be translated to epithelial cells of the airways?
THP-1 cells are widely used for the study of caspase activity. However, we also show M2 cleavage in MDCK cells and HAP1 cells. PR8 infection of A549 cells does not induce significant amounts of cell death in the infection time points used and, as caspase activation is linked to cell death, we did not observe M2 cleavage in this cell type. We will attempt to infect some epithelial cell types to confirm this phenotype.
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Minor issues:
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Fig. 1C: There seem to be quite some differences in the cleavage efficiency of M2 between panels A, B, C, and D? Any explanations?
Different cell types (THP-1 cells and HAP1 cells) are used for the experiments mentioned above, which accounts for the different amount of M2 cleavage.
- Fig. 1: Panel E: The labeling of the first amino acids as aa 76 seems to be wrong!
We thank the reviewer for pointing this out, this will be corrected in the updated manuscript.
Line 147: ...caspase mediated disruption of the M2-LC3 interaction (Fig 2A-B). Should be Fig. 2A-C.
This sentence was referring to Figure 2A-B, as it refers to LC3B lipidation and not the coIP. This sentence will be changed in the text to reflect the intended meaning.
- Growth kinetics of the various mutant viruses are missing?
__We will provide growth kinetics for the relevant mutants _(M2D85A and M2∆86-97).___
- Line 195: The authors speculate that aa85 is important for viral fitness: That should be demonstrated!
This speculation is based on the very strong conservation of D85 in human IAV strains. The importance of D85 in viral fitness (permitting cleavage of M2) is only likely to be directly demonstrable in transmission models (for example ferrets) which is not feasible or justifiable.
Reviewer #2 (Significance (Required)):
Authors concluded that caspases regulate the interaction of M2 protein with LC3 which impacts virion production. Specifically, they propose that caspase-mediated removal of the LIR motif may enable a switch between filamentous and non-filamentous budding in response to depletion of cellular resources. However, the authors were unable to rescue a filamentous IAV with a truncated M2 protein and therefore could not provide direct proof for their guess. +<br /> +
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As stated in the response to the comments above, we will attempt to rescue LIR mutant viruses (____D87A and D88A) in a MUd background which would provide further support for our hypothesis. Our data has significance for the understanding of the cell biology of influenza infection as commented on by Reviewers 1 and 3.
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- Reviewer #3 (Evidence, reproducibility and clarity (Required)): Summary : In this article, the authors identify a caspase cleavage site in the influenza A virus (IAV) Matrix 2 protein (M2) that leads to a truncated form of M2 deleted from its C-term LC3-interacting region (LIR). This cleaved form of M2 is seen and accumulates starting at 12 hours post-infection. IAV expressing M2 delta 86-97 mutant, corresponding to cleaved M2, seems to disrupt LC3B localization to cell plasma membrane upon infection. The authors also show that the IAV M2 delta 86-97 has a reduced viral titer compared to IAV WT. Overall the data are quite exciting where the authors identify the specific caspase responsible for the cleavage and show the residues of M2 necessary for LC3 interaction. However, some of the data showing the consequence of the cleavage for viral replication could be better clarified.
We thank Reviewer 3 for their kind comments and we propose further experiments to clarify the consequences of cleavage.
Major comments: - In Fig3A-B, the authors seek to demonstrate that the localization of M2 to the plasma membrane requires LIR motif. However, the representative images for cell infected with the delta 86-97 mutant show relatively few cell are expressing M2 raising questions of the infectivity of this mutant virus or if the overall expression of M2 in this assay is less for the delta 86-97 mutant. The authors should consider first quantifying the ratio of M2 cell surface staining over total M2 staining and second re-evaluate the representative images chosen.
__We will include more examples of permeabilised cells in which comparable numbers of cells are M2 positive between mutants. We will also include high-content microscopy based quantification to support this. __To clarify, we confirm that the quantification of M2 intensity in the plasma membrane is carried out relative to the number of M2 positive cells, as the reviewer agrees is the most accurate way. To avoid confusion, we will update figure legends to describe more accurately the quantification process. A comparison between surface M2 and total M2 cannot be done on an individual cell basis, as once cells are permeabilized (to look for internal M2), robust differentiation between surface and internal M2 is difficult. The above clarification and additional data should provide the necessary support for our conclusions.
- In fig3E, it is unclear what is being quantified in the graph as the legend and text lines 222-223 mention that spot intensity was measured but the y axis indicates LC3 relocalization intensity. Given LC3 is punctated particularly in the cytosol, It is unclear which spots of LC3 they are referring to. Based on the images shown, using a graph with LC3 surface staining as performed for M2 would clarify the data. The authors should clarify the reporting of these data in the results section. Additionally, the images of the control non-infected cells should be added to 3C.
We agree with the reviewer on this point. The figure will be updated to describe more accurately what is being quantified. Additionally, images for uninfected cells in 3C will be added.
- The data in Fig4 and FigS3 need to be strengthened to be conclusive. The volcano plot in FigS3A indicates that there is more LC3B and IAV proteins in M2 D85A than M2delta86-97. However in Fig4E, both LC3 I and LC3 II are increased in virions M2 delta 86-97 compared to M2 D85A which is opposite to the authors' conclusions in lines 244-245. In other words, the total amount of lipidated LC3 is higher in virions from IAV M2 without LIR motif than M2 with LIR. LC3II/I ratio in fig4F would suggest in virions containing M2 with LIR motif, LC3B II may be preferentially incorporated compared to virions containing M2 without LIR, which incorporates both LC3B I and LC3B II. Since this is a critical point made by the authors, performing a co-immunoprecipitation of M2 D58A and M2delta86-97 in the particles and then assessing for binding of LC3 I or II would bolster their conclusions.
Figure 4F quantifies the ratio of LC3II to LC3I in infectious particles. Another two repeats used to quantify this ratio will be shown in addition, with a better representation of increased amounts of lipidated LC3II in M2D85A infectious particles, as well as an increased LC3II/LC3I ration in said particles when compared to M2∆86-97. Because of the low yield acquired from the purification of IAV virions, performing an IP would be difficult. Even if this were technically feasible it would not prove that M2 is binding LC3 inside the virion – we do not make this claim in our paper, merely that LC3B can be detected in the purified viral particles. We will clarify this point in the revised manuscript.
- In Fig4J, even if statistically significant, the PFU difference between M2 D85A and M2 delta86-97 is minimal, performing growth curve assay would help appreciate this difference over time. In Thp1 cells, as the authors show caspase cleavage of M2 at time point 12h 14h 16hpi etc... (fig1), they should also show PFU data at these same time points for M2 mutant D85A compared to WT and M2 delta 86-97.
We agree with the reviewer and indeed this was a point we attempted to make in our manuscript: Figure 4J shows a statistically significant difference between the titers. However, in the text we state that, even though statistically significant, the difference is much smaller than in other titer quantifications performed. Given the nature of a plaque assay, differences of less than a log fold cannot be considered as definitively indicating biological significance. We will clarify this in a revised manuscript. We will also provide the relevant growth kinetics (as per response to Reviewer 2).
- The title of Fig4 and FigS3 and in text line 226 should be changed as M2 incorporation into virions is not shown and not described in the text. Plus, in figS3B, the authors show that between the M2 mutants, there is no difference in the abundance of M2 and other viral proteins compared to M1.
The title of Figures 4 and S3 will be changed to more accurately reflect all of the points made by the figure.
- In the image shown in Fig4H the number of plaques is higher for M2delta86-97 even though the size in smaller than M2 WT. Could the authors clarify in the text of the results section how they quantify PFU in their plaque assay and if they used a size criterion when quantifying the number of plaques?
The images of plaques are taken at different dilutions, with the M2∆86-97 image belonging to two dilutions lower than the M2WT image. We will include the calculation used for PFU/mL, which does not take into account plaque size. Furthermore, images of the whole plate, showing plaqued serial dilutions will be shown.
- In fig3B, the legend indicates 8 hpi but on the graphs it is 9 hpi.
We thank the reviewer for pointing out this mistake. Both should read 8 hpi, this will be corrected in the new manuscript.
Reviewer #3 (Significance (Required)):
The authors demonstrated that IAV M2 binding to LC3 is regulated by caspase cleavage. The authors clearly identify the cleavage site and the caspase involved: caspase 6. The cleaved form of M2 seems relevant to IAV infection as it is accumulating after 12hpi. Using a M2 mutant D85A that cannot be cleaved by caspase 6 and truncated M2 mutant delta86-97 mimicking caspase cleaved M2, the authors are able to elegantly address the role of M2 cleavage. However, the importance of M2 caspase cleavage on IAV infection is not demonstrated. Eventually, addressing the impact of the caspase cleavage of M2 LIR motif on autophagy or CASM would be interesting. - Advance: conceptual. - Audience: basic research, specialized in virology, specialized in autophagy. - Field of expertise: virology, autophagy.
We agree with the reviewer that we have made a conceptual advance in our understanding of the cell biology of influenza A virus infection. We have also determined the structure of the terminal part of the M2 tail in complex with LC3B. The biological importance of the phenotypes we show are most likely in transmission of the virus between hosts, which for IAV would require animal experiments outside the scope of this study. We have demonstrated regulation of the LIR motif by caspase cleavage in a variety of ways, using cell biological and biochemical methods. IAV is a very significant human and animal pathogen, and we believe we have made an important advance in describing a host-pathogen interaction of relevance for viral egress.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In this article, the authors identify a caspase cleavage site in the influenza A virus (IAV) Matrix 2 protein (M2) that leads to a truncated form of M2 deleted from its C-term LC3-interacting region (LIR). This cleaved form of M2 is seen and accumulates starting at 12 hours post-infection. IAV expressing M2 delta 86-97 mutant, corresponding to cleaved M2, seems to disrupt LC3B localization to cell plasma membrane upon infection. The authors also show that the IAV M2 delta 86-97 has a reduced viral titer compared to IAV WT. Overall the data are quite exciting where the authors identify the specific caspase responsible for the cleavage and show the residues of M2 necessary for LC3 interaction. However, some of the data showing the consequence of the cleavage for viral replication could be better clarified.
Major comments:
- In Fig3A-B, the authors seek to demonstrate that the localization of M2 to the plasma membrane requires LIR motif. However, the representative images for cell infected with the delta 86-97 mutant show relatively few cell are expressing M2 raising questions of the infectivity of this mutant virus or if the overall expression of M2 in this assay is less for the delta 86-97 mutant. The authors should consider first quantifying the ratio of M2 cell surface staining over total M2 staining and second re-evaluate the representative images chosen.
- In fig3E, it is unclear what is being quantified in the graph as the legend and text lines 222-223 mention that spot intensity was measured but the y axis indicates LC3 relocalization intensity. Given LC3 is punctated particularly in the cytosol, It is unclear which spots of LC3 they are referring to. Based on the images shown, using a graph with LC3 surface staining as performed for M2 would clarify the data. The authors should clarify the reporting of these data in the results section. Additionally, the images of the control non-infected cells should be added to 3C.
- The data in Fig4 and FigS3 need to be strengthened to be conclusive. The volcano plot in FigS3A indicates that there is more LC3B and IAV proteins in M2 D85A than M2delta86-97. However in Fig4E, both LC3 I and LC3 II are increased in virions M2 delta 86-97 compared to M2 D85A which is opposite to the authors' conclusions in lines 244-245. In other words, the total amount of lipidated LC3 is higher in virions from IAV M2 without LIR motif than M2 with LIR. LC3II/I ratio in fig4F would suggest in virions containing M2 with LIR motif, LC3B II may be preferentially incorporated compared to virions containing M2 without LIR, which incorporates both LC3B I and LC3B II. Since this is a critical point made by the authors, performing a co-immunoprecipitation of M2 D58A and M2delta86-97 in the particles and then assessing for binding of LC3 I or II would bolster their conclusions.
- In Fig4J, even if statistically significant, the PFU difference between M2 D85A and M2 delta86-97 is minimal, performing growth curve assay would help appreciate this difference over time. In Thp1 cells, as the authors show caspase cleavage of M2 at time point 12h 14h 16hpi etc... (fig1), they should also show PFU data at these same time points for M2 mutant D85A compared to WT and M2 delta 86-97.
Minor comments:
- The title of Fig4 and FigS3 and in text line 226 should be changed as M2 incorporation into virions is not shown and not described in the text. Plus, in figS3B, the authors show that between the M2 mutants, there is no difference in the abundance of M2 and other viral proteins compared to M1.
- In the image shown in Fig4H the number of plaques is higher for M2delta86-97 even though the size in smaller than M2 WT. Could the authors clarify in the text of the results section how they quantify PFU in their plaque assay and if they used a size criterion when quantifying the number of plaques?
- In fig3B, the legend indicates 8 hpi but on the graphs it is 9 hpi.
Significance
The authors demonstrated that IAV M2 binding to LC3 is regulated by caspase cleavage. The authors clearly identify the cleavage site and the caspase involved: caspase 6. The cleaved form of M2 seems relevant to IAV infection as it is accumulating after 12hpi. Using a M2 mutant D85A that cannot be cleaved by caspase 6 and truncated M2 mutant delta86-97 mimicking caspase cleaved M2, the authors are able to elegantly address the role of M2 cleavage. However, the importance of M2 caspase cleavage on IAV infection is not demonstrated.<br /> Eventually, addressing the impact of the caspase cleavage of M2 LIR motif on autophagy or CASM would be interesting.
- Advance: conceptual.
- Audience: basic research, specialized in virology, specialized in autophagy.
- Field of expertise: virology, autophagy.
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Referee #2
Evidence, reproducibility and clarity
The influenza A virus (IAV) M2 protein is small transmembrane protein which plays a role in virus entry and egress. In a previous study, Beale et al. (2014) identified an LC3-interacting region (LIR) in the M2 cytoplasmic domain that was found to recruit the LC3B protein to the plasma membrane. Recombinant IAV harboring mutations in the LIR motif showed reduced particle stability and lost filamentous morphology.
In the present study, Figueras-Novoa et al. show that the LIR motif is removed in response to activation of cellular caspases. The authors demonstrate that in in IAV-infected THP-1 cells M2 is partially cleaved at the motif (82)SAVD(85)A by caspase 6. Caspase inhibitors abolished cleavage, and a mutant virus harboring the D85A substitution was found to be resistant to caspase action. A crystal structure of purified M2 C- terminus and LC3B revealed that the caspase cleavage site lies in a flexible region that is accessible to caspases.
Mutant virus encoding a truncated M2 protein (M286-97) was unable to interact with LC3, in accordance with the absence of the LIR motif. The M286-97 mutant showed reduced lipidation of LC3, while enhanced lipidation of LC3 was observed when wild-type virus-infected cells were treated with caspase inhibitors. The authors also observed that cell surface transport of M286-97 but not M2-D85A was impaired. However, in purified virus particles a mix of cleaved and uncleaved M2 was detected. The authors also demonstrated that lipidated LC3B was present in purified virions of wild-type virus particles but even more abundant in M2-D85A virions. Finally, M286-97 mutants produced significantly less infectious particles compared to wild-type virus while the D85A cleavage mutant replicated to similar titers than wt virus.
Based on these findings the authors concluded that caspases regulate the interaction of M2 protein with LC3 which impacts virion production. Specifically, they propose that caspase-mediated removal of the LIR motif may enable a switch between filamentous and non-filamentous budding in response to depletion of cellular resources. However, the authors were unable to rescue a filamentous IAV with a truncated M2 protein and therefore could not provide direct proof for their guess.
While the data are sound and presented well, they do not support the conclusions of the authors.
- To the authors opinion, the conserved caspase cleavage site in the M2 protein might provide an evolutionary advantage for the virus. However, the M2-D85A mutation has no effect on viral replication, so the biological significance of why M2 needs to be cleaved at all is unclear. The conclusion that caspase-induced M2 cleavage is a fine-tuning mechanism of IAV has not been supported by experiments.
- The finding that the permanently truncated IAV M2 mutant virus was substantially attenuated does not necessarily mean that abrogation of M2-LC3 interaction was responsible for this attenuation. As the M2 protein plays a role in virus budding at the plasma membrane (recruitment of M1 protein, induction of membrane curvature, membrane scission), the impaired transport of the truncated M2 protein might already explain that the virus was attenuated and that incorporation of the protein into the viral envelope was reduced.
- It is also not clear whether the loss of the C-terminal 11 amino acids may have affected the interaction of the M2 protein with other proteins such as TRAPPC6A-delta (Zhu et al., 2017).
- The authors did not rule out whether the truncation of the M2 protein by 11 amino acids would have an effect on proton channel activity. Proton channel activity, however, might be important to preserve the metastable conformation of HA in the secretory pathway and might be also important for virus uncoating.
- M286-97 induced less LC3 lipidation than wild-type M2 or the D85A mutant. The remaining lipidation was attributed to the ion channel activity of the M2 protein. Can the authors rule out that the truncation of the M2 protein led to reduced ion channel activity which in turn led to reduced LC3B lipidation?
- The suggested role of caspase cleavage as a regulatory switch between filamentous and spherical virions (lines 304- 313) is highly speculative as long as the authors do not provide any experimental proof for it. The authors indicated that they were unable to rescue filamentous IAV with M286-97. However, would it be possible to use caspase inhibitors to test their hypothesis?
- The authors used only the PR8 strain for their studies, a highly cell culture-adapted strain with spherical morphology. Are the findings obtained with this strain are also valid for others IAV strains?
- The authors mainly used the THP-1 cells for their studies, a human macrophage-like cell line. However, human IAV mostly replicate in epithelial cells of the respiratory tract and cause only abortive infections of macrophages. Why did the authors choose this cell line? Can the findings obtained with this cell line be translated to epithelial cells of the airways?
Minor issues:
- Fig. 1C: There seem to be quite some differences in the cleavage efficiency of M2 between panels A, B, C, and D? Any explanations?
- Fig. 1: Panel E: The labeling of the first amino acids as aa 76 seems to be wrong!
- Line 147: ...caspase mediated disruption of the M2-LC3 interaction (Fig 2A-B). Should be Fig. 2A-C.
- Growth kinetics of the various mutant viruses are missing?
- Line 195: The authors speculate that aa85 is important for viral fitness: That should be demonstrated!
Significance
Authors concluded that caspases regulate the interaction of M2 protein with LC3 which impacts virion production. Specifically, they propose that caspase-mediated removal of the LIR motif may enable a switch between filamentous and non-filamentous budding in response to depletion of cellular resources. However, the authors were unable to rescue a filamentous IAV with a truncated M2 protein and therefore could not provide direct proof for their guess.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The Matrix 2 (M2) protein of influenza A virus (IAV) is a single pass transmembrane protein known to act as a tetrameric ion channel that is important for both viral entry and egress. The paper by Figueras-Nova et al. entitled "Caspase cleavage of Influenza A virus M2 disrupts M2-LC3 interaction and regulates virion production" reports on the regulation of IAV virion production through a regulatory interplay between a caspase cleavage site and a LC3 interacting region (LIR) motif in M2. In its C-terminal cytoplasmic tail the IAV M2 protein contains a C-terminal LIR motif interacting with LC3. The authors show that this LIR motif is preceded by a functional caspase cleavage motif cleaved predominantly by caspase-6, with some contribution from caspase-3: The motif 82-SAVD-85 directs cleavage after the aspartate (D) at position 85. The cleavage leads to loss of the remaining C terminal sequence from amino acid 86 to 97. The core LIR motif 91-FVSI-94 LIR motif is then lost from M2 which can no longer bind LC3. As previously described by the same group using point mutations in the LIR motif (Ref 12.), loss of a functional LIR., here by caspase- mediated deletion of the LIR, affects the virion production and inhibits filamentous budding. LC3B lipidation is increased upon treatment with a caspase inhibitor. The authors show for the first time that LC3 is included into IAV virions via binding to M2. Furthermore, they also report a co-crystal structure of the M2 C terminus (aa 70-97), containing the caspase cleavage site and LIR, and LC3B (aa 3-125) adding new insights into this interaction and showing that the caspase cleavage site is in a flexible region N-terminal to the LIR. This work shows how caspase cleavage may modulate LC3B lipidation, trafficking to the plasma membrane, incorporation of LC3B in the virions, filamentous budding and virion production (viral titer).
Major comments:
The findings reported here are very well supported by the data shown. This is a very clearly written paper with well described and nicely visualized results that are accompanied by adequate statistical analyses. The authors report a new way the LC3B binding to the C-terminal tail of the M2 proteins is regulated and suggest that this is an adaptation the virus has made to adjust virion production to host cell status by hijacking the function of host caspases. They show that the caspase cleavage motif is evolutionary conserved and use that as an argument. Perhaps it could be discussed if it also could be an argument that the host protects itself against a too massive virion production as this could be too detrimental to the host? Would it not also be an evolutionary advantage to the virus in the long run by avoiding killing the host? A question I may raise which is optional as it may be too much work to address as part of this study is if the reported regulation of LC3B binding has any role in regulating the ion channel function of the M2 tetramer?
Minor comments:
Delete "with" in line 145. Line 217: It should be written more specifically how "cells were surface stained with M2" In the Introduction a description of what filamentous vs "spherical" budding is, could perhaps be included as I missed that reading through, although it comes in the end of the Discussion.
Significance
This is a very well performed study with a sound experimental strategy and well performed assays with clear results increasing our insight into the interplay between the Influenza A virus and host cells. Although caspase mediated cleavage of the autophagy receptor and signaling scaffold protein p62 (Ref. 25), removing the LIR and LC3-binding, has been reported before I consider this study as novel in reporting this type of regulation of LC3 binding. The cleavage of p62 deletes a large part of the protein while here it is a "clean" deletion of the LIR sequence representing a conceptual advance of regulation of LC3 binding.
The study also reports for the first time on LC3B incorporated into virions.
The effects on trafficking to the plasma membrane and viral budding and virion production are similar to those reported before (Ref. 12) using viruses with point mutations crippling the LIR motif. This research will be of interested to all studying virus- host interaction and to the autophagy field both as a non autophagic role of LC3B, and as a regulatory mechanism of LIR-LC3B interactions involving the irreversible caspase cleavage-mediated deletion of the LIR motif.
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Referee #3
Evidence, reproducibility and clarity
This study by Mordier and colleagues represents an in depth analysis to clarify the evolutionary history and processes of the rapidly evolving Schlafen gene family with a strong focus on primates and rodents.
The study is of high quality in my opinion, though I do have some minor comments:
- Fig 2 and Fig 4B present inferred phylogenetic trees of schalfens in primates and rodents - these trees appear to be unrooted or rooted on a single species rather than an outgroup/gene. I suggest that the authors consider whether an outgroup gene could be included or if an outgroup free approach could be used to estimate the position of the root. This is important because the use of an unrooted tree to make inferences on gene family evolution has important implications - for example, there are no clades in an unrooted tree (Wilkinson et al 2007, Trends Ecol Evol).
- Schlafen proteins beyond mammals are referred to as SLFN11, it is not clear why this is the case because they seem to be co-orthologous to all mammal schalfen groups (except SLFNL1) based on supplementary figure S2. In this context, perhaps this image should form part of the main text?
- For blast searches parameters should be included - what cutoffs were implied for similarity searches etc. Related to this on line 120-121 homology is described as 'significant'. Homology refers to an evolutionary relationship, sequence similarity may be significant or not based on the search performed but homology is qualitative and simply detectable or not.
- The first results section describes the results of phylogenetic analyses, however this section relies heavily on what might better be considered interpretation of these analyses, this is great and should be included but I suggest that the branching patterns in the trees and bootstrap values supporting relationships between genes are also reported in the text to link interpretations to actual results.
- Bustos 2009 included viral genes belonging to the family in their analyses and I think it may be pertinent to do so here also to determine if the results are consistent or not.
- Was a rate heterogeneity (e.g. gamma rates / +G) parameter considered in phylogenetic analyses or model testing, it is not reported here and very rare for this not to improve model fit and phylogenetic accuracy.
- The authors state that all data are available in public databases, but this is not the case for the results they generated. Making various file types produced in this study would be good - e.g. alignments, phylogenetic tree files, structures, etc.
Significance
This study is an important step forward in clarifying our understanding of schalfen evolution. I think the manuscript will be of interest to a number of research areas, including gene family evolution because of its focus on an unusually rapidly evolving gene cluster and to those working on the schalfen gene families functional importance in development and immunity. The results may also draw interest from those interested in the confluence of protein structure, function, and evolution. My expertise In the context of this study is in the phylogenetics and evolution of rapidly evolving gene families.
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Referee #2
Evidence, reproducibility and clarity
In the current manuscript, Mordier et al. combine bioinformatic searches, synteny, and phylogenetic analysis to reconstruct the duplicative history of the Schlafen Genes in rodents and primates and then use molecular evolution analyses in combination with structural modeling to make inferences regarding the role of natural selection in the evolution of this gene family. The study represents an update on Bustos et al. (2009), who had already presented evidence that Positive Darwinian selection was likely a factor in the diversification of these genes in mammals. In this context, the contribution of this paper is the identification of sites that are candidates to be evolving under natural selection, and the structural exploration of the location of these sites in the proteins. CODEML strength lies in the detection of signatures of positive selection at the codon level, but it is not that accurate when it comes to pinpointing the actual sites that might be under selection. Hence, without experimental data, these inferences remain speculative. The manuscript is well-written and represents an update on the evolution of this gene family.
Major Issues
The rationale for the choice of species included in the analyses is never presented, and some of it is hard to understand. Why do authors exclude the platypus but include non-mammalian lobe-finned vertebrates is not clear. If they are going to discuss the evolution of these genes outside mammals, the authors need to survey a much wider array of genomes. Even within mammals, there is little discussion on why some species were included and others not. I think that focusing the study on rodents and primates is OK, but I also think that providing a strong justification of the selection of species to include in the study and a tree that justifies splitting the focus on rodents and primates would also be important.
In the trees in Figures 2 and 4, several genes considered as orthologs are not in monophyletic groups. These pattern aligns well with the birth-and-death model of gene family evolution, and has implications for their molecular evolution analyses. The authors need to address this issue explicitly. I would use topology tests to evaluate whether these deviations from the expected topology are significant. In addition, the relevant tests to report here are M8 vs M7 and M8 vs M8a. The M0 vs M1a comparison does not provide evidence for positive Darwinian selection. If the M8 vs M7 and M8 vs M8a tests are not significant, the inferences about sites evolving with dN/dS>1 are not really valid.
CODEML can implements models that are designed to test patterns of gene family evolution, contrasting pre and post duplication branches, which I think would be of value in this family.
Some analyses are described very succinctly, which would make replication challenging.
Minor Issues
Could 2R be responsible for the emergence of SLFN and SLFNL1?
There are several minor issues authors should fix in a revised manuscript. In general, because results are presented before the materials and methods, I think it is easier for readers to have some of the information in the results section.
They need to be consistent in using italics for species names as well as for capitalization.
In the Alignment and maximum-likelihood phylogenies section the authors indicate that they used either Muscle or Mafft for the alignments. What was the rationale for picking one alignment over the other for a given gene? In this section, they also indicate the selected a best-fitting model of substitution using SMS, but then indicate that they used JTT for protein alignments and HKY for nucleotide alignments.
How did the authors ensure that nucleotide alignments remained in frame?
Significance
I think this is a significant contribution to our understanding of the evolution of the Schlafen gene family. There are two key contributions here: the demonstration that gene conversion is a factor obscuring relationships among genes in this gene family, and the mapping of amino acids inferred be evolving under positive selection to structurally important residues of the proteins. These residues should be of interest for functional assays that evaluate the functional role of these proteins.
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Referee #1
Evidence, reproducibility and clarity
Mordier et al. used in-depth phylogenomic methods to analyze the evolution of the mammalian Schlafen gene family. They identified a novel orphan Schlafen-related gene that arose in jawed vertebrates, and they assigned orthology between Schlafen cluster paralogs. This will allow for further accurate selection studies. Throughout the entire manuscript, the authors use nomenclature predating structural and biochemical studies. The nomenclature is purely based on sequence similarities, which are sometimes very weak and not convincing, and not based on the known function of the protein. In my opinion, this causes confusion and does not help scientists in the field. Especially in Figure 3, I wouldn't call it RNAse E (AlbA); instead, tRNA recognition site,endoribonuclease domain, SLFN core domain are the correct domain designations. Since SLFN11 is not a GTPase, why do the authors name the domain GTPase domain? Actually, the SWADL domain comprises a SWAVDL instead of a SWADL sequence motif. Hence, I would name the domain SWAVDL domain instead of SWADL domain, which is, in my opinion, misleading and was wrongly chosen in initial publications.
In e.g. Figure 3 SLFN11 structure it would be better if the authors illustrated the important residues concerning the known RNase active site and ssDNA binding site. Further, a close-up of the SLFN11 interface with labeled amino acids involved in the interaction and highlighting the residues undergoing positive selection would help understand the evolutionary adaptation.
Although, according to Metzner et al., the SLFN11 dimer is built up by two interfaces (I and II), where Interface I is situated in the C-terminal helicase domain and Interface II in the N-terminal SLFN11 core domain. It would be helpful for the reader if the authors stuck to this already introduced and widely accepted nomenclature in the field.
In addition to the antiviral function, SLFN11 expression levels have been reported to show a strong positive correlation with the sensitivity of tumor cells to DNA damaging agents (DDAs). Hence, SLFN11 can serve as a biomarker to predict the response to, e.g., platinum-based drugs. It was revealed that SLFN11 exerts its function by direct recruitment to sites of DNA damage and stalled replication forks in response to replication stress induced by DDAs. Could the authors include this different molecular function of SLFN11 in their discussion of SLFN11s evolution and positive selection?
Even though it seems unclear from the genetic and evolutionary aspect (Figure 4), mouse Slfn8 and Slfn9 complement human cells lacking SLFN11 during the replication stress response and seem to resemble the function of SLFN11 (Alvi et al. 2023). The authors of this study claim that Slfn8/9 genes may share an orthologous function with SLFN11. Could the authors comment on that discrepancy?
Significance
In general, the work is well conducted and provides valuable new insights in an important and growing field of research. However, there are some limitations to the study including the disregard of known protein function (e.g. SLFN11) and the usage of a purely sequence similarity based nomenclature.
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Reviewer #1 (Evidence, reproducibility and clarity (Required)):
#1) Summary: The transport of effector proteins across membranes from the producing bacterium into a target cell is at the core of bacterial secretion systems. How an additional layer in form of a capsule affects effector export and the susceptibility towards effector import is not fully understood. Here, Flaugnatti and colleagues combined bacterial genetics with phenotypic assays and electron microscopy to demonstrate a dual role of a bacterial capsule in preventing T6SS-mediated effector export and promoting protection from effector import by another bacterium's T6SS. The wide variety of methods used, complementation of the mutants, and validation of the findings across strains strengthen the author's conclusions. Although the main conclusions seem straight forward, the authors unravel the unexpected complexity underlying these phenotypes with strong mechanistic work. In brief, a capsule-deficient mutant (∆itra) is shown to assemble its T6SS similar to the WT, yet secretes more Hcp than the WT and is better in T6SS-mediated killing of other bacteria. A capsule-overproducing mutant (∆bfmS) shows both, a partial deficiency in T6SS assembly and an additional reduction in exported Hcp, and is worse in T6SS-mediated killing than the WT. A mutant with a capsule similar to WT and deficient in cell sensing (∆tslA) forms the least T6SS apparatuses and is yet better in T6SS-mediated killing than the overcapsulated mutant. Together, these data show an effect of the capsule on (i) T6SS apparatus assembly, (ii) effector export, (iii) effector import, and (iv) the need for clearance of accumulating non-secreted Hcp by ClpXP. The work on a clinical isolate of Acinetobacter tumefaciens and the data on an impaired T6SS activity on other cells by antibiotic-induced capsulation is a strong demonstration of the work's clinical relevance in addition to the findings' conceptual novelty.
- In my view, the manuscript is outstanding with very high quality of experimental data, very well written text and very clear presentation of the data in figures. A few minor comments and suggestions below that I think would strengthen the manuscript.*
__ Authors’ reply #1: __We thank the reviewer for their enthusiasm.
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Major comment:
#2) OPTIONAL: Fig. 4c/l. 320: Having an indirect effect of an antibiotic on T6SS activity by antibiotic-induced capsule formation is very intriguing and contributes to the clinical relevance of the overall findings. When I saw the data in Fig. 4c, the graph instantaneously reminded me of the panel in Fig. 2a, where a similar phenotype is observed by changing the predator:prey ratio in the absence of any antibiotic. The authors themselves comment on the possibility of antibiotic-induced, reduced predator growth (and thereby a change in predator:prey ratio) as a one factor impacting the phenotype here. I am wondering if this data could be strengthened or better disentangled to test more precisely if it is the antibiotic induced capsule formation per se that affects T6SS-mediated killing by A. baumanii in the presence of antibiotics. Would it help to take the bfmS mutant along as a control for direct comparison to see if antibiotic-induced capsule formation of the WT to similar levels of the mutant results in the same killing phenotype? Would it help to test for T6SS-mediated killing in the presence and absence of antibiotics at multiple predator:prey ratios? Could the effect of the antibiotic on A. baumanii growth be measured and considered when choosing the ratio at which the bacteria are mixed?
__ Authors’ reply #2: __The point raised by the reviewer is very important. As we have stated in the manuscript, the capsule-induced production using antibiotics impacts the growth of A. baumannii and could therefore change the predator-prey ratio, potentially affecting the observed phenotype. However, the antibiotic is expected to equally impact the non-encapsulated ΔitrA strain, yet this strain maintains very strong T6SS killing activity in the presence of chloramphenicol. Thus, we do not believe the predator-prey ratio is causing the observed effect. To address this point more directly, we nonetheless propose to: i) repeat the experiments with different predator-prey ratios (1:1, 2:1, and 5:1), and ii) include a bfmS mutant as a control.
Minor comments:
#3) Figure 1D, l. 155, I might have missed this, do the authors happen to have the numbers of E. cloacae as well? This would strengthen the claim on A. baumannii survival because of E. cloacae is being killed.
__ Authors’ reply #3: __The reviewer is correct; we did not include the survival of E. cloacae in the initial manuscript due to technical reasons (counter-selection of E. cloacae). However, we propose to repeat the experiment using an E. cloacae strain carrying a plasmid conferring kanamycin resistance. This will allow us to counter-select E. cloacae after contact with the A. baumannii predator to determine if E. cloacae is killed by A. baumannii in a T6SS-dependent manner.
#4) Figure 2, I suggest to write out the species name of the prey in the box with the ratio. With E. cloacae being referred to in the previous figure and starting with similar letters than E. coli, I wasn't sure at first sight what E. c. refers to.
__ Authors’ reply #4: __We appreciate the comment and will revise the figure as suggested.
#5) use of the term "T6SS activity" throughout the manuscript (e.g. l. 182, l. 187). I leave this up to the authors. To me, it seems like an umbrella term for the initial observation and I see that such a term can be very handy for the writing. I just would like to mention that the use of the term was not always intuitive to me and sometimes even a bit misleading. For example, l. 182 refers to "increased T6SS activity". As a reader, I only know about 'T6SS activity on other cells' or 'a T6SS-mediated effect on other cells' at this point. T6SS apparatus assembly/firing activity is tested for specifically later and it turns out to differ between mutants. By the time the term is used in the discussion, it captures multiple nuanced phenotypes described by then. The more precise definition of the term in l. 200 helped to capture what exactly is meant by the authors.
__ Authors’ reply #5: __We propose rephrasing the sentences to include the term "T6SS-secretion activity" when referring to Hcp secretion assays and "T6SS-mediated killing activity" when referring to killing experiments.
__#6) __l. 198-199 "Collectively, our findings indicate that CPS does not hinder the secretion process of the T6SS or the consequent elimination of competing cells". I might be missing something, I cannot entirely follow this sentence. Didn't the authors just show that the CPS does hinder T6SS-mediated elimination of competing cells in panel 2A and less secreted Hcp in the encapsulated WT compared to the non-encapsulated mutant in panel 2B?
__ Authors’ reply #6:__ We thank the reviewer for this comment. We realize that the sentence wasn’t well phrased, resulting in confusion. What we meant was that the T6SS is functional regarding its T6SS-mediated killing and secretion in the WT strain, while we also showed that the non-capsulated strain kills and secretes more T6SS material in the supernatant. Thus, there seems to be a balance between capsule production and T6SS activity in the WT. We will revise the sentence to better reflect this meaning.
#7) l. 224, typo, "in"
__ Authors’ reply #7:__ We will correct this typo. Thank you.
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#8) Two connected comments: l. 338, Just a thought, I am wondering about the title of the section. After reading it a second time, I think it is technically correct. When reading it first, I was a bit confused when getting to the data because apparatus assmebly is impaired in the capsule-overproducing strain and although "preserved", doesn't the data indicate that there is less T6SS assembly in the bfmS mutant and that this might be because of less cell sensing and isn't this a main point that there is a difference in apparatus assembly in the capsule overproducing strain compared to WT (other than no difference in apparatus assembly in the strain without capsule)? To me it seems not fully acknowledged as a finding in the interpretation of the data that less cells of the bfmS mutant have a T6SS apparatus. Isn't that interesting? A title along the lines of "Capsule-overproducing strain has preserved sensory function and assembles less T6SS apparatuses" would have been more intuitive for me. l. 352, In case I didn't miss a reference to this data earlier in the manuscript, I am wondering if it would be worth mentioning the finding on the reduced apparatus assembly of the bfmS mutant earlier, together with Figure 3 already. At least a sentence that mentions already that there is more coming later. When I got to this line in the manuscript and read the findings on the apparatus assembly, I first needed to go back to figure 3 and look at the data there again in light of this finding. It is mentioned here on the side but I think very important for the interpretation of the phenotypic data of the bfmS mutant shown earlier, isn't it? The tslA mutant is used beautifully here.
__ Authors’ reply #8:__ We thank the reviewer for the suggestion and the kind comment about the beautiful usage of the tslA mutant. We will modify the title of the corresponding paragraph as suggested to make it more intuitive.
Regarding the comment about mentioning the T6SS apparatus assembly defect in the *bfmS* mutant earlier, we respectfully disagree. While we agree that this point is important and can partially explain the difference in killing activity, we believe that showing it together with the *tslA* mutant (Figure 5) makes more sense and is easier for the reader to understand.
#9) Discussion: optional comment. On the one hand, I like the concise discussion. On the other hand, I see more potential here for bringing it all together (potentially at the expense of shortening some of the introduction). I think the subtleties of the findings are complex. For example, I could envision a graphical summary with a working model on all the effects of a capsule on the T6SS and its potential clinical relevance making the study accessible to even more readers.
__ Authors’ reply #9: __In the revised manuscript, we will include a graphical summary/model.
Significance
#10) General assessment: I consider the story very strong in terms of novelty, experimental approaches used, quality of the data, quality of the writing and figures of the manuscript. In my view, the aspects that could be improved are optional/minor and concern only one figure and some phrasing.
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Advance: I see major advance in the findings (i, mechanistic) on the mechanism of how the capsule interferes with T6SS, (ii, fundamental) on the discovery of ClpXP degrading Hcp, and (iii, clinical) on the meaning of antibiotic treatment for the T6SS of this clinically relevant and often multi-drug resistant bacterial species, which strongly complements existing work on the T6SS and antibiotics in A. baumanii (e.g. of the Feldman group). As the authors write themselves, the starting points of the study of a capsule protecting from a T6SS and the effect of a T6SS on other cells being negatively impacted by a capsule were known, although not studied in one species and not understood mechanistically.*
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Audience: I see the result of interest to a broad audience in the fields of bacteria-bacteria interactions, Acinetobacter baumanii, type VI secretion, antimicrobial resistance, bacterial capsules.*
__ Authors’ reply #10: __We once again thank the reviewer and highly appreciate their positive and constructive feedback on our work. We hope the reviewer will be satisfied with the revised version of our manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
#11) In the manuscript by Flaugnatti et al., the authors provide clear evidence of the interplay between capsule outer coat production and the Type VI secretion system (T6SS) in Acinetobacter baumannii. The authors demonstrate that the presence of the capsule or the activity of the T6SS enhances survival against attacking bacteria. However, they also show that in their model bacterium, the (over)production of the capsule likely hinders T6SS dynamics, thereby reducing overall killing efficiency. Additionally, they reveal that the amount of the T6SS component Hcp is regulated in cells that can no longer assemble and/or secrete via the T6SS, presumably by the ClpXP protease. Overall, the experiments are well designed, and most conclusions are supported by the data and appropriate controls. I have however some suggestions that could further strengthen the manuscript prior to publication.
__ Authors’ reply #11: __We are grateful for the reviewer’s enthusiasm and will implement their comments and suggestions in the revised version of the manuscript.
Major comments:
#12) Line 164. The authors use E. coli as prey to test the T6SS activity of A. baumannii. Why not directly use the E. cloacae strain (with or without T6SS) for this purpose? This would provide direct evidence that A. baumannii uses its T6SS to kill E. cloacae, thus confirming the authors conclusions in this section.
__ Authors’ reply #12: __We thank the reviewer for this comment. We used E. coli to assess the functionality of the T6SS in different strains of A. baumannii, as it is commonly done in the T6SS field. However, as suggested by reviewer 1 (see comment #3) and in response to this query, we will also provide survival data of E. cloacae in the revised manuscript using a plasmid-carrying E. cloacae derivative that allows direct selection.
#13) In Figure 2, the authors show that a non-capsulated strain kills more effectively and secretes more than a WT, but has a similar number of T6SS. They suggest in their conclusion that "the observed increase in T6SS activity in the non-capsulated strain suggests a compensatory mechanism for the absence of the protective capsule layer." This conclusion implies the presence of an "active" regulatory mechanism that would increase the number of successful T6SS firing events, which has not been demonstrated. Could it not simply be that the capsule blocks some shots that cannot penetrate and are therefore ineffective? This hypothesis is mentioned in lines 204-208. The authors should clarify the conclusion of this section. Given the challenge this may pose in A. baumannii, I suggest that the authors quantify the assembly/firing dynamics of the T6SS under WT and ΔitrA conditions. This would help distinguish between the two hypotheses explaining better firing in non-capsulated cells: i.e., if the number of assembled T6SS is the same in both strains (Fig 2C & 2D), do non-capsulated cells assemble/fire faster, indicating an adaptation in regulation, or do we observe the same dynamics, suggesting a simple physical barrier blocking the passage of certain T6SS firing events?
__ Authors’ reply #13:__ We realize that the sentence, and more specifically the word "compensatory," might have been misleading and thank the reviewer for bringing this to our attention. What we meant to convey is that there is a balance between capsule production and T6SS activity; if disturbed, the balance shifts in one direction or the other. Specifically, there is more protection through the production of a thicker capsule (e.g., in the ∆bfmSmutant or under sub-MIC conditions of antibiotics, regulated by the Bfm system, as mentioned in the text) or more T6SS activity when less capsule is present (e.g., in the ΔitrA mutant, which we propose is caused by the lack of the steric hindrance). We will rephrase this sentence in the revised manuscript to better convey this message.
Regarding the quantification of T6SS dynamic assembly/firing events between the capsulated (WT) and non-capsulated (ΔitrA) strains, we do not think this is required for this study, as the amount of secreted Hcp reflects the overall activity of the system. Importantly, we also do not have the technical means to quantify assembly/firing rates under Biosafety 2 conditions, as this requires specialized microscopes with very fast acquisition options (see, for instance, Basler, Pilhofer *et al.*, 2012, *Nature*). Indeed, very few labs in the T6SS field have been able to measure such rates.
#14) Line 428-429. It is mentioned that the deletion of lon does not have a notable effect. However, I observe that the absence of Lon alone causes a more rapid degradation of Hcp in the cells compared to the WT strain (Fig 7B). How do the authors explain that the absence of this protease (whether under conditions of Hcp accumulation or not) increases the degradation of this protein in the cell? This explanation should be included in the manuscript.
__ Authors’ reply #14: __That’s a fair point. We didn’t address this point further, as the deletion of lon didn’t resolve the issue of why Hcp is degraded. In fact, the opposite seems to be the case, as there is less Hcp in the ∆lon strain compared to the WT. While this observation is not directly relevant to the question of why Hcp is degraded late during growth in secretion-impaired strains, we will properly mention it in the revised manuscript.
Please also note that a strong growth defect of a Δ*lon*Δ*clpXP* double mutant impaired further investigation in this direction.
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Minor comments:
#15) Throughout the manuscript, the authors use the term "predator" to refer to A. baumannii. Predation is a specific phenomenon that involves killing for nourishment. To my knowledge, the T6SS has never been shown to be a predation weapon but rather a weapon for interbacterial competition, which is a different concept. If this has not been demonstrated in A. baumannii, the authors should replace the term "predator" with "attacker" (or an equivalent term) to clarify the context.
__ Authors’ reply #15: __We thank the reviewer for this comment. The term “predator,” as highlighted by the reviewer, typically implies killing for nourishment/cellular products. In the context of T6SS, it facilitates the killing of competitors, releasing DNA into the environment that can subsequently be acquired through natural competence for transformation, as observed in species like Vibrio cholerae (our work by Borgeaud et al., 2015, Science) or other Acinetobacter species such as Acinetobacter baylyi (Ringel et al., 2017, Cell Rep.; Cooper et al., 2017, eLife). The acquisition of DNA reflects the killing for cellular products of the prey. As most A. baumannii strains are also naturally competent, this justifies the usage of the predator and prey nomenclature.
Apart from this fact, it seems to be a matter of nomenclature, with many papers in the field using one term or the other. Yet, ultimately, this doesn’t change any of the scientific findings. Therefore, to satisfy the reviewer, we will change “predator” to “attacker” throughout the revised manuscript.
#16) Line 274. Since the authors stated that in the Wzc mutant, the capsule is "predominantly found in the supernatant and only loosely attached to the cell," this result is not unexpected. This finding is also consistent with the previous results from Fig. 3A & B, which show sensitivity to complement-mediated killing and the weak amount of (ab)normal CPS produced in that strain, further confirmed by Fig. 3E.
__ Authors’ reply #16__: We fully agree with the reviewer’s suggestion and will remove the statement.
#17) Line 299. The authors speculate that "... T6SS may deploy through gaps akin to arrow-slits in the capsule's mesh...". However, this is very unlikely since a WT strain kills (Fig. 3C) and secretes (Fig. 2B & 3D) less effectively than the itrA mutant, suggesting that the T6SS is not assembled in the "right places" devoid of CPS; otherwise, we would expect similar T6SS activity. Based on the results in Fig. 2 (and my earlier comment), this implies that A. baumannii assembles its T6SS randomly, and in the presence of the capsule, its shots would need to be in the right place to penetrate the envelope and reach the target. Could the authors comment on this point and provide a model figure to better visualize the interplay between the capsule and T6SS under the three major conditions: WT, non-capsulated, and capsule overproduction?
__ Authors’ reply #17: __We thank the reviewer and agree with their comment. We discussed the hypothesis of T6SS deployment through gaps, drawing a parallel to what was proposed for biofilm and T6SS in V. cholerae(Toska et al., 2018, PNAS). However, as mentioned earlier, we believe that the effect of the capsule on T6SS activity is primarily due to steric hindrance, which increases the distance between the T6SS apparatus and the prey cell. To clarify our findings further, we will include a model summarizing our results, as requested by reviewer 1 (see comment #9).
__ #18)__ In Fig. 5A, the microscopy panels should be adjusted to the same dynamic range as the WT (which represents a true signal), which does not appear to be the case for the tlsA mutant panel for instance. The image gives the impression of a large amount of free TssB-msfGFP in the cytoplasm. However, this effect is due to the dynamic range being adjusted to display a signal. This observation is consistent with the fact that the amount of TssB-msfGFP protein is identical across all strains (Fig. S2F).
__ Authors’ reply #18: __We will adjust the images to the range of the WT in the revised manuscript, as suggested. However, regardless of how these images are presented, the enumeration of T6SS structures will remain unchanged, which was the sole point of this experiment.
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#19) Unless I am mistaken, the authors do not comment on the fact that in a ΔbfmS strain, the number of T6SS is halved compared to a WT or ΔitrA strain. If capsule overproduction only partially limits the TslA-dependant T6SS assembly, how can this result be explained? Is it related to the degradation of Hcp in this background, which ultimately limits the formation of T6SS? If so, it would be interesting to mention this connection in the section "Prolonged secretion inhibition triggers Hcp degradation”
__ Authors’ reply #19: __We did mention that the T6SS assembly of the ΔbfmS mutant is reduced compared to the WT (or ΔitrA), likely due to the defect in sensing the prey (lines 369-374 and 468-472 of the initial manuscript). However, we will revise the sentence to improve clarity in the revised version of the manuscript.
Significance
#20) This work is highly intriguing as it not only delves into the specific mechanisms involved but also connects fundamental elements in bacterial competition, i.e., the necessity for self-protection and aggression for survival. The manuscript offers valuable insights into cellular dynamics at a microscale level and prompts new inquiries into the regulation of these systems on a population scale. The work is well-done and the writing is also clear. I am convinced that this work represents another significant step towards understanding bacterial mechanisms and will undoubtedly spark considerable interest in the field.
__ Authors’ reply #20: __We sincerely thank reviewer #2 for their constructive inputs, which will improve our manuscript.
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Reviewer #3 (Evidence, reproducibility and clarity (Required)):
#21) The manuscript by Flaugnatti et al investigates the relationship between functions of the T6SS in A. baumannii and production of capsular polysaccharide. The manuscript argues that (1) capsule protects A. baumannii against T6SS-mediated attack by other bacteria, (2) capsule also interferes with the bacterium's own T6SS activity, and (3) the T6SS inner tube protein Hcp is regulated by degradation by ClpXP. The main critiques regard the first two conclusions, which seem to be based solely on use of a mutant that has a confounding effect as described below; and to strengthen the third claim by further exploring the results of overexpressing Hcp and by determining whether there is a fitness benefit for Hcp regulation.
__ Authors’ reply #21: __We thank reviewer #3 for their relevant input. We will conduct additional experiments based on their comments, and these will be incorporated into the revised manuscript.
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__Main items:____ __
#22) Throughout the paper, an itrA deletion mutant is used as the capsule-deficient strain and conclusions are drawn about role of capsule based on this mutant. However, itrA deletion also eliminates the protein O-glycosylation pathway (Lees-miller et al 2013), a potential confounder. Analysis of mutants specifically deficient in the high-molecular weight capsule but not protein glycosylation, and/or mutants in the protein o-glycosylation enzyme, should be incorporated into the study to enhance the ability to make conclusions about the role of the capsule.
__ Authors’ reply #22: __Fair point. We thank the reviewer for this important suggestion. To distinguish between the O-glycosylation pathway and capsule production, we will generate a ∆pglL strain (specific to O-glycosylation), as suggested, and will repeat the key experiments (similar to Fig. 2A and 2B). We are almost done with the engineering of this mutant strain and therefore don’t expect any major delays.
#23) Evidence could be provided to support the idea raised in lines 482-483 that T6SS component accumulation is toxic ("degradation [of T6SS components] could serve as a strategy to alleviate proteotoxic stress..."). For example, growth curves of ∆clpXP strains with and without hcp could be analyzed, to determine how degrading Hcp is helping the bacteria.
__ Authors’ reply #23: __We will perform growth curves of ΔclpXP strains with and without hcp, as suggested by the reviewer. However, we are uncertain whether we will be able to observe differences between these strains, as the conditions under which such degradation is significant may be challenging to replicate under standard laboratory conditions.
__#24) __The possible ClpXP recognition sequence identified at the C terminus of Hcp is interesting-does overexpression of an Hcp variant lacking/altered in this motif alter its protein levels compared to WT Hcp?
__ Authors’ reply #24: __We thank the reviewer for this suggestion. We are in the process of performing the suggested experiment and will include the data in the manuscript.
__Minor items:____ __
#25) *A better explanation could be provided for why overexpressing hcp in WT but not in ∆hcp leads to increased Hcp protein levels. There is a statement about Hcp being regulated post transcriptionally, possibly by degradation (lines 422-423), but would that not also result in regulation in the WT strain? *
__ Authors’ reply #25: __The reviewer is absolutely correct here. Despite careful genetic engineering, we believe that the hcp mutant used may have a polar effect, causing Hcp accumulation only in the ∆hcp + p-hcp strain but not in the WT + p-hcp strain, which remains capable of secretion. The ∆hcp strain therefore mimics the secretion-impaired tssB mutant. We will clarify this in the revised manuscript.
#26) *An untreated control is needed in Fig. 4B. *
__ Authors’ reply #26: __The untreated samples were shown in all previous figures. However, we understand the reviewer's point and will repeat the experiment with the untreated control included in the same experiment.
#27) *line 179: please clarify "reflecting better invading bacteria" *
__ Authors’ reply #27: __We appreciate the reviewer mentioning this oversight. We meant to compare this to a situation where a bacterium invades an already existing community, resulting in a predator-prey ratio below 1. We will clarify this further in the revised manuscript.
#28) *line 351: consider rewording the statement that ∆tslA results in decreased in T6SS assembly and activity using the tssB-msfGFP microscopy assay; it is not clear that activity is measured in this assay. *
__ Authors’ reply #28: __The reviewer is correct. We will revise the sentence accordingly to better reflect the T6SS assembly.
#29) *lines 260-265: This experiment could use clarifying, but it would seem that it requires analysis of the secreted capsule levels in the tssB mutant to show it does not produce extracellular capsule to the same extent that ∆bfmS does. *
__ Authors’ reply #29: __We thank the reviewer for the suggestion and will include these experimental data in the revised manuscript.
#30) *Fig. 6C and 7A labelling could be improved to avoid potential confusion that the bar graphs are quantifying the western blot. E.g., could add a corresponding vertical label to the Western data, or consider changing "relative expression of hcp" to something reflecting analysis of transcript levels. *
__ Authors’ reply #30: __We will improve this figure by splitting the qPCR and Western blot data into independent panels. This will eliminate any confusion.
#31) lines 416-417 and Fig. 7A: states that "hcp mRNA levels increased significantly", but more careful wording could be used because the WT's transcript change is not significant after overexpression (though it is significant in ∆hcp).
__ Authors’ reply #31: __Point well taken. We will improve the sentence (and Figure) to make its meaning unambiguous.
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#32) lines 479-480 states that in secretion-impaired strains accumulation of Hcp is mitigated by ClpXP; while this was shown for ∆tssB, was this also the case for ∆bfmS?
__ Authors’ reply #32: __This is indeed an interesting suggestion. We are in the process of generating the double mutant ∆bfmS∆clpXP and will include the experimental results in the revised manuscript.
Significance
#33) *The strengths of the study are the focus on a clinically significant pathogen, the potential novel roles for the important capsule virulence factor of A. baumannii, and the identification of novel points of control of the T6SS. The analyses of T6SS function are thorough and carefully performed. *
__ Authors’ reply #33: __We thank the reviewer for their comments, which we believe will significantly strengthen our work, particularly regarding the capsule aspect.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Flaugnatti et al investigates the relationship between functions of the T6SS in A. baumannii and production of capsular polysaccharide. The manuscript argues that (1) capsule protects A. baumannii against T6SS-mediated attack by other bacteria, (2) capsule also interferes with the bacterium's own T6SS activity, and (3) the T6SS inner tube protein Hcp is regulated by degradation by ClpXP. The main critiques regard the first two conclusions, which seem to be based solely on use of a mutant that has a confounding effect as described below; and to strengthen the third claim by further exploring the results of overexpressing Hcp and by determining whether there is a fitness benefit for Hcp regulation.
Main items:
- Throughout the paper, an itrA deletion mutant is used as the capsule-deficient strain and conclusions are drawn about role of capsule based on this mutant. However, itrA deletion also eliminates the protein O-glycosylation pathway (Lees-miller et al 2013), a potential confounder. Analysis of mutants specifically deficient in the high-molecular weight capsule but not protein glycosylation, and/or mutants in the protein o-glycosylation enzyme, should be incorporated into the study to enhance the ability to make conclusions about the role of the capsule.
- Evidence could be provided to support the idea raised in lines 482-483 that T6SS component accumulation is toxic ("degradation [of T6SS components] could serve as a strategy to alleviate proteotoxic stress..."). For example, growth curves of ∆clpXP strains with and without hcp could be analyzed, to determine how degrading Hcp is helping the bacteria.
- The possible ClpXP recognition sequence identified at the C terminus of Hcp is interesting--does overexpression of an Hcp variant lacking/altered in this motif alter its protein levels compared to WT Hcp?
Minor items:
- A better explanation could be provided for why overexpressing hcp in WT but not in ∆hcp leads to increased Hcp protein levels. There is a statement about Hcp being regulated post transcriptionally, possibly by degradation (lines 422-423), but would that not also result in regulation in the WT strain?
- An untreated control is needed in Fig. 4B.
- line 179: please clarify "reflecting better invading bacteria"
- line 351: consider rewording the statement that ∆tslA results in decreased in T6SS assembly and activity using the tssB-msfGFP microscopy assay; it is not clear that activity is measured in this assay.
- lines 260-265: This experiment could use clarifying, but it would seem that it requires analysis of the secreted capsule levels in the tssB mutant to show it does not produce extracellular capsule to the same extent that ∆bfmS does.
- Fig. 6C and 7A labelling could be improved to avoid potential confusion that the bar graphs are quantifying the western blot. E.g., could add a corresponding vertical label to the Western data, or consider changing "relative expression of hcp" to something reflecting analysis of transcript levels.
- lines 416-417 and Fig. 7A: states that "hcp mRNA levels increased significantly", but more careful wording could be used because the WT's transcript change is not significant after overexpression (though it is significant in ∆hcp)
- lines 479-480 states that in secretion-impaired strains accumulation of Hcp is mitigated by ClpXP; while this was shown for ∆tssB, was this also the case for ∆bfmS?
Significance
The strengths of the study are the focus on a clinically significant pathogen, the potential novel roles for the important capsule virulence factor of A. baumannii, and the identification of novel points of control of the T6SS. The analyses of T6SS function are thorough and carefully performed.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript by Flaugnatti et al., the authors provide clear evidence of the interplay between capsule outer coat production and the Type VI secretion system (T6SS) in Acinetobacter baumannii. The authors demonstrate that the presence of the capsule or the activity of the T6SS enhances survival against attacking bacteria. However, they also show that in their model bacterium, the (over)production of the capsule likely hinders T6SS dynamics, thereby reducing overall killing efficiency. Additionally, they reveal that the amount of the T6SS component Hcp is regulated in cells that can no longer assemble and/or secrete via the T6SS, presumably by the ClpXP protease. Overall, the experiments are well designed, and most conclusions are supported by the data and appropriate controls. I have however some suggestions that could further strengthen the manuscript prior to publication.
Major comments:
Line 164. The authors use E. coli as prey to test the T6SS activity of A. baumannii. Why not directly use the E. cloacae strain (with or without T6SS) for this purpose? This would provide direct evidence that A. baumannii uses its T6SS to kill E. cloacae, thus confirming the authors conclusions in this section.. In Figure 2, the authors show that a non-capsulated strain kills more effectively and secretes more than a WT, but has a similar number of T6SS. They suggest in their conclusion that "the observed increase in T6SS activity in the non-capsulated strain suggests a compensatory mechanism for the absence of the protective capsule layer." This conclusion implies the presence of an "active" regulatory mechanism that would increase the number of successful T6SS firing events, which has not been demonstrated. Could it not simply be that the capsule blocks some shots that cannot penetrate and are therefore ineffective? This hypothesis is mentioned in lines 204-208. The authors should clarify the conclusion of this section. Given the challenge this may pose in A. baumannii, I suggest that the authors quantify the assembly/firing dynamics of the T6SS under WT and ΔitrA conditions. This would help distinguish between the two hypotheses explaining better firing in non-capsulated cells: i.e., if the number of assembled T6SS is the same in both strains (Fig 2C & 2D), do non-capsulated cells assemble/fire faster, indicating an adaptation in regulation, or do we observe the same dynamics, suggesting a simple physical barrier blocking the passage of certain T6SS firing events? Line 428-429. It is mentioned that the deletion of lon does not have a notable effect. However, I observe that the absence of Lon alone causes a more rapid degradation of Hcp in the cells compared to the WT strain (Fig 7B). How do the authors explain that the absence of this protease (whether under conditions of Hcp accumulation or not) increases the degradation of this protein in the cell? This explanation should be included in the manuscript.
Minor comments:
- a) Throughout the manuscript, the authors use the term "predator" to refer to A. baumannii. Predation is a specific phenomenon that involves killing for nourishment. To my knowledge, the T6SS has never been shown to be a predation weapon but rather a weapon for interbacterial competition, which is a different concept. If this has not been demonstrated in A. baumannii, the authors should replace the term "predator" with "attacker" (or an equivalent term) to clarify the context.
- b) Line 274. Since the authors stated that in the Wzc mutant, the capsule is "predominantly found in the supernatant and only loosely attached to the cell," this result is not unexpected. This finding is also consistent with the previous results from Fig. 3A & B, which show sensitivity to complement-mediated killing and the weak amount of (ab)normal CPS produced in that strain, further confirmed by Fig. 3E.
- c) Line 299. The authors speculate that "... T6SS may deploy through gaps akin to arrow-slits in the capsule's mesh...". However, this is very unlikely since a WT strain kills (Fig. 3C) and secretes (Fig. 2B & 3D) less effectively than the itrA mutant, suggesting that the T6SS is not assembled in the "right places" devoid of CPS; otherwise, we would expect similar T6SS activity. Based on the results in Fig. 2 (and my earlier comment), this implies that A. baumannii assembles its T6SS randomly, and in the presence of the capsule, its shots would need to be in the right place to penetrate the envelope and reach the target. Could the authors comment on this point and provide a model figure to better visualize the interplay between the capsule and T6SS under the three major conditions: WT, non-capsulated, and capsule overproduction?
- d) In Fig. 5A, the microscopy panels should be adjusted to the same dynamic range as the WT (which represents a true signal), which does not appear to be the case for the tlsA mutant panel for instance. The image gives the impression of a large amount of free TssB-msfGFP in the cytoplasm. However, this effect is due to the dynamic range being adjusted to display a signal. This observation is consistent with the fact that the amount of TssB-msfGFP protein is identical across all strains (Fig. S2F).
- e) Unless I am mistaken, the authors do not comment on the fact that in a ΔbfmS strain, the number of T6SS is halved compared to a WT or ΔitrA strain. If capsule overproduction only partially limits the TslA-dependant T6SS assembly, how can this result be explained? Is it related to the degradation of Hcp in this background, which ultimately limits the formation of T6SS? If so, it would be interesting to mention this connection in the section "Prolonged secretion inhibition triggers Hcp degradation."
Referee Cross-Commenting
Overall, I agree with the concerns raised by reviewers 1 and 3. This (already) very good manuscript will undoubtedly benefit from these comments.
Significance
This work is highly intriguing as it not only delves into the specific mechanisms involved but also connects fundamental elements in bacterial competition, i.e., the necessity for self-protection and aggression for survival. The manuscript offers valuable insights into cellular dynamics at a microscale level and prompts new inquiries into the regulation of these systems on a population scale. The work is well-done and the writing is also clear.
I am convinced that this work represents another significant step towards understanding bacterial mechanisms and will undoubtedly spark considerable interest in the field.
Expertise: T6SS, fluorescence microscopy, predation, interbacterial competition
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Referee #1
Evidence, reproducibility and clarity
Summary:
The transport of effector proteins across membranes from the producing bacterium into a target cell is at the core of bacterial secretion systems. How an additional layer in form of a capsule affects effector export and the susceptibility towards effector import is not fully understood. Here, Flaugnatti and colleagues combined bacterial genetics with phenotypic assays and electron microscopy to demonstrate a dual role of a bacterial capsule in preventing T6SS-mediated effector export and promoting protection from effector import by another bacterium's T6SS. The wide variety of methods used, complementation of the mutants, and validation of the findings across strains strengthen the author's conclusions.
Although the main conclusions seem straight forward, the authors unravel the unexpected complexity underlying these phenotypes with strong mechanistic work. In brief, a capsule-deficient mutant (∆itra) is shown to assemble its T6SS similar to the WT, yet secretes more Hcp than the WT and is better in T6SS-mediated killing of other bacteria. A capsule-overproducing mutant (∆bfmS) shows both, a partial deficiency in T6SS assembly and an additional reduction in exported Hcp, and is worse in T6SS-mediated killing than the WT. A mutant with a capsule similar to WT and deficient in cell sensing (∆tslA) forms the least T6SS apparatuses and is yet better in T6SS-mediated killing than the overcapsulated mutant. Together, these data show an effect of the capsule on (i) T6SS apparatus assembly, (ii) effector export, (iii) effector import, and (iv) the need for clearance of accumulating non-secreted Hcp by ClpXP.
The work on a clinical isolate of Acinetobacter tumefaciens and the data on an impaired T6SS activity on other cells by antibiotic-induced capsulation is a strong demonstration of the work's clinical relevance in addition to the findings' conceptual novelty.
In my view, the manuscript is outstanding with very high quality of experimental data, very well written text and very clear presentation of the data in figures. A few minor comments and suggestions below that I think would strengthen the manuscript.
Major comment:
OPTIONAL: Fig. 4c/l. 320: Having an indirect effect of an antibiotic on T6SS activity by antibiotic-induced capsule formation is very intriguing and contributes to the clinical relevance of the overall findings. When I saw the data in Fig. 4c, the graph instantaneously reminded me of the panel in Fig. 2a, where a similar phenotype is observed by changing the predator:prey ratio in the absence of any antibiotic. The authors themselves comment on the possibility of antibiotic-induced, reduced predator growth (and thereby a change in predator:prey ratio) as a one factor impacting the phenotype here. I am wondering if this data could be strengthened or better disentangled to test more precisely if it is the antibiotic induced capsule formation per se that affects T6SS-mediated killing by A. baumanii in the presence of antibiotics. Would it help to take the bfmS mutant along as a control for direct comparison to see if antibiotic-induced capsule formation of the WT to similar levels of the mutant results in the same killing phenotype? Would it help to test for T6SS-mediated killing in the presence and absence of antibiotics at multiple predator:prey ratios? Could the effect of the antibiotic on A. baumanii growth be measured and considered when choosing the ratio at which the bacteria are mixed?
Minor comments:
- Figure 1D, l. 155ff, I might have missed this, do the authors happen to have the numbers of E. cloacae as well? This would strengthen the claim on A. baumanii survival because of E. cloacae is being killed.
- Figure 2, I suggest to write out the species name of the prey in the box with the ratio. With E. cloacae being referred to in the previous figure and starting with similar letters than E. coli, I wasn't sure at first sight what E. c. refers to.
- use of the term "T6SS activity" throughout the manuscript (e.g. l. 182, l. 187). I leave this up to the authors. To me, it seems like an umbrella term for the initial observation and I see that such a term can be very handy for the writing. I just would like to mention that the use of the term was not always intuitive to me and sometimes even a bit misleading. For example, l. 182 refers to "increased T6SS activity". As a reader, I only know about 'T6SS activity on other cells' or 'a T6SS-mediated effect on other cells' at this point. T6SS apparatus assembly/firing activity is tested for specifically later and it turns out to differ between mutants. By the time the term is used in the discussion, it captures multiple nuanced phenotypes described by then. The more precise definition of the term in l. 200 helped to capture what exactly is meant by the authors.
- l. 198f "Collectively, our findings indicate that CPS does not hinder the secretion process of 199 the T6SS or the consequent elimination of competing cells". I might be missing something, I cannot entirely follow this sentence. Didn't the authors just show that the CPS does hinder T6SS-mediated elimination of competing cells in panel 2A and less secreted Hcp in the encapsulated WT compared to the non-encapsulated mutant in panel 2B?
- l. 224, typo, "in"
- Two connected comments: l. 338, Just a thought, I am wondering about the title of the section. After reading it a second time, I think it is technically correct. When reading it first, I was a bit confused when getting to the data because apparatus assmebly is impaired in the capsule-overproducing strain and although "preserved", doesn't the data indicate that there is less T6SS assembly in the bfmS mutant and that this might be because of less cell sensing and isn't this a main point that there is a difference in apparatus assembly in the capsule overproducing strain compared to WT (other than no difference in apparatus assembly in the strain without capsule)? To me it seems not fully acknowledged as a finding in the interpretation of the data that less cells of the bfmS mutant have a T6SS apparatus. Isn't that interesting? A title along the lines of "Capsule-overproducing strain has preserved sensory function and assembles less T6SS apparatuses" would have been more intuitive for me. l. 352, In case I didn't miss a reference to this data earlier in the manuscript, I am wondering if it would be worth mentioning the finding on the reduced apparatus assembly of the bfmS mutant earlier, together with Figure 3 already. At least a sentence that mentions already that there is more coming later. When I got to this line in the manuscript and read the findings on the apparatus assembly, I first needed to go back to figure 3 and look at the data there again in light of this finding. It is mentioned here on the side but I think very important for the interpretation of the phenotypic data of the bfmS mutant shown earlier, isn't it? The tslA mutant is used beautifully here.
- Discussion: optional comment. On the one hand, I like the concise discussion. On the other hand, I see more potential here for bringing it all together (potentially at the expense of shortening some of the introduction). I think the subtleties of the findings are complex. For example, I could envision a graphical summary with a working model on all the effects of a capsule on the T6SS and its potential clinical relevance making the study accessible to even more readers.
Significance
General assessment
I consider the story very strong in terms of novelty, experimental approaches used, quality of the data, quality of the writing and figures of the manuscript. In my view, the aspects that could be improved are optional/minor and concern only one figure and some phrasing.
Advance
I see major advance in the findings (i, mechanistic) on the mechanism of how the capsule interferes with T6SS, (ii, fundamental) on the discovery of ClpXP degrading Hcp, and (iii, clinical) on the meaning of antibiotic treatment for the T6SS of this clinically relevant and often multi-drug resistant bacterial species, which strongly complements existing work on the T6SS and antibiotics in A. baumanii (e.g. of the Feldman group). As the authors write themselves, the starting points of the study of a capsule protecting from a T6SS and the effect of a T6SS on other cells being negatively impacted by a capsule were known, although not studied in one species and not understood mechanistically.
Audience
I see the result of interest to a broad audience in the fields of bacteria-bacteria interactions, Acinetobacter baumanii, type VI secretion, antimicrobial resistance, bacterial capsules
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Reply to the reviewers
We thank all three reviewers for their insightful comments. Based on this feedback, we have performed additional experiments, and revised our manuscript. Below, we address each comment and describe the revisions.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: Ponomarova et al. showed that neomorphic idh-1 mutation results in increased levels of cellular D-2HG. The authors compared the high D-2HG phenotypes by D-2HG dehydrogenase mutant and identified vitamin B12 dependent vulnerability differences. The downregulated gene function of glycine cleavage system involved in one-carbon donor units exacerbates the phenotypes while adding one-carbone donors suppresses the phenotype. They concluded that the idh-1neo mutation imposes a dependency on the one-carbon pool. The manuscript is very interesting but I think the manuscript should be modified to be more clear for broad audiences.
Concerns: The authors mention a number of examples for metabolic changes of D-2HG in the first paragraph of introduction. I think that a metabolic map explaining the changes helps readers to understand the questions proposed by the authors.
Thank you for this suggestion. A figure illustrating the contributing factors in D-2HG metabolism has been added to the manuscript (Figure 1A).
The authors say that D-2HG affects carcinogenesis in many ways, citing previous works. They should say a higher concentration of D-2HG does affect carcinogenesis or not in dhgd loss of function, if they assume the concentration is most important for carcinogenesis.
Thank you for pointing this out. We have added this information in lines 70-72 of the revised manuscript: "Increased levels of D-2HG caused by the inhibition of D-2-hydroxyglutarate dehydrogenase activity have also been associated with different cancers (PMID: 29339485, PMID: 34296423, PMID: 35007759)."
Line 110, mode should be read as model, I guess.
Thank you - we have corrected this error.
In Figure 4C, concentrations of formate are shown; 0. 20, 40, 80, 160 mM. Is this correct? the high concentration of substrates changes the osmotic pressure of the medium. Also, high concentration of formic acid is toxic to animals. Considering the concentration of vitamin B12 was 64 nM, I wonder concentration unit of formate is also nM.
We confirm that we supplemented the media with formate in the millimolar range. The highest doses of supplemented formate somewhat slowed the development of P0 animals, but they consistently produced viable progeny. To clarify this we have added the following line to the text on lines 184-187: "The highest doses of supplemented formate somewhat slowed the development of P0 animals, but restored the survival of idh-1neo embryos to wild-type levels on a regular diet of E. coli OP50 as well as the diet of RNAi-competent E. coli HT115."
Additionally, the use of sodium formate ensured that the pH of the media remained unchanged.
I could not understand how embryonic and larval lethality confer the same mechanisms on animal carcinogenesis. Could you explain the logic link between lethal mutation and carcinogenesis. Or do the two phenotypes share only a part of metabolic changes?
Thank you for this suggestion. We have added this in lines 242-246 of the Discussion:
"While our results have focused on how the neomorphic idh-1 mutation affects the developing embryo, proliferating cancer cells also have been shown to have increased demand for 1C units, for instance, to synthesize nucleosides (33)(PMID: 24657017). Thus, we can speculate that cancers with mutated IDH1 may be increasingly sensitive to depletion of the 1C pool, also."
Vitamin B12 is an essential substance and deficiency in humans results in sever diseases. Is the lethal phenotype by treatment of idh-1neo mutants comparable to humans? Is the concentration of vitamin B12 similar in humans?
The daily dose of human vitamin B12 (cobalamin) in supplements can reach 12.5 µg per kg (PMID: 18606874), while we supplement the media fed to worms with approximately 55 µg cobalamin per kg (64 nM adenosylcobalamin). No known adverse effects are associated with excessive intake of vitamin B12 by healthy individuals; therefore, no tolerable upper intake level has been set (PMID: 23193625). However, the impact of vitamin B12 on patients with IDH1neo-positive cancers has not been studied.
Reviewer #1 (Significance (Required)):
I think that the manuscript is interesting and may lead an important progress of this field. However, in general, metabolic disorders are difficult to understand for the people outside the speciality. The authors should explain carefully the structure/property, pathways, enzyme functions, and concentration effects of substances of interest.
See above, we hope these edits are sufficient.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Increased levels of the metabolite D-2HG (derived from alpha-KG) are associated with multiple disorders. In a previous study, the authors showed that in C. elegans dhgd-1 deletion mutants, embryonic lethality resulting from the accumulation of D-2HG in is caused by a lack of ketone bodies. In this study, the authors generated a new model of D-2HG accumulation in C. elegans, idh-1neo, in order to further understand how D-2HG exerts its toxic effects in different contexts. This allele mimics mutations found in neomorphic mutations of human IDH1 that lead to abnormal D-2HG production from alpha-KG. Interestingly, the authors find that idh-1neo mutants are distinct from animals lacking the D-2HG dehydrogenase dhgd-1 previously reported. Specifically, while vitamin B12 rescues the embryonic lethality in dhgd-1 deletion animals, it enhances the lethality of idh-1neo animals. Through an elegant genetic screen, and complementation studies with specific metabolites, they provide compelling evidence that this vitamin B12-dependent enhancement is due to depletion of the 1C pool. Specifically, a reverse genetic screen revealed that inactivation of components of the 1 C-producing glycine cleavage system (GCS) results in embryonic lethality in idh-1neo, but not wildtype animals. Complementation studies with specific metabolites show that replenishing C groups is sufficient to reverse embryonic lethality.
This is a very clear, well written paper. Experiments are well controlled and executed, figures are of the highest quality and conclusions are convincing. Prior studies are appropriately referenced. No additional experiments are required by this reviewer.
Minor points 1) In Figure 2A could authors explain how beta-alanine (increased) is different from alanine (decreased). As a non-specialist this is not clear to me.
Thank you for pointing this out. We added this explanation to the figure legend (lines 510-512).
2) Did the authors test inactivation of the lipoamide dehydrogenase (dld-1) has the same effect as the other identified components of the GCS?
The dld-1 RNAi clone was present in the metabolic library that we screened but was not identified as a "hit." We have added the following in lines 164-168 of the revised manuscript: "Two other GCS genes, gcsh-2 and dld-1 were not identified as 'hits'. gcsh-2 is associated with the same reaction as gcsh-1, indicating that the latter encodes an active enzyme (30). dld-1 functions in other metabolic processes, particularly in lactate/pyruvate metabolism, and confers embryonic lethality when knocked down in wild type animals (31)".
**Referees cross-commenting**
Comments to Reviewer #3: 1/ The authors treat the idh-1neo worms with vitamin B12 to reduce 3HP concentrations. The authors should consider conducting experiments to reduce 3HP by other means also. This would help establish a causal relationship between the D-2HG accumulation and observed phenotypes.
The authors show that adding vitamin B12 to the diet of the idh-1neo significantly increased their D-2HG levels. Furthermore, dhgd-1 RNAi drives a further increase in D-2HG in idh-1neo animals and led to 100% penetrant embryonic lethality among the F1 generation of idh-1neo animals. Together I think this provided strong evidence for a causal relationship between the D-2HG accumulation and observed phenotypes. Further characterizing these phenotypes would be interesting but is beyond the scope of this paper.
4/ The authors should clarify whether it is really vitamin B12 or any other metabolite from the bacteria (like methionine) that is bringing about the phenotypes. Have they tested metabolically inactive bacteria?
the authors show that supplementing B12-treated idh-1neo animals with formate (another 1C donor) restored the survival of idh-1neo embryos, supporting a role for B12 in depletion of the 1C pool. They also show that suppressing Met/SAM cycle genes in idh-1neo prevent 1C depletion and restore availability of 1C units. So the evidence that 1C unit depletion is at the core of the observed phenotypes is pretty convincing
7/ The authors should conduct metabolomic profiling to examine changes in metabolic pathways, including 1C, glycine metabolism, glucose metabolism etc, in idh-1neo animals subjected to GCS gene knockdown, and vitamin B12 supplementation.
Not clear how these experiments would add to this story. Open up another line of research
8/ The audience will be limited to the field although the study pertains to an oncometabolite. The study value would have improved if the authors had included cancer cell data. Also, the phenotype studied has not been mechanistically linked to the oncometabolite function, making the study academic in nature.
The intetest of this study is that it is being carried out in an organismal context.
Reviewer #2 (Significance (Required)):
As a geneticist with a general interest in metabolomics I find this an elegans study that offers new insight into how IDH-1 and -2 neomorphic mutations affect metabolic rewiring in the context of a whole animal. Although similarities are observed between idh-1neo mutants and animals lacking the D-2HG dehydrogenase dhgd-1, both of which have increased levels of the metabolite D-2HG, specific metabolic differences are observed. The identification of 1C unit deficiency as a driver of lethality in idh-1neo mutants is highly significant given the central importance of 1C metabolism. This study should therefore be of interest to a wide audience.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Ponomarova et al presents a short follow up of their previous study to elucidate the role of a oncogenic variant of idh-1 that increases the 3HP levels, similar to the Ddhgd-1 mutant. Using a combination of metabolomics and genetics, they show that the defect in idh-1neo worms on high vitamin B12 diet is the draining of the 1C pool, distinct from the mechanisms of lethality observed in the Ddhgd-1 mutant. While the findings are interesting, there is a lack of mechanistic understanding of the basis of the phenotype observed. Moreover, the authors do not establish the link between the oncometabolite, that should support uncontrolled cell division, with the observed phenotype. Some control experiments are missing and should be included in the revised manuscript. there could be many other The comments on the manuscript are as follows, in no particular order:
- The authors treat the idh-1neo worms with vitamin B12 to reduce 3HP concentrations. The authors should consider conducting experiments to reduce 3HP by other means also. This would help establish a causal relationship between the D-2HG accumulation and observed phenotypes.
To further examine the link between 3HP and idh-1neo embryonic lethality, we targeted hphd-1 by RNAi, which increases 3HP levels (Ponomarova et al., 2023). Hphd-1 knockdown did not induce lethality in the wild-type or exacerbate lethality in idh-1neo animals (Figure S3), further demonstrating that lack of 3HP degradation is not linked to this phenotype (lines 143-145).
Also, see cross-comments from Reviewer #2 above.
The authors should investigate the functional impact of HPHD-1 inhibition on 3-hydroxypropionate levels and D-2HG accumulation by RNAi knockdown of HPHD-1 in idh-1neo animals.
We have now performed the suggested experiment please see response to comment 1 above.
The authors do not clearly mention clearly which diet in some of their experiments. This is imporant since the two diets used (OP50 and HT115) differ in their vitamin B12 content, and thus could have different consequences.
We added this information in figures, figure legends, and lines 259-260 of the revised manuscript.
The authors should clarify whether it is really vitamin B12 or any other metabolite from the bacteria (like methionine) that is bringing about the phenotypes. Have they tested metabolically inactive bacteria?
The reviewer correctly points out that bacterial metabolism may play a role in the effects exerted by vitamin B12. We have not tested metabolically inactivated bacteria, however, our RNAi experiments (Figure 4E) demonstrate that supplemented vitamin B12 acts through the Met/SAM cycle in idh-1neo animals. Please also see cross-comments from Reviewer #2.
The authors consistently use 64 nM of Vitamin B12. Will the hphd-1 mutant and the idh-1neo mutant have different vitamin B12 thresholds for the observed phenotypes?
Thank you for raising this interesting point. While 64 nM vitamin B12 virtually eliminates 3HP accumulation in idh-1 animals (Figure 2D), we have not tested if this dose is sufficient to eliminate 3HP accumulation in hphd-1 mutant. However, potential differences in 3HP levels in idh-1neo and hphd-1 animals treated with vitamin B12 would not contradict our conclusion that 3HP is not the cause of embryonic lethality in idh-1neo mutant animals.
Figure 3b: HT115 has inherently high levels of vitamin B12 so the RNAi effect of genes should be seen on the OP50 diet supplemented with B12.
Despite reports of elevated B12 levels in E. coli HT115, vitamin B12-induced embryonic lethality of idh-1neo on a diet of OP50 is more severe than on a diet of HT115 bacteria (Figure 4C). Therefore, it may be harder to quantify synthetic lethal interaction of idh1-neo with GCS RNAi knockdown using OP50 strains (which would need to be created).
The authors should conduct metabolomic profiling to examine changes in metabolic pathways, including 1C, glycine metabolism, glucose metabolism etc, in idh-1neo animals subjected to GCS gene knockdown, and vitamin B12 supplementation.
While these results would be interesting and further our understanding of metabolic changes that occur in idh-1neo mutant animals we think they are beyond the scope of the manuscript. Also, please see cross-comments from Reviewer #2.
Perform rescue experiments using different one-carbon donors (e.g., formate, serine) to restore embryonic viability in idh-1neo mutants under conditions of vitamin B12-induced stress. Quantify the efficacy of these interventions using developmental assays.
In addition to formate rescue experiments (Figure 4C), we supplemented idh-1neo animals with serine (Figure 4D and S7). Similar to formate, serine supplementation resulted in the rescue of idh-1neo embryonic lethality on an E. coli OP50 diet (lines 187-189). The lack of rescue on an HT115 diet could be due to HT115 bacteria containing more glycine (Gao et al., 2017), which might limit the efficiency of serine conversion to glycine needed for 1C unit production.
Provide experimental evidence to show that idh-1neo animals possess an alternative source of energy.
We have previously found that diminished production of ketone bodies in ∆dhgd-1 mutants causes embryonic lethality that can be rescued by exogenous supplementation of ketone body 3-hydroxybutyrate (Ponomarova et al., 2023). In contrast to dhgd-1 mutants, idh-1neo embryonic lethality fails to respond to supplemented 3-hydroxybutyrate (Figure S4), indicating the lethality associated with the idh-1neo mutation is caused by a different mechanism, i.e., a depletion in 1C-units.
The authors use vitamin B12 to inhibit the shunt pathway (line 127). They should explore alternate strategies to do the same, like gene knockdown.
Please see our response to comment 1 above where we discuss RNAi knock-down of the shunt pathway gene, hphd-1.
It is not clear why the authors did not follow up with the other phenotypes of the idh-1neo that were visible without the Vitamin B12 supplementation. They should follow up with this and also other phenotypes to explore the broader physiological consequences of D-2HG accumulation.
We agree that the other physiological consequences of D-2HG accumulation are interesting, and we plan to investigate them in our future studies.
The authors should include control experiments without supplementation of vitamin B12, ketone bodies etc. in each of their figures.
We thank the reviewer for this suggestion. We have added these data (Figures S5, 6, 7, and 8).
The authors posit that the idh-1neo depletes the 1C pool leading to the observed lethality. So, when they supply formate to replenish it, they rescue the lethality of the B12-treated worms. Similar results are obtained by knocking down the enzymes. So where are the 1C units going? Understanding this will provide the much-needed mechanistic understanding to this study.
We appreciate this insightful comment and expand our discussion to elaborate on this issue (lines 224-227). "We propose that a lack of 1C units in idh-1neo can impede pyrimidine biosynthesis via thymidylate synthase tyms-1, which uses 1C units to generate dTMP. Supporting this hypothesis, RNAi of tyms-1 causes embryonic lethality (36-38)."
It may be important to measure the D-2HG levels in the mitochondria vs the cytosol.
While this is an interesting point, we think that this line of inquiry is beyond the scope of this work (and is technically challenging).
The idh-1neo is an oncometabolite. The authors do not show any data to indicate whether this mutant has any defect in cell division/cell cycle in the somatic tissue or germline.
In this study we primarily focused on the molecular changes in the metabolic network that occur in idh-1neo mutant animals, which we think is an important advance in understanding the basis for how this mutation affects IDH function. Additional phenotypic outcomes of these perturbed metabolic processes will be the basis of future studies.
Reviewer #3 (Significance (Required)):
The audience will be limited to the field although the study pertains to an oncometabolite. The study value would have improved if the authors had included cancer cell data. Also, the phenotype studied has not been mechanistically linked to the oncometabolite function, making the study academic in nature.
While we agree that the link between idh-1neo, 2HG production and oncometabolite function has not been directly shown we think that our study adds important molecular understanding of metabolic changes that occur in relation to idh-1neo function which are important for future studies of how this mutation affects carcinogenesis. Also, please see cross-comments from Reviewer #2.
In addition, we specified statistical significance in Figure 2, described statistical tests used (lines 361-363) and corrected a few grammatical errors throughout the text.
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Referee #3
Evidence, reproducibility and clarity
Ponomarova et al presents a short follow up of their previous study to elucidate the role of a oncogenic variant of idh-1 that increases the 3HP levels, similar to the dhgd-1 mutant. Using a combination of metabolomics and genetics, they show that the defect in idh-1neo worms on high vitamin B12 diet is the draining of the 1C pool, distinct from the mechanisms of lethality observed in the dhgd-1 mutant. While the findings are interesting, there is a lack of mechanistic understanding of the basis of the phenotype observed. Moreover, the authors do not establish the link between the oncometabolite, that should support uncontrolled cell division, with the observed phenotype. Some control experiments are missing and should be included in the revised manuscript. there could be many other The comments on the manuscript are as follows, in no particular order:
- The authors treat the idh-1neo worms with vitamin B12 to reduce 3HP concentrations. The authors should consider conducting experiments to reduce 3HP by other means also. This would help establish a causal relationship between the D-2HG accumulation and observed phenotypes.
- The authors should investigate the functional impact of HPHD-1 inhibition on 3-hydroxypropionate levels and D-2HG accumulation by RNAi knockdown of HPHD-1 in idh-1neo animals.
- The authors do not clearly mention clearly which diet in some of their experiments. This is imporant since the two diets used (OP50 and HT115) differ in their vitamin B12 content, and thus could have different consequences.
- The authors should clarify whether it is really vitamin B12 or any other metabolite from the bacteria (like methionine) that is bringing about the phenotypes. Have they tested metabolically inactive bacteria?
- The authors consistently use 64 nM of Vitamin B12. Will the hphd-1 mutant and the idh-1neo mutant have different vitamin B12 thresholds for the observed phenotypes?
- Figure 3b: HT115 has inherently high levels of vitamin B12 so the RNAi effect of genes should be seen on the OP50 diet supplemented with B12.
- The authors should conduct metabolomic profiling to examine changes in metabolic pathways, including 1C, glycine metabolism, glucose metabolism etc, in idh-1neo animals subjected to GCS gene knockdown, and vitamin B12 supplementation.
- Perform rescue experiments using different one-carbon donors (e.g., formate, serine) to restore embryonic viability in idh-1neo mutants under conditions of vitamin B12-induced stress. Quantify the efficacy of these interventions using developmental assays.
- Provide experimental evidence to show that idh-1neo animals possess an alternative source of energy.
- The authors use vitamin B12 to inhibit the shunt pathway (line 127). They should explore alternate strategies to do the same, like gene knockdown.
- It is not clear why the authors did not follow up with the other phenotypes of the idh-1neo that were visible without the Vitamin B12 supplementation. They should follow up with this and also other phenotypes to explore the broader physiological consequences of D-2HG accumulation.
- The authors should include control experiments without supplementation of vitamin B12, ketone bodies etc. in each of their figures.
- The authors posit that the idh-1neo depletes the 1C pool leading to the observed lethality. So, when they supply formate to replenish it, they rescue the lethality of the B12-treated worms. Similar results are obtained by knocking down the enzymes. So where are the 1C units going? Understanding this will provide the much-needed mechanistic understanding to this study.
- It may be important to measure the D-2HG levels in the mitochondria vs the cytosol.
- The idh-1neo is an oncometabolite. The authors do not show any data to indicate whether this mutant has any defect in cell division/cell cycle in the somatic tissue or germline.
Significance
The audience will be limited to the field although the study pertains to an oncometabolite. The study value would have improved if the authors had included cancer cell data. Also, the phenotype studied has not been mechanistically linked to the oncometabolite function, making the study academic in nature.
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Referee #2
Evidence, reproducibility and clarity
Increased levels of the metabolite D-2HG (derived from alpha-KG) are associated with multiple disorders. In a previous study, the authors showed that in C. elegans dhgd-1 deletion mutants, embryonic lethality resulting from the accumulation of D-2HG in is caused by a lack of ketone bodies. In this study, the authors generated a new model of D-2HG accumulation in C. elegans, idh-1neo, in order to further understand how D-2HG exerts its toxic effects in different contexts. This allele mimics mutations found in neomorphic mutations of human IDH1 that lead to abnormal D-2HG production from alpha-KG. Interestingly, the authors find that idh-1neo mutants are distinct from animals lacking the D-2HG dehydrogenase dhgd-1 previously reported. Specifically, while vitamin B12 rescues the embryonic lethality in dhgd-1 deletion animals, it enhances the lethality of idh-1neo animals. Through an elegant genetic screen, and complementation studies with specific metabolites, they provide compelling evidence that this vitamin B12-dependent enhancement is due to depletion of the 1C pool. Specifically, a reverse genetic screen revealed that inactivation of components of the 1 C-producing glycine cleavage system (GCS) results in embryonic lethality in idh-1neo, but not wildtype animals. Complementation studies with specific metabolites show that replenishing C groups is sufficient to reverse embryonic lethality.
This is a very clear, well written paper. Experiments are well controlled and executed, figures are of the highest quality and conclusions are convincing. Prior studies are appropriately referenced. No additional experiments are required by this reviewer.
Minor points
- In Figure 2A could authors explain how beta-alanine (increased) is different from alanine (decreased). As a non-specialist this is not clear to me.
- Did the authors test inactivation of the lipoamide dehydrogenase (dld-1) has the same effect as the other identified components of the GCS?
Referees cross-commenting
Comments to Reviewer #3:
1/ The authors treat the idh-1neo worms with vitamin B12 to reduce 3HP concentrations. The authors should consider conducting experiments to reduce 3HP by other means also. This would help establish a causal relationship between the D-2HG accumulation and observed phenotypes.
The authors show that adding vitamin B12 to the diet of the idh-1neo significantly increased their D-2HG levels. Furthermore, dhgd-1 RNAi drives a further increase in D-2HG in idh-1neo animals and led to 100% penetrant embryonic lethality among the F1 generation of idh-1neo animals. Together I think this provided strong evidence for a causal relationship between the D-2HG accumulation and observed phenotypes. Further characterizing these phenotypes would be interesting but is beyond the scope of this paper.
4/ The authors should clarify whether it is really vitamin B12 or any other metabolite from the bacteria (like methionine) that is bringing about the phenotypes. Have they tested metabolically inactive bacteria?
the authors show that supplementing B12-treated idh-1neo animals with formate (another 1C donor) restored the survival of idh-1neo embryos, supporting a role for B12 in depletion of the 1C pool. They also show that suppressing Met/SAM cycle genes in idh-1neo prevent 1C depletion and restore availability of 1C units. So the evidence that 1C unit depletion is at the core of the observed phenotypes is pretty convincing
7/ The authors should conduct metabolomic profiling to examine changes in metabolic pathways, including 1C, glycine metabolism, glucose metabolism etc, in idh-1neo animals subjected to GCS gene knockdown, and vitamin B12 supplementation.
Not clear how these experiments would add to this story. Open up another line of research
8/ The audience will be limited to the field although the study pertains to an oncometabolite. The study value would have improved if the authors had included cancer cell data. Also, the phenotype studied has not been mechanistically linked to the oncometabolite function, making the study academic in nature.
The intetest of this study is that it is being carried out in an organismal context.
Significance
As a geneticist with a general interest in metabolomics I find this an elegans study that offers new insight into how IDH-1 and -2 neomorphic mutations affect metabolic rewiring in the context of a whole animal. Although similarities are observed between idh-1neo mutants and animals lacking the D-2HG dehydrogenase dhgd-1, both of which have increased levels of the metabolite D-2HG, specific metabolic differences are observed. The identification of 1C unit deficiency as a driver of lethality in idh-1neo mutants is highly significant given the central importance of 1C metabolism. This study should therefore be of interest to a wide audience.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Ponomarova et al. showed that neomorphic idh-1 mutation results in increased levels of cellular D-2HG. The authors compared the high D-2HG phenotypes by D-2HG dehydrogenase mutant and identified vitamin B12 dependent vulnerability differences. The downregulated gene function of glycine cleavage system involved in one-carbon donor units exacerbates the phenotypes while adding one-carbone donors suppresses the phenotype. They concluded that the idh-1neo mutation imposes a dependency on the one-carbon pool. The manuscript is very interesting but I think the manuscript should be modified to be more clear for broad audiences.
Concerns:
The authors mention a number of examples for metabolic changes of D-2HG in the first paragraph of introduction. I think that a metabolic map explaining the changes helps readers to understand the questions proposed by the authors.
The authors say that D-2HG affects carcinogenesis in many ways, citing previous works. They should say a higher concentration of D-2HG does affect carcinogenesis or not in dhgd loss of function, if they assume the concentration is most important for carcinogenesis.
Line 110, mode should be read as model, I guess.
In Figure 4C, concentrations of formate are shown; 0. 20, 40, 80, 160 mM. Is this correct? the high concentration of substrates changes the osmotic pressure of the medium. Also, high concentration of formic acid is toxic to animals. Considering the concentration of vitamin B12 was 64 nM, I wonder concentration unit of formate is also nM.
I could not understand how embryonic and larval lethality confer the same mechanisms on animal carcinogenesis. Could you explain the logic link between lethal mutation and carcinogenesis. Or do the two phenotypes share only a part of metabolic changes?
Vitamin B12 is an essential substance and deficiency in humans results in sever diseases. Is the lethal phenotype by treatment of idh-1neo mutants comparable to humans? Is the concentration of vitamin B12 similar in humans?
Significance
I think that the manuscript is interesting and may lead an important progress of this field. However, in general, metabolic disorders are difficult to understand for the people outside the speciality. The authors should explain carefully the structure/property, pathways, enzyme functions, and concentration effects of substances of interest.
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Reply to the reviewers
__Reviewer #1 (Evidence, reproducibility and clarity (Required)):_ _ __ In this manuscript, Jones et al. report on a potential role for fam83fa in zebrafish hatching, radiation response and autophagy. The authors are commended for generating multiple KO lines and maternal-zygotic embryos for analysis. However, important controls are lacking and the data is circumstantial throughout with very little mechanistic insight into the precise roles, if any, of fam83f in these processes.
We thank the reviewer for recognizing the strengths of our manuscript, and highlighting areas we might improve. Please see the specific comments below addressing the points raised. In respect of mechanistic insight, while we agree that our manuscript does not provide this, it was not intended to. Rather, we aim to communicate our descriptive findings on the role of Fam83fa in vivo, providing data for follow-up studies by other researchers into the mechanistic role of Fam83fa.
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Validation of the KO phenotypes (hatching, IR sensitivity) requires rescue with WT fam83fa WT mRNA, but not 1-500 or fam83fb mRNA.
We thank the reviewer for raising the issue of rescue experiments. Such experiments are frequently used in knock-down experiments, where non-specificity may be a problem, but they are used more rarely in genetic knock-outs, where the gene defect is well defined. In the case of Fam83fa, a particular difficulty is that overexpression of fam83fa itself causes a p53-mediated DNA damage response (DDR) (Salama et al., 2019). Moreover, we have shown by both qRT-PCR and western blotting that injection of fam83fa mRNA into zebrafish embryos (the traditional technique by which rescue experiments are performed) induces a p53-mediated DDR. As a result, it would be very difficult to interpret the results of any rescue experiment, because one would have to be absolutely certain that levels of fam83fa re-expression recapitulate and do not exceed endogenous levels. As a tool for specificity, we therefore used more than one fam83fa-/- mutant line, carrying a different genomic mutation, and validated that the same phenotype was present in both. We are happy to provide the qRT-PCR and western blot data confirming the results of fam83fa mRNA injection, if required. We have included an additional section into the manuscript detailing this issue. 2.
While the hatching phenotype (Fig 3) is convincing, there is no data on HG development in the null embryos. Does the HG develop normally in the absence of fam83fb? If so, this would support the authors conclusions that the role of fam83fb is functional rather than developmental (indirect effect). In situs as in Fig.1 might be helpful here.
Thank you to the reviewer for this helpful suggestion. We agree that we did not investigate whether the hatching gland develops normally in the MZ-fam83fa-/- mutant embryos. No gross morphological differences were observed that led us to investigate this, although we agree it is an interesting question for a future project. In terms of functional vs developmental effects, we are confident that MZ-fam83fa-/- mutant embryos develop at a normal temporal rate, as evidenced by the machine learning based classifier used to assess temporal developmental trajectory (Figure S3 and Jones et al., 2022, 2024). This strongly suggests that the effect of fam83fa KO is functional rather than indirect and caused by (for example) developmental delay.
While the IR sensitivity phenotype (Fig S4) is convincing, IR-induced cell death/apoptosis was not analyzed. There is a large literature describing straightforward assays for cell death/apoptosis detection in zebrafish with assays such as acridine orange or TUNEL labeling, or active casp3 whole-mount IF. Is IR-induced cell death enhanced in fam83fa KOs?
We thank the reviewer for their positive comments and agree that investigating the nature of the cell death occurring following IR would be very interesting. We did make use of both acridine orange and TUNEL labeling following injection of fam83fa mRNA (see 1 above), and whilst the assays themselves were relatively straightforward, due to technical issues the quantification of fluorescence intensity was not. Similarly, we suspect that a significant degree of necrosis is also occurring, which further complicates the issue of data interpretation from both these approaches. We do, however, think this is an important avenue of questioning, and hope that other researchers will explore the mechanism of IR induced cell death in the MZ-fam83fa-/- mutants in the future,
Similarly, there are multiple tools to assay autophagy in zebrafish (e.g., Moss et al., Histochem Cell Biol 2020, PMC7609422; Mathai et al., Cells 2017, PMC5617967). Is autophagy affected in the KOs, with or without IR? These experiments might directly implicate fam83fa in autophagy.
We agree that there are exciting tools with which to assay autophagy in zebrafish, and although we considered some of these, including caudal fin regeneration, we deemed these experiments to be beyond the descriptive scope of this paper, given the time and resources available to us. We hope that other researchers will use our data as a basis for investigating the role of Fam83fa in autophagy further, using assays such as these suggested by the reviewer.
Figure 4: Isn't there a slight reduction in p53 induction at 10 hours?
Although the western blot in Figure 4A gives this impression, this is probably due to loading variability (see the anti-β-actin loading control band). Moreover, over three independent experiments (Figure 4B), this apparent difference is not statistically significant. Taken together with other evidence that the p53-mediated DNA damage response is not affected in MZ-fam83fa-/- mutants, we are confident there is no detectable change in the level of stabilized p53 in the MZ-fam83fa-/- mutants compared to WT.
Given the widely documented, dominant role of p53 in zebrafish IR-sensitivity, the authors should test if the IR sensitivity of fam83fa KO animals is p53-dependent, ideally via a cross into p53 null, but at least via injection of p53 morpholinos.
We agree that p53 is widely documented as playing an essential role in the IR induced DNA damage response in zebrafish. All our experiments suggest there is no difference between the levels of p53 (protein or mRNA) or any of the p53-induced downstream effectors (that we tested) in MZ-fam83fa-/- mutants compared to WT embryos. This was true whether or not the embryos were subjected to genotoxic stressors, including IR treatment. We therefore conclude that the increased sensitivity phenotype we observe as a result of loss of Fam83fa is not caused by a change in p53 activity, at least not as part of the DNA damage response.
Do autophagy inhibitors phenocopy the hatching and IR-sensitivity defects of fam83fa embryos? Do the inhibitors exacerbate the mutant phenotypes or synergize with M or Z mutant phenotypes? (I may have missed this but do M and Z fam83fa null embryos have any phenotype? Or do the phenotypes only manifest in MZ embryos?)
This is an excellent question, and indeed one we attempted to address. We tried to optimize several autophagy inhibitors including bafilomycin A1, chloroquine and wortmannin, as well as the proteasomal inhibitor MG132. In addition, we tried to optimize the autophagy promoters Torin1 and rapamycin. Unfortunately, we regularly saw global effects in zebrafish embryos that were difficult to characterize and control by dosage. At the same time, we were also working to confirm the specific effects of these drugs on autophagy using p62 and LC3-I and LC3-II western blots, which themselves were difficult to optimize. We attempted to optimize these experiments for 6 months before the COVID lockdown occurred, at which point they were abandoned. We would be delighted for future researchers to continue these experiments, as we are now unable to pursue this further due to closure of the Smith lab, but we agree that these are very pertinent questions. We hope the descriptive data provided in our paper will prompt other researchers in the autophagy field to further explore the role of Fam83fa in autophagy. In response to the zygotic phenotype question, this was something we did not investigate. As there was no immediately apparent phenotype in the zygotic generation, for ease of screening larger numbers of embryos we proceeded immediately to the maternal-zygotic (MZ) generation.
Reviewer #1 (Significance (Required)):
The role of Fam83f is not known. This study in zebrafish might be the first to clarify the function of this protein in vivo.
We thank the reviewer for this positive insight, and we agree that our work is the first do so in vivo.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Fam83f is one of the proteins about which little is known. The authors Jones et al., tried to shed light on Fam83f function by knocking out the gene in zebrafish. Here they found that fam83 is expressed in the hatching gland and that larvae without Fam83f hatch significantly earlier than wild-type animals. The authors furthermore investigated the response of fam83f knock-out animals to DNA damage and found increased sensitivity to ionizing radiation and MMS. In order to find out more about Fam83f function in the DNA damage response, the authors performed RNA-seq after employing DNA damage and here they saw upregulation of several autophagy/lysosome-associated proteins and downregulation of some phosphatidylinositol-3-phosphate binding proteins, among others. Finally, the authors found that Fam83f is targeted to the lysosome. The manuscript is overall well written and clear in its general statement.
We thank the reviewer for their encouraging comments.
In the manuscript, the authors describe the investigation of several aspects of Fam83f function and particularly the role in hatching seems to be important for Fam83f as the gene is strongly expressed in the hatching gland and its absence leads to a clear and considerable earlier hatching. Unfortunately, all aspects of Fam83f function that are described in the manuscript are investigated very superficially, the conclusions are not supported by data and important controls are lacking. As such, the RNA-seq results are not confirmed by qRT-PCR, the role of the Fam83f LIR domain is not confirmed by co-IPs and it has not been investigated whether the presence of Fam83f in lysosomes is due to its degradation or whether it has a function in this cellular compartment.
We thank the reviewer for their input and will address each point raised below: -
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All aspects of Fam83f function are investigated superficially.
We agree that we have not provided an in-depth analysis of the mechanistic role of Fam83fa. It was because there were so many roles that we decided to make this paper rather descriptive in nature, hoping that the observations will prove useful to other researchers who may wish to define the mechanistic roles of Fam83fa more deeply. Even without in-depth investigation, our findings are previously unreported and the phenotypes we report are clear. We have amended our manuscript to make it apparent that this paper is intended to be descriptive in nature, and we hope this addresses this issue.
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Important controls are lacking - RNA-seq results are not confirmed by qRT-PCR
We thank the reviewer for their comment. We did not include qRT-PCR data as a control for the RNA-seq data because 1) each RNA-seq experiment was repeated on three biological replicates across three independent experiments and 2) we conducted RNA-seq on two different MZ-fam83fa-/- mutant lines and only considered genes that were mis-regulated in both mutants. Taken together, we considered this to be sufficient validation for the manuscript. However, we also performed confirmatory qRT-PCR for several of the differentially expressed genes identified, including the three main PI(3)P binding genes. We have now included these data in the supplementary information as an additional control - see Figure S6G which is now also referred to in the main text, and additional primer sequences have been added to Table S1.
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The role of the Fam83f LIR domain is not confirmed by co-Ips
We agree with the reviewer that this is an important experiment, and we worked closely with Dr Brian Ludwig and Dr Karen Vousden (The Francis Crick Institute) to test this. We tried to express zebrafish Atg8 and Gabarap (the two main ATG8 proteins that bind to LIR domains) but were unable to express sufficient levels of protein to perform the co-Ips. The text in the manuscript has now been amended to reflect that this experiment is required to confirm the role of the putative LIR domain in Fam83fa.
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*it has not been investigated whether the presence of Fam83f in lysosomes is due to its degradation or whether it has a function in this cellular compartment *
Whilst we agree with the reviewer that this is an important question, we did not intend this paper to expand beyond a descriptive role of the observations we made following the loss of Fam83fa in vivo. These are important questions to follow up on to determine the mechanism of action of Fam83fa, and we hope that other researchers will pursue these avenues of investigation following the publication of our observations.
Also, there is no leading concept in the manuscript. Starting from a role in hatching, the authors go to the DNA damage response and finally to the presence of Fam83f in lysosomes. How are these different aspects linked? Is the presence of Fam83f in lysosomes important for the suppression of hatching and how does Fam83f delays this process? (One would have wished that the authors would not have been that broad and were more focused on a particular aspect which then could have been investigated in depth.)
We agree with the reviewer that the paper gives a broad overview of our observations and does not examine the underlying mechanisms in detail. However, we believe that descriptive papers such as this, where observations following genetic perturbation are reported, are equally important, providing as they do important foundational data for other researchers to take forward. We do postulate on the links between the hatching, DNA damage and lysosomal phenotypes we observe in the discussion section, and we have expanded on this following the reviewers' comments, to make our hypothesized link between these phenomena clearer.
Specific comments: - All materials should be described in material and methods including the antibodies that have been used
The antibodies used together with concentrations and catalog numbers are now in Materials and Methods
- Abbreviations should be explained
The manuscript has been revised to ensure all abbreviations are explained. We thank the reviewer for bringing this oversight to our attention.
- Figure 4A: Levels of p53 should also be shown for untreated fam83f -/-KO1 and KO2 animals
The authors thank the reviewers for raising this point. Extracts from untreated MZ-fam83fa-/- KO1 and KO2 embryos were not included on this particular blot, as p53 was observed to be undetectable in all embryos, across all our experiments (WT and both mutants) unless genotoxic stress was applied. No quantification could therefore be performed as the expression level was essentially zero. However, we have now included an example p53 western blot in Supplemental Figure 5A, which shows WT, MZ-fam83fa-/- KO1 and MZ-fam83fa-/- KO2 untreated blots for p53 (all undetectable) alongside treated embryos (detected).
- Some references are missing (e.g. page 17, lane 320/321: As this group of cells arises....)
This citation and reference have now been added; thank you to the reviewer for highlighting this omission.
- Lane 369: The authors write about 4 KO lines but only two are shown in the figure.
We thank the reviewer for this observation. In Figure 2B only KO1 and KO2 schematic diagrams are shown for simplicity (as these are the lines taken forward for further investigation). We have now amended the manuscript text to make this clear.
- Lane 374/375: The NMD is not proven
Absolutely - we have now revised the text to change this sentence accordingly and thank the reviewer for noting this.
- Lane 380: how can RNA levels of fam83fa be upregulated when the gene has been knocked out? Why are these genes only upregulated in KO1? How relevant is this?
This was a typographical error, and we are very grateful to the reviewer for picking up on this. It should have read 'fam83fb'. As nonsense-mediated decay and associated transcriptional adaptation have been previously reported in zebrafish, this finding may be of considerable interest to the community. It is a side observation, and not necessarily directly related to the role of Fam83fa in vivo, but we felt it important to include. Indeed, as a result of this observation we have recently shared our MZ-fam83fa-/- lines with another group who are planning to investigate precisely this question - why are fam83fb and fam83g only upregulated in KO1?
- Figure 3C is not mentioned in the text and lacks any labelling
Figure 3C is now clearly referred to in the text and a label added to the figure.
- Lane 434/435: all relevant data should be shown (can be done as supplementary figure)
We have now amended this to include an additional supplemental figure (Figure S5A).
- Lane 434: The reference to the figure seems to be incorrect (5A4A)
Amended accordingly - thank you for pointing out this mistake.
- Figure 4C and 4D: what is the difference?
Thank you to the reviewer for noticing this omission. These data are from t1 (+2hrs) and t2 (+10hrs) and have now been labelled accordingly.
- S5C and S5D: why are there 3 clusters?
We thank the reviewer for raising this as it has provided us with an opportunity to present our data more clearly. There are 3 clusters that represent the combination of the two first principal components, which are time and treatment. Therefore, the clusters represent i) untreated at t1, ii) treated at t1 and iii) treated at t2. However, having two plots with different color schemes made this confusing/misleading. We have now replaced the two PCA plots with one that is colored and labelled accordingly with the 3 aforementioned clusters.
- Lane 495 to 505: What does this mean that the GO analysis shows upregulation and downregulation of endopeptidases and why "in contrast"?
We thank the reviewer for this comment, and we agree that this paragraph was misleading/confusing. This has now been rewritten in the main text, clarifying that endopeptidases were consistently upregulated at both timepoints.
Reviewer #2 (Significance (Required)):
The strength of the manuscript is certainly that it provides inside into Fam83f function as there is not much known about Fam83f.
We thank the reviewer for the positive comment, and we agree that very little is known about this highly conserved protein.
These study is probably most interesting for people in the zebrafish and related fields as the authors convincingly show the expression of Fam83f in the hatching gland and also the earlier hatching in the absence of the protein is very clear.
Thank you for the positive feedback.
The weakness of the study is clearly that it does not provide an in-depth analysis. As such, it shows that Fam83f is involved in hatching and can delay the process but it remains elusive how this is achieved. (Likwise, also the investigation into the DNA damage response remains very superficial and does not prove a specific role for Fam83f in the DNA damage response or whether the increased sensitivity is more unspecifically caused by the absence of a gene or eventually even connected to the earlier hatching.
Please refer to responses above (and changes made to the manuscript) clarifying that this study is intended to be descriptive, and provides important foundational data for further in-depth mechanistic studies by other researchers interested in the role of Fam83fa in vivo.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):_ _ __ In their manuscript "Zebrafish reveal new roles for Fam83f in hatching and the DNA damage-mediated autophagic response", Jones et al. provide an interesting exploration for the function of a poorly studied protein, Fam83f in embryonic development. Using the zebrafish as a model organism, the study combines loss-of-function genetics, phenotypic analysis and RNA-sequencing to characterize and explore the result of Fam83f loss. Upon critical review of the manuscript and the results we offer suggestions to improve the manuscript (see 'minor technical issues'). Additionally, we would like to highlight a weakness of the study in making the connection between Fam83f to the observed phenotype (increased sensitivity to DNA damage), see 'major issues'.
Major issues:
Most of our concern stems from relatively incomplete connection of the loss of fam83f to increased sensitivity to DNA-damage and lysosome function.
Please refer to comments above and changes made to the manuscript to clarify this is a descriptive paper that is not intended to provide in-depth mechanistic insight into the role of Fam83fa.
Is the increased sensitivity in fam83f KO embryos a direct effect to fam83f loss? A rescue experiment (by introduction of Fam83fa mRNA into their KO2 fish line) in the presence of ionizing radiation would help us understand the functional role of this protein in this process. Furthermore, can overexpression of any of the down-regulated genes involved in lysosome function restore the early hatching phenotype or the sensitivity to DNA damage? Fam83fa rescue experiments would be very difficult to interpret - please see comments above and the corresponding changes to our manuscript.
In terms of over-expressing some of the downregulated genes identified in the RNA-seq and qRT-PCR to see if the phenotype can be rescued, we feel these are excellent suggestions and we hope other researchers in future will attempt such experiments.
Minor technical issues:
-Methods line 203, clarify how many embryos were used per sample for RNA-seq (this was only described as 15 embryos in the main body results text).
Text has been amended to clarify this. We thank the reviewer for noticing this oversight.
-Comment about the expansion of fam83f orthologs in mammals (8) as opposed to only 2 in zebrafish
We apologize for any confusion: mammals do not have 8 fam83f orthologs. Mammals and zebrafish have 8 FAM83 genes (FAM83A-FAM83H). Zebrafish, unlike mammals, have genome duplication and although mammals have only one FAM83F gene, zebrafish have two: Fam83fa and Fam83fb. We trust this clarifies this issue and believe this to be clear in our main text. However, we are happy to make any suggested amendments should the reviewer consider our wording confusing.
-Supplementary figure 1C: please include representative images of secondary axis formation in fam83fa overexpressed Xenopus embryos.
We have not included any images as these are already published in our related paper on FAM83F (Dunbar et al., 2020) which we refer to in the figure legend text. No additional images were captured specifically for this publication.
-Provide more information about the mis-regulated genes in the RNA-seq analysis, how many are up or down regulated? Perhaps a better plot than a Venn diagram can be an MA-plot with the Venn diagram moved to a supplementary figure.
The Venn diagrams in Figure 5A-C are to illustrate the number of differentially expressed genes that are shared between KO1 and KO2 (whether up or down regulated), and only those that are common to both lines are taken forward. Following the reviewer's comments, we have now displayed the behavior of the common genes across all replicates in one heatmap, with the data normalized to the WT untreated samples, and the normalized variance stabilized count indicates whether a gene is up or down regulated across each of the replicates and conditions. We believe this addresses the reviewer's comment as these data are now displayed in a more direct way and the genes that are consistently up or downregulated across all replicates (and indeed those that are not) can be clearly seen. We thank the reviewer for raising this and improving our data representation.
-A better comparison of mis-regulated genes in the fam83f knockouts would be a comparison of KO2 and perhaps KO3, as the compensatory effects in KO1 can lead to additional indirect effect on the transcriptome. We understand the time and cost involved in this experiment and suggest that the differential gene expression analysis be performed individually on up or down regulated genes from KO2, or a comparison of such analysis will be provided with the differential gene expression analysis that was performed on shared mis-regulated genes between KO1 and KO2.
The reviewer raises an excellent point. At the time of experimental design, we were concerned that omitting KO1 in favor of another line (e.g. KO3) would bias our results by excluding potentially important data. Similarly, as transcriptional adaptation occurs in a sequence specific manner, and the phenotype was present in KO1 regardless, we didn't want to exclude these data. However, with hindsight, we agree that it may have been prudent to exclude KO1 on this basis, and we may have seen an increased concordance of differentially expressed genes (DEGs) between KO2 and KO3. However, this is not possible to repeat now due to the Smith lab closing, and our documented findings are valid and important regardless. We acknowledge however that, with hindsight, what the reviewer suggests may have been better experimental design.
-Can you confirm with the RNA-seq analysis that fam83g is upregulated in KO1 as opposed to KO2? (i.e. can the compensatory analysis you have observed with qRT-PCR be confirmed with the RNA-seq data?)
This is an excellent question, and we thank the reviewer for raising this. fam83fb passed our threshold for significance to be deemed as differentially expressed (upregulated) in KO1 only, in accordance with our qRT-PCR data. fam83g did not pass the significance threshold, but perhaps this is not surprising as both fam83fb and fam83g are expressed at particularly low levels to start with and would probably require much greater sequencing depth to be detected.
Reviewer #3 (Significance (Required)):
There is fundamental value in clarifying the in vivo function of poorly characterized protein-coding genes. This study fills a gap in the literature, but the broader conceptual impact is limited. The authors do a thorough job at generating and characterizing CRISPR/Cas9 mediated knock-out zebrafish animals. It is further commended that the authors do a meticulous job in a quantitative description of the resulting phenotype. This is a thorough study, with the only major concern being the lack of rescue experiments that would be needed to substantiate the the role of fam83f in sensitivity to DNA damage and lysosome function.
We thank the reviewer for their comments and trust we have addressed the issues concerned with the changes described above.
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Referee #3
Evidence, reproducibility and clarity
In their manuscript "Zebrafish reveal new roles for Fam83f in hatching and the DNA damage-mediated autophagic response", Jones et al. provide an interesting exploration for the function of a poorly studied protein, Fam83f in embryonic development. Using the zebrafish as a model organism, the study combines loss-of-function genetics, phenotypic analysis and RNA-sequencing to characterize and explore the result of Fam83f loss. Upon critical review of the manuscript and the results we offer suggestions to improve the manuscript (see 'minor technical issues'). Additionally, we would like to highlight a weakness of the study in making the connection between Fam83f to the observed phenotype (increased sensitivity to DNA damage), see 'major issues'.
Major issues:
Most of our concern stems from relatively incomplete connection of the loss of fam83f to increased sensitivity to DNA-damage and lysosome function.
Is the increased sensitivity in fam83f KO embryos a direct effect to fam83f loss? A rescue experiment (by introduction of Fam83fa mRNA into their KO2 fish line) in the presence of ionizing radiation would help us understand the functional role of this protein in this process. Furthermore, can overexpression of any of the down-regulated genes involved in lysosome function restore the early hatching phenotype or the sensitivity to DNA damage?
Minor technical issues:
- Methods line 203, clarify how many embryos were used per sample for RNA-seq (this was only described as 15 embryos in the main body results text).
- Comment about the expansion of fam83f orthologs in mammals (8) as opposed to only 2 in zebrafish
- Supplementary figure 1C: please include representative images of secondary axis formation in fam83fa overexpressed Xenopus embryos.
- Provide more information about the mis-regulated genes in the RNA-seq analysis, how many are up or down regulated? Perhaps a better plot than a Venn diagram can be an MA-plot with the Venn diagram moved to a supplementary figure.
- A better comparison of mis-regulated genes in the fam83f knockouts would be a comparison of KO2 and perhaps KO3, as the compensatory effects in KO1 can lead to additional indirect effect on the transcriptome. We understand the time and cost involved in this experiment and suggest that the differential gene expression analysis be performed individually on up or down regulated genes from KO2, or a comparison of such analysis will be provided with the differential gene expression analysis that was performed on shared mis-regulated genes between KO1 and KO2.
- Can you confirm with the RNA-seq analysis that fam83g is upregulated in KO1 as opposed to KO2? (i.e. can the compensatory analysis you have observed with qRT-PCR be confirmed with the RNA-seq data?)
Significance
There is fundamental value in clarifying the in vivo function of poorly characterized protein-coding genes. This study fills a gap in the literature, but the broader conceptual impact is limited. The authors do a thorough job at generating and characterizing CRISPR/Cas9 mediated knock-out zebrafish animals. It is further commended that the authors do a meticulous job in a quantitative description of the resulting phenotype. This is a thorough study, with the only major concern being the lack of rescue experiments that would be needed to substantiate the the role of fam83f in sensitivity to DNA damage and lysosome function.
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Referee #2
Evidence, reproducibility and clarity
Fam83f is one of the proteins about which little is known. The authors Jones et al., tried to shed light on Fam83f function by knocking out the gene in zebrafish. Here they found that fam83 is expressed in the hatching gland and that larvae without Fam83f hatch significantly earlier than wild-type animals. The authors furthermore investigated the response of fam83f knock-out animals to DNA damage and found increased sensitivity to ionizing radiation and MMS. In order to find out more about Fam83f function in the DNA damage response, the authors performed RNA-seq after employing DNA damage and here they saw upregulation of several autophagy/lysosome-associated proteins and downregulation of some phosphatidylinositol-3-phosphate binding proteins, among others. Finally, the authors found that Fam83f is targeted to the lysosome. The manuscript is overall well written and clear in its general statement. In the manuscript, the authors describe the investigation of several aspects of Fam83f function and particularly the role in hatching seems to be important for Fam83f as the gene is strongly expressed in the hatching gland and its absence leads to a clear and considerable earlier hatching. Unfortunately, all aspects of Fam83f function that are described in the manuscript are investigated very superficially, the conclusions are not supported by data and important controls are lacking. As such, the RNA-seq results are not confirmed by qRT-PCR, the role of the Fam83f LIR domain is not confirmed by co-IPs and it has not been investigated whether the presence of Fam83f in lysosomes is due to its degradation or whether it has a function in this cellular compartment. Also, there is no leading concept in the manuscript. Starting from a role in hatching, the authors go to the DNA damage response and finally to the presence of Fam83f in lysosomes. How are these different aspects linked? Is the presence of Fam83f in lysosomes important for the suppression of hatching and how does Fam83f delays this process? (One would have wished that the authors would not have been that broad and were more focused on a particular aspect which then could have been investigated in depth.)
Specific comments:
- All materials should be described in material and methods including the antibodies that have been used
- Abbreviations should be explained
- Figure 4A: Levels of p53 should also be shown for untreated fam83f -/-KO1 and KO2 animals
- Some references are missing (e.g. page 17, lane 320/321: As this group of cells arises....)
- Lane 369: The authors write about 4 KO lines but only two are shown in the figure.
- Lane 374/375: The NMD is not proven
- Lane 380: how can RNA levels of fam83fa be upregulated when the gene has been knocked out? Why are these genes only upregulated in KO1? How relevant is this?
- Figure 3C is not mentioned in the text and lacks any labelling
- Lane 434/435: all relevant data should be shown (can be done as supplementary figure)
- Lane 434: The reference to the figure seems to be incorrect (5A<->4A)
- Figure 4C and 4D: what is the difference?
- S5C and S5D: why are there 3 clusters?
- Lane 495 to 505: What does this mean that the GO analysis shows upregulation and downregulation of endopeptidases and why "in contrast"?
Significance
The strength of the manuscript is certainly that it provides inside into Fam83f function as there is not much known about Fam83f.
These study is probably most interesting for people in the zebrafish and related fields as the authors convincingly show the expression of Fam83f in the hatching gland and also the earlier hatching in the absence of the protein is very clear.
The weakness of the study is clearly that it does not provide an in-depth analysis. As such, it shows that Fam83f is involved in hatching and can delay the process but it remains elusive how this is achieved. (Likwise, also the investigation into the DNA damage response remains very superficial and does not prove a specific role for Fam83f in the DNA damage response or whether the increased sensitivity is more unspecifically caused by the absence of a gene or eventually even connected to the earlier hatching.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Jones et al. report on a potential role for fam83fa in zebrafish hatching, radiation response and autophagy. The authors are commended for generating multiple KO lines and maternal-zygotic embryos for analysis. However, important controls are lacking and the data is circumstantial throughout with very little mechanistic insight into the precise roles, if any, of fam83f in these processes.
- Validation of the KO phenotypes (hatching, IR sensitivity) requires rescue with WT fam83fa WT mRNA, but not 1-500 or fam83fb mRNA.
- While the hatching phenotype (Fig 3) is convincing, there is no data on HG development in the null embryos. Does the HG develop normally in the absence of fam83fb? If so, this would support the authors conclusions that the role of fam83fb is functional rather than developmental (indirect effect). In situs as in Fig.1 might be helpful here.
- While the IR sensitivity phenotype (Fig S4) is convincing, IR-induced cell death/apoptosis was not analyzed. There is a large literature describing straightforward assays for cell death/apoptosis detection in zebrafish with assays such as acridine orange or TUNEL labeling, or active casp3 whole-mount IF. Is IR-induced cell death enhanced in fam83fa KOs?
- Similarly, there are multiple tools to assay autophagy in zebrafish (e.g., Moss et al., Histochem Cell Biol 2020, PMC7609422; Mathai et al., Cells 2017, PMC5617967). Is autophagy affected in the KOs, with or without IR? These experiments might directly implicate fam83fa in autophagy.
- Figure 4: Isn't there a slight reduction in p53 induction at 10 hours?
- Given the widely documented, dominant role of p53 in zebrafish IR-sensitivity, the authors should test if the IR sensitivity of fam83fa KO animals is p53-dependent, ideally via a cross into p53 null, but at least via injection of p53 morpholinos.
- Do autophagy inhibitors phenocopy the hatching and IR-sensitivity defects of fam83fa embryos? Do the inhibitors exacerbate the mutant phenotypes or synergize with M or Z mutant phenotypes? (I may have missed this but do M and Z fam83fa null embryos have any phenotype? Or do the phenotypes only manifest in MZ embryos?)
Significance
The role of Fam83f is not known. This study in zebrafish might be the first to clarify the function of this protein in vivo.
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Reply to the reviewers
We thank the reviewer for their careful evaluation and constructive criticisms of our manuscript. We also appreciate the positive review by all three reviewers. The reviewers noted:
- "The computational model in this manuscript can be a tool to discover unknown molecular pathways interactions in cardiomyocyte proliferation."
- "This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression."
- "The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation." We have responded to all reviewer comments and have outlined the corresponding additions and changes to the manuscript.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary:
In the manuscript by Harris et al. titled "Dynamic map illuminates Hippo to cMyc module crosstalk driving cardiomyocyte proliferation," the authors developed a computational model of cardiac proliferation signaling that incorporates various regulatory networks (cytokinesis, mitosis, DNA replication, etc.) to predict molecular drivers (genes) that support cardiomyocyte proliferation. Published research articles on cardiomyocyte proliferation in multiple contexts (different species, ages, in vitro and in vivo, etc) were used to build and validate the computational model. The authors found using their model that different processes during cardiomyocyte proliferation may or may not be context-dependent. For example, DNA replication is regulated differently in conditions with high Neuregulin compared to high YAP, whereas mitosis and cytokinesis regulation is similar in these conditions. To experimentally validate their model, the authors used an in vitro system to test the effects of YAP on 3 connected pathways; in the context of YAP activation, inhibition of PI3K, cMyc, or FoxM1 was combined to assay cell-cycle markers in cultured neonatal rat ventricular cardiomyocytes. Cell-cycle marker expression in cardiomyocytes was attenuated by inhibition of cMyc or PI3K, suggesting that these pathways are involved in YAP-mediated cardiomyocyte proliferation. While this model can be a good tool to gain new insights on interactions between molecular pathways, there are a few questions to be addressed prior to publication.
We appreciate the Reviewer's positive remarks about important findings in our manuscript and the ability of our model to be a tool to gain insights on interactions between molecular pathways to regulate cardiomyocyte proliferation. We have strived to address their points, as shown below.
Major Comments:
- One of the potential uses for this computational model is to discover new interactions between known pathways that are involved in cardiomyocyte proliferation. However, this would be more powerful if factors such as species, age (neonate vs. adult), and experimental design (in vivo vs. in vitro) were accounted for, as new node inputs or a combination of existing node input activity values. This is very important because cardiomyocyte proliferation can drastically vary depending on these experimental factors. We agree that future extensions of this model accounting for species, age, and experimental design may enable an understanding of how these factors regulate proliferation. While this model's predictions are most relevant to immature cardiomyocytes, we note that it is the first systems model of the molecular network regulating cardiomyocyte proliferation. We extensively validated it against neonatal cardiomyocyte literature and then made new predictions regarding Hippo-cMyc pathways, which we validated in new cardiomyocyte experiments and against data in adult mice. This provides a strong foundation for future extensions. We now address this potential in the Discussion:
"While our model's predictions are most relevant to immature cardiomyocytes, it is the first systems model of the molecular network regulating cardiomyocytes. In the future, we hope that we and others may extend this model to identify how factors like species, age, and experimental design regulate proliferation. However, these endeavors would span multiple manuscripts, and the field currently lacks sufficient stage-specific data. For example, a previous foundational computational model of cardiomyocyte electrophysiology (Luo and Rudy, Circ Res 1994) focused on adult guinea pigs. This model became the foundation for a range of developmental and species-specific models in electrophysiology (Tusscher et al, AJP 2004,; Courtemanche eta al, AJP 1998; Paci et al, ABME 2013). We believe the open availability of our code will enable similar dissemination and extension for additional factors." Line 651-661
For reference:
Luo and Rudy, Circ Res 1994, >2.1k citations; Tusscher et al, AJP 2004, >1.7k citations; Courtemanche et al, AJP 1998, 1.5K citations; Paci et al, ABME 2013, 147 citations
The finding that cardiomyocyte proliferation is context-dependent is very exciting and warrants further investigation/validation. The authors state that different sets of nodes/modules are affected by neuregulin activation compared to YAP activation. This should be experimentally validated - qPCR/Western blots on sets of genes that are predicted to be differentially regulated in the high neuregulin context vs the high YAP context.
We agree that the model's prediction of context-dependent cardiomyocyte proliferation is very exciting. To further validate these predictions, we have performed additional experiments to validate context-dependent changes of phospho-ERK treated with Nrg and TT10. Using a high throughput capillary electrophoresis western blot system, we observed that with a short treatment of 30 min, Nrg induces greater phosphor-ERK compared to TT10, which validates our model predictions at short time intervals. Additionally, the model predicted greater p-AKT with 30 min treatment with Nrg compared to TT10. To validate this prediction, we now compare to Western blots from Hara et al. examining p-AKT in Nrg and TT10-treated cells. Validating our model predictions, their data show that Nrg induces greater p-AKT than with TT10. We have added new panels C, D, and E to Figure 4.
Figure 4: Influence of node knockdowns shifts with context, revealing crosstalk from Hippo to Growth Factor modules.
(A) Total influence of node knockdowns on the DNA replication, mitosis, and cytokinesis modules, compared across multiple signaling contexts: baseline, high Nrg, and high YAP. Total influence sums the overall effect of a node knockdown on a network module. (B) The total influence of each network module varies depending on whether a basal state, high Nrg, or high YAP signaling context is applied. (C) Capillary electrophoresis western blot for phosphorylated ERK, beta-actin, and GAPDH from neonatal cardiomyocytes treated with Nrg or TT10 for 30 min. (D) Model predictions of AKT and ERK activity of acute response to Nrg or TT10 (time constants for gene expression set to 100). (E) Quantification of effects of Nrg or TT10 treatment on p-ERK (from Western blot in panel C, n = 3) or p-AKT (from Western blot from (Hara et al., 2018), n = 1).
The overall description of the model can be improved. For example, how are the input and parameters set to validate or predict different experimental observations? What is the steady state activity of each of the nodes and does this make sense biologically? Including a few more sentences to explain the model would help with overall understanding for an uninformed reader.
We have addressed the following questions provided by the reviewer in the methods and results section of the manuscript:
How are the input and parameters set to validate or predict different experimental observations?
__ __"At baseline, input reaction weight parameters (w) were set based on information from the literature describing the baseline state of these inputs in the heart (each input reaction weight can be found in Supplemental File 1). To simulate experiments with biochemical stimuli, input reaction weights were increased to 0.8 or 1. To simulate experiments with inhibition or knockdown, the corresponding maximum species value (ymax) was set to 0.1 or 0. Complete annotations for all validation simulations are provided in Supplemental File 2." Line 154-160
What is the steady-state activity of each of the nodes and does this make sense biologically?
"Steady-state activity of model nodes was obtained by running the model until there was a __ __
Minor Comments:
Line 124 - The use of "species" and "reactions" is confusing to uninformed readers. Do you mean nodes and interactions/bridges?
We now further clarify these terms in the manuscript:
"As in past network models (Zeigler et al., PMID 27017945; Tan et al., PMID 29131824; Kraeutler et al PMID 21087478), species (or nodes) refer to a small molecule, gene, protein, or process. Reactions (or edges) are activating or inhibiting relationships between network species." Line 143-146
Line 130 - I could not find Supplementary File 2, which includes the references
We apologize for the error. Supplementary File 2 references articles and resources used to build the model. These files are now attached.
Line 257 - What is the meaning of the directional arrows in Fig 1A?
We clarified the Fig 1A legend:
"Arrows between modules represent one or more reactions that link species from one module to species in another module. " Line 594-595
Line 301 - Unclear what default values mean here. Please elaborate and provide an example of how this is reasonable.
We have added further descriptions of default values in reference to the parameters to the manuscript.
"A previous study identified default values of the parameters (ymax, EC50, W, etc.) that most accurately predict the results of knockdown screens compared to a model where all biochemical parameters were measured experimentally (Kraeutler et al 2010). Subsequent studies started from these default values and further demonstrated that model accuracy was robust to random variation in the parameters (Tan et all 2017, Zeigler et al 2017). Consistent with these prior models, we performed robustness analysis that demonstrates that the CM proliferation model accuracy (compared against 78 experiments) is maintained at >80% with up to 35% variation in ymax, 30% variation with w, and a variation of >50% with EC50 (Figure S4)." Line 305-312
Supplemental FigS2 - Why would knockdown of PKA, Lats1 or SMAD3 have the exact same effects on node activation? This is seen with multiple other genes was well (IGF and FGF for example).
PKA, Lats1, and SMAD3 all inhibit cell cycle progression in part through cMyc. Therefore, their knockdown have similar effects on downstream signaling and proliferation. Similarly, IGF and FGF both stimulate Ras and PI3K via similar mechanisms, which is consistent with experimental studies of IGF- and FGF-dependent proliferation.
Reviewer #1 (Significance (Required)):
The computational model in this manuscript can be a tool to discover unknown molecular pathway interactions in cardiomyocyte proliferation. The novelty lies in the ability to adjust any parameter or the entire setting/context. While this sounds very exciting, improvement of the model to account for age, experimental conditions (in vivo vs in vitro), and species (human, pig, mouse) could lead to increase prediction accuracy. Additionally, more robust validation of context-dependent interactions between signaling pathways would also increase overall enthusiasm for the manuscript. Readers interested in a systems biology approach to cardiomyocyte proliferation, or researchers probing molecular interactions during cardiomyocyte proliferation would be interested in using such a model to discover novel contexts/combinations in which cardiomyocyte proliferation is more likely.
The reviewer comes from a varied training background and is qualified to evaluate this manuscript in full - BS in biomedical engineering and mathematics. PhD in biomedical engineering (molecular biology, cardiac electrophysiology). Postdoctoral training in cardiac regeneration and immunity.
We appreciate the positive comments about our model of the cardiomyocyte proliferation network. As described above, we believe that we have addressed the concerns with additional experimental validation.
The manuscript submitted by Harris and colleagues collates a molecular map of cardiomyocyte cell cycle activation through mathematical modeling of previously published experimental results. They attempt to validate the constructed model several ways: 1) through testing results compiled from additional literature, 2) through in vitro analysis, and 3) through in vivo supporting data. When validating through additional literature the model proves quite reliable particularly for prediction of effects on synthesis, mitosis, and cytokinetic entry, but was less reliable (or insufficiently tested) at predicting completion of these stages as determined by polyploidization and multinucleation. A potentially novel observation which arose from the model - that hippo nodule connects to the growth factor nodule through PI3K, Myc, and FoxM1 - was partially confirmed with in vitro experiments, though a few experiments are warranted.
We appreciate the reviewer's recognition of the important contributions of this model of the cardiomyocyte proliferation network. We have addressed the concerns below.
Major comments:
- The model is admittedly weakest in its handling of completion of cytokinesis resulting in new daughter cells (i.e. proliferation) versus failure to complete either M phase or cytokinesis resulting in the much more common cellular phenotypes - polyploidy and multinucleation. Notably, very few molecules were "tested" for this output (figure 2) and this proved the least reliable aspect of the model/map. I wonder if the authors consulted the literature on somatic polyploidization at all when building the model (files not provided as indicated, see minor comment 1 below )? And if not, would doing so help strengthen this arm of their map? There are some great reviews on the topic (see PMIDs 25921783, 23849927, 30021843) - while admittedly much of the work is done on other cell types (i.e. trophoblast giant cells and hepatocytes) maybe understanding the molecular intricacies in these cells could be incorporated to strengthen the predictive model in cardiomyocytes. Notably, PMID 23849927 even provides a table of citations about key nodes in the model influencing polyploidy. To validate this model, we used entirely cardiomyocyte specific studies. We appreciate the reviewer's reference to PMID 23849927, which enabled us to add two additional experiments to the validation table in Figure 2. That paper found that overexpression of either cMyc or cyclin D increases polyploidy, which both matched our new simulations in the updated Figure 2.
Motivated by the reviewer's citation of PMID 23849927, we further validated the model against polyploidization data from multiple cell types, finding an 85.7% accuracy (6 of 7 experiments) as now shown in Supplementary Figure S7.
We included an additional discussion of polyploidization in the manuscript.
"Our model validation is notably weakest in predicting experiments on polyploidization, indicating a need to better characterize polyploidy and cytokinesis pathways. Because such data are limited in cardiomyocytes, we performed an additional validation against polyploidization experiments from other cell types as summarized in Pandit et al. Our CM proliferation model predicted 85% (6 of 7) experiments. Future experiments are needed to identify conserved or differential mechanisms of polyploidization and cytokinesis in cardiomyocytes." Line 587-594
- Paragraph on the cytokinesis module (lines 364-377) is confusing - not sure what the takeaway message is. Also, while progression through G1/S and G2/M are "required" for cytokinesis they on their own are not sufficient (lines 366-368), this perhaps goes back to major comment 1. We agree this sentence was confusing, it was meant to be introductory rather than stating a particular result. We removed that sentence and further revised our description of the output module to clarify the model structure:
"The output module interlinks the phenotypic outputs of the other modules, representing how experimentally measured aspects of cell cycle activity (DNA replication by EdU or Ki67), mitosis by phospho-Histone 3 (pHH3), abscission by cytokinetic midbody converge on polyploidy, binucleation, or cytokinesis (e.g. completed proliferation) (Figure 1G)." Line 283-286
Minor comments:
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Use of the word "Proliferation" should be reserved for situations where the authors can clearly say a new daughter cell was born. In many instances, "cell cycle activation" or "cell cycle progression" might be better terms. As suggested by the reviewer, we now use "cell cycle progression" in 7 instances, reserving "proliferation" for cell cycle progression through cytokinesis. In the remaining 90 instances, we refer to proliferation based on the model's predictions of completed cell division based on the combined DNA replication, mitosis, and cytokinesis pathways in the "output module". We retain "proliferation" in the title because the model encompasses the entire proliferation process from cell cycle entry through cytokinesis.
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Supplementary Files 1 & 2 or Supplementary Document 2 were not provided or not found during review, thus we were unable to confirm which literature were used to build and validate the model. Thank you, we have included Supplementary Files 1 and 2 along with supplementary document 2 in the submission.
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Figures are too small, particular Figure 1 We have enlarged Figure 1.
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"E2F" should be specified as E2F1-3 yield quite distinct results from E2F7/8. We have changed "E2F" to "E2F123"
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Text corresponding to Figure 5 does not reference most of the panels in the Figure. i.e. figures are not "cited" in the text We have made sure that each panel in Figure 5 is referenced in the text addressing the figure. We have also bolded all references to Figure 5.
- Figure 5C - why is there no bars for PI3K. Text claims it was predicted by the model, but the data are missing? We apologize for the confusion regarding Figure 5C, in which the bar for PI3K was near zero. We now clarify this in the legend.
"Predicted DNA replication and mitosis activity is close to zero when PI3K is inhibited alone and when PI3K is inhibited in combination with TT10 treatment."
- Data provided in figure 5D & E are insufficient on their own to claim "proliferation". Perhaps adding total cardiomyocyte numbers, where one would expect expansion compared to control. We agree that Ki67 and pH3 are not sufficient to claim "proliferation", so we modified the Figure 5 legend to:
"Prediction and experimental validation of cardiomyocyte cell cycle progression mediated by the Hippo pathway via PI3K, cMyc, and FoxM1."
We previously found that cardiomyocyte numbers without live tracking are not sufficient to robustly measure proliferation (Woo et al, J Mol Cardiol, 2019).
- Consider adding a details about the p-values to the figure legend in figure 5. Thank you for this suggestion p-value information has been added to the legend of Figure 5. We use *** Our literature-based validation in Figure 2 focused on 78 experiments that examined well-established and corroborated aspects of cardiomyocyte proliferation. Later in the paper, we focused on a newly predicted mechanism of cardiomyocyte proliferation involving small number of comparisons that would naturally have a lower a priori probability of validation in vitro neonatal experiments (Figure 5) and adult mouse experiments (Figure 6). Therefore, in the revised text we focus on the specific comparisons rather than statistics.
"Based on predictions from this validated model, we hypothesized that YAP drove proliferation via PI3K, cMyc and FoxM1. To test this model-driven hypothesis, we accurately predicted TT10-induced DNA replication that is suppressed by inhibition of PI3K, cMyc and to a lesser extent FoxM1 (Figure 5D). These model predictions were further validated using RNA-seq and ATAC-seq data from adult mouse hearts showing that constitutively active YAPS5A induces expression of Myc and FoxM1 as well as increased chromatin accessibility at PI3Kca and Myc." Line 454-468
In the discussion, we add:
"Further model revision is needed based on these molecular mechanisms of YAP-TEAD-Myc interactions to distinguish between chromatin accessibility, transcription factor binding, and gene expression." Line 649-651
As it stands now, the generated map largely constitutes already known details offering few if any new insights; however, if updated as new results arise AND made available as a public tool, the model could prove to be a highly valuable resource to the field.
We thank the reviewer for recognizing our model as a valuable resource and public tool. We have made our model publicly available on GitHub at https://github.com/saucermanlab/Cardiomyocyte-Proliferation-Network.
The virtual knockdown screens in Figure 3, 4 and 5 provide a wide range of new insights, which we clarify in new text.
"Because this is a literature-based network model, each component or direct interaction has been studied individually. However, our model makes much broader predictions of how these components interact to regulate proliferation, beyond the ~30 papers available for validation on the response of this system to perturbations shown in Figure 3. For example, Supplemental Figure S3 provides ~5000 predictions of how each protein responds to knockdown of every other protein. These predictions led to new insights into how YAP regulates proliferation via cMyc (experimentally validated in Figure 5 in vitro and Figure 6 in vivo), as well as many other insights that can be validated in future studies. These future studies will be aided by the open-source availability of our model on GitHub." Line 563-571
__ I have expertise in cardiomyocyte cell cycle and polyploidization.__
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The authors generated a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. Interestingly, the model correctly predicts the outcome of 95% independent experiments from the literature. The model also elucidated crosstalk between the growth factor and Hippo modules and the authors identified key hubs for which the Hippo signaling pathway regulates cardiomyocyte proliferation. The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation.
Reviewer #3 (Significance (Required)):
This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. The program may provide a convenient framework prioritizing potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation. However, the overall impact of the study appears modest since it is unclear whether the study allows elucidation of the unique properties of cardiomyocyte proliferation in adult hearts (i.e. they hardly proliferate) and the validation study was conducted only in neonatal myocytes. The field has seen many studies with neonatal myocytes but the findings are not always translatable to adult cardiomyocytes.
We thank the reviewer for recognizing the importance of our work that provides a framework for prioritizing potential signaling drivers of therapeutic modulators of CM proliferation.
Neonatal studies are the most prevalent with cardiomyocyte proliferation literature, making it the most robust starting point that allows for rigorous validation. Based on the high performance of the model against neonatal data, in the future we expect this model to be a stepping stone towards adaptions to understand differences in the adult cardiomyocyte proliferation network. We have updated our model discussion on future directions on this point.
"While our model's predictions are most relevant to immature cardiomyocytes, it is the first molecular network model of cardiomyocyte proliferation. In the future, this model will enable extensions to identify how factors like species, age and experimental design regulate proliferation. However, such endeavors would span multiple manuscripts and the field currently lacks sufficient stage-specific data. For example, the highly influential computational model of Luo and Rudy focused on adult guinea-pig cardiomyocyte electrophysiology (Luo and Rudy, Circ Res 1994). That model became the foundation for a wide range of development- and species-specific models in electrophysiology (Tusscher et al, AJP 2004,; Courtemanche eta al, AJP 1998; Paci et al, ABME 2013). We believe the open availability of our code will enable similar dissemination and extension for additional factors regulating cardiomyocyte proliferation." Line 655-665
The authors described that "Literature articles used for model development came from multiple cell types due to limited CM data." It is unclear whether this would allow the identification of unique mechanisms present in cardiomyocytes. As the authors admitted, the fact that the model predictions and experimental observations for polyploidization did not match clearly suggests the complexity surrounding the possibility of cell phenotypes in cardiomyocyte populations. The authors could have addressed whether this model allows the identification of unique mechanisms mediating cardiomyocyte proliferation in the adult heart.
Although we necessarily included literature on other cell types to support network reactions, all of the experimental validation in Figure 2 was with cardiomyocyte data (~33 publications). 80% of experiments were from neonatal CMs, 10% from adult CMs, 5% from in vivo studies, and the other 5% from hiPSC-derived cardiomyocytes as annotated in Supplemental File 3.
At this time, there is insufficient data from which to make a model focused only on adult CMs. The mode's open-source availability enables future extensions that examine age and species-dependent mechanisms of cardiomyocyte proliferation. We updated the manuscript, addressing the ability of our model to adapt to new information.
"This model provides an initial network framework for integrating additional discoveries in cardiomyocyte proliferation. As more information becomes available in cardiomyocyte proliferation literature the model can be adapted. Additionally, the field can use our open-sourced model to adapt this model to other developmental stages or species." Line 671-674
Acknowledging the limited data on cardiomyocyte polyploidy, we performed a new separate validation of 7 experiments in non-myocytes from PMID 23849927, finding an 85.7% accuracy (new Supplementary Figure S7).
Please provide more information regarding the rationale for having six modules in the authors' model, including the growth factor and the Hippo pathway.
We revised the text to clarify the motivation for the six modules:
"Our initial review of the literature indicated multiple complex molecular pathways that regulate cardiomyocyte proliferation, including growth factors, Hippo signaling, G1/S transition, G2/M transition, or cytokinesis pathways (Hashmi and Ahmad, PMID: 31205684; Payan et al, PMID: 30930108; Moral et al., PMID: 35008660; Wang et al., PMID: 30111784; Johnson et al., PMID: 34360531). Several review articles (Zheng et al, PMID: 32664346; Mia and Singh, PMID: 31632964; Diaz Del Moral et al, PMID: 35008660; Besson et al, PMID: 18267085; Wang et al, PMID: 19216791)) also organized the literature based on these distinct pathways or processes, which we used to define the boundaries of the six modules. However, how these molecular pathways work together is not well characterized. Therefore, we designed the model to incorporate each of these established modules and how they work together to drive cardiomyocyte proliferation." Line 550-557
The extent of cardiomyocyte proliferation at baseline is very low in the adult heart. The model identified 25 nodes that may influence baseline proliferation. Is there any evidence to support the involvement of these mechanisms in baseline cardiomyocyte proliferation in vivo?
We agree with the reviewer that proliferation at baseline is very low in the adult heart, and also rather low in neonatal cardiomyocytes. As shown in Figure S4A, we performed a virtual knockdown screen under baseline conditions that showed that no genetic knockdowns caused a substantial decrease in DNA replication or cytokinesis, consistent with a low baseline proliferation rate.
We describe this point about baseline proliferation in revised text:
"A complete virtual knockdown screen of the model was done under baseline conditions in Figure S4A, which showed that no knockdowns caused substantial decreases in DNA replication or cytokinesis. This is consistent with a low baseline proliferation rate described in cardiomyocyte literature." Line 354-357
The validation study was conducted with neonatal rat ventricular cardiomyocytes. This study could have been repeated with adult cardiomyocytes since they are more resistant to proliferation and, thus, the Myc may not work as expected. In addition, the authors could have commented on the mechanism through which chromatin opening and YAP allow transcription of Myc in the heart.
We agree that Myc is likely less proliferative in adult hearts. While our model was extensively validated against neonatal cardiomyocytes (Figure 2 for literature, Figure 5 for new neonatal experiments), only 10% of literature-based validations in Figure 2 are from adult cardiomyocytes due to limited data. However, in Figure 6 we validate YAP-dependent signaling to Myc, PI3K, and FOXM1 using RNA-seq and ATAC-seq data from Monroe et al. from adult mouse cardiomyocytes in vivo. While molecular mechanisms of YAP regulation of Myc are not characterized in the heart, based on the reviewer's suggestion, we add new discussion on YAP-Myc interaction in other cells:
"Overexpression of Myc induces cardiomyocyte proliferation in vitro and in vivo in several contexts, with open chromatin and Myc binding near mitotic genes (PMID: 32286286). But to our knowledge, crosstalk of YAP with Myc has not been reported in the heart. Our model prediction and experiments in neonatal cardiomyocytes support a YAP-TEAD-Myc pathway for cardiomyocyte proliferation. Further, our analysis of ATAC-seq and RNA-seq data from Monroe et al. validate that YAP induces Myc chromatin availability and gene expression in adult mouse hearts.
In MDA-MB-231 breast cancer cells, YAP/TAZ/TEAD bind directly to Myc enhancers through chromatin looping, with decreased acetylation of H3K27 and cell proliferation upon YAP/TAZ knockdown (26258633). YAP-TEAD-Myc signaling regulates the proliferation of cancer cells (26258633), tumorigenesis (29416644), and the growth of Drosophila imaginal discs (20951343). In the future, computational models and experiments are needed to better resolve how YAP promotes proliferation via Myc in the adult heart, including regulation by Mycn (30315164), cyclin T1 (32286286)."Line 632-644
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Referee #3
Evidence, reproducibility and clarity
The authors generated a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. Interestingly, the model correctly predicts the outcome of 95% independent experiments from the literature. The model also elucidated crosstalk between the growth factor and Hippo modules and the authors identified key hubs for which the Hippo signaling pathway regulates cardiomyocyte proliferation. The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation.
Significance
This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. The program may provide a convenient framework prioritizing potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation. However, the overall impact of the study appears modest since it is unclear whether the study allows elucidation of the unique properties of cardiomyocyte proliferation in adult hearts (i.e. they hardly proliferate) and the validation study was conducted only in neonatal myocytes. The field has seen many studies with neonatal myocytes but the findings are not always translatable to adult cardiomyocytes.
The authors described that "Literature articles used for model development came from multiple cell types due to limited CM data." It is unclear whether this would allow the identification of unique mechanisms present in cardiomyocytes. As the authors admitted, the fact that the model predictions and experimental observations for polyploidization did not match clearly suggests the complexity surrounding the possibility of cell phenotypes in cardiomyocyte populations. The authors could have addressed whether this model allows the identification of unique mechanisms mediating cardiomyocyte proliferation in the adult heart.
Please provide more information regarding the rationale for having six modules in the authors' model, including the growth factor and the Hippo pathway.
The extent of cardiomyocyte proliferation at baseline is very low in the adult heart. The model identified 25 nodes that may influence baseline proliferation. Is there any evidence to support the involvement of these mechanisms in baseline cardiomyocyte proliferation in vivo?
The validation study was conducted with neonatal rat ventricular cardiomyocytes. This study could have been repeated with adult cardiomyocytes since they are more resistant to proliferation and, thus, the Myc may not work as expected. In addition, the authors could have commented on the mechanism through which chromatin opening and YAP allow transcription of Myc in the heart.
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Referee #2
Evidence, reproducibility and clarity
The manuscript submitted by Harris and colleagues collates a molecular map of cardiomyocyte cell cycle activation through mathematical modeling of previously published experimental results. They attempt to validate the constructed model several ways: 1) through testing results compiled from additional literature, 2) through in vitro analysis, and 3) through in vivo supporting data. When validating through additional literature the model proves quite reliable particularly for prediction of effects on synthesis, mitosis, and cytokinetic entry, but was less reliable (or insufficiently tested) at predicting completion of these stages as determined by polyploidization and multinucleation. A potentially novel observation which arose from the model - that hippo nodule connects to the growth factor nodule through PI3K, Myc, and FoxM1 - was partially confirmed with in vitro experiments, though a few experiments are warranted.
Major comments:
- The model is admittedly weakest in its handling of completion of cytokinesis resulting in new daughter cells (i.e. proliferation) versus failure to complete either M phase or cytokinesis resulting in the much more common cellular phenotypes - polyploidy and multinucleation. Notably, very few molecules were "tested" for this output (figure 2) and this proved the least reliable aspect of the model/map. I wonder if the authors consulted the literature on somatic polyploidization at all when building the model (files not provided as indicated, see minor comment 1 below)? And if not, would doing so help strengthen this arm of their map? There are some great reviews on the topic (see PMIDs 25921783, 23849927, 30021843) - while admittedly much of the work is done on other cell types (i.e. trophoblast giant cells and hepatocytes) maybe understanding the molecular intricacies in these cells could be incorporated to strengthen the predictive model in cardiomyocytes. Notably, PMID 23849927 even provides a table of citations about key nodes in the model influencing polyploidy.
- Paragraph on the cytokinesis module (lines 364-377) is confusing - not sure what the takeaway message is. Also, while progression through G1/S and G2/M are "required" for cytokinesis they on their own are not sufficient (lines 366-368), this perhaps goes back to major comment 1.
Minor comments:
- Use of the word "Proliferation" should be reserved for situations where the authors can clearly say a new daughter cell was born. In many instances "cell cycle activation" or "cell cycle progression" might be better terms.
- Supplementary Files 1 & 2 or Supplementary Document 2 were not provided or not found during review, thus we were unable to confirm which literature were used to build and validate the model.
- Figures are too small, particular Figure 1
- "E2F" should be specified as E2F1-3 yield quite distinct results from E2F7/8.
- Text corresponding to Figure 5 does not reference most of the panels in the Figure. i.e. figures are not "cited" in the text
- Figure 5C - why is there no bars for PI3K. Text claims it was predicted by the model, but the data are missing?
- Data provided in figure 5D & E are insufficient on their own to claim "proliferation". Perhaps adding total cardiomyocyte numbers, where one would expect expansion compared to control.
- Consider adding a details about the p-values to the figure legend in figure 5.
- Data presented in figure 6 do not "validate" the model. Rescue experiments as were provided in vitro would be necessary or at minimum YAP/TEAD binding to the promoters (ATAC insufficient). Alternatively, walking back these statements, might be easiest.
- Validation studies through the literature suggested ~94% fidelity. The invitro validation suggests 66% reliability of model? The in vivo 33%? Perhaps this should be added as a discussion point - can the authors comment on the loss of fidelity as the rigor/complexity of the experiment increased?
Significance
As it stands now, the generated map largely constitutes already known details offering few if any new insights; however, if updated as new results arise AND made available as a public tool, the model could prove to be a highly valuable resource to the field.
I have expertise in cardiomyocyte cell cycle and polyploidization
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Referee #1
Evidence, reproducibility and clarity
Summary:
In the manuscript by Harris et al. titled "Dynamic map illuminates Hippo to cMyc module crosstalk driving cardiomyocyte proliferation," the authors developed a computational model of cardiac proliferation signaling that incorporates various regulatory networks (cytokinesis, mitosis, DNA replication, etc.) to predict molecular drivers (genes) that support cardiomyocyte proliferation. Published research articles on cardiomyocyte proliferation in multiple contexts (different species, ages, in vitro and in vivo, etc) were used to build and validate the computational model. The authors found using their model that different processes during cardiomyocyte proliferation may or may not be context-dependent. For example, DNA replication is regulated differently in conditions with high Neuregulin compared to high YAP, whereas mitosis and cytokinesis regulation is similar in these conditions. To experimentally validate their model, the authors used an in vitro system to test the effects of YAP on 3 connected pathways; in the context of YAP activation, inhibition of PI3K, cMyc, or FoxM1 was combined to assay cell-cycle markers in cultured neonatal rat ventricular cardiomyocytes. Cell-cycle marker expression in cardiomyocytes was attenuated by inhibition of cMyc or PI3K, suggesting that these pathways are involved in YAP-mediated cardiomyocyte proliferation. While this model can be a good tool to gain new insights on interactions between molecular pathways, there are a few questions to be addressed prior to publication.
Major Comments:
- One of the potential uses for this computational model is to discover new interactions between known pathways that are involved in cardiomyocyte proliferation. However, this would be more powerful if factors such as species, age (neonate vs. adult), experimental design (in vivo vs. in vitro) are accounted for, as new node inputs or a combination of existing node input activity values. This is very important because cardiomyocyte proliferation can drastically vary depending on these experimental factors.
- The finding that cardiomyocyte proliferation is context-dependent is very exciting and warrants further investigation/validation. The authors state that different sets of nodes/modules are affected by neuregulin activation compared to YAP activation. This should be experimentally validated - qPCR/Western blots on sets of genes that are predicted to be differentially regulated in the high neuregulin context vs the high YAP context.
- The overall description of the model can be improved. How are the modules and overarching model built from published results? For example, how are the input and parameters set to validate or predict different experimental observations? What is the steady-state activity of each of the nodes and does this make sense biologically? Includng a few more sentences to explain the model would help with overall understanding for an uninformed reader.
Minor Comments:
Line 124 - The use of "species" and "reactions" is confusing to uninformed readers. Do you mean nodes and interactions/bridges?
Line 130 - I could not find Supplementary File 2, which includes the references?
Line 251 - "theseanal"
Line 257 - What is the meaning of the directional arrows in Fig 1A?
Line 301 - Unclear what default values mean here. Please elaborate and provide an example of how this is reasonable?
Supplemental Fig S2 - Why would knockdown of PKA, Lats1, or SMAD3 have the exact same effects on node activation? This is seen with multiple other genes as well (IGF and FGF for example).
Significance
The computational model in this manuscript can be a tool to discover unknown molecular pathway interactions in cardiomyocyte proliferation. The novelty lies in the ability to adjust any parameter or the entire setting/context. While this sounds very exciting, improvement of the model to account for age, experimental conditions (in vivo vs in vitro), and species (human, pig, mouse) could lead to increase prediction accuracy. Additionally, more robust validation of context-dependent interactions between signaling pathways would also increase overall enthusiasm for the manuscript. Readers interested in a systems biology approach to cardiomyocyte proliferation, or researchers probing molecular interactions during cardiomyocyte proliferation would be interested in using such a model to discover novel contexts/combinations in which cardiomyocyte proliferation is more likely.
The reviewer comes from a varied training background and is qualified to evaluate this manuscript in full - BS in biomedical engineering and mathematics. PhD in biomedical engineering (molecular biology, cardiac electrophysiology). Postdoctoral training in cardiac regeneration and immunity.
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #2
Evidence, reproducibility and clarity
Sang and colleagues present in vitro evidence for purified PARP1 forming condensates with DNA under certain conditions. They report the PARP1/DNA/NaCl concentration ranges under which these condensates form, and the impact of adding the PARP1 substrate NAD+ that allows poly(ADP-ribose) production. The results are presented clearly for the most part. One wonders if these in vitro conditions are really representative of DNA repair foci; however, the study adds new information that will be useful to the field. As noted below, there is some concern about the lack of reversibility of the condensates.
- DNA curtains assay. The imaging buffer is listed as including 25 mM NaCl and 2 mM MgCl2. Were these experiments performed in conditions of higher ionic strength? The lack of a response to the addition of NAD+ is puzzling. It seems that the condensing of the DNA is not reversible. For the data in panel C, would the DNA return to extended form upon further flow of buffer only? The data give the impression that the assay conditions promote a one-way road to an irreversible state, and it is hard to see how this should be interpreted. A different single-molecule study indicated that PARP1 condensation of long DNA is reversible with NAD+ addition (PMID 34380612). The different outcomes should be discussed.
- DNA ligase assay. Figure 5E,F. How can it be certain that the ligation is actually taking place in the condensates under these conditions? Can the Cy5 signal be shown? Is there a way to separate the condensates from the dilute phase, and then analyze the ligation state of the DNA? Also, the reactions could be performed under the same conditions (e.g. uM concentrations of LigIII and XRCC1) as presented in the rest of the figure.
The discussion mentions the impact of HPF1 on PARP1 and that the research team has used HPF1 in the past. Is there a reason for excluding it from the current study? In particular in an effort to address the reversibility/mobility of the condensates?
Supp. Figure 1F. It would be useful to convert the ng/uL concentrations to micromolar concentrations, perhaps in the legend for each nucleic acid. This would make the results easier to relate to the rest of the data that is generally listed in micromolar.
Figure 6A. ZnF3, BRCT, and WGR domains of PARP1 also bind to DNA and should probably be included, as it could help explain why full-length PARP1 is needed for the most robust condensate formation.
Supp. Figure 1G. The legend for this panel indicates absence or presence of NAD+, and that the DNA concentration is indicated, but this does not seem consistent with the figure panel.
Results, first sentence. There is a missing parenthesis.
Figure 2G,H. It would be useful to see the plot of the other replicates of the fusion experiments.
page 3. "(data not shown)" Probably worth including.
page 4. "(Supp. Fig. 3B and 3D)" Also Supp. Fig. 3C ?
Referees cross-commenting
Reviewer #1 comments are clearly stated and justified. There is good overlap in the feedback.
Significance
Strength: the study provides parameters for studying PARP1 condensate formation. Differential impact on repair factors is interesting.
Limitation: the reconstitution is missing elements that could have a very big impact (e.g. nucleosomes).
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Referee #1
Evidence, reproducibility and clarity
PARP1 plays important roles in the recognition and repair of DNA damage, primarily by catalyzing the formation of PAR at DNA breaks, which assembles various repair factors and other PAR-binding proteins. The anionic PAR scaffold was previously proposed to induce condensation of phase separating proteins including FUS (PMIDs: 26317470, 26286827), and PARP1 was recently reported to form condensates at DNA breaks to promote nucleosome dynamics and DNA end synapsis (PMIDs: 38320550, 38215753). However, several aspects of PARP1-dependent repair condensate formation and how such condensates contribute functionally to repair have remained unclear. Here, Sang et al. show that purified PARP1 forms viscous droplets in vitro in a DNA binding-dependent manner, enhanced by PAR. Interestingly, the downstream DNA repair factors XRCC1, POLB, DNA Ligase III, and FUS co-assemble with PARP1 and damaged DNA, albeit with different enrichment patterns, resulting in multiphase condensates. Functionally, the authors not only confirm a DNA end bridging function by PARP1-mediated condensation, but also report enhanced DNA end ligation. Their in vitro experiments, which are state-of-the-art and are overall well controlled, despite lacking an in vivo counterpart, provide an important step forward in the reconstitution of multi-step DNA repair reactions in repair condensates.
Major comments:
- In addition to the short oligonucleotides that were used in this study to evaluate DNA-dependent PARP1 condensation (Table S2), the use of circular plasmid DNA (nicked or broken to resemble SSBs or DSBs, respectively) should be considered to corroborate key findings.
- Although not essential for the main conclusions, it would be very interesting to address the role of PARG on PAR-dependent multiphase condensates. Based on the methods section, the authors have purified full-length PARG, so experiments to address the consequences of PARG-dependent PAR degradation on repair condensates and their disassembly seem feasible.
- If PAR increases the internal dynamics and mobility of PARP1 in condensates, why does it not seem to affect the DNA end bridging function?
- Catalytically inactive mutants of PARP1 could be employed to separate the PARP1-dependent DNA end bridging function from PAR-dependent modulation of PARP1 dynamics in condensates. Additionally, it would help to show that the DNA end bridging function depends on the ZnF domains and can be modulated by conditions that alter PARP1 condensation (see also point 6).
- The differential organization of XRCC1, LIG3, POLB, and FUS is intriguing, but the implications of this behavior remain unclear. Can the droplet assays be adapted to inform about the sequence of events from DNA damage recognition by PARP1 and PAR induction to the handing over of the break site to LIG3 for end ligation?
- The increase in end ligation, which correlates with PARP1- and PAR-dependent condensate formation, is very interesting. However, from the experimental setup it seems unclear if the observed effect is due to condensation or simply PARylation. Additional controls would be needed to substantiate a functional role of condensation for ligation (as implied also in the title of the manuscript). Perhaps it is feasible to modulate condensation (e.g. enhanced by crowding agents, reduced by salt or by 1,6-hexanediol) without affecting PARylation, and then reassess how this affects ligation.
Minor comments:
- Please double-check if previous studies reaching similar conclusions are referenced appropriately.
- Please carefully double-check if all references to figure panels are correct.
- Please carefully double-check if the methods descriptions and discussion match the displayed experimental procedures and results.
- Supplemental Figure 3 seems to contain only negative results. Consider showing experimental conditions with the same proteins or protein combinations that result in droplet formation as well.
Referees cross-commenting
The comments by reviewer #2 seem comprehensible and justified. Similar points are addressed, e.g. major points 2, 3, and 6 in review #1.
Significance
Several new and exciting findings on PARP1-dependent and PAR-modulated repair condensate formation are presented, including the multiphase behavior and the functional contribution to DNA compaction, end synapsis, and ligation. The study extends previous work on PARP1/PAR-triggered liquid demixing and complements very new and recently published work by the Alberti and Kay labs on PARP1 condensation at DSBs. The study makes an important step forward towards the reconstitution of DNA repair reactions inside multi-component repair condensates in vitro, which may eventually allow making testable predictions for repair condensate functions in vivo. Strengths of the current study include complementary state-of-the-art in vitro techniques such as biochemical assays, multi-component droplet assays, and single molecule experiments, which were conceived and conducted carefully. Limitations relate primarily to the undemonstrated relevance in cells, which may, however, be beyond the scope of the current study. A broad audience of basic researchers in the areas of genome stability and biomolecular condensates will likely be interested in this study.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required):
The majority of the conclusions are well supported by strong experimental evidence. The only area where that is not fully the case is the role of Pak1 as a downstream effector of FoxG1-FoxO6 and its effects on macropinocytosis. To further strengthen this claim, the authors should demonstrate that ablation of Pak1 can rescue the functional consequences of forced FoxO6 expression and whether overexpression of Pak1 rescues quiescence exit in FoxO6 knockout. Thank you to the reviewer for these helpful suggestions. To investigate the effects of Pak1 ablation, and therefore more directly the link between FOXG1 and FoxO6 and macropinocytosis, we tested the published Pak1 inhibitor IPA-3. Unfortunately, to distinguish the role of Pak1 in quiescence exit and macropinocytosis, we would need a dosage of IPA-3 that is efficacious but does not affect cell proliferation. It was not possible to optimise such a dosage (a dosage of 10uM is shown to be efficacious at inhibiting Pak1 (Verma et al, 2020; Wong et al, 2013) however even at 2.5uM we see significant cell death in our cells. Indeed, this is potentially due to pleiotropic roles for Pak1.
Also, it is not feasible to overexpress Pak1 in the FoxO6 KO cells with inducible FOXG1. To ensure we are investigating quiescence exit this would need to be in an inducible manner; however, re-transfecting cells using the PiggyBac system would potentially alter FOXG1 transgene levels by excising the existing transgene.
As shown in Figure S3, we do not observe clear vacuole formation in F6 (FOXG1-inducible) cells upon Dox addition. As detailed in the discussion, we hypothesise that FoxO6-induced macropinocytosis could represent a stalled state, with other pathways downstream of FOXG1 necessary to be activated concomitantly to ensure cell cycle re-entry, e.g., through increased pinocytic flux that cannot be assessed within our experimental timeframes. Indeed, active Pak1 has been found to modulate pinocytic cycling, enhancing both FITC-dextran uptake and efflux (Dharmawardhane et al, 2000). We therefore would not hypothesise that high Pak1 levels alone would be sufficient to drive quiescence exit.
Alternatively, the macropinocytosis observed may be a metabolic stress response because of the hyperactivation of signalling pathways upon FoxO6 overexpression. Hyperactivation of Ras signalling, canonical Wnt and PI3K signalling have all been shown to play roles in inducing macropinocytosis (Overmeyer et al, 2008; Tejeda-Muñoz et al, 2019; Recouvreux & Commisso, 2017).
We believe the observed macropinocytosis phenotype upon Foxo6 overexpression, and the changes in Pak1 expression upon Foxo6 loss or FOXG1 induction provide interesting insights into the function of this underexplored FoxO family member. However, currently we are unable to demonstrate a direct link between these processes and have therefore modified the text to reflect this (see lines 292-4, 330-3, 365-8).
- The manuscript stresses the role of NSC quiescence exit in GBM and demonstrates that FoxG1 KO reduces FoxO6 levels in a murine GBM cell line but a BMP4-mediated quiescence and dox-induced FoxG1 over-expression or an abolishment of cell cycle re-entry thereof by reduced FoxO6 levels in the case of FoxG1 KO is lacking. But this would significantly substantiate the relevance of the findings. *
Mouse GBM cells have elevated levels of FoxG1 and have been shown to be refractory to BMP4-mediated quiescence entry, maintaining colony formation following BMP treatment (Bulstrode et al, 2017). It is therefore challenging to specifically investigate cell cycle re-entry/ quiescence exit using these mouse GBM cells, or indeed any GBM cell line due to their inability to respond fully to BMP cues (Caren et al, 2015). It has also been shown by Bulstrode et al, 2017 that Foxg1 null mouse neural stem cells show an increased propensity to exit cycle in response to BMP treatment, and reduced colony formation on return to EGF/FGF-2 growth factors. FOXG1 null cell lines therefore show a reduced response to BMP cues, making it difficult to explore quiescence exit per se.To navigate this, instead we investigated Dox-induced FOXG1 overexpression in FoxO6 WT and KO mouse NS cells, which display similar quiescence characteristics upon BMP treatment (Figure 4).
- In the introduction and discussion, FoxO6 is mentioned for its oncogenic roles in various cancers but no reference to GBM specifically is cited. It feels like a missed opportunity to not show evidence of this in the IENS cell line that has reduced levels of FoxO6; is there an effect in their proliferative capacity? What are the expression levels of Pak1 following FoxG1 KO in IENS cells? *
Thank you for the helpful suggestion. It is indeed true the literature on FoxO6 in GBM is lacking, explaining the absence of citations on this. On investigation of expression of the proliferation marker Ki67 in these cells we found no significant difference in expression, now shown in Figure 1H. This is in fitting with previous findings of our lab (Bulstrode et al, 2017) which show that FOXG1 is dispensable for the maintenance of continued NSC or GSC proliferation in vitro. We investigated the expression levels of Pak1 following FOXG1 KO in IENS and found a decrease in both KO lines compared to parental cells (updated Figure 6F).
As explained in our discussion, these data suggest that Foxg1/FoxO6/Pak1 are not functionally important in sustaining GSC/NSC proliferation, as shown by the lack of proliferation defects upon Foxg1 or FoxO6 deletion (Bulstrode et al, 2017), but impact regulatory transitions, as cells prepare to exit quiescence into the proliferative radial-glia like state.
*Minor comments *
- Fig1A shows 4 and 2-fold respectively for the two mouse NSC lines, not 17 and 4-fold increase as written on manuscript, please adjust accordingly.
The qRT-PCR data are presented as log2(fold change) or - ddCt, where this value equals zero for the calibrator sample, as indicated in the figure legends and axes. The data are presented in this way to enable accurate visualisation of up- and down-regulation of gene expression. Data are stated as ‘fold increase’ in the text for ease of reading, which we have clarified in the text and figure legends (e.g. lines 154 and 176).
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- Fig2G manuscript reports a 235-fold upregulation, but graph looks more like a 7 or 8-fold as shown on Fig1A for the F6 NSC line. I would recommend checking the fold changes reported throughout the paper. *
See previous comment above. The qRT-PCR data are presented as log2(fold change) or - ddCt, where this value equals zero for the calibrator, as indicated in the figure legends and axes. The data are presented in this way to enable accurate visualisation of up- and down-regulation of gene expression. Data are stated as ‘fold increase’ in the text for ease of reading, which we have clarified in the text and figure legends (e.g. lines 154 and 176).
- The manuscript describes the increase of FOXG1 after BMP4-induced cell cycle exit as compared to non-BMP4 treated cells (p.8 first paragraph), but I am wondering if this expression is rather compared to dox negative and not vs BMP4 negative treatment. *
Data are presented relative to the non-BMP treated (EGF/FGF-2) control throughout the manuscript for consistency. This is to enable changes in expression between -Dox and +Dox to be visualised throughout the quiescence-exit time course relative to the initial starting population in EGF/FGF-2 growth media, prior to BMP treatment.
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- In Fig2G it is interesting that FoxO6 is upregulated in BMP4 treated throughout the experiment with highest values at day10 post treatment. At the same time, non-BMP4 treated cells keep decreasing their FoxO6 levels dramatically but there is no mention or reference to this effect.*
In Figure 2G, all cells have been treated with BMP4, prior to return to growth media (EGF/FGF) with or without Dox. It is true that in the +Dox condition with FOXG1 induction, FoxO6 levels continue to increase up to Day 10, perhaps reflective of the expansion of a highly proliferative radial glia-like population.
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- Fig2 would benefit from a western blot like Fig1D where FoxG1 and FoxO6-HA protein levels are also shown in dox-treated comparing BMP4-treated vs non-treated. *
Due to the lack of specific FoxO6 antibodies and the absence of a FoxO6-HA tag in this cell line, it is not possible to perform protein analysis of FoxO6 levels in this figure as for Figure 1D.
- The colonies in Fig3E should be quantified, as their ability to form neurospheres seems somewhat compromised upon FoxO6 KO. Fig3B and 3F could perhaps be consolidated into one panel in the interest of space and presentation. *
Good suggestion. We have now consolidated Fig 3B and 3F into one panel (now Figure 3F) as suggested by the reviewer. We performed additional replicates for Figure 3E to quantify the colony formation efficiency. This showed a small but insignificant decrease in colony forming ability in the KO cells (Figure 3E). Importantly the FoxO6 null cells do form colonies, and our results show that FoxO6 is not essential for proliferation or colony formation of NSCs in EGF/FGF-2 – this therefore does not account for the complete loss in colony formation we see the in the FoxO6 KO cells upon FOXG1 induction.
- Fig4A shows vs "parental" non-BMP on y axis but wouldn't this show fold change of dox+ parental vs parental. The authors should clarify this. *
All samples in Figure 4A are compared to parental cells in EGF/FGF-2, i.e. non-BMP treated, as the calibrator sample where log2(fold change) equals zero. We chose to set a single calibrator sample for all data (parental and FoxO6 KO cells included) to allow us to compare changes in FOXG1 transgene across the entire experiment.
- Perhaps the authors can add a non-BMP4 treated count of % FOXG1 positive cells to Fig4C for reference. *
As shown in Figure 4A, both parental and FoxO6 KO cells show similar, i.e. negligible, FOXG1 transgene expression without Dox, compared to the parental non-BMP4 treated control, therefore negligible FOXG1-V5 positive cells are seen by ICC. We have edited Figure 4A to include a non-BMP treated and BMP-treated control to show the negligible FOXG1-V5 expression by qPCR as controls.
- The sentence mentioning Fig5D for the first time (p.10 third paragraph) needs rephrasing for clarity and should also call out Fig5C for the mCherry expression live cell imaging data where appropriate. Fig5D does not appear to be live imaging as implied by the text. If vacuole formation is observed already as early as 10-11h after Dox induction, then it should be shown somewhere in Fig5. Vacuole formation is shown with a higher magnification image inset only in the 22h timepoint image. I think Fig5E should be more substantiated with some sort of quantification, e.g. % of vacuoles positive for EEA1 and/or LAMP1. *
We apologise for this. The first reference to Figure 5D one line 234 should refer to Figure 5C, this has now been corrected in the text. Vacuoles are visible in Figure 5C panel 10 h 30 min, however, to make this clearer we have also supplied an accompanying movie of the live imaging (Movie 1). The imaging in Fig 5E has not been quantified as this imaging was performed with the purpose of confirming the vacuole structures seen are not simply enlarged lysosomes, due to their similarity in appearance to those published elsewhere (Ramosaj et al, 2021; Leeman et al, 2018). Instead, we have provided Western blotting data in Figure S5E to support this conclusion that there is no clear increase in EEA1 or LAMP1 (early endosomal or lysosomal) expression upon FoxO6-HA induction.
*- Could the authors comment on the lack of proliferative advantage of the FoxO6 overexpression. FigS3 shows Edu staining, but there is no proliferation assay in either Fig5 or S3. What would be the effect of FoxO6 overexpression on BMP4-mediated quiescence with or without FoxG1 over-expression? *
Induction of FoxO6-HA overexpression does not provide a proliferative advantage to the cells. Looking at individual cells, those with high FoxO6-HA levels seem to associate with EdU negativity. In Figure S3 we provide quantitative EdU incorporation assay as a proliferation assay (quantification of the number of cells cycling, therefore incorporating EdU, within a 24h pulse period). Quantification of the EdU staining in Figure S3G is provided in Figure S3H. We have now clarified this in the text on page 11, lines 263-4.
Unfortunately, due to transgene overexpression using the PiggyBac transposon method, it is not feasible to overexpress FoxO6 and FOXG1 in the same cell line, as re-transfecting cells using the PiggyBac system would potentially alter FOXG1 transgene levels and make results difficult to interpret. Given the association of vacuolated cells with EdU negativity, we predict that FoxO6 overexpression would not give an advantage for quiescence exit. Indeed, BMP-treated cells with FoxO6 overexpression show a decrease in EdU positivity, as shown in Figure S3H. As discussed in the text, we hypothesise that cells with FoxO6 overexpression are in a stalled state, potentially due to signalling hyperactivation. While this may not be physiological, it gives us clues as to the function and downstream targets of FoxO6, which remain uncharacterised.
*- Can the authors clarify if there is a proliferation change in F6 cells in Fig6F as in Fig2F? Fig6F shows Pak1 is already upregulated in quiescent NSCs, what are the expression levels of Pak1 in FoxO6 -/- ANS4 cells upon FoxG1-mediated quiescence exit as shown in Fig4? Is there a particular reason why the F6 cell line data is shown only up to day2 post Dox-induction rather than d4 or d10? For consistency with the rest of similar experimental data this timeline should be extended. Does Pak1 remain elevated, plateaus or keeps reducing further post day2? *
The data is (previous) Figure 6F is the same assay and cell line as presented in Figure 2, but at an early timepoint (Day 2) during the quiescence exit assay. We have provided in the panel qRT-PCR analysis of Ki67 to show that cells begin to show increased proliferation at this timepoint. Due to our hypothesis that Pak1 is required at an early transition point, we decided to analyse this expression at an earlier timepoint than Figure 2. We have also repeated this at D10 (data below), showing Pak1 levels continue to increase with time, along with FoxO6 and the proliferative marker Ki67. Due to technical issues with variable FOXG1 transgene levels we were unable to analyse Pak1 expression levels in FoxO6+/- ANS4 cells upon FOXG1-mediated quiescence exit.
*15 . Reviewer #1 (Significance (Required)): *
The study provides a conceptual advance for exit from stem cell quiescence. There is strong evidence provided for murine neural stem cells, but the link to GBM cancer stem cells is less developed (but perhaps this is the subject of a separate manuscript).
While FoxG1 is a known regulator of neurodevelopment and glioblastoma, the functions of FoxO6 have not been studied in the context of neural stem cells. In my view, this study should be of high interest to audiences in both neurodevelopment and cancer research. * Expertise: glioblastoma, cancer stem cells, neurodevelopment *
We have edited the text and title to clarify that neural stem cells are used here as a model for GSCs with high levels of FOXG1 (e.g. lines 36 and 69).
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
*Major comments: *
-The choice of NSCs as a main experimental model to understand the effects of FoxG1 and FoxO6 is not fully justified. The authors had previously shown that FoxG1 is expressed at very low levels in NSCs (Fig. 1A in Bulstrode et al. 2017). FoxO6 also seems to be barely expressed in NSCs (Fig. 1 of the current manuscript) and, in addition, its levels seem to go further down as cells exit quiescence (-Dox line in Fig. 2H). Therefore, these two genes do not seem to play an important role in the normal exit from quiescence of NSCs, with FoxO6 only affecting FoxG1 overexpression-induced exit from quiescence. * * *If the aim is to mimic a GBM-like state by FoxG1 overexpression, this should be made much clearer in the text, including title and abstract. In that case, the authors should also show a direct comparison of the levels of FoxG1 in GBM and upon Dox-induced overexpression in NSCs. *
We agree with this criticism and suggestion to fix this. It is indeed our aim to mimic a GBM-like state by inducing FOXG1 overexpression and we should have made that more explicit. All experiments are performed in the context of high FOXG1 level. Like Foxg1, FoxO6’s homeostatic roles may be subtle in adulthood, and mostly involved in neural plasticity (Yu et al, 2019). This is in keeping with our finding that basal FoxO6 levels are low in adult NSCs and not required for sustained proliferation but are important for cell state transitions. If the FoxO6 levels activated by elevated FOXG1 represent an acquired dependency of GBM, there may be a therapeutic window to target this pathway. However, given the poorly understood roles of FoxO6, further work is needed to determine its specific value as a therapeutic target. We have modified the title and the text to make this clearer. This is also stated in the first paragraph of the results section on page 7 (line 148).
We have provided below a Western Blot (Bulstrode, 2016) in which FOXG1 levels in F6 cells induced with Dox (1000 ng/ml the dosage used) with the GBM cell lines G7 and G144, and the normal NS cell line U5. This shows that the FOXG1 levels induced are significantly higher than found in normal neural stem cells (mouse or human). This model has been previously used and published in Bulstrode et al, 2017, upon which this manuscript expands.
*-While the authors state that they aim to study NSC quiescence, they use a protocol that is closer to modelling astrocytic differentiation. In fact, in their previous work, they use this very same protocol (removal of growth factors and addition of BMP) to study the role of FoxG1 and Sox2 on astrocyte de-differentiation (Bulstrode et al. 2017). While there is arguably no perfect in vitro model of NSC quiescence, the current standard in the field is treatment with both BMP and FGF for 48 to 72 hours (e.g.: Mira et al., 2010, Martynoga et al., 2013, Knobloch et al., 2017, Leeman et al., 2020). BMP alone is regarded as a pro-astrocytic differentiation cue, and 24 hours might not be enough for NSCs to fully commit to either differentiation or quiescence. Therefore, either the claims in the paper are changed to match the astrocytic differentiation model, or a standard quiescence protocol should be used throughout to confirm the findings also apply to the exit from quiescence of NSCs. *
We agree with the reviewer that there is indeed no perfect in vitro model of NSC quiescence and thank the reviewer for this useful discussion. Coincident with this project, this was an active area of research from our laboratory as explored by Marques-Torrejon et al, 2021 (Nature Comms). After 24 h BMP4 treatment, we found that adult mouse NS cells: exit cell cycle, are growth factor unresponsive, obtain an astrocytic morphology, upregulate astrocytic markers such as Gfap and Aqp4, and downregulate radial glia/NS cell markers such as Nestin and Olig2 (Figure 3).
We therefore initially viewed them as terminally differentiated. However, the exact state of these cells is difficult to define due to the lack of definitive markers and transcriptional differences that can distinguish terminally differentiated GFAP-expressing astrocytes from quiescent type B SVZ NS cells (which also express GFAP) (Bulstrode et al, 2017; Doetsch et al, 1999; Codega et al, 2014). Findings from our laboratory later suggested some NS cell markers are maintained following BMP4 treatment and these cells can be forced back into cycle with combined Wnt/EGF signalling, or FGF/BMP signalling (Marques-Torrejon et al 2021). This suggests in vitro NS cells may lie along a continuous spectrum of states from dormant quiescent, activated quiescent (primed for cell cycle re-entry) to actively proliferating, similar to that observed in vivo in the mouse SVZ (Dulken et al, 2017). Indeed, after 24 h BMP4 treatment, we observe a minimal level of colony formation in no Dox controls following 10 days of exposure to the growth factors EGF/FGF-2 (Figure 2D-F).
These non-cycling BMP4-induced astrocytic cells might therefore be better viewed as dormant quiescent NSCs, hence our reference as quiescent NSCs. The assay conditions used in this manuscript differ to those of Marques-Torrejon et al, in terms of density and length of BMP4 treatment; it is therefore likely that our BMP-treated cells are at different stages along the continuum between dormancy and primed quiescent states. Importantly, regardless of the exact cell type induced by 24 h BMP4 treatment, we have considered the changes induced by FOXG1 overexpression, in comparison to the effect of NS cell media alone.
*-The FoxO6-induced vacuole formation in NSCs is a very interesting finding. However, so far it was only observed upon FoxO6 overexpression. To claim vacuolization is required for quiescence exit, the authors should show whether this phenomenon is also observed upon normal exit from quiescence and FoxG1-induced reactivation of NSCs. From the author's own data, Pak1 (which induces vacuolization) is unlikely to reactivate NSCs, as its expression is highest in BMP-treated cells (Figure 6F). The authors should show whether some vacuolization is present at these stage in NSCs and if not, discuss the possible interplay between Pak1 and FoxO6 in vacuole formation and quiescence exit. *
As detailed in the discussion, we hypothesise that FoxO6- induced macropinocytosis could represent a stalled state, with other pathways downstream of FOXG1 necessary to be activated concomitantly to ensure cell cycle re-entry, e.g., through increased pinocytic flux that cannot be assessed within our experimental timeframes. Indeed, active Pak1 has been found to modulate pinocytic cycling, enhancing both FITC-dextran uptake and efflux (Dharmawardhane et al, 2000). Alternatively, the macropinocytosis observed may be a metabolic stress response because of hyperactivation of signalling pathways upon FoxO6 overexpression Hyperactivation of Ras signalling, canonical Wnt and PI3K signalling have all been shown to play roles in inducing macropinocytosis (Overmeyer et al, 2008; Tejeda-Muñoz et al, 2019; Recouvreux & Commisso, 2017).
We do not see clear evidence of vacuoles in FOXG1-induced reactivation of NSCs – this supports that the macropinocytosis seen upon FoxO6 overexpression is a stalled state or due to hyperactivation. While this may not be physical, it gives us clues as to the function and downstream targets of FoxO6, which remain uncharacterised (such as a link of FoxO6 and FOXG1 with Pak1-related pathways). Demonstrating a requirement for vacuolisation in quiescence exit is outwidth this manuscript and therefore we are careful not to claim this. We have modified the text to clarify this.
As the reviewer noted, it is interesting that Pak1 is highest in BMP-treated cells; it seems that BMP signalling itself is triggering elevated Pak1 levels, likely as cells undergo extensive cell shape changes during the transition from proliferation to quiescence. However, in EGF/FGF-2, Pak1 levels decrease, and our data suggests that FOXG1/FoxO6 are required to increase or maintain Pak1, potentially to again enable the cell shape/metabolic changes required on quiescence exit. We have added to the text to expand upon this observation on page 14 (lines 330-333). -Finally, the data on the regulation of Pak1 expression by FoxO6 is insufficient to draw any strong conclusions. Downregulation of Pak1 in FoxO6 cells is not enough evidence to claim a direct regulation. The authors should show whether Pak1 levels are increased after FoxO6 overexpression and whether FoxG1 is downregulated in FoxO6 KO NSCs (indirectly affecting Pak1 expression).
We have performed qRT-PCR analysis of Foxg1 expression in FoxO6 KO NSCs and see no consistent difference in expression, indicating this is not indirectly affecting Pak1 expression (see below, 1). We have also investigated Pak1 levels upon FoxO6 overexpression, over a time course following Dox addition (see below, 2). Interestingly, when FoxO6 is overexpressed, Pak1 is not clearly upregulated at any time-point. It may be that as Pak1 is already expressed in the -Dox controls, due to its roles in a variety of cellular functions, that the levels are saturated already. It is clear that Pak1 expression decreases upon FoxO6 loss in EGF/FGF (without coincident Foxg1 downregulation) and in F6 cells, higher FOXG1 correlates with higher Pak1 in EGF/FGF. Together with the induction of macropinocytosis upon FoxO6 overexpression, these data provide interesting insights into the potential pathways downstream of Foxo6 in controlling quiescence exit, directly or indirectly related to Pak1 signalling. We have modified the text to reflect this on page 14 (lines 330-333).
Minor comments: * Please state in the main text that NSCs are derived from the SVZ. *
This has been added to the text on page 7 (line 149) and is in the methods ‘Cell Culture’ section.
Reviewer #2 (Significance (Required)):
As I said before, I find this work tackles a very important question, how is the exit from quiescence controlled in NSCs. This manuscript will be of interest to researchers in the fields of adult stem cell biology and adult neurogenesis. While my expertise lies mostly on NSC biology, this work is of potential great interest for the cancer field, particularly for brain cancer research. Elucidating the mechanisms GBM cells use to exit quiescence is crucial in order to avoid the relapse of this aggressive form of brain cancer. To increase the relevance of the work to the cancer community, some of the key findings should be reproduced with GBM cells. It would be particularly important to show whether Pak1 induced vacuolization and macropinocytosis can be observed in GBM cells.
As detailed in the discussion, we hypothesise that FoxO6- induced macropinocytosis could represent a stalled state, with other pathways downstream of FOXG1 necessary to be activated concomitantly to ensure cell cycle re-entry, e.g., through increased pinocytic flux that cannot be assessed within our experimental timeframes. Alternatively, the macropinocytosis observed may be a metabolic stress response because of hyperactivation of signalling pathways upon FoxO6 overexpression Hyperactivation of Ras signalling, canonical Wnt and PI3K signalling have all been shown to play roles in inducing macropinocytosis (Overmeyer et al, 2008; Tejeda-Muñoz et al, 2019; Recouvreux & Commisso, 2017). We do not see clear evidence of vacuoles in FOXG1-indued reactivation of NSCs– this supports that the macropinocytosis seen upon FoxO6 overexpression is a stalled state or due to hyperactivation. We do not therefore think macropinocytosis per se would be observed in quiescence exit of GBM cells – indeed a normal form of macropinocytosis-induced cell death called methuosis has been observed in GBM cells with hyperactivated Ras signalling (Overmeyer et al, 2008). However, this phenotype still gives us clues as to the function of FoxO6 in quiescence exit in GSCs and the downstream signalling pathways it may regulate, such as Pak1-related signalling (discussed on lines 330-3 and 366-9).
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Summary: * The overall objective of the paper is to investigate the mechanisms by which co-option of the activity of developmental master lineage regulators by cancer cells allows them to gain fitness. To answer this question, they focus on FOXG1. This TF acts during the specification of the telecephalon. Its expression can be increased in Glioblastoma (GBM) and, more importantly for the paper, FOXG1 has previously been shown to promote exit from quiescence of glioblastoma stem cells (GSCs) and non-transformed neural stem cells (NSCs). In a previous screen, the authors identified FoxO6 as a potential direct target gene of FOXG1. In this paper, they showed that with the gain of expression for FOXG1 in NSCs and loss of FOXG1 in GSCs, FoxO6 is increased or decreased, respectively. Loss of FoxO6 in NSCs does not alter their cell cycle or cell shape and specification. Yet, loss of FoxO6 in NSCs blocks FOXG1-mediated exit from quiescence. To understand the mechanisms, they decided to overexpress FoxO6 in NSCs and demonstrated that the cells undergo macropinocytosis, a process by which cells can engulf large amount of nutriments from the external medium. It remains to be determined whether this macropinocytosis occurs in cells overexpressing FOXG1 and GSCs. The authors provide a first answer by showing that overexpression of FOXG1 induces not only FoxO6 but also the expression of PAK1, one of the key kinases that regulates the membrane engulfment of macropinocytosis in NSCs. In GSC lines, the decrease of FOXO6 decreases PAK1 levels. *
Major comments: * The paper describes interesting and convincing results (number of cell lines, repeated experiments seems sufficient) but it is difficult to reconcile them all in a single model, and this diminishes the impact of the study. Epistatic interactions between FoxG1, FoxO6, PAK1 and macropinocytosis are not always studied in the same cell models. Whether FOXG1-induced exit from quiescence of NSCs is dependent on a FOXG1-->FOXO6-->PAK1-->Macropinocytosis axis remains to be demonstrated. Also does such an axis operate in tumor cells remains to be fully assessed? In particular, if FoxO6 overexpression in NSCs can induce macropinocytosis, is this cellular process induced by FoxO6 downstream of FOXG1 activity during NSC quiescence exit? Is PAK1 a relay of FoxO6? Experiments looking at macropinocytosis and the involvement of PAK1 in the cell models of Figure 4 will definitely help to bridge the different results all together. *
We thank the reviewer for this useful insight and discussion for future work.
To directly investigate the effects of Pak1 ablation, and therefore more directly the link between FOXG1 and FoxO6 and macropinocytosis, we tested the published Pak1 inhibitor IPA-3. Unfortunately, to distinguish the role of Pak1 in quiescence exit and macropinocytosis, we would need a dosage of IPA-3 that is efficacious but does not affect cell proliferation. It was not possible to optimise such a dosage (a dosage of 10uM is shown to be efficacious at inhibiting Pak1 (Verma et al, 2020; Wong et al, 2013) however even at 2.5uM we see significant cell death in our cells. Indeed, this is potentially due to the variety of cellular functions Pak1 is involved in. Conversely, it is not feasible to overexpress Pak1 in the FoxO6 KO cells with inducible FOXG1. To ensure we are investigating quiescence exit this would need to be in an inducible manner; however, re-transfecting cells using the PiggyBac system would potentially alter FOXG1 transgene levels (through excision of the existing transgene) and therefore make results difficult to interpret.
We hypothesise that FoxO6- induced macropinocytosis could represent a stalled state, with other pathways downstream of FOXG1 necessary to be activated concomitantly to ensure cell cycle re-entry, e.g., through increased pinocytic flux that cannot be assessed within our experimental timeframes (as detailed in the text discussion). Alternatively, the macropinocytosis observed may be a metabolic stress response because of hyperactivation of signalling pathways upon FoxO6 overexpression Hyperactivation of Ras signalling, canonical Wnt and PI3K signalling have all been shown to play roles in inducing macropinocytosis (Overmeyer et al, 2008; Tejeda-Muñoz et al, 2019; Recouvreux & Commisso, 2017). We do not see clear evidence of vacuoles in FOXG1-induced reactivation of NSCs– this supports that the macropinocytosis seen upon FoxO6 overexpression is a stalled state or due to hyperactivation and therefore not a physiological process in quiescence exit. We do not therefore think macropinocytosis per se would be observed in quiescence exit of GBM cells – indeed a normal form of macropinocytosis-induced cell death called methuosis has been observed in GBM cells with hyperactivated Ras signalling (Overmeyer et al, 2008).
However, we believe the observed macropinocytosis phenotype upon Foxo6 overexpression, and the changes in Pak1 expression upon Foxo6 loss or FOXG1 induction provide interesting insights into the function of this underexplored FoxO family member, in GSCs and the downstream signalling pathways it may control, such as Pak1-related signalling. We have modified the text to reflect the limitations of our current data and discuss this (lines 330-3 and 366-9).
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Referee #3
Evidence, reproducibility and clarity
Summary:
The overall objective of the paper is to investigate the mechanisms by which co-option of the activity of developmental master lineage regulators by cancer cells allows them to gain fitness. To answer this question, they focus on FOXG1. This TF acts during the specification of the telecephalon. Its expression can be increased in Glioblastoma (GBM) and, more importantly for the paper, FOXG1 has previously been shown to promote exit from quiescence of glioblastoma stem cells (GSCs) and non-transformed neural stem cells (NSCs). In a previous screen, the authors identified FoxO6 as a potential direct target gene of FOXG1. In this paper, they showed that with the gain of expression for FOXG1 in NSCs and loss of FOXG1 in GSCs, FoxO6 is increased or decreased, respectively. Loss of FoxO6 in NSCs does not alter their cell cycle or cell shape and specification. Yet, loss of FoxO6 in NSCs blocks FOXG1-mediated exit from quiescence. To understand the mechanisms, they decided to overexpress FoxO6 in NSCs and demonstrated that the cells undergo macropinocytosis, a process by which cells can engulf large amount of nutriments from the external medium. It remains to be determined whether this macropinocytosis occurs in cells overexpressing FOXG1 and GSCs. The authors provide a first answer by showing that overexpression of FOXG1 induces not only FoxO6 but also the expression of PAK1, one of the key kinases that regulates the membrane engulfment of macropinocytosis in NSCs. In GSC lines, the decrease of FOXO6 decreases PAK1 levels.
Major comments:
The paper describes interesting and convincing results (number of cell lines, repeated experiments seems sufficient) but it is difficult to reconcile them all in a single model, and this diminishes the impact of the study. Epistatic interactions between FoxG1, FoxO6, PAK1 and macropinocytosis are not always studied in the same cell models. Whether FOXG1-induced exit from quiescence of NSCs is dependent on a FOXG1-->FOXO6-->PAK1-->Macropinocytosis axis remains to be demonstrated. Also does such an axis operate in tumor cells remains to be fully assessed? In particular, if FoxO6 overexpression in NSCs can induce macropinocytosis, is this cellular process induced by FoxO6 downstream of FOXG1 activity during NSC quiescence exit? Is PAK1 a relay of FoxO6? Experiments looking at macropinocytosis and the involvement of PAK1 in the cell models of Figure 4 will definitely help to bridge the different results all together.
Minor comments:
No minor comments
Significance
Understanding how hijacking of developmental programs by tumour cells contributes to their fitness is important for the design of cancer therapies, as these programs often confer resistance to tumour cells. Although it has been shown that FOXG1, this master TF of telencephalon specification, can give cells the ability to leave quiescence, the downstream mechanisms were unknown. The identification of FoxO6 as a relay for FOXG1 and the suggestion that this may involve macropinocytosis and the PAK1 enzyme is interesting. FoxO6 acts differently from other members of the FoxO family and PAK1 could indeed be targeted. If the authors can integrate several of their findings into a single model, their paper should be of interest to oncologists, developmental biologists and cell biologists.
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Referee #2
Evidence, reproducibility and clarity
This manuscript by Ferguson et al. identifies FoxO6 as a FoxG1 target that promotes the reactivation of neural stem cells (NSCs). Quite remarkably, FoxO6 is dispensable for the proliferation or entry into quiescence of NSCs but required for their FoxG1-dependent reactivation. The authors claim that Pak1-induced macropinocytosis is required for quiescence exit and show that Pak1 expression depends on both FoxG1 and FoxO6. These findings are very interesting and could potentially help better understand the regulation of NSC quiescence. In addition, as the authors point out, they could shed light on the regulation of the exit of quiescence of glioblastoma multiforme (GBM) cells, which express higher levels of FoxG1 and FoxO6 than NSCs. The experiments are overall of high quality, with the authors making an appropriate and efficient use of CRISPR technologies to control the expression of their genes of interest in cultured NSCs. However, the findings are mostly the result of overexpression in NSCs (which do not seem to express FoxG1 or FoxO6) and the quiescence model used is neither the standard in the field nor appropriate to draw strong conclusions about quiescence exit in NSCs or GBM cells.
Major comments:
- The choice of NSCs as a main experimental model to understand the effects of FoxG1 and FoxO6 is not fully justified. The authors had previously shown that FoxG1 is expressed at very low levels in NSCs (Fig. 1A in Bulstrode et al. 2017). FoxO6 also seems to be barely expressed in NSCs (Fig. 1 of the current manuscript) and, in addition, its levels seem to go further down as cells exit quiescence (-Dox line in Fig. 2H). Therefore, these two genes do not seem to play an important role in the normal exit from quiescence of NSCs, with FoxO6 only affecting FoxG1 overexpression-induced exit from quiescence. If the aim is to mimic a GBM-like state by FoxG1 overexpression, this should be made much clearer in the text, including title and abstract. In that case, the authors should also show a direct comparison of the levels of FoxG1 in GBM and upon Dox-induced overexpression in NSCs.
- While the authors state that they aim to study NSC quiescence, they use a protocol that is closer to modelling astrocytic differentiation. In fact, in their previous work, they use this very same protocol (removal of growth factors and addition of BMP) to study the role of FoxG1 and Sox2 on astrocyte de-differentiation (Bulstrode et al. 2017). While there is arguably no perfect in vitro model of NSC quiescence, the current standard in the field is treatment with both BMP and FGF for 48 to 72 hours (e.g.: Mira et al., 2010, Martynoga et al., 2013, Knobloch et al., 2017, Leeman et al., 2020). BMP alone is regarded as a pro-astrocytic differentiation cue, and 24 hours might not be enough for NSCs to fully commit to either differentiation or quiescence. Therefore, either the claims in the paper are changed to match the astrocytic differentiation model, or a standard quiescence protocol should be used throughout to confirm the findings also apply to the exit from quiescence of NSCs.
- The FoxO6-induced vacuole formation in NSCs is a very interesting finding. However, so far it was only observed upon FoxO6 overexpression. To claim vacuolization is required for quiescence exit, the authors should show whether this phenomenon is also observed upon normal exit from quiescence and FoxG1-induced reactivation of NSCs. From the author's own data, Pak1 (which induces vacuolization) is unlikely to reactivate NSCs, as its expression is highest in BMP-treated cells (Figure 6F). The authors should show whether some vacuolization is present at these stage in NSCs and if not, discuss the possible interplay between Pak1 and FoxO6 in vacuole formation and quiescence exit.
- Finally, the data on the regulation of Pak1 expression by FoxO6 is insufficient to draw any strong conclusions. Downregulation of Pak1 in FoxO6 cells is not enough evidence to claim a direct regulation. The authors should show whether Pak1 levels are increased after FoxO6 overexpression and whether FoxG1 is downregulated in FoxO6 KO NSCs (indirectly affecting Pak1 expression).
Minor comments:
Please state in the main text that NSCs are derived from the SVZ.
Significance
As I said before, I find this work tackles a very important question, how is the exit from quiescence controlled in NSCs. This manuscript will be of interest to researchers in the fields of adult stem cell biology and adult neurogenesis. While my expertise lies mostly on NSC biology, this work is of potential great interest for the cancer field, particularly for brain cancer research. Elucidating the mechanisms GBM cells use to exit quiescence is crucial in order to avoid the relapse of this aggressive form of brain cancer. To increase the relevance of the work to the cancer community, some of the key findings should be reproduced with GBM cells. It would be particularly important to show whether Pak1 induced vacuolization and macropinocytosis can be observed in GBM cells.
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Referee #1
Evidence, reproducibility and clarity
The authors investigate mechanisms of quiescence and cell cycle entry in neural stem cells. Expanding on previous work, they show that FoxO6 is a target gene of FOXG1 in neural stem cells and glioma cancer stem cells. FoxO6 is upregulated following activation of stem cells from quiescence but is not required for proliferation. Continued over expression of FoxO6 leads to macropinocytosis through Pak1, indicating a link between FoxO6 and actin remodelling.
Major comments
The majority of the conclusions are well supported by strong experimental evidence. The only area where that is not fully the case is the role of Pak1 as a downstream effector of FoxG1-FoxO6 and its effects on macropinocytosis. To further strengthen this claim, the authors should demonstrate that ablation of Pak1 can rescue the functional consequences of forced FoxO6 expression and whether overexpression of Pak1 rescues quiescence exit in FoxO6 knockout.
The manuscript stresses the role of NSC quiescence exit in GBM and demonstrates that FoxG1 KO reduces FoxO6 levels in a murine GBM cell line but a BMP4-mediated quiescence and dox-induced FoxG1 over-expression or an abolishment of cell cycle re-entry thereof by reduced FoxO6 levels in the case of FoxG1 KO is lacking. But this would significantly substantiate the relevance of the findings. In the introduction and discussion, FoxO6 is mentioned for its oncogenic roles in various cancers but no reference to GBM specifically is cited. It feels like a missed opportunity to not show evidence of this in the IENS cell line that has reduced levels of FoxO6; is there an effect in their proliferative capacity? What are the expression levels of Pak1 following FoxG1 KO in IENS cells?
Minor comments
- Fig1A shows 4 and 2-fold respectively for the two mouse NSC lines, not 17 and 4-fold increase as written on manuscript, please adjust accordingly.
- Fig2G manuscript reports a 235-fold upregulation, but graph looks more like a 7 or 8-fold as shown on Fig1A for the F6 NSC line. I would recommend checking the fold changes reported throughout the paper.
- The manuscript describes the increase of FOXG1 after BMP4-induced cell cycle exit as compared to non-BMP4 treated cells (p.8 first paragraph), but I am wondering if this expression is rather compared to dox negative and not vs BMP4 negative treatment.
- In Fig2G it is interesting that FoxO6 is upregulated in BMP4 treated throughout the experiment with highest values at day10 post treatment. At the same time, non-BMP4 treated cells keep decreasing their FoxO6 levels dramatically but there is no mention or reference to this effect.
- Fig2 would benefit from a western blot like Fig1D where FoxG1 and FoxO6-HA protein levels are also shown in dox-treated comparing BMP4-treated vs non-treated.
- The colonies in Fig3E should be quantified, as their ability to form neurospheres seems somewhat compromised upon FoxO6 KO. Fig3B and 3F could perhaps be consolidated into one panel in the interest of space and presentation.
- Fig4A shows vs "parental" non-BMP on y axis but wouldn't this show fold change of dox+ parental vs parental. The authors should clarify this.
- Perhaps the authors can add a non-BMP4 treated count of % FOXG1 positive cells to Fig4C for reference.
- The sentence mentioning Fig5D for the first time (p.10 third paragraph) needs rephrasing for clarity and should also call out Fig5C for the mCherry expression live cell imaging data where appropriate. Fig5D does not appear to be live imaging as implied by the text. If vacuole formation is observed already as early as 10-11h after Dox induction, then it should be shown somewhere in Fig5. Vacuole formation is shown with a higher magnification image inset only in the 22h timepoint image. I think Fig5E should be more substantiated with some sort of quantification, e.g. % of vacuoles positive for EEA1 and/or LAMP1.
- Could the authors comment on the lack of proliferative advantage of the FoxO6 overexpression. FigS3 shows Edu staining, but there is no proliferation assay in either Fig5 or S3. What would be the effect of FoxO6 overexpression on BMP4-mediated quiescence with or without FoxG1 over-expression?
- Can the authors clarify if there is a proliferation change in F6 cells in Fig6F as in Fig2F? Fig6F shows Pak1 is already upregulated in quiescent NSCs, what are the expression levels of Pak1 in FoxO6 -/- ANS4 cells upon FoxG1-mediated quiescence exit as shown in Fig4? Is there a particular reason why the F6 cell line data is shown only up to day2 post Dox-induction rather than d4 or d10? For consistency with the rest of similar experimental data this timeline should be extended. Does Pak1 remain elevated, plateaus or keeps reducing further post day2?
Significance
The study provides a conceptual advance for exit from stem cell quiescence. There is strong evidence provided for murine neural stem cells, but the link to GBM cancer stem cells is less developed (but perhaps this is the subject of a separate manuscript). While FoxG1 is a known regulator of neurodevelopment and glioblastoma, the functions of FoxO6 have not been studied in the context of neural stem cells. In my view, this study should be of high interest to audiences in both neurodevelopment and cancer research.
Expertise: glioblastoma, cancer stem cells, neurodevelopment
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #2
Evidence, reproducibility and clarity
In this ms, the authors searched using bioinformatic approaches for the presence of natural antisense RNAs (cis-NATs) linked to LRR-RLK genes, a large gene family containing many major regulators of plant growth, to detect a potential novel mechanism for their post-transcriptional regulation. They detected a large proportion of LRR-RLK genes containing cis-NATs. TO address their potential functions, they overexpressed specific cis-NATs against Bri1, CLV1 and SOBIR1 and observed phenotypes reminiscent of the respective mutant in these genes. This means that these cis-NATs can act in trans on the endogenous loci. The authors detected reduction in expression of the cognate LRR-RLK in several transgenic lines except for SOBIR1 where a minor change in protein production was found. Then, they prepare transgenic GUS lines with the promoters of the NATs and detect variable levels of expression. In addition, expressing the NATs under epidermal specific promoters but not in a control promoter induced differences in BRI1 expression specifically in the plants. Finally, they search for the presence of NATs linked to LRR-RLK loci in other plants.
The paper is interesting but the message about NAT regulation is overstated and there are several conclusions that require major additional experiments
- There are no experiences with mutant NATs to confirm a potential linkage to the regulation of LRR-RLK complementary transcript. Overexpressing a NAT may lead to several artefacts, including the artificial silencing of the endogenous loci (expressing a complementary RNA). Hence, it cannot be concluded that this regulation occurs in planta only based on overexpressing lines. The use of rdr6 mutants or similar may also serve to discard potential alternative NAT regulations.
- Complementing a bri1 mutant (or in other RLK) with construct expressing the BRI1 locus with or without the NAT (e.g. with or without its promoter) is a much cleaner manner to show NAT regulation. CRISPR or other manipulation may allow to create mutants in the NAT alone to see the effects on its cis-target as well as its eventual trans action on other LRR-RLK (as apparently acts in trans).
- The trans-NAT experiments will minimally require genome-wide RNAseq studies to see the level of cross-talk of a trans NAT. Even though the phenotype is related, there may be very general misregulation triggered by the NAT. A minimum is to pass all studied RLK in all the lines to define certain "specificity" of action.
- To propose a translational regulation for SOBIR1 with its present data is an overstatement for me. Is the antisense nuclear or accumulate in ribosomes? Can the NAT lead to a change in the recruitment into ribosomes (without changing mRNA levels). There may be indirect effects that can explain these changes in protein accumulation
- The GUS plants need to be shown in detail, with a more precise tissular localisation and compared to the cognate LRR-RLK. Can BRI1 expression be monitored in TRANS-NAT BRI1 vs wt using in situ or BRI1-GFP fusions. TO show the expression patterns of the antisense and the target will further support the proposed specificity regulation. What happens with other LRR-RLKs? Kinetics of expression along development in trans NATs and wt will be a possibility
- The epidermal connection is interesting but there is any evidence about the "cell specificity" expression of BRI1 or the differential expression of BRI1 in cells containing or not the NATs?
- The extrapolation to crops might be interesting if some features are conserved (e.g. BRI1 homologs contain NATs of the same size or similar cell-specific regulation). As many genes have NATs, this evolutionary argument of the presence of NATs is not convincing to conclude about a regulatory mechanism permitting "to engineer improved crop performances".
Significance
The paper in its present form is very limited and the major strength is the occurrence of similar phenotypes when NATs are overexpressed and the mutant cognate mRNAs. Alternative explanations of the reported data are possible and should be discarded and better presented. This paper does not advance in NAT regulatory mechanisms to be relevant for a broad audience in life sciences. However, the subject of LRR-RLK regulation is of great interest to the plant community as several critical growth regulators act through LRR-RLKs.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The authors investigate the role of antisense transcription (cis-NATs) at Leucine-rich repeat receptor-like kinases (LRR-RLK) genes in plants. They find that many LRR-RLK genes are associated with cis-NATs, through data-mining and RT-PCR in Arabidopsis. For functional studies, they selected three cis-NATs for over-expression, from the BRI1, CLV1 and SOBIR genes, where over-expression has phenotypic consequences for the plants. They use reporter gene assays to study cisNAT expression is regulated across development. The authors examine the relationship between cis-NATs and LRR-RLKs in tomato and rice, where they also detect a high fraction of LRR-RLKs associated with cis-NATs.
Major comments:
- Over-expression of cis-NATs is used to support a functional role. Here, the experimental design leaves open if the effect is explained by a trans-acting role on the corresponding sense RNA (as the authors interpret), or, if the over-expression constructs trigger co-suppression of the corresponding sense RNAs. The authors should distinguish these possibilities prior to publication in a journal. It makes a big difference. Co-suppression would be a very different mechanism that is independent of the RNA functions the authors propose. The authors need to rule it out.
Minor comments:
- The authors use older resources to examine antisense transcription. In some sense, this makes it even more impressive that so many LRR-RLK genes are associated with antisense transcription, but it would be nice to include more recent data support the conclusions. In particular Araport11 is not considered a high-quality annotation (PMID: 34266383). The manuscript could benefit from the integration of some more recent genome-annotations, or genome browser screenshots for some the genes (e.g. BRI1, CLV1 and SOBIR) that shows some of the more recent methods (reviewed in PMID: 36259932). A lot of these data are even accessible on the TAIR website.
Significance
General assessment:
The authors observe cisNATs at LRR-RLK genes. These findings hint at a contribution of cisNATs to LRR-RLK regulation that has not received much attention yet. For functional characterization, the authors rely on over-expression of cis-NATs. Here, I am sceptical, this the results could also fit a model where cisNAT OE may trigger siRNA formation and silencing of the endogenous locus (TGS/co-suppression).
Advance: The manuscript uncovers cisNATs as potential large-scale regulators of LRR-RLK genes.
Audience: Plant scientists interested in gene expression and cellular roles of LRR-RLK genes.
Reviewer expertise: Plant lncRNA, genome annotation, plant gene expression, epigenetics.
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Reply to the reviewers
We would like to thank all reviewers for their detailed and constructive feedback, which substantially helped improve the manuscript. We apologise for the time taken for the revisions, which was partially due to the first author (successfully) writing and defending her PhD thesis in the same time frame. We would like to point out already here that, based on reviewers' feedback, main figure 6 is completely redone and the conclusions of this figure have changed substantially. We no longer suggest RNA chaperoning activity (it was identified as being due to the high concentration of TEV protease, in a control suggested by the reviewers). Instead, our refined assay conditions with lower TEV protease concentration identified ribonuclease activity of membrane-bound full-length 2C, which is consistent with a publication from 2022 (PMID: 35947700).
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Evidence, reproducibility, and clarity
Summary:
In this study by Shankar and colleagues, the authors aim to understand the structure and function of the enterovirus 2C protein, a putative viral helicase with AAA+ ATPase activity. Using poliovirus (as a model enterovirus) 2C, the author's propose the protein contains two amphipathic helices (AH1 and AH2) at the N-terminus that are divided by a conserved glycine. Using purified MBP-tagged 2C and N-terminal 2C truncations, their data suggests AH1 is primarily responsible for clustering at membranes, whilst AH2 is the main mediator of 2C oligmerisation and membrane binding. Furthermore, 2C was suggested to be able to recruit RNA to membranes, with a preference for dsRNA, and the author's data implies that the helicase activity of 2C is ATP-independent. Instead, the ATP activity appears to be required for 2C hexamer formation or chaperone activity. The manuscript is generally well written /presented and the author's present very interesting data which raises several questions, some of which require additional experimentation to help support the author's conclusions. Specific comments are as follows.
We thanks the reviewer for the overall positive assessment, as well as the specific comments below.
Major Comments:
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The authors use four main constructs throughout the paper: full-length 2C, 2C with deletion of AH1 (ΔAH1), 2C with both AH1 and AH2 deleted (ΔMBP) and 2C with an extended N-terminal deletion. From this, the author's draw conclusions on the function of both AH1 and AH2. One of the author's main conclusions is that AH2 is the main mediator of 2C membrane association (e.g., in line 169). However, is it possible to conclude the relative importance of AH1 vs AH2 without testing a construct containing the deletion of AH2 only (ΔAH2)? This should be generated and used alongside this data to fully define the relative importance of AH1 and AH2 in these assay and remove the possibility that the deletion of AH1 changes the structure and/or function of AH2, which could also result in the observed differences.
This was a very good suggestion. We expressed and purified the ΔAH2 protein requested by the reviewer and characterized its oligomeric state as well as its membrane binding. It turns out, as suspected, that the ΔAH2 protein behaves very similarly to the ΔMBD protein (i.e. it does not form higher order oligomers and does not bind membranes). The changes in the manuscript due to this addition are many but can primarily be found in main figures 2-3 and their associated supplementary figures.
Previous structural predictions of 2C do not appear to have two separate AHs at the N-terminus. Are the AH1 and AH2 structures predicted to be formed in the context of the entire 2C protein, 2BC precursors and polyprotein? Are there structural approaches that could provide experimental evidence for two separate AH at the N-terminus?
This is a good point. Previous predictions were not that detailed, partially since they were done in the pre-alphafold era. Unfortunately, we cannot think of a tractable experimental method that could verify the split nature of the amphipathic helix in the only context that would matter: the protein bound to a membrane. A long-term goal would be in situ structures of full-length 2C on membranes using cryo-electron tomography, but our current sample and data sets are not sufficient for this. We added a mention of the long-term need for experimental structures of full-length 2C on lines 315-318 in the discussion.
Why are the 2C dimers (lines 137-138) not apparent on the mass photometry data presented (figure 2)?
Different constructs were measured by mas photometry and SEC-MALS. Also, the required concentration is 100-1000x lower for mass photometry which will affect a dynamic equilibrium in case the same construct were measured by the two methods.
It appeared that binding of ΔMBD-2C was better when POPS is in the membrane (line 174). What is the explanation for this and was this finding significant?
Well spotted. It may mean that 2C has a second, lower affinity membrane-binding site which is charge-dependent somewhere outside the MBD. We now added a mention of this in the discussion, lines 321-323.
From the author's data on lipid drop clustering they conclude ΔAH1 is more effective for clustering, however, the ΔAH1 construct produces pentamers not hexamers (from Figure 2). Is formation of hexamers related to or required for membrane clustering?
ΔAH1 is LESS effective at clustering, not more. As for the mention of pentamers in the original submission: we now think this was an unfortunate choice of words. The mass photometry data for 2C(ΔAH1) could more parsimoniously be interpreted as a mix of hexamers and other (unknown to us) smaller oligomers such as trimers. We have removed all mentions of pentamers.
The replicon data presented in Figure 7 should include a replication-defective control (e.g., polymerase mutant), in order to compare how defective in replication ΔAH1 and ΔMBP deletions are compared to a fully-defective construct. Likewise, deletion of ΔAH1 in this construct is likely to affect processing of the viral polyprotein where several previous studies with picornaviruses have demonstrated that the residues in the P2'-P4' positions can change cleavage efficiency (e.g., PMID: 2542331), or the structure of 2C, leading to the reduction of replication.
Thanks for these good comments. We made the polymerase-dead (GDD-to-GAA) replicon and remeasured it side by side with the 2C replicons. It has a similar luciferase activity indicating that no replication takes place in the 2C deletion replicons. This is shown in the new figure 7. As for the possibility or processing defects, we mentioned this in the original discussion and have now cited the reference suggested by the reviewer in this context (line 324).
How does the author's model of ATPase-independent helicase activity and an APT-dependent required RNA chaperone activity fit with 2 step model for RNA binding and ATPase activity suggested by Yeager et al (PMID: 36399514)?
Acting upon comments from other reviewers, we completely redid the "helicase assay" in the revised manuscript. It turns out that the ATP-independent unwinding activity in the original submission was an artefact of the assay conditions (specifically, of the TEV protease at the higher concentration we used in the old assay). In our improved assay we neither see helicase activity nor ATP-independent RNA chaperoning activity.
Optional major comments that would increase the significance of the work:
All of the optional comments below are exceptionally interesting. But given the long time needed for the several major changes to this manuscript (e.g. the ΔAH2 protein characterization and reoptimisation of the helicase assay) we believe it is more sensible to address them in future studies, for which the 2C reconstitution system can be used.
The preference for dsRNA over ssRNA appears to be quite small (Figure 5d). In the context of a viral infection where ssRNA is likely to outnumber dsRNA at different times during infection is this preference physiologically relevant? In relation to this, what size stretch of dsRNA is required for preference, and could this correspond to cis-acting RNA structural elements, dsRNA as it escapes 3D polymerase or as part of the RF and RI forms (PMID: 9343205)? What is the proposed mechanism of how dsRNA outcompetes membrane tethering of 2C? OPTIONAL The author's study has been conducted in the absence of other viral non-structural proteins. What is the physiological importance of the observations, such as membrane interaction/clustering or RNA binding when presented in the context of the other replication machinery. OPTIONAL Do 2C monomers, dimers and hexamers have different functions in viral replication perhaps at different stages of replication and which of these forms are relevant during viral infection or can they all be detected during infection? Can any suggested separate functional arrangements be separated by genetic complementation experiments? OPTIONAL
Minor comments:
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The author's appear to interchange between naming/nomenclature of the constructs which makes it confusing to follow (for example, ΔMBD is the same as 2C(41-329) likewise, 2C(Δ115) is sometimes called 2C(116-329)). It would be much easier to follow if the naming of constructs was consistent throughout (unless I am misunderstanding some subtlety in the difference between such constructs).
Thanks very much for spotting this. We have fixed it.
The author's suggest a pentamer arrangement for the ΔAH1 construct, however in the mass photometry data (figure 2D), a hexamer is indicated with the arrow. It would be helpful to change the label to indicate the size of the pentamer where this is being generated, not the hexamer.
As mentioned above, we think the "pentamer" designation of the original manuscript was unfortunate. It is more parsimonious to interpret this as a mix of states, hexamer and undefined snaller.
In most figures, data for full-length 2C, ΔAH1 and ΔMBP is shown. However data for ΔMBP is missing in Figure 4. Using ΔMBP may demonstrate even lower clustering, hinting that AH2 is also involved in this process.
Thanks for this comment. In our view, it can be derived from figure 3 (which shows lack of binding to PC/PE membranes) that the ΔMBD construct would not cluster membranes under the conditions of the assay (clustering requires concomitant binding to two membranes). We now describe our rationale for this on lines 220-222. However, we did include the ΔMBD protein in the new negative staining TEM supplementary figure where it and ΔAH2 show no signs of clustering (figure S10).
I think it would be better for normalise the data in the flotation experiments such that the percentage of 2C in the upper faction is presented as relative to the amount of lipid in the upper fraction (presented in Figure S4).
The change suggested by the reviewer would make it impossible to show the important no-liposome control (leftmost bar in Fig. 3C) in the same plot as the other measurements. We believe that would unnecessarily complicate the figure. Thus, we opted to keep the measurement that are normalised by lipid fluorescence in the supplementary figure. Instead, we now added another mention of this supplementary figure in the legend to main figure 3.
At several places (e.g., lines 232 and 272) the author's refer to "realistic systems". I think the term "physiologically relevant" might be more appropriate.
Agreed and changed throughout.
Line 237: I think "y" is a typo and should read "by".
Thanks. This text was reworked due to the major changes to figure 6.
Reviewer #1 (Significance (Required)):
Significance
I have limited expertise with structural biology but specialise my research on positive-sense RNA virus replication, structure and function. This research is of interest to a broad audience of researchers investigating many positive-sense RNA viruses, which extends beyond the viral family studied here. The work utilises novel techniques to begin to understand the specific roles of 2C in poliovirus replication. The author's data add important incremental new insight into recent studies on viral helicase proteins as referenced in the study, however, a key limitation is understanding the importance/relevance of their observations during a viral infection.
We thanks the reviewer for this positive and nuanced appraisal of our work.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The authors present an alternative assay system to investigate picornavirus 2C, a protein that is tricky to analyze biochemically in its full length form because of an amphipathic helix at the N-terminus. Poliovirus 2C is expressed with an N-terminal MBP tag, a 50kD protein that helps with solubility as is commonly used for 2C investigations. A difference here is that liposomes are included to mimic membranes for 2C attachment. The key findings are that 2C induces clustering of of liposomes, that double stranded RNA binding by 2C impacts this clustering effect and that a free N-terminus (after cleavage of MBP by TEV protease) is needed for RNA binding and an ATP independent (ie non helicase) RNA duplex separation activity.
Major:
In the floatation assays in figure 3 the authors use a system where MBP-2C is fluorophore-labeled with ATTO488 on exposed cysteines. Poliovirus and other enterovirus 2C has a very well characterized zinc finger domain that has cysteines coordinating a zinc ion. Mutation experiments previously showed that these cysteines are necessary for viral replication and 2C stability. Have the authors controlled for disruption of the zinc finger domain by the labelling of cysteines with ATT0488 and checked if the protein remains folded?
We completely agree with the reviewer and apologise for the omission in the original submission. We have now included a Zn content measurement, which shows unchanged levels between labelled and unlabelled 2C protein (Figure S7). Also, we now in the revised manuscript explicitly describe our original reasoning for labelling on native cysteines: the presence of two cysteines which are not necessary for viral replication and which are more solvent exposed-exposed (and thus more likely to be labelled) in the crystal structure of the soluble fragment of 2C (lines 176-181).
In the analysis of the amphipathic helix, did the authors include membranes in their structural predictions o just the free helix? How does inclusion of membranes impact the predictions? In the predictions in Figure D, only 2 of 4 show a kink and there doesn't seem to be a correlation between those that predict a kink or not and whether the hydrophobic side is aligned in Figure S1.
Unfortunately, predicting a protein structure with the interacting membrane is beyond what is currently doable with protein prediction methods (one would have to combine protein structure predictions with molecular dynamics simulations including a membrane). Based on general principles of protein structure, it is likely that there is some flexibility around G17. Thus there may not be a single "kink angle" for any given virus, but we believe that the presence of the kink (and offset hydrophobic surfaces) for a number of viruses lends credibility and robustness to the observation. We added some descriptions of this thinking on lines 126-127.
Based on previous structures of 2C from different viruses the N-terminal amphipathic helix containing region is predicted to localize on one face of the predicted hexametric structure tethering 2C to the membrane. How does the authors hypothesized model explain 2C dependent clustering? is there evidence that 2C hexamers can oligomerize further into dodecamers for example, maintaining separate faces to enable N-terminal interaction with different membranes? What is the distance between the liposomes in figure 4 at the points of density attributed to 2C? How does this compare to the size of 2C determined in previous structural studies? Is it consistent with one hexamer/2 hexamers sitting on top of one another?
These are very interesting questions but we believe it is prudent to limit our speculation at this point. Eventually, we hope that larger data sets of cryo-electron tomography, coupled to subtomogram averaging, may provide a more definitive answer. What we managed to do with our current cryo-electron tomography data set is to estimate the volume of individual protein densities, and from the volume calculate an estimated molecular mass of the individual complexes seen in the tomograms. This correlates very well with 2C hexamers (new figure 4D).
In the Discussion lines 278-285 the authors suggest that having MBP attached may reflect the polyprotein condition. Can they make a construct with MBP-2B2C to examine interaction with liposomes and assess 2C function?
This is a highly relevant question, but the biochemistry of 2BC is even more challenging than 2C, and we are unfortunately nowhere near being able to work with purified 2BC at the moment.
Discussion lines 293-296, the possibility of two different populations of 2C, binding RNA or membranes cannot be excluded, there is much more 2C around late in infection that present in early infection- the model in figure 8 doesn't acknowledge/capture this.
We have changed the model figure such that more 2C is seen later, and the clustering function is also seen late in infection. The original discussion text referred to (which is unchanged) talks about a "preferential role in RNA replication and particle assembly at later time points" specifically for this reason. We hope the new figure 8 is better at conveying this message.
Discussion lines 313-317, the authors don't reference a study where a mutant of foot-and-mouth disease virus 2C lacking the n-terminal amphipathic helix that could bind but not hydrolyze ATP, hexamerized in the presence of RNA that seems pertinent here (PMID: 20507978).
Thanks for the suggestion. However, after the extensive changes we made to the revised to figure 6 based on excellent reviewer comments (essentially: the RNA chaperoning activity turned out to be an artefact, the improved assay shows no sign of RNA unwinding but instead of 2C-mediated ribonuclease activity), these sentence of the original discussion lost most of their context and we opted to remove them.
Some evidence of MBP-2C cleavage by TEV in the different assays used should be presented as this is a major focus of discussion and currently no gels show TEV cleavage is happening.
Thanks for the suggestion - we agree. We now show these in the new supplementary figures S5 and S12.
Reviewer #2 (Significance (Required)):
The work presents an additional methodology to investigate a a protein that has previously been difficult to study. The authors acknowledge that there is still a lot of 2C biology that remains to be discovered.
Thanks, we agree.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript provides insights into the role of the N-terminus in membrane binding and its importance in the various functions of 2C.
Major issues
Line 103-119. Is this novel? I thought people had done a lot of bioinformatic analysis of PV 2C (especially Wimmer) who also did mutational work to analyse the importance of various amino acids in the N-terminal helix. I feel like the paper in general, and this section in particular, underplays the large body of work that has been done on the amphipathic helix by various groups.
We apologise if our original manuscript didn't sufficiently acknowledge previous work in the field. In the first sentence of the mentioned paragraph (now lines 112-113) , we did however cite several papers that have previously addressed the amphipathic nature of the N-terminus of 2C. We have now added two more references along the same line, and changed the wording in a way that we hope better bring across that the amphipathic nature per se has been studies before. We would be happy to add more specific references if the reviewer has any suggestions. However, the rest of our analysis IS indeed novel for the following reasons: (i) we show that the amphipathic region is not a simple, single amphipathic helix, but instead has a conserved glycine (helix breaker/destabiliser residue) and two distinct amphipathic stretches before and after this region, (ii) we use alphafold2 (not available at the time of the earlier work) to provide the first reliable structural models of the membrane-binding domain. These models consistently, across several enterovirus 2C proteins, reveal that the hydrophobic surfaces of the first and second amphipathic regions, on either side of the conserved glycine 17, are offset from one another. This lends additional credibility to the distinct nature of these regions which have not previously been identified as such and which we also show in the biochemical assays to be functionally distinct. We have now also added a clarification to the Discussion that the N-terminus of 2C had previously been identified as its membrane-binding domain and we cite references for this. We hope that these changes will sufficiently acknowledge earlier work in the field while clearly pointing out the advance that our paper makes.
Line 132. Did you validate your column with known MW standards? The peak for full length and deltaAH1 look fairly standard for 2C, in that you have a mixture of species. Not sure you can say it is a hexamer when it is such a broad peak. C doesn't really help you too much since the counts at 400 (pentamer) and 480 (hexamer) are almost the same with quite large error bars. Like most people that have worked with 2C I think the best you can say is that you are making some kind of oligomerized 2C that includes hexamer, pentamer, etc. Why no dimer for MBP-2C and MBP-2C(delta AH1) when compared to the other constructs?
We did not calibrate the gel filtration column since the outcome would anyway be a more crude estimate of molecular mass than the mass photometry and SEC-MALS measurements. But we do agree with the reviewer on the broad mass photometry peaks. To address this experimentally, we compared the existing MBP-2C spectra to new recordings on apoferritin, a highly stable homomultimeric protein complex of a similar mass to aa MBP-2C hexamer. The apoferritin mass estimate is overlayed with the full-length MBP-2C in the new figure 2D and the corresponding supplementary figure S3. This indeed shows that the MBP-2C peak is broader, i.e. consistent with a mix of species which are predominantly but not only hexamers. We describe and discuss this on lines 145-149. As for the mention of pentamers in the original submission: we now think this was an unfortunate choice of words. The mass photometry data for 2C(ΔAH1) could more parsimoniously be interpreted as a mix of hexamers and other (unknown to us) smaller oligomers such as trimers. We have removed all mentions of pentamers.
Line 143. Does your data show that there are two amphipathic helices? Bioinformatics suggests it but your experiments just show the importance of the two areas in oligomerization, not that it is forming two helices.
We agree that the choice of words was not idea and have now changed it to "structure predictions indicate" (lines 162).
Figure S2. Your preps are still relatively dirty, which isn't ideal for biochemical assays. Especially lane 3, where you are looking at 50-60% purity. I don't want you to re-run experiments but I think you need to comment on the purity of the protein you are working with. Also I don't like that you removed the top and bottom of the SDS-PAGE. How much protein never entered the gel. Is there a big fat band at 20 kDa? You need to have the full gel here. Did you measure 260 nm of the preps as well to see if you had bound RNA to the 2C?
Thanks for the comment, we agree that our original submission lacked detail in the description of the protein purification. This is now addressed with the new figure S2 which shows size exclusion chromatograms of the fluorophore-labelled proteins (same chromatograms as in figure 2) and the corresponding uncropped gels imaged both in the stain-free channel (showing all proteins) and in the fluorescence channel. The A260/A280 ratio measured for all proteins shows that they are free of nucleic acids at the point of imaging. The protein preps are not 100% homogeneous but we do believe that they are more than 50-60% pure.
Lines 170. Wasn't this done in the recent "An Amphipathic Alpha-Helix Domain from Poliovirus 2C Protein Tubulate Lipid Vesicles"? I don't see it referenced. What is novel about the current work when compared to that paper? Any differences?
Thanks for pointing this out. The referenced study worked with a synthesized, isolated peptide corresponding to AH2 (i.e. not with full protein). An amphipathic peptide outside the context of its protein cannot be expected to recapitulate the properties of the entire protein, e.g. since it is not spatially constrained in how it interactis with membranes. As one example (relating to the title of that paper) we don't see full-length 2C protein tubulating membranes the way the isolated peptide does. As for the reviewer's question about novelty, the paper mentioned does not identify the split nature of the amphipathic region, does not consider the role of AH1, does not characterise the membrane-binding properties of full-length 2C with respect to liposome membrane composition and size, does not identify and characterise the membrane clustering properties of 2C, nor its interactions with nucleic acid when bound to a membrane. However, we do agree that we should have cited the paper in our manuscript. We now cite it in the discussion, lines 320-321.
I'm surprised by the lack of electron microscopy (negative stain mostly) of both the oligomerized 2C and the various liposomes. I know the Carlson group is a microscopy group so why the lack of validation using electron microscopy of the various DLS experiments? I know you did cryo-ET for one of the constructs but I think negative stain electron microscopy of other constructs would be useful.
Thanks for the suggestion. As suggested, we have now expanded the analysis with negative staining EM of several more constructs studied by DLS. It can be found in the new supplementary figure S10.
Figure 4C. What evidence is there that this is 2C apart from you added it to the liposomes? It also comes back to the relative impurity of your protein prep. Could this be E.coli contamination?
Thanks for this comment. We have now added a new supplementary figure (S5) showing SDS-PAGE gels of the reactions used for flotation and DLS assays - which are identical to the cryo-ET samples. In addition, we estimated the molecular mass of the individual, putative 2C desities in the cryo-electron tomograms by measuring their volume. This analysis, which can be found in the new figure 4D, shows that the estimated mass of individual protein densities is consistent with a hexamer of full-length 2C. In addition, we mention in the discussion the long-term need to determine high-resolution structures of membrane-bound 2C using cryo-ET and subtomogram averaging (lines 315-318).
Figure 8. Is this model supported by the data in this paper? Your cryo-ET says that 2C is there but that isn't supported by any other data. How is the dsRNA protected from the innate immune system in this model? is it just sat out in the cytosol? How is the nascent ssRNA packeged into the capsid? Is there competition between the dsRNA and capsid for 2C binding (which your model suggests)? I know it sounds like I am being overly critical of the model but in my opinion there are still too many unanswered questions in the field to come up with a half decent model.
Thanks for this comment. We are the first to agree that our understanding of the roles of 2C is far from complete! We should have been more clear that the model figure represents some of the roles of 2C identified to date, and does not claim to be complete. However we do feel that a model figure serves a purpose of putting our findings into a context, and also providing testable hypotheses for future research . As for the question, some of the roles of 2C shown in the model figure (in particular, particle assembly) are rather supported but earlier work of ourselves and others. We have now produced a new model figure and changed the figure legend to better reflect the incompleteness of the current understanding, and the origin of the different parts of the model figure. In addition, we extended the final paragraph of the discussion (which lists still-unknown aspects of 2C) with the reviewer's mention of dsRNA shielding from innate immunity (lines 374-375). The other aspects mentioned by the reviewer as not yet fully understood are already mentioned in that paragraph.
Minor issues
Lines 43-45: I feel like you underplay the success of the poliovirus vaccination program. Approximately 30 of WPV1 in 2022 and the full eradication of WPV2 and 3. Vaccine derived polio is still an issue but even that is relatively low compared to where the world was in the 1950s.
We agree that the previous wording was not ideal. We replaced it and added another recent reference - related to the type 2 vaccine switch (lines 47-49).
Line 66. I agree there are 11 individual proteins but I feel like this leaves out the fact that some of the uncleaved precursors appear to have some functions, for example 2BC.
Good point. We have now added a mention of 2BC and the fact that it has distinct functions to the introduction (lines 70-71). 2BC is also mentioned in the legend of the model figure (figure 8).
Line 56: LD needs to be defined.
Well spotted thanks. Since the abbreviation was not used anywhere else we opted to spell it out instead (line 59).
Line 75. I think you have misrepresented Xia et al here. They clearly say that in their study that they show helicase and chaperone activity. I never managed to repeat that work but you should still report what they claim. One major thing is that they used insect expressed protein, whereas most people (including myself and in the paper under review) use E.coli expressed protein. Do post translational modifications play an important role in function?
You are right that the reference to their paper for this statement was incorrect. We have now made this part of the introduction more explicit (lines 82-83) and we also in the new discussion mention the possibility of e.g. post-translational modifications affecting 2C helicase activity, under reference to Xia et al (lines 359-361)
Line 103. Need to make it clear here it is poliovirus 2C.
Thanks, we added it (line 112).
Line 135. I assume you mean kDa instead of uM?
It should actually be μM. It is the solution concentration at which the assay was performed. We added some words to clarify this (line 154).
Figure 3. What do you mean by "Only 2C"? Is that MBP-2C? Maybe I am reading the data wrong but adding TEV does nothing? How do you know TEV is removing the MBP? It looks like MBP-2C binds to the liposomes just the same as cleaved MBP-2C. I see in line 165 you acknowledge this. Could an alternative conclusion for line 168 be that MBP isn't being cleaved off but that AH2 is too small to be exposed in that construct? Did you do that construct without MBP being cleaved? I think you need to confirm that MBP is being cleaved off.
Thanks for spotting this mistake. It should indeed be MBP-2C (in the absence of liposomes). We corrected figure 3. Also, in response to this comment and similar ones, we have now added a new supplementary figure showing SDS-PAGE gels of the reaction loaded onto flotation assays and DLS (figure S5). It shows that MBP-2C is cleaved.
Line 184. Is there a reason you use the 2019 paper as a reference instead of the far earlier Bienz et al papers? I'd suggest they are the seminal papers on 2C membrane association. Once again how is this work different from the recent "An Amphipathic Alpha-Helix Domain from Poliovirus 2C Protein Tubulate Lipid Vesicles" paper?
See our response above of the paper mentioned here (which we have now cited). As for why we cite the 2019 paper here: our statement pertains specifically to the contact sites between lipid droplets and replication organelles, not to the membrane binding of 2C per se. We have now added a more general mention of membrane remodelling by non-structural proteins in the introduction, where we cite on of the Bienz papers (lines 75-77).
Figure 5D. So only 1-3% of RNA is found in the upper fraction? Is that significant enough to say that dsRNA was recruited significantly more than ssRNA? How confident are you in your quantification of the starting amounts of RNA?
We agree that the fraction is low, however, the fluorescence signal is very clearly above background. We are thus confident in the measurement. The low percentage at the end of the experiment likely has a simple physico-chemical explanation: in a dynamic equilibrium in a density gradient, whatever RNA dissociates during the run will migrate away from the 2C-vesicle fraction and not be able to rebind. We still tried to address this concern by a complementary experiment where we used fluorescence anisotropy to measure binding of RNA to 2C on vesicles. While the measurements showed the same tendency, they curves were not clean enough to be published, which we think is due to the complex system with 2C bound to vesicles and clusters of vesicles. Still, in view of the relatively low percentage of measured recruitment we opted to adjust the paper title and the title of figure 5 (including the subheading related to figure 5) to put less emphasis on the dsRNA recruitment.
Line 223. Any idea why the MBP needs to be cleaved off? Clearly the MDB is accessible or it would not bind to the liposomes.
Since we have no data directly supporting this we prefer not to speculate in the paper. But one guess would be that the NTD of 2C, as implicated by previous publications, has a dual role in membrane binding and RNA binding. It may be that it can bind membrane while conjugated to MBP, but needs MBP to be removed in order to simultaneously bind membrane and RNA.
Line 237: missing "b" in "by"
Thanks. This paragraph was rewritten in the light of the changes to figure 6.
Figure 6. I don't fully understand the results here. Earlier you showed that the delta MBD didn't really bind SUV. So presumably it isn't really membrane bound. Why does it have similar activity to full-length MBP in your helicase assay if membrane is important? Did you do SUV and TEV protease only control?
We are very grateful to this reviewer (and others) for pointing out the need for a TEV control. When performing the control, we found that the TEV protease, at the high concentrations initially used, surprisingly had an artefactual RNA chaperone-like effect on its own. We then proceeded to titrate down the TEV protease concentration to the point where it no longer interfered. At this TEV protease concentration, although 2C was substantially cleaved (see the new supplementary figure S12), we could no longer detect an RNA chaperone activity. Thus, the contents of the new figure 6, and its conclusions, have been substantially changed. We now focused our attention on the remaining effect that 2C has on RNA: single-strand ribonuclease activity. These experiments were all conducted in the presence of RNase inhibitors, and the presence of Mg2+-dependent ribonuclease activity parallels a recent publication that found this for truncated 2C from hepatitis A and several enteroviruses.
Line 257: "staring"?
Thanks, corrected. A staring glycine would indeed be something strange.
Line 336. Need to change the u to mu.
Thanks, corrected.
Any discussion on your observation in Figure 1D that EV71 and CVB3 don't appear to have AH1 and AH2 or do you think that the domains are conserved across the different viruses?
Thanks for bringing this up. Based on this and a comment from another reviewer, we have now clarified our thinking around this. Since the glycine will introduce some flexibility between AH1 and AH2, we cannot say from the single alphafold predictions that this is THE kink angle. The presence of the kink in the predictions of several MBDs lends more credibility to the robustness of the observation, but most importantly the hydrophobic surfaces in AH1 and AH2 are non-aligned for ALL sequences we looked at. This is now described on lines 126-128.
Table 1 (and possibly elsewhere): an apostrophe is not the prime symbol. 5' compared to 5′.
Thanks, we corrected this throughout.
Line 702 "and" should be "an".
Thanks, corrected.
I couldn't open one of the movies (140844_0_supp_2820374_a2g272.avi).
Sorry to hear this, we will check the movie again.
Reviewer #3 (Significance (Required)):
Overall I liked the paper and is worth publishing. One of the issues in the 2C field is the difficulty in making pure 2C and carrying out in vitro assays that correlate with what is observed in the natural infection. I think this paper suffers from similar struggles with a 2C preparation that doesn't appear that pure. I think it also suffers from not having 2C from a wild-type infection. I don't think that it is feasible to get that kind of 2C but by once again using a recombinant protein from E.coli we are left with another manuscript that provides conflicting evidence of the functions of 2C without a definitive answer. The experiments are well done, although are missing some controls and the manuscript is laid out in a logical manner and is relatively easy to follow.
We thanks the reviewer for these comments. We believe that we have now provided better information regarding the purification of the recombinant 2C protein, and we do think that the controls present in the original manuscript and the revised manuscript alleviate the concerns about lack of specificity. Of course, isolating 2C vesicles from wildtype infection would be another interesting way of approaching its function, but such an approach would come with its own set of challenges related e.g. to the presence of confounding host factors.
Reviewer #4 (Evidence, reproducibility and clarity (Required)):
This is an interesting manuscript that reports the development of an in vitro membrane assay for probing the biochemical functions of the enterovirus 2C protein. The technique is interesting because it can be applied to 2C proteins from other members of the picornavirus family, an important group of mammalian pathogens. It has the capacity to probe different functions (e.g. membrane clustering, ATPase activity, RNA-binding and manipulation activities).
Overall, the manuscript is well written and gives a clear account of the work undertaken. It adds insight to previous studies of enteroviral (and picornaviral) 2C proteins, providing confirmation of some earlier work in a more physiological context and some new insights, particularly into the membrane and RNA binding aspects of 2C.
That said, there are a number of places where some amendment of the claims made is required to provide a more precise statement of the findings of this work. These are listed below.
We thank the reviewer for this positive feedback on our work, as well as for the specific comments below.
Line 21 (Abstract) - The authors claim to have shown that a conserved glycine divides the N-terminal membrane-binding domain into 2 helices. I would suggest instead what they have produced are computational predictions that this is the case - some way short of an experimental demonstration. Sequence analysis predicts helical secondary structure in the N-terminus and indeed Alphafold2 also predicts a helical structure, but these predictions require experimental verification. The authors should therefore rewrite sections that claim to have shown the presence of 2 helices. In doing so, they should perhaps also comment on the fact that Alphafold2 does not predict 2 helices in this region for all enteroviruses (see Fig 1D). Moreover, the sequence analysis in Fig. S1 shows the presence of two Lys residues in the segment 17-38; it would be interesting for the reader to have these indicated in the figures showing the Alphafold2 prediction - do they in any way interrupt the hydrophobic face of the predicted helix?
Thanks very much for this comment, which is in line with what other reviewers also wrote. We agree, and changed the abstract sentence. We have also rewritten the manuscripts in several places to address the limits of structure predictions and the eventual need for an experimental structure of full-length membrane-bound 2C (lines 126-128 and 315-318).
Line 82 (Introduction) - The authors write that the membrane binding domain (MBD) of poliovirus has been shown to mediate hexamerisation, citing Adams et al (2009) - reference 43. However, that is not what this paper shows. Rather it provides evidence of aggregation of an MBP-2C fusion protein into forms that ranged from tetramer to octamer, but no evidence that these aggregates assume functional forms (e.g. the presumed hexameric ring structure characteristic of the AAA+ ATPase family to which 2C belongs). As far as I am aware the first demonstration of hexameric ring formation by a picornaviral 2C protein was for the 2C of foot-and-mouth disease virus (see Sweeney et al, JBC, 2010). Although this is not an enterovirus, this finding was later confirmed for Echovirus 30 (ref 51). I should declare an interest here: the Sweeney paper is from my lab. I will leave it to the editor and the authors to determine how to write a more precise account of the early observations of hexamerisation in picornaviral and enteroviral 2C proteins.
Thanks very much for this insightful comment. As a response to this and other similar comments, we are much more cautious about our wording in the revised manuscript (see also response to comment below. In the part of the introduction discussed here (now lines 89-91) we now use the original wording of the Adams paper ("oligomerization"). In the context of that new text we didn't feel that Sweeney et al paper was a suitable reference, but we now cite it in the later mention of 2C's oligomeric/hexameric state in the first part of the Results (lines 137-138 ).
Line 132 - the authors used mass photometry to investigate oligomeric forms of their MBP-2C constructs and state that for the full length 2C protein "the high-mass peak closely corresponds to a hexamer". While it is true that the peak shown in Fig 2C aligns with the expected MW for an MBP-2C hexamer, the peak is very broad, indicative of the presence of other oligomeric states with lower and higher numbers of monomers. This should be commented on. Indeed, the finding seems to echo the early findings of Adams et al (ref 43) with poliovirus MBP-2C.
Thanks for this comment, which was also made by another reviewer. We cite here what we replied to that reviewer
...we do agree with the reviewer on the broad mass photometry peaks. To address this experimentally, we compared the existing MBP-2C spectra to new recordings on apoferritin, a highly stable homomultimeric protein complex of a similar mass to aa MBP-2C hexamer. The apoferritin mass estimate is overlayed with the full-length MBP-2C in the new figure 2D and the corresponding supplementary figure S3. This indeed shows that the MBP-2C peak is broader, i.e. consistent with a mix of species which are predominantly but not only hexamers. We describe and discuss this on lines 145-149.
Line 143 - for the reasons given above, this summary paragraph represents too strong a statement of what has been observed.
We agree, and changed the paragraph. It now only refers to "oligomerization" (lines 162-164).
Line 197 - I note that the authors did not test the membrane clustering capabilities of the 2C(41-329) construct. Although the 2C(deltaAH1) construct had already shown a significant loss of activity, the shorter construct could still have been a useful control. I don't think it is necessary for this experiment to be done, but if the authors have a rationale for not performing the experiment, perhaps they could include it in a revised manuscript.
Thanks for the suggestion. The rationale is that a protein that doesn't bind a membrane in the first place will also not cluster them (an action that requires binding TWO membranes). We now describe our reasoning on lines 220-222. Nevertheless, we did test these constructs in the new supplementary figure showing negative staining TEM (figure S10).
Line 223 - typo. I think you mean MBD.
Thanks! Corrected (now line 257).
Line 215 - the authors observed that the presence of ssDNA reduced membrane clustering and conclude that "nucleic acid binding partially outcompetes membrane tethering activity". Two things: (1) although I agree is it likely that this effect is due to binding of DNA to 2C, binding has not been demonstrated experimentally so the authors should be more careful in how they describe their result; (2) there is no data presented to show that RNA binding reduces membrane tethering so at best I think the conclusion has to be that the data are consistent with the notion that DNA binding reduces membrane tethering. It would of course be interesting to see the effects of RNA and I'm curious to know why the assay was not performed.
Thanks for the comment. The honest answer is that previous publications (primarily Yeager et al, NAR 2022) convinced us that the outcome should be near-identical with DNA, so we chose DNA oligos because they are cheaper and easier to work with. But we agree with the reviewer that RNA is of course more relevant. We now present a comparison at 5 μM of ssDNA and ssRNA, which in fact shows a slightly stronger effect on membrane clustering by RNA (figure 5C). In the light of this additional experiment, we feel that some of the text changes suggested by the reviewer may no longer be necessary.
Line 237 - typo: by, not y
Thanks. In the light of the extensive changes to figure 6 this text was removed.
Line 284 - the authors claim that 2C may only bind RNA after the N-terminus is liberated from 2B in infected cells, since cleavage of the MBP tag from their construct was needed for 2C to bind RNA in their in vitro assay. However, this does not automatically follow given the large structural differences between MBP and 2B and the fact that the authors have not tested the RNA binding capacity of a 2BC fusion protein. Their claim here is too strong and should be re-written.
We agree, and have added a discussion along the lines suggested by the reviewer (line 330-332).
Line 293 - The authors speculate that RNA binding might cause a shift between the membrane clustering activities and the role of the protein in RNA replication. However, since they have not shown that RNA binding reduces membrane clustering, this is too speculative.
In our revised manuscript we have studied the effect of RNA on membrane binding, thus we feel that this text is relevant in the context of the extended experiments.
Line 299-317 - within this discussion is the assumption that in their assay system enterovirus 2C adopts the ring-like hexameric structure typical of AAA+ ATPases. While I agree this may well be the case, it has not been demonstrated in this study so the authors should make clear they are making this assumption. The same applies to the legend of Fig 8.
This part of the discussion was extensively rewritten after our changes to figure 6. We now only refer to "hexamer" once in the corresponding part of the discussion, where we talk about structural models of hexamers produced by other groups who have crystallised fragments of 2C. There we believe we should refer to hexamers to accurately cite their work.
We are not sure what the reviewer is referring to when it comes to the legend for figure 8: the original legend had no reference to the oligomeric state of 2C. We have substantially changed figure 8 and its legend and the new figure and legend make no references to hexamers/oligomers.
Line 302 - the authors claim to have shown that 2C is 'selective' for dsRNA. I think at best they have shown a preference for binding dsRNA over ssRNA.
We changed the wording (line 349). We have also changed the title of the paper where we removed "double-stranded".
Line 313 - The sentence starting "A recent study..." needs a reference.
The revised discussion no longer contains this sentence.
Line 332 - the full sequence of the synthetic gene used in this study should be made available (e.g. as supplementary information or a deposited sequence with an accession number). This is a critical point before the paper can be published.
We will of course submit the sequences as supplementary data. Thanks for the reminder.
Line 362 - the authors should describe the likely points of attachment of fluorophores and comment on how this labelling might affect 2C function.
Thanks for the comment. In response to this and a similar comment from another reviewer, we discuss the likely conjugation site of the fluorophore (lines 175-181), and also (due to the proximity to the Zn finger) provide a new measurement showing that equal amounts of Zn can be detected in the labelled and unlabelled protein (figure S7).
Line 372 - Is a single protein standard (BSA) sufficient to calibrate the SEC-MALS system?
Yes, it is the recommended procedure (note that SEC-MALS is only dependent on scattering, not elution volumes etc).
Reviewer #4 (Significance (Required)):
As stated above this is an interesting study that presents findings from a novel assay. It will be of interest to picornavirologists and the wider community interested in the mechanisms of AAA+ ATPases.
We thanks the reviewer for this positive appraisal of our work.
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Referee #4
Evidence, reproducibility and clarity
This is an interesting manuscript that reports the development of an in vitro membrane assay for probing the biochemical functions of the enterovirus 2C protein. The technique is interesting because it can be applied to 2C proteins from other members of the picornavirus family, an important group of mammalian pathogens. It has the capacity to probe different functions (e.g. membrane clustering, ATPase activity, RNA-binding and manipulation activities).
Overall, the manuscript is well written and gives a clear account of the work undertaken. It adds insight to previous studies of enteroviral (and picornaviral) 2C proteins, providing confirmation of some earlier work in a more physiological context and some new insights, particularly into the membrane and RNA binding aspects of 2C.
That said, there are a number of places where some amendment of the claims made is required to provide a more precise statement of the findings of this work. These are listed below.
Line 21 (Abstract) - The authors claim to have shown that a conserved glycine divides the N-terminal membrane-binding domain into 2 helices. I would suggest instead what they have produced are computational predictions that this is the case - some way short of an experimental demonstration. Sequence analysis predicts helical secondary structure in the N-terminus and indeed Alphafold2 also predicts a helical structure, but these predictions require experimental verification. The authors should therefore rewrite sections that claim to have shown the presence of 2 helices. In doing so, they should perhaps also comment on the fact that Alphafold2 does not predict 2 helices in this region for all enteroviruses (see Fig 1D). Moreover, the sequence analysis in Fig. S1 shows the presence of two Lys residues in the segment 17-38; it would be interesting for the reader to have these indicated in the figures showing the Alphafold2 prediction - do they in any way interrupt the hydrophobic face of the predicted helix?
Line 82 (Introduction) - The authors write that the membrane binding domain (MBD) of poliovirus has been shown to mediate hexamerisation, citing Adams et al (2009) - reference 43. However, that is not what this paper shows. Rather it provides evidence of aggregation of an MBP-2C fusion protein into forms that ranged from tetramer to octamer, but no evidence that these aggregates assume functional forms (e.g. the presumed hexameric ring structure characteristic of the AAA+ ATPase family to which 2C belongs). As far as I am aware the first demonstration of hexameric ring formation by a picornaviral 2C protein was for the 2C of foot-and-mouth disease virus (see Sweeney et al, JBC, 2010). Although this is not an enterovirus, this finding was later confirmed for Echovirus 30 (ref 51). I should declare an interest here: the Sweeney paper is from my lab. I will leave it to the editor and the authors to determine how to write a more precise account of the early observations of hexamerisation in picornaviral and enteroviral 2C proteins. Line 132 - the authors used mass photometry to investigate oligomeric forms of their MBP-2C constructs and state that for the full length 2C protein "the high-mass peak closely corresponds to a hexamer". While it is true that the peak shown in Fig 2C aligns with the expected MW for an MBP-2C hexamer, the peak is very broad, indicative of the presence of other oligomeric states with lower and higher numbers of monomers. This should be commented on. Indeed, the finding seems to echo the early findings of Adams et al (ref 43) with poliovirus MBP-2C.
Line 143 - for the reasons given above, this summary paragraph represents too strong a statement of what has been observed.
Line 197 - I note that the authors did not test the membrane clustering capabilities of the 2C(41-329) construct. Although the 2C(deltaAH1) construct had already shown a significant loss of activity, the shorter construct could still have been a useful control. I don't think it is necessary for this experiment to be done, but if the authors have a rationale for not performing the experiment, perhaps they could include it in a revised manuscript.
Line 223 - typo. I think you mean MBD.
Line 215 - the authors observed that the presence of ssDNA reduced membrane clustering and conclude that "nucleic acid binding partially outcompetes membrane tethering activity". Two things: (1) although I agree is it likely that this effect is due to binding of DNA to 2C, binding has not been demonstrated experimentally so the authors should be more careful in how they describe their result; (2) there is no data presented to show that RNA binding reduces membrane tethering so at best I think the conclusion has to be that the data are consistent with the notion that DNA binding reduces membrane tethering. It would of course be interesting to see the effects of RNA and I'm curious to know why the assay was not performed.
Line 237 - typo: by, not y
Line 284 - the authors claim that 2C may only bind RNA after the N-terminus is liberated from 2B in infected cells, since cleavage of the MBP tag from their construct was needed for 2C to bind RNA in their in vitro assay. However, this does not automatically follow given the large structural differences between MBP and 2B and the fact that the authors have not tested the RNA binding capacity of a 2BC fusion protein. Their claim here is too strong and should be re-written.
Line 293 - The authors speculate that RNA binding might cause a shift between the membrane clustering activities and the role of the protein in RNA replication. However, since they have not shown that RNA binding reduces membrane clustering, this is too speculative.
Line 299-317 - within this discussion is the assumption that in their assay system enterovirus 2C adopts the ring-like hexameric structure typical of AAA+ ATPases. While I agree this may well be the case, it has not been demonstrated in this study so the authors should make clear they are making this assumption. The same applies to the legend of Fig 8.
Line 302 - the authors claim to have shown that 2C is 'selective' for dsRNA. I think at best they have shown a preference for binding dsRNA over ssRNA.
Line 313 - The sentence starting "A recent study..." needs a reference.
Line 332 - the full sequence of the synthetic gene used in this study should be made available (e.g. as supplementary information or a deposited sequence with an accession number). This is a critical point before the paper can be published.
Line 362 - the authors should describe the likely points of attachment of fluorophores and comment on how this labelling might affect 2C function.
Line 372 - Is a single protein standard (BSA) sufficient to calibrate the SEC-MALS system?
Significance
As stated above this is an interesting study that presents findings from a novel assay. It will be of interest to picornavirologists and the wider community interested in the mechanisms of AAA+ ATPases.
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Referee #3
Evidence, reproducibility and clarity
The manuscript provides insights into the role of the N-terminus in membrane binding and its importance in the various functions of 2C.
Major issues
Line 103-119. Is this novel? I thought people had done a lot of bioinformatic analysis of PV 2C (especially Wimmer) who also did mutational work to analyse the importance of various amino acids in the N-terminal helix. I feel like the paper in general, and this section in particular, underplays the large body of work that has been done on the amphipathic helix by various groups.
Line 132. Did you validate your column with known MW standards? The peak for full length and deltaAH1 look fairly standard for 2C, in that you have a mixture of species. Not sure you can say it is a hexamer when it is such a broad peak. C doesn't really help you too much since the counts at 400 (pentamer) and 480 (hexamer) are almost the same with quite large error bars. Like most people that have worked with 2C I think the best you can say is that you are making some kind of oligomerized 2C that includes hexamer, pentamer, etc. Why no dimer for MBP-2C and MBP-2C(delta AH1) when compared to the other constructs?
Line 143. Does your data show that there are two amphipathic helices? Bioinformatics suggests it but your experiments just show the importance of the two areas in oligomerization, not that it is forming two helices.
Figure S2. Your preps are still relatively dirty, which isn't ideal for biochemical assays. Especially lane 3, where you are looking at 50-60% purity. I don't want you to re-run experiments but I think you need to comment on the purity of the protein you are working with. Also I don't like that you removed the top and bottom of the SDS-PAGE. How much protein never entered the gel. Is there a big fat band at 20 kDa? You need to have the full gel here. Did you measure 260 nm of the preps as well to see if you had bound RNA to the 2C?
Lines 170. Wasn't this done in the recent "An Amphipathic Alpha-Helix Domain from Poliovirus 2C Protein Tubulate Lipid Vesicles"? I don't see it referenced. What is novel about the current work when compared to that paper? Any differences?
I'm surprised by the lack of electron microscopy (negative stain mostly) of both the oligomerized 2C and the various liposomes. I know the Carlson group is a microscopy group so why the lack of validation using electron microscopy of the various DLS experiments? I know you did cryo-ET for one of the constructs but I think negative stain electron microscopy of other constructs would be useful.
Figure 4C. What evidence is there that this is 2C apart from you added it to the liposomes? It also comes back to the relative impurity of your protein prep. Could this be E.coli contamination?
Figure 8. Is this model supported by the data in this paper? Your cryo-ET says that 2C is there but that isn't supported by any other data. How is the dsRNA protected from the innate immune system in this model? is it just sat out in the cytosol? How is the nascent ssRNA packeged into the capsid? Is there competition between the dsRNA and capsid for 2C binding (which your model suggests)? I know it sounds like I am being overly critical of the model but in my opinion there are still too many unanswered questions in the field to come up with a half decent model.
Minor issues
Lines 43-45: I feel like you underplay the success of the poliovirus vaccination program. Approximately 30 of WPV1 in 2022 and the full eradication of WPV2 and 3. Vaccine derived polio is still an issue but even that is relatively low compared to where the world was in the 1950s.
Line 66. I agree there are 11 individual proteins but I feel like this leaves out the fact that some of the uncleaved precursors appear to have some functions, for example 2BC.
Line 56: LD needs to be defined.
Line 75. I think you have misrepresented Xia et al here. They clearly say that in their study that they show helicase and chaperone activity. I never managed to repeat that work but you should still report what they claim. One major thing is that they used insect expressed protein, whereas most people (including myself and in the paper under review) use E.coli expressed protein. Do post translational modifications play an important role in function?
Line 103. Need to make it clear here it is poliovirus 2C.
Line 135. I assume you mean kDa instead of uM?
Figure 3. What do you mean by "Only 2C"? Is that MBP-2C? Maybe I am reading the data wrong but adding TEV does nothing? How do you know TEV is removing the MBP? It looks like MBP-2C binds to the liposomes just the same as cleaved MBP-2C. I see in line 165 you acknowledge this. Could an alternative conclusion for line 168 be that MBP isn't being cleaved off but that AH2 is too small to be exposed in that construct? Did you do that construct without MBP being cleaved? I think you need to confirm that MBP is being cleaved off.
Line 184. Is there a reason you use the 2019 paper as a reference instead of the far earlier Bienz et al papers? I'd suggest they are the seminal papers on 2C membrane association. Once again how is this work different from the recent "An Amphipathic Alpha-Helix Domain from Poliovirus 2C Protein Tubulate Lipid Vesicles" paper?
Figure 5D. So only 1-3% of RNA is found in the upper fraction? Is that significant enough to say that dsRNA was recruited significantly more than ssRNA? How confident are you in your quantification of the starting amounts of RNA?
Line 223. Any idea why the MBP needs to be cleaved off? Clearly the MDB is accessible or it would not bind to the liposomes.
Line 237: missing "b" in "by"
Figure 6. I don't fully understand the results here. Earlier you showed that the delta MBD didn't really bind SUV. So presumably it isn't really membrane bound. Why does it have similar activity to full-length MBP in your helicase assay if membrane is important? Did you do SUV and TEV protease only control?
Line 257: "staring"?
Line 336. Need to change the u to mu.
Any discussion on your observation in Figure 1D that EV71 and CVB3 don't appear to have AH1 and AH2 or do you think that the domains are conserved across the different viruses?
Table 1 (and possibly elsewhere): an apostrophe is not the prime symbol. 5' compared to 5′.
Line 702 "and" should be "an".
I couldn't open one of the movies (140844_0_supp_2820374_a2g272.avi).
Significance
Overall I liked the paper and is worth publishing. One of the issues in the 2C field is the difficulty in making pure 2C and carrying out in vitro assays that correlate with what is observed in the natural infection. I think this paper suffers from similar struggles with a 2C preparation that doesn't appear that pure. I think it also suffers from not having 2C from a wild-type infection. I don't think that it is feasible to get that kind of 2C but by once again using a recombinant protein from E.coli we are left with another manuscript that provides conflicting evidence of the functions of 2C without a definitive answer. The experiments are well done, although are missing some controls and the manuscript is laid out in a logical manner and is relatively easy to follow.
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Referee #2
Evidence, reproducibility and clarity
The authors present an alternative assay system to investigate picornavirus 2C, a protein that is tricky to analyze biochemically in its full length form because of an amphipathic helix at the N-terminus. Poliovirus 2C is expressed with an N-terminal MBP tag, a 50kD protein that helps with solubility as is commonly used for 2C investigations. A difference here is that liposomes are included to mimic membranes for 2C attachment. The key findings are that 2C induces clustering of of liposomes, that double stranded RNA binding by 2C impacts this clustering effect and that a free N-terminus (after cleavage of MBP by TEV protease) is needed for RNA binding and an ATP independent (ie non helicase) RNA duplex separation activity.
Major:
In the floatation assays in figure 3 the authors use a system where MBP-2C is fluorophore-labeled with ATTO488 on exposed cysteines. Poliovirus and other enterovirus 2C has a very well characterized zinc finger domain that has cysteines coordinating a zinc ion. Mutation experiments previously showed that these cysteines are necessary for viral replication and 2C stability. Have the authors controlled for disruption of the zinc finger domain by the labelling of cysteines with ATT0488 and checked if the protein remains folded?
In the analysis of the amphipathic helix, did the authors include membranes in their structural predictions o just the free helix? How does inclusion of membranes impact the predictions? In the predictions in Figure D, only 2 of 4 show a kink and there doesn't seem to be a correlation between those that predict a kink or not and whether the hydrophobic side is aligned in Figure S1.
Based on previous structures of 2C from different viruses the N-terminal amphipathic helix containing region is predicted to localize on one face of the predicted hexametric structure tethering 2C to the membrane. How does the authors hypothesized model explain 2C dependent clustering? is there evidence that 2C hexamers can oligomerize further into dodecamers for example, maintaining separate faces to enable N-terminal interaction with different membranes? What is the distance between the liposomes in figure 4 at the points of density attributed to 2C? How does this compare to the size of 2C determined in previous structural studies? Is it consistent with one hexamer/2 hexamers sitting on top of one another?
In the Discussion lines 278-285 the authors suggest that having MBP attached may reflect the polyprotein condition. Can they make a construct with MBP-2B2C to examine interaction with liposomes and assess 2C function?
Discussion lines 293-296, the possibility of two different populations of 2C, binding RNA or membranes cannot be excluded, there is much more 2C around late in infection that present in early infection- the model in figure 8 doesn't acknowledge/capture this.
Discussion lines 313-317, the authors don't reference a study where a mutant of foot-and-mouth disease virus 2C lacking the n-terminal amphipathic helix that could bind but not hydrolyze ATP, hexamerized in the presence of RNA that seems pertinent here (PMID: 20507978).
Some evidence of MBP-2C cleavage by TEV in the different assays used should be presented as this is a major focus of discussion and currently no gels show TEV cleavage is happening.
Significance
The work presents an additional methodology to investigate a a protein that has previously been difficult to study. The authors acknowledge that there is still a lot of 2C biology that remains to be discovered.
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this study by Shankar and colleagues, the authors aim to understand the structure and function of the enterovirus 2C protein, a putative viral helicase with AAA+ ATPase activity. Using poliovirus (as a model enterovirus) 2C, the author's propose the protein contains two amphipathic helices (AH1 and AH2) at the N-terminus that are divided by a conserved glycine. Using purified MBP-tagged 2C and N-terminal 2C truncations, their data suggests AH1 is primarily responsible for clustering at membranes, whilst AH2 is the main mediator of 2C oligmerisation and membrane binding. Furthermore, 2C was suggested to be able to recruit RNA to membranes, with a preference for dsRNA, and the author's data implies that the helicase activity of 2C is ATP-independent. Instead, the ATP activity appears to be required for 2C hexamer formation or chaperone activity. The manuscript is generally well written /presented and the author's present very interesting data which raises several questions, some of which require additional experimentation to help support the author's conclusions. Specific comments are as follows.
Major Comments:
- The authors use four main constructs throughout the paper: full-length 2C, 2C with deletion of AH1 (ΔAH1), 2C with both AH1 and AH2 deleted (ΔMBP) and 2C with an extended N-terminal deletion. From this, the author's draw conclusions on the function of both AH1 and AH2. One of the author's main conclusions is that AH2 is the main mediator of 2C membrane association (e.g., in line 169). However, is it possible to conclude the relative importance of AH1 vs AH2 without testing a construct containing the deletion of AH2 only (ΔAH2)? This should be generated and used alongside this data to fully define the relative importance of AH1 and AH2 in these assay and remove the possibility that the deletion of AH1 changes the structure and/or function of AH2, which could also result in the observed differences.
- Previous structural predictions of 2C do not appear to have two separate AHs at the N-terminus. Are the AH1 and AH2 structures predicted to be formed in the context of the entire 2C protein, 2BC precursors and polyprotein? Are there structural approaches that could provide experimental evidence for two separate AH at the N-terminus?
- Why are the 2C dimers (lines 137-138) not apparent on the mass photometry data presented (figure 2)?
- It appeared that binding of ΔMBD-2C was better when POPS is in the membrane (line 174). What is the explanation for this and was this finding significant?
- From the author's data on lipid drop clustering they conclude ΔAH1 is more effective for clustering, however, the ΔAH1 construct produces pentamers not hexamers (from Figure 2). Is formation of hexamers related to or required for membrane clustering?
- The replicon data presented in Figure 7 should include a replication-defective control (e.g., polymerase mutant), in order to compare how defective in replication ΔAH1 and ΔMBP deletions are compared to a fully-defective construct. Likewise, deletion of ΔAH1 in this construct is likely to affect processing of the viral polyprotein where several previous studies with picornaviruses have demonstrated that the residues in the P2'-P4' positions can change cleavage efficiency (e.g., PMID: 2542331), or the structure of 2C, leading to the reduction of replication.
- How does the author's model of ATPase-independent helicase activity and an APT-dependent required RNA chaperone activity fit with 2 step model for RNA binding and ATPase activity suggested by Yeager et al (PMID: 36399514)? Optional major comments that would increase the significance of the work:
- The preference for dsRNA over ssRNA appears to be quite small (Figure 5d). In the context of a viral infection where ssRNA is likely to outnumber dsRNA at different times during infection is this preference physiologically relevant? In relation to this, what size stretch of dsRNA is required for preference, and could this correspond to cis-acting RNA structural elements, dsRNA as it escapes 3D polymerase or as part of the RF and RI forms (PMID: 9343205)? What is the proposed mechanism of how dsRNA outcompetes membrane tethering of 2C? OPTIONAL
- The author's study has been conducted in the absence of other viral non-structural proteins. What is the physiological importance of the observations, such as membrane interaction/clustering or RNA binding when presented in the context of the other replication machinery. OPTIONAL
- Do 2C monomers, dimers and hexamers have different functions in viral replication perhaps at different stages of replication and which of these forms are relevant during viral infection or can they all be detected during infection? Can any suggested separate functional arrangements be separated by genetic complementation experiments? OPTIONAL
Minor comments:
- The author's appear to interchange between naming/nomenclature of the constructs which makes it confusing to follow (for example, ΔMBD is the same as 2C(41-329) likewise, 2C(Δ115) is sometimes called 2C(116-329)). It would be much easier to follow if the naming of constructs was consistent throughout (unless I am misunderstanding some subtlety in the difference between such constructs).
- The author's suggest a pentamer arrangement for the ΔAH1 construct, however in the mass photometry data (figure 2D), a hexamer is indicated with the arrow. It would be helpful to change the label to indicate the size of the pentamer where this is being generated, not the hexamer.
- In most figures, data for full-length 2C, ΔAH1 and ΔMBP is shown. However data for ΔMBP is missing in Figure 4. Using ΔMBP may demonstrate even lower clustering, hinting that AH2 is also involved in this process.
- I think it would be better for normalise the data in the flotation experiments such that the percentage of 2C in the upper faction is presented as relative to the amount of lipid in the upper fraction (presented in Figure S4).
- At several places (e.g., lines 232 and 272) the author's refer to "realistic systems". I think the term "physiologically relevant" might be more appropriate.
- Line 237: I think "y" is a typo and should read "by".
Significance
I have limited expertise with structural biology but specialise my research on positive-sense RNA virus replication, structure and function. This research is of interest to a broad audience of researchers investigating many positive-sense RNA viruses, which extends beyond the viral family studied here. The work utilises novel techniques to begin to understand the specific roles of 2C in poliovirus replication. The author's data add important incremental new insight into recent studies on viral helicase proteins as referenced in the study, however, a key limitation is understanding the importance/relevance of their observations during a viral infection.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This manuscript provides a detailed analysis of RNA and protein dynamics during transmission of the rodent malaria model P. yoelii from the mouse host to an in vitro ookinete culture setting (mimicking the mosquito midgut environment). This group and others have shown experimentally that a substantial number of mRNAs is stored in the female Plasmodium gametocyte, ready to be translated following initiation of ookinete development. The process is akin to maternal deposition of mRNA in oocytes of metazoans. With this manuscript the authors provide a significant contribution to the field of translational control in Plasmodium parasites as they explore the translational activation during the early hours of zygote-to-ookinete development. The paper presents RNAseq and mass-spec analyses of female gametocytes and for the first time for 6-hour zygotes (ie a fertilized female gamete); the zygote datasets are much improved and more comprehensive than the only other performed in 2008 in P. gallinaceum. Using comparative analyses of transcriptome and proteome data (including published datasets) the authors arrive at a list of 198 transcripts that are translationally repressed in the gametocyte and translated within 6 hours of fertilization in the zygote. Many of these mRNAs are known to be involved in zygote to ookinete transformation. BioID is finally used to explore changes in mRNP protein composition between the female gametocyte and the zygote.
The paper is generally well written. The authors present a lot of data (also in comparison with published data). Sometimes perhaps the main message could be simplified / streamlined in section titles (Quantitative Proteomics by DIA-MS is not very informative. The outcome of the analysis would be more telling).
Response: We have revised section headers to clarify the content.
A considerable proportion of the DIA mass-spec proteomics results section is very technical. The paper describes a biological phenomenon rather than a technical mass-spec advance. Can these technical details be moved to the methods section?
Response: As this is one of the first published instances of using DIA-MS to Plasmodium, we want to keep this information in the main text to help our community adopt these approaches. While these details are highly technical, they are also some of the major advances of this project.
On the other hand, a bit more detail could be provided in the main text. For example, the age of the zygotes is never mentioned. This is important, please add this. The main manuscript text has 16 mentions of the word "many". As the authors are in possession of the data, please provide, if missing, (in parenthesis) the absolute numbers, maybe in an "x out y" format. Please clearly state the number of biological and/or technical replicates used for transcriptome and proteome analyses in the main text, figures and/or figure legends. How many protein coding genes are encoded in the P. yoelii genome?
Response: Several of these requested details are noted in the materials and methods. We have added this information to the main manuscript now as well. We have also revised the manuscript to replace some instances of “many” with specific numbers unless it adversely impacted the flow of the sentence to do so.
The authors claim that only zygotes (fertilized females) have surface-exposed Pys25 (a surface protein they use to affinity-purify zygotes) but not gametocytes. I could not find the experimental data for this in the paper. The cited reference #22 also does not appear to show this. In Figure 2C Pys25 is shown to be translated in gametocytes. In this context it may be important to note that in the related P. berghei the related protein P28 is expressed even in the absence of fertilization (Billker 2004; DOI: 10.1016/s0092-8674(04)00449-0). It may not be relevant whether translation requires fertilization, but the authors claim it affects trafficking of the Pys25 protein to the surface, so it needs to be shown. A reference to an infertile P. yoelii line would be great.
Response: We have corrected the reference supporting the surface exposure of p25 on zygotes. The observation by Billker and colleagues about Pbs28 is also of interest, but outside of the scope of this study as we did not investigate the fertilization event itself here.
It is highly commendable that all data is provided throughout the manuscript. For readability, may I suggest that the authors add labels to individual sheets within an excel file from A to Z, and do so also within the manuscript. That would really help; the most relevant data sets could then be identified quickly. For example, line 184 refers to 276 zygote proteins in which sheet of which table?
Response: While this labeling system would also be effective, we have provided a README tab for our files that quickly directs the reader to the relevant tab (as we do for our previous publications).
Section 176 onwards: here the authors combine P. falciparum and P. yoelii proteomics data. Please explain why you excluded any of the available P. berghei proteome data such as the male and female gametocyte proteome? The same question applies to 294 onwards.
Response*: We compared our datasets with those of Lasonder et al. NAR 2016 because that study was also focused on translational repression of mRNAs and provided both RNA-seq and proteomic datasets of female gametocytes (although not of zygotes). *
The comparative transcriptome-proteome analysis arrives at 198 translationally repressed mRNAs. Could the authors provide one or two alternatives using less stringent parameters? The list in P. falciparum and P. berghei is considerably larger (500+ and 700+).
Response: We could have reduced the stringency of our thresholds to arrive at a far larger number, but prefer to retain higher confidence in those we are scoring as translationally repressed and then released for translation. We provide all of the pertinent data in the supplemental files if readers would like to adjust these thresholds to see which additional mRNAs may also be regulated.
The turboID data is informative but somewhat speculative in regard to spatial rearrangements within these mRNPs. Figure 6 presents the RNA helicase to bind the 5' end of mRNAs that are associated with polyribosomes and I assume being translated. Is this association realistic? The RNA helicase DOZI homolog of yeast (Dhh1) is also involved in decapping. Response: We provide Figure 6 as our working model of how the reorganization of the DOZI/CITH/ALBA complex could occur based on available data from this study and others. Future studies are warranted to determine if DOZI remains associated with monosomes vs. polysomes, but current data indicate that DOZI can bind to eIF4E when translational repression is not imposed.
Specific comments:
title Is global the appropriate word? Some transcripts appear to be translated later.
Response: We believe it does apply appropriately to these data.
Line 30/32 Please re-phrase the sentence. There is: Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.
Response: We conclude that the sentence is correct as written, even in considering Sebastian et al. Cell Host & Microbe 2012.
30 Perhaps add ookinete that establishes infection rather than the zygote. For a general readership, a brief description of the sexual life cycle might be useful
Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.
32 DOZI/CITH/ALBA complex would require some explanation for a more general reader
Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.
36-37 I believe zygotes were collected 6 hours after fertilization. Does that qualify as soon after fertilization? Motile ookinetes are generated within 20 hours and motility can be seen before that.
Response: Yes, we think this qualifies as the process is not synchronous, but relies on when male gametes encounter and fuse with female gametes.
37 Essential functions for what?
Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.
39 Is the spatial arrangement of this mRNP known?
Response*: Some interactions of members of this complex were known (DOZI with eIF4E, ALBA4 with PABP1), but not the overall spatial arrangement. These findings are novel to this study. *
40 Can you briefly allude to the "recent, paradigm-shifting models of translational control"
Response: It is not possible to get into these nuances in the Abstract. This information is covered in the main text and the works that are cited.
44 Products = mRNA
Response: We have stated it as products because the maternal cell provides more than just mRNAs that are essential to further development post-fertilization.
45 Oocyte in metazoans ?
Response: Yes, this is the correct term. The context here is in higher eukaryotes.
60/62 Please re-phrase the sentence. There is: Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.
Response: We conclude that the sentence is correct as written, even in considering Sebastian et al. Cell Host & Microbe 2012.
81 PbDozi Plasmodium berghei DOZI
Response: We have added this clarifying text here as suggested.
84/85 Please rephrase and cite Nucleic Acids Res. 2008 Mar;36(4):1176-86. doi: 10.1093/nar/gkm1142. Epub 2007 Dec 23. and Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.
Response: As noted above for other comments, we hold that the current phrasing is accurate even when considering these important publications.
88 Please define the timepoints throughout this manuscript. What age are the zygotes? How many hours post-induction? Please define the time for ookinete development somewhere in the introduction
Response: The timepoint used for zygote collection is now included in the main text in addition to its previous inclusion in the Materials and Methods section. As we have not studied the ookinete stage here, we have opted to keep the introduction focused on the key details for this study.
104 Please add the age (in hours) of these zygotes from the time of starting the in vitro cultures. From the methods section it looks like 6 hours.
Response: The timepoint used for zygote collection is now included in the main text in addition to its previous inclusion in the Materials and Methods section.
103/105 I can find no evidence for P25 (Pys25) expression relying on fertilization in the cited paper (22). The SOM has no reference to Pys25 either. Please show data or reference published data that there is no translation and trafficking of Pys25 in unfertilized female gametes, ie those that are placed in ookinete medium. In this respect it may be important to note that unfertilized Plasmodium berghei females placed in ookinete medium translate P28, the P25 paralog (https://www.sciencedirect.com/science/article/pii/S0092867404004490?via%3Dihub)
Response: We have corrected the reference supporting the surface exposure of p25 on zygotes. The observation by Billker and colleagues about Pbs28 is also of interest, but outside of the scope of this study as we did not investigate the fertilization event itself here.
104 What cell line was used for the zygotes?
Response*: The PyApiAP2-O::GFP transgenic parasite line was used here. These details are included in the manuscript and supporting information. *
114 The number of transcripts detected in gametocytes is quite small compared to the twice as large proteomics dataset. See for example also Lasonder 2016 for P. falciparum detected transcripts: 4477 different sense transcripts were identified, 98% of which were shared between MG and FG.
Response: Yes, the number of mRNAs or proteins scored as detected differs based on thresholds applied. We prefer to err on the side of higher stringency as noted above.
117 Does the 194 up-in-gametocytes dataset include the 81 not found in zygotes?
Response: No, these 194 are detected in both datasets, but are more abundant in gametocytes than zygotes.
117 Could you indicate some of the genes in the plot?
Response: Several hits of special note are described in the text. We have opted to keep the figure clear and streamlined.
Fig1 How were the upregulated transcripts identified? 1647 are shown to be specific to zygotes in 1B, yet only 685 are shown in 1C to be upregulated. Do the transcripts found exclusively in zygotes not count? Are these transcripts likely the result of de novo transcription? How old are these zygotes when the libraries are made?
Response: The details of the RNA-seq processing are provided in the MakeFile, the supplementary tables, and the manuscript. The README tab provides descriptions of what processing occurred between sequential tabs. As noted above, zygotes were collected at 6 hours.
132 Many? How many? Please provide a precise number.
Response: These details are now in the revised manuscript.
134 Please explain why p28 would be differentially abundant in the zygote rather than the female gametocyte. That would require de novo transcription of this gene. If there is experimental evidence for the de novo transcription of p28 and other translationally repressed transcripts in the zygote please cite the references. Can you name a few more examples here? P25 for example, ap2-o, or anything published and experimentally validated. What about AP2-o and AP2-Z? Both are known to be translationally repressed.
Response: We state in the original manuscript that there is not a significantly different mRNA abundance of pys28.
139 Please define how many members of the IMC?
Response*: We have now replaced “many” with the number of IMC members we have detected, which is also shown in supporting tables. *
156 Can you provide a number of how many parasites were used in total or per run. And how many biological and technical replicates were analysed?
Response: These details are provided in the Materials and Methods.
169 The number of proteins detected in the gametocyte sample is twice the size of transcripts. IS this to be expected?
Response*: This reflects the sensitivity of the assays run for transcriptomics and proteomics. *
170 How many samples were analyzed? One gametocyte and one zygote sample?
Response: Yes, for the creation of the DIA-MS spectral library, a single biological replicate was used in addition to in silico library approaches. This information is provided in the next sentence.
176 Why did you not include P. berghei in the meta-analysis?
Response: We compared these results to all of the published Plasmodium proteomes in PlasmoDB.
184 Please refer to an excel table here.
Response: We have pointed to the relevant supporting files in this section.
184 145 proteins: do you mean orthologs in general or orthologs with a gene/protein annotation other than unknown function?
Response: We use the standard form of ortholog throughout the manuscript.
190 142 proteins: do they all have orthologs in P. falciparum?
Response: No, not all proteins in our dataset have unambiguous orthologues in P. falciparum, and this is accounted for in our data processing approaches.
Figure 2C P25 is not exclusive to zygotes here and also found in the gametocyte sample.
Response: That is correct. It is known that p25 is expressed in female gametocytes, but that the localization changes in the zygote.
190 shortlist
Response: The spelling of “short list” as two words is an appropriate American spelling of this term.
219 onwards Does the list of 198 transcripts exclusively arise from your RNAseq and proteomics comparison? Or does it include falciparum data as outline in section 176 onwards, ie the list of 276 proteins that only are detected in zygotes?
Response: Yes, this list of 198 mRNAs is derived from our datasets only using our defined thresholds. The details of this are provided in the manuscript.
224 Early zygote? At 6 hours do the parasites not start to transform, elongate?
Response: This process is not synchronous, as it is affected by the timing of gamete fusion.
225 >5-fold. Is this an arbitrary decision?
Response: This threshold has been used by our group and others in prior studies, and was partially informed by the behavior of previously characterized transcripts.
227 1417 mRNAs: they are from which dataset?
Response: These are from our datasets with P. yoelii, as described in the manuscript.
228/229 Please explain why DOZI and CITH are in the list of 198 repressed transcripts? They are present in the gametocyte. Are they upregulated>5 fold?
Response: Yes, they meet our criteria for this regulation, and in the manuscript we note that we believe that they are self-regulated and likely have continuing roles in early mosquito stage development.
259 ... as they are already translated in the gametocyte?
Response: Yes. Translational repression allows for the existence of some of the protein in the initial timepoint. This differs from translational silencing which does not.
295 Is this from the 198 TR list S4?
Response: No. Transcripts that remain repressed would not be in the list of 198, as the protein was not detected in zygotes.
294 onwards How many putatively falciparum transcripts are there? How many were identified in P. berghei? How many are common to all? A Venn diagram perhaps to compare the different studies
Response: There is substantial overlap between the species with respect to the presence of syntenic orthologues in this dataset. However, because we did not conduct experiments with P. falciparum or P. berghei here, we do not want to make claims that they are similarly regulated or potentially have a reader misinterpret a figure to that effect.
301 How many transcripts were found associated with Plasmodium berghei DOZI and/or CITH in female gametocytes? How many of those were abundantly detected as protein in zygotes, or had no difference in protein abundance between gametocytes and zygotes, or even greater abundance in female gametocytes?
Response: These details are now provided in the revised manuscript.
303/305 Please indicate the numbers of translationally repressed transcripts identified for P. falciparum and berghei.
Response: These data are provided in Supporting Information Table 4.
317/319 Please add the promoter used for tid-GFP
Response: We have now added this information to the Materials and Methods.
320 Please elaborate on the spatial organization of the DCA complex.
Response: This has not been previously characterized, and this entire section is dedicated to the experimental data and interpretations of how the DOZI/CITH/ALBA complex may be organized.
321/322 Have precise binding sites of DOZI and ALBA4 really been shown experimentally in the cited papers? In relation to 5' and 3' ends of the mRNA? Please cite Braks et al. paper.
Response: Yes. The association of DOZI with eIF4E and ALBA4 with PABP1 are established in the literature, in some cases by multiple independent laboratories. The Braks publication does not address the binding of these proteins, and thus is not cited.
323 What is the first generation BioID enzyme? BirA*
Response: Yes. The first generation enzyme is called BirA*
323 Please cite relevant Kyle Roux and Alice Ting for the original enzymes
Response: We have now added these citations to this sentence.
327 Could you show images of ALBA4::TurboID::GFP, DOZI::TurboID::GFP and cytosolic (free) TurboID? Perhaps stained with fluorescently labelled streptavidin and / or against GFP? In the gametocyte and zygote samples?
Response: We attempted to stain with monoclonal antibodies that are reactive against biotin and there was insufficient specificity, hence why such data is not included. We conclude that all of the other data that supports this approach suffices to demonstrate its rigor.
331 What is the age of these zygotes? Where they affinity purified?
Response: As throughout the manuscript, zygotes were collected at 6 hours. Details of experimental purifications are provided in the materials and methods.
Fig S4 Please indicate whether ALBA4 and DOZI were tagged endogenously
Response: Yes. The endogenous loci for both ALBA4 and DOZI were modified to include the C-terminal TurboID and GFP tags.
421/430 Please add a few references here
Response: We do not believe that specific references are warranted for these general statements.
429 translational repression?
Response: Yes. These statements set the stage for the use of translational repression.
445 966 proteins in gallinaceum? The zygote cultures in that study were 2-3 hours. How old were the cultures in your study?
Response: As throughout the manuscript, zygotes were collected at 6 hours.
481 Please explain / cite why repression is energetically costly.
Response: These details are provided in both the introduction and discussion sections. The energetic cost of translational repression is both the cost to produce the transcripts without immediately/fully utilizing it for translation, in addition to the energetic cost to impose the regulation.
501 Please add the time-point of RNA and protein sampling. How many hours into ookinete development? What is the time from cardiac puncture through FACS sampling of gametocytes.
Response: We have provided all of these details in the materials and methods for female gametocytes and zygotes. We did not look at ookinetes in this study.
711/713 Do you have any images that show the successful purification of zygotes away from gametocytes? Secondly, please provide a reference for the statement that unfertilized female gametocyte do not express surface exposed Pys25.
Response*: We do not have captured images of these zygotes, but confirmed them during collection using microscopy. The reference for surface exposure of Pbs25 is now provided earlier in the manuscript as well. *
711/716 Were parasites lysed and mechanically homogenised?
Response: We have provided all of these details in the materials and methods for female gametocytes and zygotes.
Figure 6 What is the evidence that DOZI stays associated with mRNA that is being translated? Rather than mRNA that is being decapped. Please add the references that unequivocally show that DOZI and ALBA4 bind to opposite ends of repressed mRNAs.
Response: This is our working model of these data. It is feasible that these complexes could form off of mRNA as well. Publications describing the interactions of DOZI with eIF4E and ALBA4 with PABP1 are provided in the manuscript. It is well established that eIF4E binds to the m7G cap of the 5’ end of mRNAs, and PABP1 binds to the poly(A) tail at the 3’ end of mRNAs.
Reviewer #1 (Significance (Required)):
The experiments in the manuscript are carefully conducted. Apart from a P. gallinaceum study from 2009 this is the first comprehensive analysis of the transcriptome and proteome of a Plasmodium zygote (developing ookinete) at 6 hours post-fertilization. The data are used to explore the temporal aspect of activation of translation during the first quarter of the 20-24 hour ookinete developmental period. The study will be of interest to the field, specifically those scientists working to understand translational control, ookinete development, and those developing intervention strategies to prevent mosquito infection and thus malaria transmission.
Response: We appreciate Reviewer 1’s extensive feedback and positive remarks about the significance of our study. We have revised our manuscript to reflect this constructive feedback.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Main findings
Taking a multi-omic approach, the authors provide quantitative evidence for translation repression of ~200 mRNAs in Plasmodium yoelii female gametocytes. These mRNAs are then translated, and proteins detected by 6 hours after activating gametocytes. They accomplish this by performing a comparative global analysis of the transcriptome and proteome between female gametocytes and early zygotes that provides an intresting resource. The authors also use proximity labelling of the DOZI/CITH/ALBA4 repression complex, and these data suggest the complex may disassemble in the zygote or change its composition.
Major points
Line 181-184: The authors state that there is no evidence of how the DCA complex selects specific mRNAs for translation repression. While the exact mechanisms have not been fully elucidated, Braks et al (2008, doi:10.1093/nar/gkm1142) suggested a role of the untranslated regions (UTRs) in translation repression of transcripts in Plasmodium berghei female gametocytes. They identified a uridine-rich 47-base element in the 5'UTR and or 3'UTR that was associated with translationally repressed transcripts and validated it experimentally. Considering this finding, I would recommend an amendment of the statement and to include the earlier work. I would also like to see additional analysis to check if this U-rich motif or other motifs are associated with the translationally repressed transcripts identified in the current study. The current study should be better powered to conduct such an analysis.
Response: We have now added a comment and citation in the revised text about this study in Lines 86-88. Understanding the full importance of this element is challenging, as the Plasmodium transcriptome is highly enriched in A’s and U’s due to the highly skewed A/T content of its genome. Perhaps for this reason, we did not see an association of this motif with the identified mRNAs.
The authors used zygotes that expressed GFP tagged AP2-O, however, there is no explanation of the significance of using this line.
Response: This line is described in the Materials and Methods and supporting information. It was used to provide further validation of the production of zygotes.
Minor points
In line 106-107, the authors refer to figure SI, this figure is about genomic locus and genotyping PCR for the PyApiAP2-O::GFP parasites but there is no intext description of why this specific line was used.
Response: We have provided this information in the revised manuscript.
Statement in line 122-124 "It is likely that....." should go into the discussion not results.
Response: We have placed this single sentence immediately after presenting these data here to aid reader comprehension.
Statement in line 171-175: "In addition to providing confirmatory...." Should be in the discussion not on the results.
Response: We view this sentence as a concluding remark of this section of data that also places this information in context for the reader.
In Fig. 4 A and B, could the colour scheme be changed so that the proteins that are not in both samples (and probably contain many unspecifically detected proteins) appear less prominent?
Response: We appreciate this suggestion and have adjusted these plots accordingly in the revised manuscript.
Reviewer #3 (Significance (Required)):
Why is the paper interesting. Translation repression of mRNA at a global level in the female gametocytes has been studied previously in rodent malaria parasites investigated, but prior to the current study, the release of mRNA from translation repression in the mosquito stages has only been demonstrated for specific transcripts. By characterizing and quantitating changes in protein abundance between macrogamete and zygote, coupled with transcriptomic analysis, the current work broadens our understanding of zygotic translation activation that is key to successful malaria parasite transmission to the mosquito.
This dataset provides a useful resource for the Plasmodium research community as it provides a more comprehensive view of how transcripts behave during the transitions from the mammalian host to the vector. It is one step in a broader endeavour towards finding genes crucial for parasite transmission that could be targeted for interventions.
How translational repression and derepression is regulated remains unknown, although some of the molecular players have been identified. This paper shows proximity labelling and expansion microscopy data of the ribonuclear protein complex thought to mediate repression. Although the specific mechanistic insights provided by the experiments shown here remain relatively limited, the work demonstrates interesting new avenues for how translational derepression in Plasmodium can be studied.
Response: We also appreciate Reviewer 3’s excellent feedback and positive remarks about the significance of our study. The revised manuscript addresses these comments, and we believe it is further strengthened because of it.
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Referee #2
Evidence, reproducibility and clarity
Main findings
Taking a multi-omic approach, the authors provide quantitative evidence for translation repression of ~200 mRNAs in Plasmodium yoelii female gametocytes. These mRNAs are then translated, and proteins detected by 6 hours after activating gametocytes. They accomplish this by performing a comparative global analysis of the transcriptome and proteome between female gametocytes and early zygotes that provides an intresting resource. The authors also use proximity labelling of the DOZI/CITH/ALBA4 repression complex, and these data suggest the complex may disassemble in the zygote or change its composition.
Major points
- Line 181-184: The authors state that there is no evidence of how the DCA complex selects specific mRNAs for translation repression. While the exact mechanisms have not been fully elucidated, Braks et al (2008, doi:10.1093/nar/gkm1142) suggested a role of the untranslated regions (UTRs) in translation repression of transcripts in Plasmodium berghei female gametocytes. They identified a uridine-rich 47-base element in the 5'UTR and or 3'UTR that was associated with translationally repressed transcripts and validated it experimentally. Considering this finding, I would recommend an amendment of the statement and to include the earlier work. I would also like to see additional analysis to check if this U-rich motif or other motifs are associated with the translationally repressed transcripts identified in the current study. The current study should be better powered to conduct such an analysis.
- The authors used zygotes that expressed GFP tagged AP2-O, however, there is no explanation of the significance of using this line.
Minor points
In line 106-107, the authors refer to figure SI, this figure is about genomic locus and genotyping PCR for the PyApiAP2-O::GFP parasites but there is no intext description of why this specific line was used.<br /> Statement in line 122-124 "It is likely that....." should go into the discussion not results. Statement in line 171-175: "In addition to providing confirmatory...." Should be in the discussion not on the results. In Fig. 4 A and B, could the colour scheme be changed so that the proteins that are not in both samples (and probably contain many unspecifically detected proteins) appear less prominent?
Significance
Why is the paper interesting.
Translation repression of mRNA at a global level in the female gametocytes has been studied previously in rodent malaria parasites investigated, but prior to the current study, the release of mRNA from translation repression in the mosquito stages has only been demonstrated for specific transcripts. By characterizing and quantitating changes in protein abundance between macrogamete and zygote, coupled with transcriptomic analysis, the current work broadens our understanding of zygotic translation activation that is key to successful malaria parasite transmission to the mosquito.
This dataset provides a useful resource for the Plasmodium research community as it provides a more comprehensive view of how transcripts behave during the transitions from the mammalian host to the vector. It is one step in a broader endeavour towards finding genes crucial for parasite transmission that could be targeted for interventions.
How translational repression and derepression is regulated remains unknown, although some of the molecular players have been identified. This paper shows proximity labelling and expansion microscopy data of the ribonuclear protein complex thought to mediate repression. Although the specific mechanistic insights provided by the experiments shown here remain relatively limited, the work demonstrates interesting new avenues for how translational derepression in Plasmodium can be studied.
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Referee #1
Evidence, reproducibility and clarity
This manuscript provides a detailed analysis of RNA and protein dynamics during transmission of the rodent malaria model P. yoelii from the mouse host to an in vitro ookinete culture setting (mimicking the mosquito midgut environment). This group and others have shown experimentally that a substantial number of mRNAs is stored in the female Plasmodium gametocyte, ready to be translated following initiation of ookinete development. The process is akin to maternal deposition of mRNA in oocytes of metazoans. With this manuscript the authors provide a significant contribution to the field of translational control in Plasmodium parasites as they explore the translational activation during the early hours of zygote-to-ookinete development. The paper presents RNAseq and mass-spec analyses of female gametocytes and for the first time for 6-hour zygotes (ie a fertilized female gamete); the zygote datasets are much improved and more comprehensive than the only other performed in 2008 in P. gallinaceum. Using comparative analyses of transcriptome and proteome data (including published datasets) the authors arrive at a list of 198 transcripts that are translationally repressed in the gametocyte and translated within 6 hours of fertilization in the zygote. Many of these mRNAs are known to be involved in zygote to ookinete transformation. BioID is finally used to explore changes in mRNP protein composition between the female gametocyte and the zygote.
The paper is generally well written. The authors present a lot of data (also in comparison with published data). Sometimes perhaps the main message could be simplified / streamlined in section titles (Quantitative Proteomics by DIA-MS is not very informative. The outcome of the analysis would be more telling).
A considerable proportion of the DIA mass-spec proteomics results section is very technical. The paper describes a biological phenomenon rather than a technical mass-spec advance. Can these technical details be moved to the methods section?
On the other hand, a bit more detail could be provided in the main text. For example, the age of the zygotes is never mentioned. This is important, please add this. The main manuscript text has 16 mentions of the word "many". As the authors are in possession of the data, please provide, if missing, (in parenthesis) the absolute numbers, maybe in an "x out y" format. Please clearly state the number of biological and/or technical replicates used for transcriptome and proteome analyses in the main text, figures and/or figure legends. How many protein coding genes are encoded in the P. yoelii genome?
The authors claim that only zygotes (fertilized females) have surface-exposed Pys25 (a surface protein they sue to affinity-purify zygotes) but not gametocytes. I could not find the experimental data for this in the paper. The cited reference #22 also does not appear to show this. In Figure 2C Pys25 is shown to be translated in gametocytes. In this context it may be important to note that in the related P. berghei the related protein P28 is expressed even in the absence of fertilization (Billker 2004; DOI: 10.1016/s0092-8674(04)00449-0). It may not be relevant whether translation requires fertilization, but the authors claim it affects trafficking of the Pys25 protein to the surface, so it needs to be shown. A reference to an infertile P. yoelii line would be great.
It is highly commendable that all data is provided throughout the manuscript. For readability, may I suggest that the authors add labels to individual sheets within an excel file from A to Z, and do so also within the manuscript. That would really help; the most relevant data sets could then be identified quickly. For example, line 184 refers to 276 zygote proteins in which sheet of which table?
Section 176 onwards: here the authors combine P. falciparum and P. yoelii proteomics data. Please explain why you excluded any of the available P. berghei proteome data such as the male and female gametocyte proteome? The same question applies to 294 onwards.
The comparative transcriptome-proteome analysis arrives at 198 translationally repressed mRNAs. Could the authors provide one or two alternatives using less stringent parameters? The list in P. falciparum and P. berghei is considerably larger (500+ and 700+).
The turboID data is informative but somewhat speculative in regard to spatial rearrangements within these mRNPs. Figure 6 presents the RNA helicase to bind the 5' end of mRNAs that are associated with polyribosomes and I assume being translated. Is this association realistic? The RNA helicase DOZI homolog of yeast (Dhh1) is also involved in decapping.
Specific comments:
title Is global the appropriate word? Some transcripts appear to be translated later.
Line 30/32 Please re-phrase the sentence. There is: Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.
30 Perhaps add ookinete that establishes infection rather than the zygote. For a general readership, a brief description of the sexual life cycle might be useful
32 DOZI/CITH/ALBA complex would require some explanation for a more general reader
36-37 I believe zygotes were collected 6 hours after fertilization. Does that qualify as soon after fertilization? Motile ookinetes are generated within 20 hours and motility can be seen before that.
37 Essential functions for what?
39 Is the spatial arrangement of this mRNP known?
40 Can you briefly allude to the "recent, paradigm-shifting models of translational control"
44 Products = mRNA
45 Oocyte in metazoans ?
60/62 Please re-phrase the sentence. There is: Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.
81 PbDozi Plasmodium berghei DOZI
84/85 Please rephrase and cite Nucleic Acids Res. 2008 Mar;36(4):1176-86. doi: 10.1093/nar/gkm1142. Epub 2007 Dec 23. and Cell Host Microbe 2012 Jul 19;12(1):9-19. doi: 10.1016/j.chom.2012.05.014.
88 Please define the timepoints throughout this manuscript. What age are the zygotes? How many hours post-induction? Please define the time for ookinete development somewhere in the introduction
104 Please add the age (in hours) of these zygotes from the time of starting the in vitro cultures. From the methods section it looks like 6 hours.
103/105 I can find no evidence for P25 (Pys25) expression relying on fertilization in the cited paper (22). The SOM has no reference to Pys25 either. Please show data or reference published data that there is no translation and trafficking of Pys25 in unfertilized female gametes, ie those that are placed in ookinete medium. In this respect it may be important to note that unfertilized Plasmodium berghei females placed in ookinete medium translate P28, the P25 paralog (https://www.sciencedirect.com/science/article/pii/S0092867404004490?via%3Dihub)
104 What cell line was used for the zygotes?
114 The number of transcripts detected in gametocytes is quite small compared to the twice as large proteomics dataset. See for example also Lasonder 2016 for P. falciparum detected transcripts: 4477 different sense transcripts were identified, 98% of which were shared between MG and FG.
117 Does the 194 up-in-gametocytes dataset include the 81 not found in zygotes?
117 Could you indicate some of the genes in the plot?
Fig1 How were the upregulated transcripts identified? 1647 are shown to be specific to zygotes in 1B, yet only 685 are shown in 1C to be upregulated. Do the transcripts found exclusively in zygotes not count? Are these transcripts likely the result of de novo transcription? How old are these zygotes when the libraries are made?
132 Many? How many? Please provide a precise number.
134 Please explain why p28 would be differentially abundant in the zygote rather than the female gametocyte. That would require de novo transcription of this gene. If there is experimental evidence for the de novo transcription of p28 and other translationally repressed transcripts in the zygote please cite the references. Can you name a few more examples here? P25 for example, ap2-o, or anything published and experimentally validated. What about AP2-o and AP2-Z? Both are known to be translationally repressed.
139 Please define how many members of the IMC?
156 Can you provide a number of how many parasites were used in total or per run. And how many biological and technical replicates were analysed?
169 The number of proteins detected in the gametocyte sample is twice the size of transcripts. IS this to be expected?
170 How many samples were analyzed? One gametocyte and one zygote sample?
176 Why did you not include P. berghei in the meta-analysis?
184 Please refer to an excel table here.
184 145 proteins: do you mean orthologs in general or orthologs with a gene/protein annotation other than unknown function?
190 142 proteins: do they all have orthologs in P. falciparum?
Figure 2C P25 is not exclusive to zygotes here and also found in the gametocyte sample.
190 shortlist
219 onwards Does the list of 198 transcripts exclusively arise from your RNAseq and proteomics comparison? Or does it include falciparum data as outline in section 176 onwards, ie the list of 276 proteins that only are detected in zygotes?
224 Early zygote? At 6 hours do the parasites not start to transform, elongate?
225 >5-fold. Is this an arbitrary decision?
227 1417 mRNAs: they are from which dataset?
228/229 Please explain why DOZI and CITH are in the list of 198 repressed transcripts? They are present in the gametocyte. Are they upregulated>5 fold?
259 ... as they are already translated in the gametocyte?
295 Is this from the 198 TR list S4?
294 onwards How many putatively falciparum transcripts are there? How many were identified in P. berghei? How many are common to all? A Venn diagram perhaps to compare the different studies
301 How many transcripts were found associated with Plasmodium berghei DOZI and/or CITH in female gametocytes? How many of those were abundantly detected as protein in zygotes, or had no difference in protein abundance between gametocytes and zygotes, or even greater abundance in female gametocytes?
303/305 Please indicate the numbers of translationally repressed transcripts identified for P. falciparum and berghei.
317/319 Please add the promoter used for tid-GFP
320 Please elaborate on the spatial organization of the DCA complex.
321/322 Have precise binding sites of DOZI and ALBA4 really been shown experimentally in the cited papers? In relation to 5' and 3' ends of the mRNA? Please cite Braks et al. paper.
323 What is the first generation BioID enzyme? BirA*
323 Please cite relevant Kyle Roux and Alice Ting for the original enzymes
327 Could you show images of ALBA4::TurboID::GFP, DOZI::TurboID::GFP and cytosolic (free) TurboID? Perhaps stained with fluorescently labelled streptavidin and / or against GFP? In the gametocyte and zygote samples?
331 What is the age of these zygotes? Where they affinity purified?
Fig S4 Please indicate whether ALBA4 and DOZI were tagged endogenously
421/430 Please add a few references here
429 translational repression?
445 966 proteins in gallinaceum? The zygote cultures in that study were 2-3 hours. How old were the cultures in your study?
481 Please explain / cite why repression is energetically costly.
501 Please add the time-point of RNA and protein sampling. How many hours into ookinete development? What is the time from cardiac puncture through FACS sampling of gametocytes.
711/713 Do you have any images that show the successful purification of zygotes away from gametocytes? Secondly, please provide a reference for the statement that unfertilized female gametocyte do not express surface exposed Pys25.
711/716 Were parasites lysed and mechanically homogenised?
Figure 6 What is the evidence that DOZI stays associated with mRNA that is being translated? Rather than mRNA that is being decapped. Please add the references that unequivocally show that DOZI and ALBA4 bind to opposite ends of repressed mRNAs.
Significance
The experiments in the manuscript are carefully conducted. Apart from a P. gallinaceum study from 2009 this is the first comprehensive analysis of the transcriptome and proteome of a Plasmodium zygote (developing ookinete) at 6 hours post-fertilization. The data are used to explore the temporal aspect of activation of translation during the first quarter of the 20-24 hour ookinete developmental period. The study will be of interest to the field, specifically those scientists working to understand translational control, ookinete development, and those developing intervention strategies to prevent mosquito infection and thus malaria transmission.
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Reply to the reviewers
1. General Statements [optional]
We thank all the reviewers for their constructive and critical comments. We provide a point-by-point response to the reviewers' comments, as detailed below. By responding to them, we believe that our revised manuscript will significantly improve so that it will be of interest for researchers in the field of cell biology, signaling pathways, physiology and nutrition.
Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity):
Summary: The manuscript by Yusuke Toyoda and co-workers describes that the phosphorylation of the a-arrestin Aly3 downstream of TORC2 and GAD8 (AKT) negatively regulates endocytosis of the hexose transporter Ght5 in S.pombe under glucose limiting growth conditions.
To arrive at these conclusions, the researchers define a set of redundant c-terminal phosphorylation sites in Aly3 that are downstream by GAD8. Phosphorylation of these sites reduces Ght5 ubiquitination and endocytosis. For ubiquitination, Aly3 interacts with the ubiquitin ligases Pub1/3.
We thank the reviewer for his/her time and reporting advantages and issues of this study.
Major points:
Figure 3B: it would be interesting to compare Aly3 migration pattern (and hence potential phosphorylation) under glucose replete or limiting growth conditions. Can the authors provide direct evidence that Aly3 phosphorylation changes in response to glucose availability? Also please explain the 'smear' in lanes aly3(4th Ala), aly3(4th Ala, A584S), aly3(4th Ala, A586T).
While it is an interesting possibility that the Aly3 migration pattern changes in response to glucose concentrations in medium, we think that this is unlikely and that examining this possibility is beyond the scope of this study. Because a phospho-proteomics study reported by Dr. Paul Nurse's lab showed Tor1-dependent phosphorylation of Aly3 at S584 under high glucose (2%) conditions (Mak et al, EMBO J, 2021), the Aly3 phosphorylation (migration) pattern is likely to be constant regardless of glucose conditions. Glucose conditions affect the mRNA and protein levels of Ght5, but supposedly not its endocytosis to vacuoles (Saitoh et al, Mol Biol Cell, 2015; Toyoda et al, J Cell Sci, 2021).
As for the smear in Aly3(4th A), Aly3(4th A;A584S), Aly3(4th A; A586T), we suspect that some posttranslational modification occurs on these mutant Aly3 proteins, but the identity of the modification is unclear. We did not mention the smear signals in the original manuscript, because the presence or absence of the smear did not necessarily correlate with cell proliferation in low glucose and thus vacuolar localization of Ght5, which is the main topic of this study. In the revised manuscript, we will mention this point more clearly.
Figure 4: Ght5 localization should be analyzed + / - thiamine and in media with different glucose levels. Also, a co-localization with a vacuolar marker (FM4-64) would be nice (but not necessary). Ideally, the authors should add WB analysis of Ght5 turnover to complement the imaging data. Also, would it be possible to measure directly the effects on glucose uptake (using eg: 2-NBDG).
In this revision, we plan to observe Ght5 localization under the conditions indicated by the reviewer (+/- thiamine and high/low glucose levels) to unambiguously show that the vacuolar localization of Ght5 occurs in a manner dependent solely on expression of the mutant Aly3 protein.
We thank the reviewer for the suggestion of co-staining with FM4-64. Indeed, because we previously reported that the cytoplasmic Ght5 signals were surrounded by FM4-64 signals in the TORC2-deficient tor1Δ mutant cells (Toyoda et al, J Cell Sci, 2021), the cytoplasmic Ght5-GFP signals in Figure 4 are very likely to co-localize with vacuoles. We will modify the text to clarify this point.
As suggested, we plan to add Western blot analysis of Ght5 turnover in Aly3-expressing cells, to complement the imaging data (Figure 4) in the revised manuscript. Persistent appearance of GFP in Western blot would be a good support for vacuolar transport of Ght5-GFP.
While regulation of glucose uptake is an important issue, measurement of Ght5-dependent glucose uptake using 2-NBDG was very difficult in our hands. Another reviewer (Reviewer #2) also mentioned the difficulty of this measurement in the Referees cross-commenting section.
Figure 5: Given the localization of Ght5 shown in Figure 4, I'm surprised that it is possible in to detect full length Ght5, and its ubiquitination in the phospho-mutants of Aly3. I expected that the majority of Ght5 would be constitutively degraded, and that one would need to prevent endocytosis and/or vacuolar degradation to detect full length Ght5 and ubiquitination. Please explain the discrepancy. Also it seems that the quantification in B was performed on a single experiment.
As the aim of Figure 5 is to compare the ubiquitinated species of Ght5 among the samples expressing different species of Aly3, the loading amount of each sample was adjusted so that the abundance of immunoprecipitated Ght5 is same across them. Therefore, as the reviewer points out, before the adjustment, abundance of the full-length Ght5 might be different in these samples. In the revised manuscript, we will add explanation on this point; why the anti-GFP blot of Figure 5A has the similar intensities in those samples.
In the revised manuscript, we will add two additional replicates of the same experiment as Figure 5 in Supplementary material to show reproducibility of the result.
Figure 6: Which PPxY motif of Aly3 is used for interaction with Pub1/3 and does their interaction depend on (de)phosphorylation?
In the revised manuscript, we will discuss that "both PY motifs of Aly3 might be required for full interaction with Pub1/3," by citing the following published knowledge:
(a) Mutation of both PPxY motif of budding yeast Rod1 and Rog3 (Aly3 homologs) diminished their interaction with the ubiquitin ligase Rsp5 (Andoh et al, FEBS Lett, 2002).
(b) Mutating either one of two PPxY motifs of budding yeast Cvs7/Art1 greatly decreased interaction with WW domain, and mutating both abolished the interaction (Lin et al, Cell, 2008).
Our preliminary results indicated that Pub3 interacted with Aly3, Aly3(4th A) and phospho-mimetic Aly3(4th D), and thus suggested that the Aly3-Pub1/3 interaction does not depend on the phosphorylation status of Aly3. Consistently, budding yeast Rod1 reportedly interacts with Rsp5 regardless of its phosphorylation status (e.g. Becuwe et al, J Cell Biol, 2012). While we have partially mentioned this point in the original manuscript (L499-503), we will discuss this point more clearly in the revised manuscript.
Reviewer #1 (Significance):
The results are well presented and clear cut (with few exceptions, please see major points). They provide further evidence that metabolic cues instruct the phosphorylation of a-arrestins. Phosphorylation then negatively regulates a-arrestin function in selective endocytosis and is essential to adjust nutrient uptake across the plasma membrane to the given biological context.
We thank the reviewer for finding significance of our study. We believe that adding new results of the requested experiments and responding to the raised comments will clarify the significance of our revised manuscript.
Reviewer #2 (Evidence, reproducibility and clarity):
**Summary / background. This paper focuses on the regulation of endocytosis of the hexose transporter, Ght5, in S. pombe by nutrient limitation through the arrestin-like protein Aly3. Ght5 is induced when glucose is limiting and is required for growth and proliferation in these conditions. ght5+ encodes the only high-affinity glc transporter from fission yeast. ght5+ is induced in low glucose conditions at the transcriptional level and is translocated to the plasma membrane to allow glc import. Ght5 is targeted to the vacuole in conditions of N limitation. Mutations in the TORC2 pathway lead to the same process, thus preventing growth on low glucose medium, as shown in the gad8ts mutant, mutated for the Gad8 kinase acting downstream of TORC2. Previously, the authors demonstrated that the vacuolar delivery of Ght5 in the gad8ts mutant is suppressed by mutation of the arrestin-like protein Aly3. Arrestin-like proteins are in charge of recognising and ubiquitinating plasma membrane proteins to direct their vacuolar targeting by the endocytosis pathway. This suggested that Aly3 is hyperactive in TORC2 mutants, and accordingly, Ght5 ubiquitination was increased in gad8ts.
**Overall statement This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments below.
We thank the reviewer for pointing both advantages and issues of our manuscript.
We admit that phosphorylation of Aly3 was not experimentally shown in our manuscript, although its phosphorylation has already been shown in phospho-proteomic studies by other groups. For this issue, we plan to add an experiment and modify the text, as explained below.
The other major issue raised by this reviewer is that detection of Ght5 ubiquitination by immunoprecipitation in a native condition cannot be conclusive. Although we noticed that many studies perform affinity purification after denaturing and precipitating proteins with TCA or acetone to detect ubiquitination of the affinity-purified protein (e.g. Lin et al, Cell, 2008), we disagree with this opinion of the reviewer #2. In a review article describing methods to study ubiquitination by immunoblotting (Emmerich and Cohen, Biochem Biophys Res Comm, 2015), affinity purification of the protein of interest in a native condition is mentioned as one major choice. Moreover, a denaturing condition was not applicable to detect ubiquitinated Ght5 because the Ght5 protein that is once denatured and precipitated with TCA cannot be re-solubilized for immune-purification and -blotting. As the reviewer points out, a pitfall of detection of ubiquitinated Ght5 in a native condition is the presence of co-immunoprecipitated proteins. In our previous study (Toyoda et al, J Cell Sci, 2021), we purified GFP-tagged Ght5 and showed that a 110 kDa band detected in an anti-Ub immunoblot was also recognized by an anti-GFP antibody, confirming that the detected 110 kDa band corresponded to an ubiquitinated species of Ght5, but not a co-immunoprecipitated protein. Similarly, in the revised manuscript, we will add a panel of high-contrast (over-exposed) anti-GFP immunoblot, in which the indicated 110 kDa band was clearly detected by an anti-GFP antibody, in Figure 5A.
We appreciate these issues raised by the reviewer #2. By responding to them, we believe that conclusions of our study will be more rigorous and undoubtful in the revised manuscript.
**Major statements and criticism.
*Fig 1. Based on the hypothesis that TORC2-mediated phosphorylation regulate Ght5 endocytosis, the authors first considered a possible phosphorylation of Ght5. They mutagenised 11 **possible** phosphorylation sites on the Ct of Ght5, but none affected the growth on low glucose in the absence of thiamine, suggesting that they don't contribute to the observed TORC2-mediated regulation. However, I disagree with the statement that "phosphorylation of Ght5 is dispensable for cell proliferation in low glucose", given that the authors do not show 1- that Ght5 is phosphorylated and 2-that this is abolished by these mutations. They should either provide data on this or tone down and say that these residues are not involved in the regulation, without implying phosphorylation which is not proven.
Although we did not experimentally test whether these 11 residues of Ght5 was phosphorylated in our hand, these residues have been shown to be phosphorylated in phospho-proteomics studies by other groups (Kettenbach et al, Mol Cell Proteomics, 2015; Swaffer et al, Cell Rep, 2018; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021; Mak et al, EMBO J, 2021). In the revised manuscript, we plan to be more precise by replacing this conclusion with the following statement: "11 Ser/Thr residues of Ght5, which are reportedly phosphorylated, are not essential for cell proliferation in low glucose."
In the presence of Thiamine (Supp fig 1), it seems that the ST/A mutant grows better in low glucose, and this is not explained nor commented. Since the transporter is not expressed, could the authors provide an explanation to this? If the promoter is leaky and some ght5-ST/A is expressed, it may be more stable and allow better growth than the WT, which would tend to indicate that impairing phosphorylation prevents endocytosis (which is classical for many transporters, see the body of work on CK1-mediated phosphorylation of transporters). Have the authors tried to decrease glc concentration lower than 0.14% in the absence of thiamine to see if this also true when the transporters is strongly expressed? (OPTIONAL)
Improved growth of Ght5(ST11A)-expressing cells in the presence of thiamine was mentioned in the legend of Supplementary Figure 1A. In the revised manuscript, we will mention this observation also in the main text for better description of the results.
Adding thiamine to medium does not completely shut off transcription from the nmt1 promoter but allows some transcription, as previously reported (Maundrell, J Biol Chem, 1990; Forsburg, Nuc Acid Res, 1993). In the revised manuscript, we will mention this "leakiness" of the nmt1 promoter and, by citing the suggested studies, will discuss a possibility that the ST11A mutations might prevent endocytosis of Ght5 and consequently promote cell proliferation in low glucose conditions.
We found that, in the absence of thiamine, cells expressing ght5+ and ght5(ST11A) proliferated to the comparable extent on medium containing 0.08% glucose. This result will be added to the revised manuscript.
*Fig 2. The authors then follow the hypothesis that TORC2 exerts its Ght5-dependent regulation through the phosphorylation of Aly3. They mutagenised 18 **possible** phosphorylation sites on Aly3. This led to a strong defect in growth in low-glc medium. Mutation of the possible Gad8 site (S460) did not recapitulate this phenotype, suggesting that it is not sufficient, however, mutations of 4 ST residues in a CT cluster (582-586) mimicked the full 18ST/A mutation, suggesting these are the important residues for Ght5 endocytosis.
We thank the reviewer for appreciating the results in Fig. 2. As we explain below, we plan to perform an additional experiment to show that the Aly3 C-terminus is phosphorylated. With this result, our model will gain another experimental support.
*Fig 3A. Further dissection did not allow to pinpoint this regulation to a specific residue, beyond the dispensability of the T586 residue. Fig 3B. The authors look at the effects of mutation of Aly3 on these sites at the protein level. They had to develop an antibody because HA-epitope tagging did not lead to a functional protein (Supp fig 2). Whereas I agree that the mutations causing a phenotype lead to a change in the migration pattern, I disagree with the statement that "This observation indicated that slower migrating bands were phosphorylated species of Aly3" (p.9 l.271). First, lack of phosphorylation usually causes a slower mobility on gel, which is not clear to spot here. Second, a smear appears on top of the mutated proteins (eg. 4th Ala) which is possibly caused by another modification. There are many precedents in the literature about arrestins being ubiquitinated when they are not phosphorylated (see the work on Bul1, Rod1, Csr2 in baker's yeast from various labs). My gut feeling is that lack of phosphorylation unleashes Aly3 ubiquitination leading to change in pattern. All in all, it is impossible to state about the phosphorylation of a protein without addressing its phosphorylation properly by phosphatase treatment + change in migration, or MS/MS. Thus, whereas the data looks promising, this hypothesis that Aly3 is phosphorylated at the indicated sites is not properly demonstrated.
We disagree with the reviewer's opinion that a lack of phosphorylation usually causes slower mobility on gel. There are many examples in which phosphorylation causes slower mobility on gel, including budding yeast Rod1 (Alvaro et al, Genetics, 2016), and mammalian TXNIP (Wu et al, Mol Cell, 2013). In the revised manuscript, we will cite these reports to support our interpretation that the slower migrating bands are likely phosphorylated species of Aly3 (L270-271).
Smear-like signals in Aly3(4th Ala), Aly3(4th A;A584S) and Aly3(4th A;A586T) might result from some modification, but identity of the modification is unknown. As the reviewer #2 mentioned, phosphorylation on Aly3 might negatively regulate another modification. The precedent studies revealed that budding yeast Rod1 and Rog3 arrestins tend to be ubiquitinated in snf1/AMPK-deficient cells (Becuwe et al, J Cell Biol, 2012; O'Donnell et al, Mol Cell Biol, 2015), and that Bul1 arrestin is dephosphorylated and ubiquitinated in budding yeast cells deficient in Npr1 kinase (Merhi and Andre, Mol Cell Biol, 2012). Also, budding yeast Csr2 arrestin is deubiquitinated and phosphorylated upon glucose replenishment, while non-phosphorylated Csr2 is ubiquitinated and activated by Rsp5 (Hovsepian et al, J Cell Biol, 2012). While the smear-like signals are interesting, we noticed that the smear-like signals did not necessarily correlate with cell proliferation defects in low glucose. We therefore think that clarifying the identity of the smear-like signals is beyond the scope of this study. We will discuss the smear-like signals only briefly in the revised manuscript, and would address this issue in our future work, hopefully.
While the 4 S/T residues at the C-terminus of Aly3 as well as the other 14 S/T residues have been already shown to be phosphorylated in the precedent studies (Kettenbach et al, Mol Cell Proteomics, 2015; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021), we will confirm that the slower migrating Aly3 is indeed phosphorylated by phosphatase treatment in the revised manuscript. This planned experiment will further strengthen our study and support our conclusion and model.
*Fig 4. The authors now look at the functional consequences of these mutations on ALy3 on Ght5 localisation. The data clearly shows that mutation of the 4 identified S/T residues (Aly3-4th A) causes aberrant localisation of the transporter to the vacuole, likely to cause the observed growth defect on low glucose. There is a nice correlation between the vacuolar localisation and growth in low-glucose for the various aly3 mutants. (A final proof could be to express this in the context of an endocytic mutant, which should restore membrane localisation and suppress the aly3-4thA phenotype - OPTIONAL). However, I still disagree with the statement that "These results indicate that phosphorylation of Aly3 at the C-terminal 582nd, 584th, and/or 585th serine residues is required for cell-surface localization of Ght5." given that phosphorylation was not properly demonstrated.
While phosphorylation of the 582nd, 584th and/or 585th serine residues of Aly3 is not experimentally demonstrated in our hands, they have been shown to be phosphorylated in phospho-proteomics studies by other groups (Kettenbach et al, Mol Cell Proteomics, 2015; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021; Mak et al, EMBO J, 2021). Among them, the 584th serine residue (S584) was reported to be phosphorylated in a TORC2-dependent manner (Mak et al, EMBO J, 2021), consistent with our model. To explicitly demonstrate that S584 is phosphorylated, we plan to make a strain expressing a mutant Aly3 protein in which all the possible phosphorylation sites except S584 are replaced with alanine, namely Aly3(ST17A;S584). Hopefully, we can properly show the phosphorylation of S584 by measuring the mobility of the Aly3(ST17A;S584) on gel with/without phosphatase treatment or gad8 mutation.
We thank the reviewer for suggestion of the experiment using an endocytic mutant. Previously we reported that vacuolar localization of Ght5 in gad8 mutant cells was suppressed by mutations in not only aly3 but also genes encoding ESCRT complexes (Toyoda et al, J Cell Sci, 2021). We therefore think that in cells expressing Aly3(ST18A) or Aly3(4th Ala), Ght5 is subject to endocytosis and ensuing selective transport to vacuoles via endosome-localized ESCRT complexes. We will discuss this point in the revised manuscript.
*Fig 5. Here, the authors question the role of Aly3 mutations on Ght5 ubiquitination. They immunoprecipitate Ght5 and address its ubiquitination status in various Aly3 mutants. The data is encouraging for a role in Aly3 phosphorylation (?) in the negative control of Ght5 ubiquitination. My main problem with this experiment is that it seems that Ght5 immunoprecipitations were made in non-denaturing conditions, which leads to the question of what is the anti-ubiquitin revealing here (Ght5 or a co-immunoprecipitated protein, for example Aly3 itself, or the Pub ligases, or an unknown protein). It seems that this protocol was previously used in their previous paper, but I stand by my conclusion that ubiquitination of a given protein can only be looked in denaturing conditions. The experiments should be repeated in buffers classical for the study of protein ubiquitination to be able to conclude unambiguously that we are looking at Ght5 ubiquitination itself, especially in the absence of a non-ubiquitinable form of Ght5 as a negative control. Could the authors comment on the fact that S-A or S-D mutations display the same phenotype regarding the possible Ght5 ubiquitination?
As mentioned above, immunoprecipitation of Ght5 in denaturating conditions is not feasible. Ght5 can be affinity-purified only in a non-denaturing condition. In addition, affinity purification in a native condition is considered as a major choice to examine its ubiquitination according to a literature by Emmerich and Cohen (Emmerich and Cohen, Biochem Biophys Res Comm, 2015). A drawback of native condition is, as the reviewer points out, that the affinity-purified fraction might include non-bait (non-Ght5) proteins. The 110 kDa band indicated by an arrow in Fig. 5A was confirmed to be Ght5, not a non-bait protein, as a band at the identical position was detected in the immunoblot with anti-GFP antibody. Because this band in the anti-GFP immunoblot was too faint to be visible in Fig. 5A of the original manuscript, we will add an additional panel showing the contrast-enhanced anti-GFP immunoblot in which the 110 kDa band is clearly visible.
As for the result that "S-A or S-D mutations display the same phenotype regarding the possible Ght5 ubiquitination," we are afraid that the reviewer #2 misunderstood the labels of the samples. We apologize for confusing notational system of the sample name. Full description of samples is as follows; In Aly3(4th A), all of S582, S584, S585 and T586 are replaced with A; In Aly3(4th A;A584S), S582, S585 and T586 are replaced with A, whereas S584 remains intact; In Aly3(4th A;A584D), S582, S585 and T586 are replaced with A, and S584 is replaced with phospho-mimetic D. Because cells expressing Aly3(4th A;A584S) and Aly3(4th A;A584D) exhibited similarly low levels of Ght5 ubiquitination, we speculated that phosphorylation at S584 of Aly3 negatively regulates ubiquitination of Ght5.
In the revised manuscript, we plan to add a table showing amino acid sequence of each species of Aly3 (just like Figure 3A) to avoid confusion.
*Fig 6. The authors want to document the model whereby Aly3 may interact with some of the Nedd4 ligases (Pub1/2/3) to mediate its Ght5-ubiquitination function. They actually use the Aly3-4thA mutant, it should have been better with the WT protein. But the results indicate a clear interaction with at least Pub1 and Pub3. By the way, are the Pub1/2/3 fusions functional? Nedd4 proteins are notoriously affected in their function by C-terminal tagging and are usually tagged at their N-terminus (See Dunn et al. J Cell Biol 2004).
We plan to test whether Pub1-myc is functional by comparing proliferation of the Pub1-myc-expressing strain and pub1Δ strain, as pub1Δ cells reportedly show proliferation defects at a high temperature (Tamai and Shimoda, J Cell Sci, 2002). As deletion of pub2 or pub3 reportedly exhibited no obvious defects (Tamai and Shimoda, J Cell Sci, 2002; Hayles et al, Open Biol, 2013), it is not easy to assess functionality of the myc-tagged genes.
Please note that C-terminally tagged Pub1/2/3 proteins have been widely used in studies with fission yeast. Both Pub1-HA and non-tagged Pub1 were reported to be ubiquitinated (Nefsky and Beach, EMBO J, 1996; Strachan et al, J Cell Sci, 2023). Pub1-GFP, which complemented the high temperature sensitivity of pub1Δ, localized to cell surface and cytoplasmic bodies (Tamai and Shimoda, J Cell Sci, 2002). Pub2-GFP, overexpression of which arrested cell growth just like overexpression of non-tagged Pub2, localized to cell surface, and consistently Pub2-HA was detected in membrane-enriched pellet fractions after ultracentrifugation (Tamai and Shimoda, J Cell Sci, 2002). They also reported ubiquitin conjugation of the HECT domain of Pub2 fused with myc epitope at its C-terminus. Pub3-GFP localized to cell surface (Matsuyama et al, Nat Biotech, 2006).
Regardless of functionality of the myc-tagged Pub1/2/3, we believe that results of this experiment (Figure 6) support our model, because the aim of this experiment, which is to identify the HECT-type and WW-domain containing ubiquitin ligase(s) that interact with Aly3, is irrelevant to functionality of the myc-tagged Pub proteins.
*Fig 7. The authors want to provide genetic interaction between the Pub ligases and the growth defects in low glc due to alterations in Ght5 trafficking. It is unclear how the gad8ts pub1∆ mutant was generated since it doesn't seem to grow on regular glc concentration (Supp fig 5), could the authors provide some information about this? It is also not clear whether it can be stated thatches mutant is "more sensitive" to glc depletion because of the low level of growth to begin with (even at 3%). Altogether, the data show that deletion of pub3+ is able to suppress the growth defect of the gad8ts mutant on low glc medium, suggesting it is the relevant ligase for Ght5 endocytosis. This is confirmed by microscopy observations of Ght5 localisation. However, I would again tone down the main conclusion, which I feel is far-reaching: "Combined with physical interaction data, these results strongly suggest that Aly3 recruits Pub3, but not Pub2, for ubiquitination of Ght5." Work on Rsp5 in baker's yeast has shown that Rsp5 function goes beyond cargo ubiquitination, including ubiquitination of arrestins (which is often required for their function as mentioned in the introduction) or other endocytic proteins (epsins, amphyphysin etc). I agree that the data are compatible with this model but there are other possible explanations. Anything that would block endocytosis would supposedly suppress the gad8ts phenotype.
gad8ts pub1Δ was produced at 26 {degree sign}C, a permissive temperature of the gad8ts mutant. While this is described in the Methods section of the original manuscript, we will mention this more clearly in the Results section of the revised manuscript.
We did not conclude low glucose sensitivity of gad8ts pub1Δ cells in the indicated part (L376-377). Rather, we compared proliferation of gad8ts single mutant and pub1Δ single mutant cells in low glucose, and we found that the pub1Δ single mutant exhibited the higher sensitivity. In the revised manuscript we will correct the text to clarify that we compared proliferation of two single mutants (but not gad8ts pub1Δ mutant).
We agree with the opinion that the recruited Pub3 may ubiquitinate proteins other than Ght5. In the revised manuscript, we will correct our conclusion of the Figure 7 experiment (L388-390), not to limit the possible ubiquitination target(s) to Ght5.
In a genetic screen, we found that mutations in aly3+ and genes encoding ESCRT complexes suppressed low-glucose sensitivity and vacuolar transport of Ght5 of gad8ts mutant cells (Toyoda et al, J Cell Sci, 2021). This finding appears consistent with the reviewer's opinion that blocking endocytosis would supposedly suppress the gad8ts phenotype. We will mention this point in the revised manuscript.
*Discussion Some analogy with the regulation of the Bul arrestins by TORC1/Npr1 and PP2A/Sit4 could be mentioned (Mehri et al. 2012), at the discretion of the authors. The possibility that phosphorylation may neutralise a basic patch on Aly3 Ct, possibly involved in electrostatic interactions with Ght5 is very interesting. Regarding the effect of the mutations on Aly3 localisation (p.15 l.498), did the authors tag Aly3 with GFP? There are examples where proteins tagged with HA are not functional whereas tagging with GFP does not alter their function (eg. Rod1, Laussel et al. 2022) - and here Supp Fig 2 only relates to HA-tagging. Proof of a change in Aly3 localisation upon mutation would definitely be a plus (OPTIONAL).
We thank the reviewer for the suggestion of a reference. In the revised manuscript, we will cite the indicated report in the corresponding part for an additional support of TORC1-mediated control of Aly3 (de)phosphorylation.
While examining localization of Aly3 by GFP-tagging is interesting, we do not believe that it is necessary in this study. We would like to produce Aly3-GFP and to examine its functionality and localization in our future study. We thank the reviewer's insightful suggestion.
**Minor comments.
*Introduction: - I believe the text corresponding to the work on TXNIP is incorrect (p.5 l.127). TXNIP is degraded after its phosphorylation, not "rectracted" from the surface.
In the revised manuscript, we will correct the text accordingly.
- For the sake of completion, the authors could add other references concerning the regulation of Rod1 in budding yeast such as Becuwe et al. 2012 J Cell Biol and O'Donnell et al. 2015 Mol Cell Biol, in addition to Llopis-Torregrosa et al. 2016.
In the revised manuscript, we will add the suggested references and correct the text in the corresponding part of the Introduction (L123-138).
- Other examples of the requirement for arrestin ubiquitination beyond Art1 (p.5 l.136-137) are listed in the ref cited: Kahlhofer et al. 2021.
We will cite the indicated review to navigate readers for more examples of arrestin ubiquitination (and transporter ubiquitination).
*Figures: In general, I think it would be clearer if the authors showed on the figures that the background strain in which the XXX gene is added (or its mutant forms) is a xxx∆ strain.
We will modify the figures to clearly show the genetic background of the strains used.
**Referees cross-commenting**
Cross review of Reviewer 1 - *I don't believe that the authors "define a set of redundant c-terminal phosphorylation sites in Aly3", because phosphorylation is not proven. *I thinks the points raised for Fig 3B are valid but the authors should focus on making their story conclusive before expanding to other data (except for the explanation of the smear, see my review). Also, I don't think 2NBDG actually works to measure Glc uptake. * same for Fig 6 - not sure the interaction site mapping between Aly3 and Pubs would bring much value since there are more urgent things to do to make the story solid.
As mentioned above, we will experimentally show phosphorylation of the Aly3 C-terminus in the revised manuscript. Such experiments would make our story more solid and conclusive. We truly appreciate the comments and suggestions.
We agree with the comments on difficulty of measuring glucose uptake using 2-NBDG. In fact, we tried and failed measuring Ght5-mediated glucose uptake using 2-NBDG.
Cros review of Reviewer 3 - we have many overlaps, so briefly : *I agree that the bibliography is incomplete (mentioned in my review) *I agree that there is no demonstration of the phospho-status of Aly3, and it is a problem *I agree that the results can be better quantified, esp. in the light of the points raised by this referee concerning the variability of expression of ST18A Other specific comments : *I agree that the statement that dephosphorylation activates alpha-arresting should be toned down - this was observed in several instances but there are examples of arrestin-mediated endocytosis which does not require their prior dephosphorylation. *I fully agree that efforts could be made regarding the classification/nomenclature of arrestins in S. pombe, this had escaped my attention
As detailed in the individual point raised by the reviewers, we will add the suggested references and accordingly correct the text in the revised manuscript.
In addition to experimentally showing Aly3 phosphorylation, we will quantify the immunoblot result.
Our statement that dephosphorylation activates alpha-arrestins might be too generalized. We will mention reports in which arrestin-mediated endocytosis does not require prior dephosphorylation (e.g. O'Donnell et al, Mol Biol Cell, 2010; Gournas et al, Mol Biol Cell, 2017; Savocco et al, PLoS Biol, 2019), and modify the text precisely.
Reviewer #2 (Significance):
*strengths and limitations This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins in S. pombe. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand, including the discovery of Aly3 as the main arrestin for this regulation, and a signalling pathway (TORC2/Gad8) acting upstream. The main question is now to understand at the mechanistic level how TORC2 signaling impinges on the regulation of this arrestin.
Overall, the authors nicely demonstrate that C-terminal Ser/Thr residues are crucial for the function of Aly3 in Ght5 endocytosis. They propose a model whereby Aly3 phosphorylation by an unknownn kinase inhibits its function on Ght5 ubiquitination, which would favour its endocytosis. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments above.
*Advance
This study, if completed carefully, would provide among the first examples of mapping of phosphorylation sites on arrestins, which are usually phosphorylated at many sites and are thus difficult to study. Few studies went down to this level in this respect (see Ivshov et al. eLife 2020). There are no changes in paradigms or new conceptual insights, but this work is a nice example of the conservation of these regulatory mechanisms.
We appreciate that this study is highly evaluated by this reviewer. We understand the main problems raised by the reviewer, and as we detailed above, we plan to perform an experiment and make explanation to respond to the problems. With the raised issues answered, we believe that conclusions of the revised manuscript will be more rigorous.
Our study reveals mechanisms regulating vacuolar transport of the Ght5 hexose transporter via the TORC2 pathway in fission yeast. The serine residues at the Aly3 C-terminus (582nd, 584th and 585th serine residues), which are probably phosphorylated in a manner dependent on the TORC2 pathway, are required for sustained Ght5 localization to cell surface and cellular adaptation to low glucose. To our knowledge, there is no such study, and thus we think that this study is novel. By responding to the reviewers' comments and adding new data as explained above, the revised manuscript will be able to present novelty of our study more clearly. Comparison of our study in fission yeast to related studies in other model organisms may reveal the conservation and diversity of these regulatory mechanisms.
*Audience Should be of interest for people studying basic research in the field of cell biology, signalling pathways, transporter regulation by physiology. Reviewer background is on the regulation of transporter endocytosis by signalling pathways and arrestin-like proteins.
Reviewer #3 (Evidence, reproducibility and clarity): (Authors' response in blue)
In this manuscript, the authors work to address how phospho-regulation of a-arrestin Aly3 in S. pombe regulates the glucose transporter Ght5. The authors use a series of phospho-mutants in Aly3 and assess function of these mutants using growth assays and localization of Ght5. My main concerns with the manuscript are that 1) there is a lack of appreciation for the similar work that has been done in S. cerevisiae to define a-arrestin phospho-regulation, which is evidenced by the severe lack of referencing throughout the document, 2) the sites mutated on Aly3 are not demonstrated to change phospho-status of Aly3 and so all interpretations of these mutants need to be better contextualized and 3) almost none of the findings are quantified (imaging or immunoblots) making it difficult to assess the rigor of the outcomes. More detailed comments are provided below.
We thank the reviewer for thorough reading of the manuscript and the detailed comments. As explained below, we will respond to the points raised by the reviewer and accordingly modify the manuscript.
Minor Comments
Immunoblotting or immunostaining to define the levels and localization of phospho-mutants - In Figure 1, an immunoblot or immunostaining to define the abundance/localization of WT Ght5 vs its ST11A mutant would be appreciated. It is very difficult to know if ST11A is as functional as WT or not without an assessment of the levels and localization of the WT and mutant proteins to accompany the spot assays. Perhaps a version of Ght5 that is a phospho-mimetic would be more useful here as well since that version should not be dephosphorylated and then presumably would be internalized and not allow for growth on low glucose medium.
We plan to add fluorescence microscopy data of WT Ght5 and Ght5(ST11A) in the revised manuscript, to compare the localization and abundance of these two Ght5 species. In our preliminary observation, those of two Ght5 species seemed to be indistinguishable.
We'd like to emphasize that the primary aim of this study is to reveal mechanisms regulating Ght5 localization and consequently ensuring cell proliferation in low glucose. While analyzing a phospho-mimetic Ght5 mutant (e.g. Ght5(ST11D)) is interesting in terms of understanding of the nature of Ght5, we believe that such an analysis is out of the scope on this study. As Ght5(ST11A)-expressing cells proliferated comparably to Ght5(WT)-expressing cells and WT and ST11A Ght5 indistinguishably localize on the cell surface, phosphorylation of the ST residues of Ght5 is not likely to be the primary mechanism regulating Ght5 localization and function. We would like to assess a phospho-mimetic Ght5 mutant protein in our future studies.
For the Aly3 mutants where the abundance of Aly3 appears lower via immunoblotting (i.e., 4thA-A582S or S582A) how is the near perfect functional readout explained when the levels of the protein are much lower than WT? For the ST18A mutant, this is a particularly important point since the authors indicate on lines 194-197 that based on the functional data for ST18A, some of these ST residues are needed for phospho-regulation of Aly3. However, in Figure 3B the authors clearly show that there is very little ST18A protein in cells, and so these mutations have impacted Aly3 stability, which may or may not be linked to its phospho-status. The authors should be upfront about this finding on lines 194-197 and should not present this phospho-model as the only reason for why ST18A may not be functional. On lines 265-276 for the authors indicate that ST18A is expressed equivalently to WT Aly3, which is just not the case in Figure 3B. Perhaps quantification of replicate data would help clarify this issue. Further, if the authors wish to conclude that the upper MW bands in Figure 3B are due to phosphorylation, perhaps they should perform phosphatase treatments of their extracts to collapse these bands. However, most certainly the overall abundance of the single band for ST18A is reduced compared to the total bands of WT Aly3.
We disagree with the opinion that the levels of the mutant Aly3 are much lower than WT. For semi-quantitative measurement of the protein abundance, 2-fold dilution series of the WT Aly3 sample were loaded in the leftmost 3 lanes of Figure 3B. Although the levels of Aly3(4th A;A582S), Aly3(S582A) and Aly3(ST18A) were lower than that of WT Aly3, those are 50% or more of the WT, judging from the intensities of the serially-diluted WT samples. To clearly show that the expression of these Aly3 proteins is within comparable levels, we plan to add a column chart of the quantified expression levels and to mention abundances of the Aly3 proteins more quantitatively in the revised text. We do not think that replicate data (of Western blots as in Figure 3B) helps clarify this issue, because nmt1 promoter-driven gene transcription is induced with a small variation (Forsburg, Nuc Acid Res, 1993). We will cite this report and mention this point in the revised text.
We are afraid that this reviewer seems to consider that Aly3(ST18A) is not functional, but it is not a case and we do not intend to claim so. While deletion of aly3 did not interfere with cell proliferation in low glucose (see vector controls in Figures 2B, 2C and 3A, -Thiamine), expression of the ST18A mutant clearly hinders cell proliferation in low glucose, indicating that the ST18A performs dominant negative function to inhibit cell proliferation. That is, even though the expression level and/or stability of the ST18A is reduced, it is still sufficiently abundant to perform the dominant negative function. We propose the phospho-model not due to dysfunctionality of ST18A, but its dominant negative functionality. The 18 S/T residues of Aly3, which are shown to be phosphorylated in precedent phospho-proteomics studies, seem to be required to down-regulate Aly3's function to inhibit cell proliferation in low glucose. We apologize for this confusion, and we will modify the text and figures to clarify these points in the revised manuscripts.
To obtain an experimental support for our description that the slower migrating bands in Figure 3B are due to phosphorylation, we plan to perform a phosphatase treatment experiment as suggested.
Figure 2A - how do the phosphorylation sites identified in Aly3 compare to those identified in Rod1 from S. cerevisiae? See PMID 26920760 or SGD for more information. I am confused as to why the Aly3 protein has an arrowhead at the C-terminus. What does this denote?
We will mention reported phosphorylation sites of Aly3 and budding yeast Rod1/Art4 in the revised manuscript, by referring to the indicated report and database. It should be noted that similarity between amino acid sequences of Aly3 and S. cerevisiae Rod1 is not so high and limited in Arrestin-N and -C domains. The C-terminal half of Aly3, in which most of the potential phosphorylation sites are found, is not similar to Rod1. Thus, these sites are unlikely to be conserved between them.
An arrowhead indicates the direction of transcription (from N to C-terminus). We will describe it explicitly in the revised figure legend.
Figure 2 - The WT and Aly3-ST18A are expressed in S. pombe from a non-endogenous locus under the control of the Nmt1 promoter. However, are these mutants present in cells that contain WT copies of Aly3 at other genomic loci? If so, this would surely muddy the interpretations of these data as a- and b-arrestins are capable of multimerizing and the effect of multimerization on their activities can vary.
As mentioned in L188, an aly3 deletion mutant strain (aly3Δ) was used as a host, and thus all strains harboring an nmt1-driven aly3 gene lack the endogenous aly3 gene. We will add an illustration clearly showing that the host strain lacks the endogenous aly3+ gene and modify the legend of Figure 2.
Functional readouts for Aly3 using Ght5 localization - The reduced surface levels of Ght5 does correspond to the spot assay growth in low glucose for the various Aly3 mutants used. However, it would be useful if these assays incorporated an endocytosis inhibitor to help prevent the activities of these Aly3 plasmids to see if the transporter is retained at the PM. At the end of these mutational analyses, the authors conclude that phosphorylation of Aly3 at any of 3 sites is required for Ght5 trafficking to the vacuole in low glucose, however no experiment is done to demonstrate that these sites are phosphorylated residues. A phosphatase assay would be useful to help demonstrate that the modifications in 3B really are phosphorylation and a quantification of the phosphorylated bands in these WBs would also be useful to solidify the statement made on lines 306-309.
We thank the reviewer for suggestion of the experiment using an endocytosis inhibitor. Previously we reported that vacuolar localization of Ght5 in gad8ts mutant cells was suppressed by mutations in not only aly3 but also genes encoding ESCRT complexes (Toyoda et al, J Cell Sci, 2021). We therefore think that, in cells expressing Aly3(ST18A) or Aly3(4th Ala), Ght5 is subject to endocytosis and subsequent selective transport to vacuoles via ESCRT complexes. We will mention these previous findings in the revised manuscript.
As mentioned in responses to the comments above and other reviewer's, we will perform a phosphatase treatment experiment and its quantification in the revised manuscript. Here, we'd like to emphasize that these 3 sites have been shown to be phosphorylated in phospho-proteomic studies by other researchers (Kettenbach et al, Mol Cell Proteomics, 2015; Tay et al, Cell Rep, 2019; Halova et al, Open Biol, 2021; Mak et al, EMBO J, 2021), although we do not show it directly in this study.
Phosphorylation assessments - in general, it would be good to not only build the non-phosphorylatable versions of Aly3 but also the phospho-mimetic forms.
We produced a phospho-mimetic mutant Aly3 (i.e. Aly3(4th A;A584D)), and showed the result in Figure 5A; cells expressing Aly3(4th A;A584D) exhibited a low ubiquitination of Ght5, similarly to Aly3(WT)- and Aly3(4th A;A584S)-expressing cells. According to our experiences, replacing S/T with D/E does not necessarily mimic phosphorylation. Thus, we do not believe that systematic production of phospho-mimetic Aly3 mutants would help achieve the aim of this study.
Pub1, 2, and 3 - It would be helpful if the authors indicated what genes Pubs 1-3 correspond to in S. cerevisiae, where Rsp5 is the predominant Ub ligase interacting with a-arrestins. Is there no ortholog of Rsp5 in S. pombe?
Pub1, Pub2 and Pub3 are regarded as orthologs of budding yeast Rsp5, according to the fission yeast database PomBase. We will perform a homology search for these E3 proteins, and based on the result, we will add a description in the revised manuscript.
Pub-Aly3 interactions - could the authors please comment on the reason why so very little Aly3 is copurified with Pub1 or Pub2? Can any clear conclusion be drawn about pub2 given how very little Pub2 is present in the IPs? Based on my understanding of these data I do not think that this can be cleanly interpreted. What is is the identity of the ~50kDa MW band in Figure 6 in the upper MYC detection panel?
We do not have an accurate answer for the result that a small amount of Aly3 is copurified with Pub1 or Pub3. The Pub1/3-Aly3 interaction may be weak or transient. We will discuss this point in the revised manuscript.
Regarding whether Aly3 interacts with Pub2, we agree with the reviewer. As described in the Results (L360-362), we could not conclude anything about Aly3-Pub2 interaction by this immunoprecipitation experiment alone. On the other hand, the genetic interaction experiment (Figure 7A) suggests that pub2+ is not involved in defects caused by the gad8ts mutation (while pub3+ and aly3+ are). By this experiment, we think that Pub2 is not a partner of Aly3.
In the revised manuscript, we will describe that Pub2 is not a partner of Aly3 in a paragraph describing the Figure 7A experiment.
Because the 50 kDa band found in the IP fraction of all the samples appears even in "beads only" (Figure 6), those are supposedly derived from mouse IgG dissociated from the beads used for immunoprecipitation. We will mention this in the legend of Figure 6.
Phosphorylation and ubiquitination of a-arrestins - The paragraph from lines 123-138 is very superficial in addressing what is known about phosphorylation and ubiquitination of a-arrestins. The way this section is written, it feels misleading to the reader as it omits many of the details for regulation that would help place the current study in context. The discussion of Rod1 phosphorylation by AMPK for example, which is directly relevant to this study, is underdeveloped. I would recommend splitting this into two paragraphs and providing a more in depth, and accurate, view of the literature on this topic, with a focus on the regulation that is relevant for the ortholog of Aly3 in S. cerevisiae. For example, Rod1 phosphorylation by AMPK is greatly expanded upon in the following papers (PMID 22249293 and 25547292) and AMPK regulation of C-tail phosphorylation of a-arrestins is defined further in PMID 26920760. These references are each particularly important to compare with the current findings presented in this manuscript. Torc2 regulation ofa-arrestins is also reviewed in PMID 36149412 and references therein should be considered.
Because the primary aim of this study is to reveal mechanisms regulating Ght5 localization in fission yeast, but not to dissect modification and regulation of α-arrestins, we decided not to get into the details of phosphorylation and ubiquitination of α-arrestins. Furthermore, although budding yeast Rod1 and Rog3 are found to be downstream of the TORC2-Ypk1 signaling in the context of internalization of the Ste2 pheromone receptor, it is not clear whether TORC2-Ypk1 signaling also regulate α-arrestin-mediated internalization of hexose transporters in budding yeast. For these reasons, we focused on limited literatures essential for interpretation of the results and omitted many references describing the details of α-arrestin regulation. However, as this reviewer commented, we realize that our decision makes the discussion superficial and misleading to the reader. We sincerely apologize for this inconvenience.
In the revised manuscript, we will reorganize the paragraphs in the discussion and include the suggested references. Regarding budding yeast Rod1, we will cite the study reporting Ypk1-mediated phosphorylation on Rod1 in mating pheromone response via regulation of Ste2 endocytosis (Alvaro et al, Genetics, 2016). We will also mention other reports (Becuwe et al, J Cell Biol, 2012; O'Donnell et al, Mol Cell Biol, 2015) about AMPK-dependent phosphorylation of Rod1 in the corresponding part (e.g. L129-130). In addition, we will mention that Aly2, Rod1 and Rog3 α-arrestins were found downstream of the TORC2-Ypk1 signaling (Muir et al, eLife, 2014; Thorner, Biochem J, 2022).
As a further detailed example, there is far more work done on ubiquitination of a-arrestins in S. cerevisiae than the single citation provided by the authors on line 137. The way this section is written it feels misleading. Considerable effort has been spent on defining how mono- and poly-ubiquitination regulate a-arrestins and the authors should consider the data provided in the following citations and revise the two sentences they provide in this introduction to better reflect the breadth of our understanding rather than simply indicate that the 'mechanisms that regulate functions of a-arrestisn are not fully understood'. (PMIDs 23824189; 22249293; 17028178; 28298493)
Ubiquitination of α-arrestin itself is not the topic of this study, and physiological consequences of ubiquitination of Aly3 remain unknown. Because of these reasons, we did not describe the details of ubiquitination of α-arrestins in the original manuscript. However, we never intend to mislead the reader, and thus to avoid it, we will revise the indicated sentences and cite the suggested literatures (O'Donnell et al, J Biol Chem, 2013; Becuwe et al, J Cell Biol, 2012; Kee et al, J Biol Chem, 2006; Ho et al, Mol Biol Cell, 2017) in the revised manuscript.
Context of the findings and lack of citations - The referencing in this manuscript is very poor as many of the key papers that report analogous findings in the budding yeast Saccharomyces cerevisiae are not cited. This oversight in citing the appropriate literature must be remedied before this manuscript can be considered further for publication. Examples of these omissions occur at the following places:
We will modify the text and carefully cite more literatures describing analogous finding in budding yeast and other organisms in the revised manuscript. We appreciate the insightful suggestions by this reviewer. It should be noted, however, that it is not evident whether budding yeast Rod1 and Rog3 are orthologous to fission yeast Aly3. Although Rod1 and Aly3 share overlapping roles, amino acid sequence similarity of them is not high and limited only in domains which are generally conserved among α-arrestin-family proteins.
Line 90 - The Puca and Brou citations is one example of this but the first examples come from Daniela Rotin's work looking at Rsp5 interactions in budding yeast, which is where the association between HECT-domain Ub ligases and a-arrestins is also documented by Scott Emr and Hugh Pelham's labs. Here are some PMID numbers to improve the citations of this section (PMID 17551511; 18976803; 19912579) and each of these references long predates the Puca and Brou publication.
In the revised manuscript, we will improve the citations by including the suggested studies (Gupta et al, Mol Syst Biol, 2007; Lin et al, Cell, 2008; Nikko and Pelham, Traffic, 2009).
Lines 123-126 - Phosphorylation can also increase vacuole-dependent degradation of alpha-arrestins as demonstrated in PMID 35454122. The interaction with 14-3-3 proteins that is driven by phosphorylation of a-arrestins was first demonstrated by the Leon group in PMID 22249293). Lines 129-132 - Here again the Leon reference that helps demonstrate the 14-3-3 inhibition of Rod1 is lacking (PMID 22249293).
We will cite the suggested studies in description of these topics (Bowman et al, Biomolecules, 2022; Becuwe et al, J Cell Biol, 2012).
Lines 130-132 - Please include references for the statement that dephosphorylation activates a-arrestin activity. There are no citations on this statement and there are many to choose from and I would urge the authors to cite the primary literature on these points.
We will cite studies for the statement "Conversely, dephosphorylation is thought to activate α-arrestins and to promote selective endocytosis of transporter proteins" (L130-132).
These are just a few examples from the Introduction, but the Discussion is similarly wrought with issues in referencing and framing the experimental results within the context of the larger field, including what is known about Rod1/Rog3 regulation in S. cerevisiae. For example, the Llopis-Torregrosa et al reference and statement on lines 508-510 is incorrect. There are other phosphorylation sites defined in the C-terminus of Rod1, as described in Alvaro et al. PMID: 26920760.
We will carefully correct Discussion by citing the suggested references (e.g. Alvaro et al, Genetics, 2016) and framing the obtained results within the context of the larger field.
Of note, a combination of α-arrestin, upstream kinase(s) and distinct phosphorylation sites appears to determine the target transporter (Kahlhofer et al, Biol Cell, 2021; Thorner, Biochem J, 2022), and it has not been explicitly proved that TORC2-Ypk1 signaling also regulate α-arrestin-mediated internalization of hexose transporters in budding yeast. For these reasons, we stated "S. cerevisiae Rod1 and Rog3 are phosphorylated solely by Snf1p/AMPK" in the context of internalization of hexose transporters. We will also discuss this point in the revised manuscript.
Minor Comments Clarification needed - Lines 107-121 - The relationship between the S. pombe arrestins and those in other organisms is somewhat unclear. Frist, all the arrestins in humans and S. cerevisiae can be sorted into the alpha, beta and Vps26 classes. However, the authors indicate that the S. pombe genome has 11 arrestin-like proteins but only 4 of these are a-arrestins. What classes do the other 7 arrestins belong to? It would be appreciated if this point was clarified.
To our knowledge, fission yeast arrestins are not well classified yet. We will perform a phylogenetic tree analysis to classify them, and modify the description of the indicated part accordingly. We will also cite our previous report (Toyoda et al, J Cell Sci, 2021), in which the overall protein structure and domains of 11 fission yeast arrestin-like proteins were reported.
Next, for the 4 a-arrestins identified in S. pombe the authors indicate that Aly3 is the homolog of Rod1/Art4 and Rog3/Art7 from S. cerevisiae. What is the relationship of Rod1 in S. pombe to Rod1 in S. cerevisiae? Are these also homologs? You can see how the nomenclature is confusing and, given the functional overlap of S. cerevisiae Rod1/Rog3 proteins it is important to know if Aly3 is the only version of these a-arrestins or if there is an additional counterpart in S. pombe. This point becomes somewhat more confusing when on lines 134-136 the authors talk about Arn1/Any1 as an arrestin related protein in S. pombe yet this protein was not included on the list of a-arrestins in the preceding section. What class of arrestin is this protein?
According to PomBase, both Aly3 and Rod1 are assigned as the orthologue of budding yeast Rod1 and Rog3. However, as mentioned in responses above, it is unclear whether Aly3 is really orthologous to budding yeast Rod1/Rod3. In the revised manuscript, we will perform a homology search for these 4 proteins, and add information on how much these arrestins share homology.
Arn1/Any1 is regarded as a β-arrestin (Nakase et al, J Cell Sci, 2013). We will also mention this in the revised manuscript.
Alpha-arrestin homology - On lines 127-129 the authors indicate that TXNIP is the mammalian homolog of Aly3. To my knowledge, there are no evolutionary analyses that can draw these lines of homology between the a-arrestins in humans and those in yeasts. It would be appreciated if the authors could cite the work that leads to this conclusion or revise the sentence to more accurately reflect what is known on this topic. It certainly appears that, given their functional overlap in regulating glucose transporters, Txnip and Rod1/Rog3 in humans and S. cerevisiae are functionally connected. I urge the authors to use more caution when describing this protein family.
Among human α-arrestins, ARRDC2 (22%) but not TXNIP (20%) has the highest amino acid identity to Aly3 (Toyoda et al, J Cell Sci, 2021). However, as TXNIP has been reported to regulate endocytosis of hexose transporters, GLUT1 and 4 (Wu et al, Mol Cell, 2013; Waldhart et al, Cell Rep, 2017), we think that TXNIP and Aly3 share physiological roles. We will revise the sentence (L127-129) more accurately.
Text editing - The text could use editing as there are awkward and grammatically incorrect sentences in several places. Here are a few examples to help the authors:
Please note that the original manuscript is edited by a professional editor, who is a native English (American) speaker and has edited thousands of research papers, before initial submission. We will ask an editor to check the revised draft again before submission.
Lines 57-60 - the protein is not expressed over the entire cell surface, but is localized to the entire cell surface.
We will correct this wording.
Lines 80-83 - this sentence is very confusing
We will correct this part by changing the phrase "Unlike TORC1," into a clause.
Line 86 - Is there more than one gene encoding Aly3 in S. pombe?
No, there is only one gene encoding Aly3. We will correct this part so as to avoid being misunderstood.
Line 88, 109, - these sentences need to start with a capitol so either capitalize the A in arrestin or write out Alpha with a capitol A.
We will correct the sentence as suggested.
Lines 145-148 - unclear as written
We will clarify the meaning of the sentence by changing the voice.
Line 224 - why are these amino acids being referred to as hydroxylated? Perhaps hydroxyl-containing amino acids or 18 amino acids with hydroxyl side chains would be better choices?
We will correct the word as suggested.
Line 300 - very confusing sentence structure
We will correct this part by simplifying the structure of the sentence.
And elsewhere....
We will carefully check the revised text before submission.
Reviewer #3 (Significance):
The authors provide some information as to the residues needed in the Aly3 C-tail for Ght5 trafficking in S. Pombe. These results are not places in the context of similar phosphor-regulatory work done for a-arrestins in S. cerevisiae, and this is needed for appreciation of the significance of the study.
Overall, it appears that the model put forth is very similar to the one already proposed in S. cerevisiae where phosphorylation impedes a-arrestin-mediated trafficking of glucose transporters. It is interesting to see this similarity hold in S. Pombe, but it does not dramatically alter our appreciation of a-arrestin biology.
The significance of the findings are somewhat underscored by the fact that very little quantification of data are presented, making the rigor of the work difficult to assess.
We thank the reviewer for careful reading and evaluation of our study. As the reviewer states, the results are not placed in the context of similar phospho-regulatory works done for α-arrestins in S. cerevisiae. This may partly come from the fact that it remains unclear whether internalization of hexose transporters is regulated by TORC2-dependent phosphorylation in S. cerevisiae. We believe that our study is novel and significant for this reason. By performing the additional experiments/quantification and revising the text as suggested by the reviewers, the manuscript will be further strengthened, and we will be able to clearly conclude that TORC2-dependent phosphorylation of Aly3 regulates localization of the Ght5 hexose transporter and cellular responses to glucose shortage stress.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors work to address how phospho-regulation of a-arrestin Aly3 in S. pombe regulates the glucose transporter Ght5. The authors use a series of phospho-mutants in Aly3 and assess function of these mutants using growth assays and localization of Ght5. My main concerns with the manuscript are that 1) there is a lack of appreciation for the similar work that has been done in S. cerevisiae to define a-arrestin phospho-regulation, which is evidenced by the severe lack of referencing throughout the document, 2) the sites mutated on Aly3 are not demonstrated to change phospho-status of Aly3 and so all interpretations of these mutants need to be better contextualized and 3) almost none of the findings are quantified (imaging or immunoblots) making it difficult to assess the rigor of the outcomes. More detailed comments are provided below.
Minor Comments
Immunoblotting or immunostaining to define the levels and localization of phospho-mutants - In Figure 1, an immunoblot or immunostaining to define the abundance/localization of WT Ght5 vs its ST11A mutant would be appreciated. It is very difficult to know if ST11A is as functional as WT or not without an assessment of the levels and localization of the WT and mutant proteins to accompany the spot assays. Perhaps a version of Ght5 that is a phospho-mimetic would be more useful here as well since that version should not be dephosphorylated and then presumably would be internalized and not allow for growth on low glucose medium.
For the Aly3 mutants where the abundance of Aly3 appears lower via immunoblotting (i.e., 4thA-A582S or S582A) how is the near perfect functional readout explained when the levels of the protein are much lower than WT? For the ST18A mutant, this is a particularly important point since the authors indicate on lines 194-197 that based on the functional data for ST18A, some of these ST residues are needed for phospho-regulation of Aly3. However, in Figure 3B the authors clearly show that there is very little ST18A protein in cells, and so these mutations have impacted Aly3 stability, which may or may not be linked to its phospho-status. The authors should be upfront about this finding on lines 194-197 and should not present this phospho-model as the only reason for why ST18A may not be functional. On lines 265-276 for the authors indicate that ST18A is expressed equivalently to WT Aly3, which is just not the case in Figure 3B. Perhaps quantification of replicate data would help clarify this issue. Further, if the authors wish to conclude that the upper MW bands in Figure 3B are due to phosphorylation, perhaps they should perform phosphatase treatments of their extracts to collapse these bands. However, most certainly the overall abundance of the single band for ST18A is reduced compared to the total bands of WT Aly3.
Figure 2A - how do the phosphorylation sites identified in Aly3 compare to those identified in Rod1 from S. cerevisiae? See PMID 26920760 or SGD for more information. I am confused as to why the Aly3 protein has an arrowhead at the C-terminus. What does this denote?
Figure 2 - The WT and Aly3-ST18A are expressed in S. pombe from a non-endogenous locus under the control of the Nmt1 promoter. However, are these mutants present in cells that contain WT copies of Aly3 at other genomic loci? If so, this would surely muddy the interpretations of these data as - and -arrestins are capable of multimerizing and the effect of multimerization on their activities can vary.
Functional readouts for Aly3 using Ght5 localization - The reduced surface levels of Ght5 does correspond to the spot assay growth in low glucose for the various Aly3 mutants used. However, it would be useful if these assays incorporated an endocytosis inhibitor to help prevent the activities of these Aly3 plasmids to see if the transporter is retained at the PM. At the end of these mutational analyses, the authors conclude that phosphorylation of Aly3 at any of 3 sites is required for Ght5 trafficking to the vacuole in low glucose, however no experiment is done to demonstrate that these sites are phosphorylated residues. A phosphatase assay would be useful to help demonstrate that the modifications in 3B really are phosphorylation and a quantification of the phosphorylated bands in these WBs would also be useful to solidify the statement made on lines 306-309.
Phosphorylation assessments - in general, it would be good to not only build the non-phosphorylatable versions of Aly3 but also the phospho-mimetic forms.
Pub1, 2, and 3 - It would be helpful if the authors indicated what genes Pubs 1-3 correspond to in S. cerevisiae, where Rsp5 is the predominant Ub ligase interacting with -arrestins. Is there no ortholog of Rsp5 in S. pombe?
Pub-Aly3 interactions - could the authors please comment on the reason why so very little Aly3 is copurified with Pub1 or Pub2? Can any clear conclusion be drawn about pub2 given how very little Pub2 is present in the IPs? Based on my understanding of these data I do not think that this can be cleanly interpreted. What is is the identity of the ~50kDa MW band in Figure 6 in the upper MYC detection panel?
Phosphorylation and ubiquitination of -arrestins - The paragraph from lines 123-138 is very superficial in addressing what is known about phosphorylation and ubiquitination of a-arrestins. The way this section is written, it feels misleading to the reader as it omits many of the details for regulation that would help place the current study in context. The discussion of Rod1 phosphorylation by AMPK for example, which is directly relevant to this study, is underdeveloped. I would recommend splitting this into two paragraphs and providing a more in depth, and accurate, view of the literature on this topic, with a focus on the regulation that is relevant for the ortholog of Aly3 in S. cerevisiae. For example, Rod1 phosphorylation by AMPK is greatly expanded upon in the following papers (PMID 22249293 and 25547292) and AMPK regulation of C-tail phosphorylation of -arrestins is defined further in PMID 26920760. These references are each particularly important to compare with the current findings presented in this manuscript. Torc2 regulation of-arrestins is also reviewed in PMID 36149412 and references therein should be considered. As a further detailed example, there is far more work done on ubiquitination of -arrestins in S. cerevisiae than the single citation provided by the authors on line 137. The way this section is written it feels misleading. Considerable effort has been spent on defining how mono- and poly-ubiquitination regulate -arrestins and the authors should consider the data provided in the following citations and revise the two sentences they provide in this introduction to better reflect the breadth of our understanding rather than simply indicate that the 'mechanisms that regulate functions of -arrestisn are not fully understood'. (PMIDs 23824189; 22249293; 17028178; 28298493)
Context of the findings and lack of citations - The referencing in this manuscript is very poor as many of the key papers that report analogous findings in the budding yeast Saccharomyces cerevisiae are not cited. This oversight in citing the appropriate literature must be remedied before this manuscript can be considered further for publication. Examples of these omissions occur at the following places:
Line 90 - The Puca and Brou citations is one example of this but the first examples come from Daniela Rotin's work looking at Rsp5 interactions in budding yeast, which is where the association between HECT-domain Ub ligases and -arrestins is also documented by Scott Emr and Hugh Pelham's labs. Here are some PMID numbers to improve the citations of this section (PMID 17551511; 18976803; 19912579) and each of these references long predates the Puca and Brou publication.
Lines 123-126 - Phosphorylation can also increase vacuole-dependent degradation of alpha-arrestins as demonstrated in PMID 35454122. The interaction with 14-3-3 proteins that is driven by phosphorylation of -arrestins was first demonstrated by the Leon group in PMID 22249293).
Lines 129-132 - Here again the Leon reference that helps demonstrate the 14-3-3 inhibition of Rod1 is lacking (PMID 22249293).
Lines 130-132 - Please include references for the statement that dephosphorylation activates -arrestin activity. There are no citations on this statement and there are many to choose from and I would urge the authors to cite the primary literature on these points.
These are just a few examples from the Introduction, but the Discussion is similarly wrought with issues in referencing and framing the experimental results within the context of the larger field, including what is known about Rod1/Rog3 regulation in S. cerevisiae. For example, the Llopis-Torregrosa et al reference and statement on lines 508-510 is incorrect. There are other phosphorylation sites defined in the C-terminus of Rod1, as described in Alvaro et al. PMID: 26920760.
Minor Comments
Clarification needed - Lines 107-121 - The relationship between the S. pombe arrestins and those in other organisms is somewhat unclear. Frist, all the arrestins in humans and S. cerevisiae can be sorted into the alpha, beta and Vps26 classes. However, the authors indicate that the S. pombe genome has 11 arrestin-like proteins but only 4 of these are -arrestins. What classes do the other 7 arrestins belong to? It would be appreciated if this point was clarified. Next, for the 4 -arrestins identified in S. pombe the authors indicate that Aly3 is the homolog of Rod1/Art4 and Rog3/Art7 from S. cerevisiae. What is the relationship of Rod1 in S. pombe to Rod1 in S. cerevisiae? Are these also homologs? You can see how the nomenclature is confusing and, given the functional overlap of S. cerevisiae Rod1/Rog3 proteins it is important to know if Aly3 is the only version of these -arrestins or if there is an additional counterpart in S. pombe.
This point becomes somewhat more confusing when on lines 134-136 the authors talk about Arn1/Any1 as an arrestin related protein in S. pombe yet this protein was not included on the list of -arrestins in the preceding section. What class of arrestin is this protein?
Alpha-arrestin homology - On lines 127-129 the authors indicate that TXNIP is the mammalian homolog of Aly3. To my knowledge, there are no evolutionary analyses that can draw these lines of homology between the -arrestins in humans and those in yeasts. It would be appreciated if the authors could cite the work that leads to this conclusion or revise the sentence to more accurately reflect what is known on this topic. It certainly appears that, given their functional overlap in regulating glucose transporters, Txnip and Rod1/Rog3 in humans and S. cerevisiae are functionally connected. I urge the authors to use more caution when describing this protein family.
Text editing - The text could use editing as there are awkward and grammatically incorrect sentences in several places. Here are a few examples to help the authors:
Lines 57-60 - the protein is not expressed over the entire cell surface, but is localized to the entire cell surface.
Lines 80-83 - this sentence is very confusing
Line 86 - Is there more than one gene encoding Aly3 in S. pombe?
Line 88, 109, - these sentences need to start with a capitol so either capitalize the A in arrestin or write out Alpha with a capitol A.
Lines 145-148 - unclear as written
Line 224 - why are these amino acids being referred to as hydroxylated? Perhaps hydroxyl-containing amino acids or 18 amino acids with hydroxyl side chains would be better choices?
Line 300 - very confusing sentence structure
And elsewhere....
Significance
The authors provide some information as to the residues needed in the Aly3 C-tail for Ght5 trafficking in S. Pombe. These results are not places in the context of similar phosphor-regulatory work done for a-arrestins in S. cerevisiae, and this is needed for appreciation of the significance of the study.
Overall, it appears that the model put forth is very similar to the one already proposed in S. cerevisiae where phosphorylation impedes a-arrestin-mediated trafficking of glucose transporters. It is interesting to see this similarity hold in S. Pombe, but it does not dramatically alter our appreciation of a-arrestin biology.
The significance of the findings are somewhat underscored by the fact that very little quantification of data are presented, making the rigor of the work difficult to assess.
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Referee #2
Evidence, reproducibility and clarity
Summary/background.
This paper focuses on the regulation of endocytosis of the hexose transporter, Ght5, in S. pombe by nutrient limitation through the arrestin-like protein Aly3. Ght5 is induced when glucose is limiting and is required for growth and proliferation in these conditions. ght5+ encodes the only high-affinity glc transporter from fission yeast. ght5+ is induced in low glucose conditions at the transcriptional level and is translocated to the plasma membrane to allow glc import. Ght5 is targeted to the vacuole in conditions of N limitation. Mutations in the TORC2 pathway lead to the same process, thus preventing growth on low glucose medium, as shown in the gad8ts mutant, mutated for the Gad8 kinase acting downstream of TORC2. Previously, the authors demonstrated that the vacuolar delivery of Ght5 in the gad8ts mutant is suppressed by mutation of the arrestin-like protein Aly3. Arrestin-like proteins are in charge of recognising and ubiquitinating plasma membrane proteins to direct their vacuolar targeting by the endocytosis pathway. This suggested that Aly3 is hyperactive in TORC2 mutants, and accordingly, Ght5 ubiquitination was increased in gad8ts.
Overall statement
This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments below.
Major statements and criticism.
- Fig 1. Based on the hypothesis that TORC2-mediated phosphorylation regulate Ght5 endocytosis, the authors first considered a possible phosphorylation of Ght5. They mutagenised 11 possible phosphorylation sites on the Ct of Ght5, but none affected the growth on low glucose in the absence of thiamine, suggesting that they don't contribute to the observed TORC2-mediated regulation. However, I disagree with the statement that "phosphorylation of Ght5 is dispensable for cell proliferation in low glucose", given that the authors do not show 1- that Ght5 is phosphorylated and 2-that this is abolished by these mutations. They should either provide data on this or tone down and say that these residues are not involved in the regulation, without implying phosphorylation which is not proven. In the presence of Thiamine (Supp fig 1), it seems that the ST/A mutant grows better in low glucose, and this is not explained nor commented. Since the transporter is not expressed, could the authors provide an explanation to this? If the promoter is leaky and some ght5-ST/A is expressed, it may be more stable and allow better growth than the WT, which would tend to indicate that impairing phosphorylation prevents endocytosis (which is classical for many transporters, see the body of work on CK1-mediated phosphorylation of transporters). Have the authors tried to decrease glc concentration lower than 0.14% in the absence of thiamine to see if this also true when the transporters is strongly expressed? (OPTIONAL)
- Fig 2. The authors then follow the hypothesis that TORC2 exerts its Ght5-dependent regulation through the phosphorylation of Aly3. They mutagenised 18 possible phosphorylation sites on Aly3. This led to a strong defect in growth in low-glc medium. Mutation of the possible Gad8 site (S460) did not recapitulate this phenotype, suggesting that it is not sufficient, however, mutations of 4 ST residues in a CT cluster (582-586) mimicked the full 18ST/A mutation, suggesting these are the important residues for Ght5 endocytosis.
- Fig 3A. Further dissection did not allow to pinpoint this regulation to a specific residue, beyond the dispensability of the T586 residue. Fig 3B. The authors look at the effects of mutation of Aly3 on these sites at the protein level. They had to develop an antibody because HA-epitope tagging did not lead to a functional protein (Supp fig 2). Whereas I agree that the mutations causing a phenotype lead to a change in the migration pattern, I disagree with the statement that "This observation indicated that slower migrating bands were phosphorylated species of Aly3" (p.9 l.271). First, lack of phosphorylation usually causes a slower mobility on gel, which is not clear to spot here. Second, a smear appears on top of the mutated proteins (eg. 4th Ala) which is possibly caused by another modification. There are many precedents in the literature about arrestins being ubiquitinated when they are not phosphorylated (see the work on Bul1, Rod1, Csr2 in baker's yeast from various labs). My gut feeling is that lack of phosphorylation unleashes Aly3 ubiquitination leading to change in pattern. All in all, it is impossible to state about the phosphorylation of a protein without addressing its phosphorylation properly by phosphatase treatment + change in migration, or MS/MS. Thus, whereas the data looks promising, this hypothesis that Aly3 is phosphorylated at the indicated sites is not properly demonstrated.
- Fig 4. The authors now look at the functional consequences of these mutations on ALy3 on Ght5 localisation. The data clearly shows that mutation of the 4 identified S/T residues (Aly3-4th A) causes aberrant localisation of the transporter to the vacuole, likely to cause the observed growth defect on low glucose. There is a nice correlation between the vacuolar localisation and growth in low-glucose for the various aly3 mutants. (A final proof could be to express this in the context of an endocytic mutant, which should restore membrane localisation and suppress the aly3-4thA phenotype - OPTIONAL). However, I still disagree with the statement that "These results indicate that phosphorylation of Aly3 at the C-terminal 582nd, 584th, and/or 585th serine residues is required for cell-surface localization of Ght5." given that phosphorylation was not properly demonstrated.
- Fig 5. Here, the authors question the role of Aly3 mutations on Ght5 ubiquitination. They immunoprecipitate Ght5 and address its ubiquitination status in various Aly3 mutants. The data is encouraging for a role in Aly3 phosphorylation (?) in the negative control of Ght5 ubiquitination. My main problem with this experiment is that it seems that Ght5 immunoprecipitations were made in non-denaturing conditions, which leads to the question of what is the anti-ubiquitin revealing here (Ght5 or a co-immunoprecipitated protein, for example Aly3 itself, or the Pub ligases, or an unknown protein). It seems that this protocol was previously used in their previous paper, but I stand by my conclusion that ubiquitination of a given protein can only be looked in denaturing conditions. The experiments should be repeated in buffers classical for the study of protein ubiquitination to be able to conclude unambiguously that we are looking at Ght5 ubiquitination itself, especially in the absence of a non-ubiquitinable form of Ght5 as a negative control. Could the authors comment on the fact that S-A or S-D mutations display the same phenotype regarding the possible Ght5 ubiquitination?
- Fig 6. The authors want to document the model whereby Aly3 may interact with some of the Nedd4 ligases (Pub1/2/3) to mediate its Ght5-ubiquitination function. They actually use the Aly3-4thA mutant, it should have been better with the WT protein. But the results indicate a clear interaction with at least Pub1 and Pub3. By the way, are the Pub1/2/3 fusions functional? Nedd4 proteins are notoriously affected in their function by C-terminal tagging and are usually tagged at their N-terminus (See Dunn et al. J Cell Biol 2004).
- Fig 7. The authors want to provide genetic interaction between the Pub ligases and the growth defects in low glc due to alterations in Ght5 trafficking. It is unclear how the gad8ts pub1∆ mutant was generated since it doesn't seem to grow on regular glc concentration (Supp fig 5), could the authors provide some information about this? It is also not clear whether it can be stated thatches mutant is "more sensitive" to glc depletion because of the low level of growth to begin with (even at 3%). Altogether, the data show that deletion of pub3+ is able to suppress the growth defect of the gad8ts mutant on low glc medium, suggesting it is the relevant ligase for Ght5 endocytosis. This is confirmed by microscopy observations of Ght5 localisation. However, I would again tone down the main conclusion, which I feel is far-reaching: "Combined with physical interaction data, these results strongly suggest that Aly3 recruits Pub3, but not Pub2, for ubiquitination of Ght5." Work on Rsp5 in baker's yeast has shown that Rsp5 function goes beyond cargo ubiquitination, including ubiquitination of arrestins (which is often required for their function as mentioned in the introduction) or other endocytic proteins (epsins, amphyphysin etc). I agree that the data are compatible with this model but there are other possible explanations. Anything that would block endocytosis would supposedly suppress the gad8ts phenotype.
Discussion
Some analogy with the regulation of the Bul arrestins by TORC1/Npr1 and PP2A/Sit4 could be mentioned (Mehri et al. 2012), at the discretion of the authors. The possibility that phosphorylation may neutralise a basic patch on Aly3 Ct, possibly involved in electrostatic interactions with Ght5 is very interesting. Regarding the effect of the mutations on Aly3 localisation (p.15 l.498), did the authors tag Aly3 with GFP? There are examples where proteins tagged with HA are not functional whereas tagging with GFP does not alter their function (eg. Rod1, Laussel et al. 2022) - and here Supp Fig 2 only relates to HA-tagging. Proof of a change in Aly3 localisation upon mutation would definitely be a plus (OPTIONAL).
Minor comments.
Introduction:
- I believe the text corresponding to the work on TXNIP is incorrect (p.5 l.127). TXNIP is degraded after its phosphorylation, not "rectracted" from the surface.
- For the sake of completion, the authors could add other references concerning the regulation of Rod1 in budding yeast such as Becuwe et al. 2012 J Cell Biol and O'Donnell et al. 2015 Mol Cell Biol, in addition to Llopis-Torregrosa et al. 2016.
- Other examples of the requirement for arrestin ubiquitination beyond Art1 (p.5 l.136-137) are listed in the ref cited: Kahlhofer et al. 2021.
Figures: In general, I think it would be clearer if the authors showed on the figures that the background strain in which the XXX gene is added (or its mutant forms) is a xxx∆ strain.
Referees cross-commenting
Cross review of Reviewer 1
- I don't believe that the authors "define a set of redundant c-terminal phosphorylation sites in Aly3", because phosphorylation is not proven.
- I thinks the points raised for Fig 3B are valid but the authors should focus on making their story conclusive before expanding to other data (except for the explanation of the smear, see my review). Also, I don't think 2NBDG actually works to measure Glc uptake.
- same for Fig 6 - not sure the interaction site mapping between Aly3 and Pubs would bring much value since there are more urgent things to do to make the story solid.
Cros review of Reviewer 3 - we have many overlaps, so briefly :
- I agree that the bibliography is incomplete (mentioned in my review)
- I agree that there is no demonstration of the phospho-status of Aly3, and it is a problem
- I agree that the results can be better quantified, esp. in the light of the points raised by this referee concerning the variability of expression of ST18A
Other specific comments :
- I agree that the statement that dephosphorylation activates alpha-arresting should be toned down - this was observed in several instances but there are examples of arrestin-mediated endocytosis which does not require their prior dephosphorylation.
- I fully agree that efforts could be made regarding the classification/nomenclature of arrestins in S. pombe, this had escaped my attention
Significance
strengths and limitations
This study aims at deepening our understanding of the regulation of endocytosis by signalling pathways through arrestin-like proteins in S. pombe. Ght5 is a nice model to study a physiological regulation, and the authors have a great set of tools at hand, including the discovery of Aly3 as the main arrestin for this regulation, and a signalling pathway (TORC2/Gad8) acting upstream. The main question is now to understand at the mechanistic level how TORC2 signaling impinges on the regulation of this arrestin.
Overall, the authors nicely demonstrate that C-terminal Ser/Thr residues are crucial for the function of Aly3 in Ght5 endocytosis. They propose a model whereby Aly3 phosphorylation by an unknownn kinase inhibits its function on Ght5 ubiquitination, which would favour its endocytosis. However, I think the conclusions are not always rigorous and the conclusions are sometimes far-reaching. The main problem is that much of the conclusions concern a potential phosphorylation of Aly3 which is not experimentally addressed. An additional issue is the fact that they look at Ght5 ubiquitination by co-immunoprecipitation in native conditions (or at least, it seems to me) which cannot be conclusive. Overall, I think some experiments should be performed to address (at least) these 2 points before the manuscript can be published, see detailed comments above.
Advance
This study, if completed carefully, would provide among the first examples of mapping of phosphorylation sites on arrestins, which are usually phosphorylated at many sites and are thus difficult to study. Few studies went down to this level in this respect (see Ivshov et al. eLife 2020). There are no changes in paradigms or new conceptual insights, but this work is a nice example of the conservation of these regulatory mechanisms.
Audience
Should be of interest for people studying basic research in the field of cell biology, signalling pathways, transporter regulation by physiology. Reviewer background is on the regulation of transporter endocytosis by signalling pathways and arrestin-like proteins.
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Referee #1
Evidence, reproducibility and clarity
Summary: The manuscript by Yusuke Toyoda and co-workers describes that the phosphorylation of the a-arrestin Aly3 downstream of TORC2 and GAD8 (AKT) negatively regulates endocytosis of the hexose transporter Ght5 in S.pombe under glucose limiting growth conditions.
To arrive at these conclusions, the researchers define a set of redundant c-terminal phosphorylation sites in Aly3 that are downstream by GAD8. Phosphorylation of these sites reduces Ght5 ubiquitination and endocytosis. For ubiquitination, Aly3 interacts with the ubiquitin ligases Pub1/3.
Major points:
Figure 3B: it would be interesting to compare Aly3 migration pattern (and hence potential phosphorylation) under glucose replete or limiting growth conditions. Can the authors provide direct evidence that Aly3 phosphorylation changes in response to glucose availability? Also please explain the 'smear' in lanes aly3(4th Ala), aly3(4th Ala, A584S), aly3(4th Ala, A586T).
Figure 4: Ght5 localization should be analyzed + / - thiamine and in media with different glucose levels. Also, a co-localization with a vacuolar marker (FM4-64) would be nice (but not necessary). Ideally, the authors should add WB analysis of Ght5 turnover to complement the imaging data. Also, would it be possible to measure directly the effects on glucose uptake (using eg: 2-NBDG).
Figure 5: Given the localization of Ght5 shown in Figure 4, I'm surprised that it is possible in to detect full length Ght5, and its ubiquitination in the phospho-mutants of Aly3. I expected that the majority of Ght5 would be constitutively degraded, and that one would need to prevent endocytosis and/or vacuolar degradation to detect full length Ght5 and ubiquitination. Please explain the discrepancy. Also it seems that the quantification in B was performed on a single experiment.
Figure 6: Which PPxY motif of Aly3 is used for interaction with Pub1/3 and does their interaction depend on (de)phosphorylation?
Significance
The results are well presented and clear cut (with few exceptions, please see major points). They provide further evidence that metabolic cues instruct the phosphorylation of a-arrestins. Phosphorylation then negatively regulates a-arrestin function in selective endocytosis and is essential to adjust nutrient uptake across the plasma membrane to the given biological context.
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Reply to the reviewers
Reviewer #1
i) "Enhancers dependent on TPR during senescence are enriched for binding sites of inflammatory transcription factors". *Proximity to genes does not confirm an enhancer role for that gene, although Tasdemir et al., 2016 suggested this. At that time, HI-C and Hi-CHiP techniques were not well-established. Nowadays, without combining HI-C and H3K27ac ChIP, Hi-ChIP alone cannot definitively identify actual enhancer regions. If we repeatedly use the Tasdemir et al., 2016 map, we risk incorrect mapping of enhancers of SASP. The authors should either use other public Hi-C databases to map the enhancer of SASP or temper their conclusions about enhancers. Otherwise, this could set a precedent for the SASP enhancer region that might not be entirely accurate. *
The enhancer mapping for SASP is outdated, as advancements in Hi-C have significantly developed this area. Therefore, the claimed enhancers of SASP may not be accurate.
__Response: __We agree with the reviewer that enhancers are not easy to define, or to pair with their target gene(s). Indeed, we would argue that even combined HI-C and H3K27ac does not define enhancers or enhancer-gene pairs and that the gold-standard evidence for an enhancer is genetics – does its deletion/mutation abrogate gene activation. We would also point out that we did not actually use the Tasdemir data to call enhancers. In response to the reviewer’s comment, we will temper our terminology and now refer to our inter-and intra-genic ATAC-seq peaks only as “putative enhancers”.
ii) “Many of these include putative enhancers located close to key SASP genes, such as IL1B and IL8 (Figure 1D).” I have the same concern as mentioned above (i). However, I am interested in knowing the other key SASP genes where DNA is accessible near the genes. A supplementary table listing key SASP genes along with their distances to the TSS and affected by TPR knock-down would be helpful.
__Response: __We thank the reviewer for this suggestion. We will provide tables listing the TPR dependent, senescent specific ATAC-seq peaks that are close to genes associated with the ‘positive regulation of inflammatory response’, ‘cytokine activity’ and ‘cytokine receptor binding’ gene ontology terms which were significant in the GREAT analysis, and which includes many SASP genes. We will also provide distances of these regions from the associated genes.
iii) "As we previously reported, knockdown of TPR (siTPR) in RAS cells blocks SAHF formation, but it also results in reduced nuclear localisation (decreased nucleocytoplasmic ratio) of NF-κB, consistent with decreased NF-κB activation (Figure 2A and B, Figure S2A)." TPR is required for CCF, SASP, and SAHF. The relationship between CCF and SASP is well established, but the relationship between SAHF and CCF/SASP remains elusive. Both SAHF and CCF are enriched with heterochromatin markers, suggesting that CCF might originate from SAHF. However, this has not been confirmed. Do the authors think that SAHF is a prerequisite for CCF in the OIS model, or is it an independent event?
Response: __We agree with the reviewer that CCFs likely originate from SAHF. Whilst we cannot definitively prove thisin our ER-Ras OIS model, in the revised manuscript we intend to further investigate the relationship between SAHF and CCF by knocking down HMGA1 during RAS-induced senescence. Like TPR, HMGA1 depletion is known to lead to loss of SAHF (Narita et al., Cell, 2006) but, unlike TPR, HMGA1 is a chromatin protein enriched on heterochromatin itself. We will assess whether loss of HMGA1 also abrogates CCF formation.__
iv) The authors suggested that "it is plausible that the decrease in CCFs produced during the early phases of OIS upon TPR knockdown may be caused by an increase in the stability of the nuclear periphery due to the heterochromatin that remains there when SAHF are not formed." I do not completely agree with this explanation because CCF starts forming at day 3-4 but culminates at later time points. According to Figure 5A, only 5-6% of cells are positive for CCFs on day 5. What happens on day 8? By day 8, the percentage of CCF-positive cells could be 20-25%, or the number of CCFs per cell might be 0.2-0.3. If TPR is not required for CCF formation at this stage, then linking CCF to SASP at day 8 becomes critical. This suggests that another mechanism might be driving SASP expression and that TPR could be regulating downstream signaling of CCF. It is possible that changes in nuclear pore density affect the localization of cGAS from the nucleus to the cytoplasm.
Response: __In our hands and using this IMR90 ER-RAS system, CCF formation decreases later in senescence (d8 - only 2% of cells) hence our focus on early timepoints after oncogenic RAS activation. At later timepoints, cGAS activation is also mediated by retrotransposons (de Cecco et al., Nature, 2019; Liu et al., Cell, 2023), as well as leakage of mitochondrial DNA (Victorelli et al., Nature, 2023; Chen et al., Nat. Comms, 2024), and so it is difficult to disentangle the net contribution of these three inputs.__
v) Additionally, the authors did not address what happens in the later stages of CCF formation in the absence of TPR. If TPR is not required for CCF formation at later stages, it fails to explain the downstream processes at these time points adequately. This suggests that TPR may also have another mechanism of SASP regulation independent of CCF formation.
__Response: __In our cellular system CCFs precede the SASP - CCFs are already present at day 3 but SASP factors are not secreted until day 5. However, CCFs are not necessarily required for maintenance of the SASP. Once initiated the SASP is maintained by cytokine feedback loops.
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Reviewer #2:
- The claim that TPR knockdown does not affect NFkappaB nuclear translocation indeed stands, but it would be nice if the authors also compared data across conditions in Fig. 2F, i.e. siCTRL+Ras CM versus siTPR+Ras CM in RAS cells and provided a p-value as it seems to me that there is some dampening of translocation intensity, which is clearly not the case for STOP cells. The authors focus on this for d3 and d5, but it seems to be also the case for later time points.
__Response: __As basal NF-κB translocation is lower in RAS cells on TPR knockdown, we would expect a dampening in NF-κB translocation between siCTRL+RAS CM and siTPR+Ras CM regardless of whether there is a transportation defect. Consistent with this, the p-value for this comparison is significant, but we did not show it because it is not important in considering whether NF-κB nuclear translocation is impeded by TPR knockdown, which is the focus here. We will add a table with median nuclear:cytoplasmic NF-κB ratios and 95% confidence intervals to make the changes in basal level (treatment with STOP CM) clearer.
Also, a comment based on literature or from the authors previous work on TPR, on the extent to which the structural integrity of the nuclear basket is at all affected upon TPR depletion would be helpful for data interpretation.
__Response: __In the revised manuscript we will refer to the literature showing that TPR is the final component added to the nuclear pore and that its absence does not affect localisation of NUP153 to the nuclear basket (Hase and Cordes., Mol. Biol. Cell 2003; Aksenova et al., Nat Comms, 2020).
Magnification of representative cells per each condition in Fig. 2E would be welcome.
__Response: __We will provide a revised figure 2E with the magnifications as requested.
Regarding the data in Figs 3 and S3: I am a bit confused about how the obviously decreased NFkappaB nuclear signal (e.g., in Fig. 3D) does not translate into a skewed N/C ratio (e.g., in Fig. 3C)? The western blots indicate that overall NFkappaB levels remain essentially unchanged? Am I missing something?
Response: __As stated in the Methods section, we used a 50-pixel expansion of the detected nuclear area as our cytoplasmic area in the analysis (see image below). This was because we found detecting and segmenting the whole cytoplasmic area in the NF-κB channel to be unreliable. At day 3 and 5, the decrease in NF-κB nuclear signal in RAS cells on TPR knockdown was accompanied by a decrease in signal in the portion of the cytoplasm closest to the nucleus. This led to no change in the nuclear:cytoplasmic ratio. We believe the redistribution of NF-κB closer to the nucleus in the RAS siCTRL sample indicates early activation and will make this clearer in the revised text. We will also quantify the NF-κB western blots (see point 5), to help clarification of this issue.____ __
Also, along these lines, d8 western blots seem to portray an overall drop in NFkappaB levels. Is this indeed so? Can the authors maybe quantify their blots' replicates and provide a box plot and statistical testing?
Response: __We will provide quantification for the NF-κB western blots, though box plots would not be appropriate as we only have two replicates.__
Regarding the ATAC-seq data from d3, I think it could be mined a bit more. For example, compare to d8 (which the authors have apparently done, but don't present in detail) and discuss which are these early regions that also become accessible by d3 and what kind of genes and motifs are associated with them. Moreover, the focus in Fig. S3E is on ATAC sites shared with d8; how about d3-specific ones? How many of these are there (if any) and how might they be affected?
__Response: __As shown in Table S2, TPR knockdown did not cause any changes in chromatin accessibility at day 3, so there are no day 3 specific TPR dependent peaks. We will edit the text to make this clearer. We will carry out motif analysis and GREAT analysis on the day 3 peaks that become accessible in RAS cells but are not accessible in STOP (RAS-specific peaks).
I trust that the authors quantified their STING blots for the conclusions they present, but since it is difficult to assess these confidently by eye, again, some quantification plots would be welcome in Figs 4C,D and S4D,E.
__Response: __We will provide quantification for the STING western blots.
As controls for Fig. 5, it would be interesting to see if active histone readouts also mark CCFs in this system.
__Response: __Ivanov et al., J. Cell Biol., 2013 showed the absence of H3K9 acetylation from chromatin in CCFs. Further exploration of the types of chromatin/sequences in CCFs is outside the scope of our current manuscript.
*The POM121 channel in Fig. 5C appears to have some small signal foci in the cytoplasm; could these be small CCFs? More generally, the authors focus on these large blobs that only appear in
__Response: __The small signal foci the reviewer is highlighting are background from the POM121 antibody staining rather than CCFs – they do not show DAPI staining, and similar foci are evident in non-senescent cells where CCFs are generally not present. Our unpublished data (see response to Reviewer 1, point iv) from day 8 cells shows that only ~2% of senescent cells are positive for CCF regardless of TPR knockdown, which is a similar number to that observed in non-senescent cells at earlier timepoints. Thus, in our hands CCF formation occurs earlier, triggering the SASP, rather than at day 8 when the SASP is already established and reinforced through positive feedback cytokine signalling.
I wonder if there is a simple experiment the authors could do to test if this mechanism is only linked to senescence, specifically oncogene-induced senescence? I don't think this is needed to support the conclusions drawn here, but it could significantly broaden the scope of their discovery of, for example, this was true in other senescence models or during proinflammatory activation in general?
__Response: __These are interesting suggestions, but setting up, characterising and quantifying other senescence models will take a substantial amount of time that would be outside the scope of our current manuscript.
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Reviewer #3
- The study uses a single cell strain IMR90 undergoing a single form of senescence, induced by activated Ras. To show the generalizability of the finding, the authors are advised to inhibit TPR in other forms of senescence in addition to IMR90. For example, IR or etoposide induces greater amount of CCF than in OIS of IMR90. BJ, MEFs, and ARPE-19 senescence also show prominent CCF.
__Response: __These are interesting suggestions, but as we responded to reviewer 2, setting up, characterising and quantifying other senescence models will take a substantial amount of time that would be outside the scope of our current manuscript.
To convincing show the CCF pathway is involved, the authors need to measure the activity of cGAS-STING pathway. Including cGAMP ELISA will be informative.
__Response: __We thank the reviewer for this suggestion, and we will try to include this assay in our revised manuscript.
The authors used conditioned media to show that TPR KD does not directly affect NFkB nuclear translocation. While this is helpful, conditions other than senescence will be more direct. For example, TNFa treatment or poly I:C transfection induces efficient NFkB nuclear translocation in IMR90 cells.
__Response: __This experiment (Fig. 2EF) was designed to simply show that knocking down TPR does not impair the ability of activated NFkB to enter the nucleus, it is not about senescence per se. Indeed, this is why we included the addition of SASP (RAS) conditioned media to non-senescence STOP cells in Fig. 2. We do not think investigating other methods of activating NFkB would add more to the question of whether TPR loss abrogates NFkB nuclear import.
Fig. 4C and Fig. S4D are identical.
Response: Though these STING immunoblots look similar; in fact they are not identical. Below we attach the raw original image in which both biological replicates (Fig 4C and S4D) for Day 3 were run on the same gel as proof of this claim.
Figure legend for Fig. S4F is mislabeled.
__Response: __We will correct this.
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Referee #3
Evidence, reproducibility and clarity
Bartlett et al reported that knockdown of TPR inhibits CCF and NFkB during oncogene-induced senescence in IMR90 cells. The manuscript is well-written, and the results are clear and convincing. I have the following suggestions.
- The study uses a single cell strain IMR90 undergoing a single form of senescence, induced by activated Ras. To show the generalizability of the finding, the authors are advised to inhibit TPR in other forms of senescence in addition to IMR90. For example, IR or etoposide induces greater amount of CCF than in OIS of IMR90. BJ, MEFs, and ARPE-19 senescence also show prominent CCF.
- To convincing show the CCF pathway is involved, the authors need to measure the activity of cGAS-STING pathway. Including cGAMP ELISA will be informative.
- The authors used conditioned media to show that TPR KD does not directly affect NFkB nuclear translocation. While this is helpful, conditions other than senescence will be more direct. For example, TNFa treatment or poly I:C transfection induces efficient NFkB nuclear translocation in IMR90 cells.
Minor:
- Fig. 4C and Fig. S4D are identical.
- Figure legend for Fig. S4F is mislabeled.
Significance
This study builds on the group's prior publication that knockdown of TPR inhibits SAHF and SASP. The current study finds that the underlying mechanism is via CCF-STING-NFkB pathway. Overall, the study broadens our understanding of CCF and SASP in senescence, and will be of general interest to the senescence field.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Bartlett et al. revisits the role of the nuclear pore component, TPR, in OIS to uncover its contribution to NFkappaB activation magnitude via the control of chromatin fragment release from the senescent nucleus. The flow of the manuscript is very good and the conclusions are supported by clear experimental evidence. This is an overall mature manuscript, and I offer below some comments that might help the authors streamline their message even more:
- The claim that TPR knockdown does not affect NFkappaB nuclear translocation indeed stands, but it would be nice if the authors also compared data across conditions in Fig. 2F, i.e. siCTRL+Ras CM versus siTPR+Ras CM in RAS cells and provided a p-value as it seems to me that there is some dampening of translocation intensity, which is clearly not the case for STOP cells. The authors focus on this for d3 and d5, but it seems to be also the case for later time points.
- Also, a comment based on literature or from the authors previous work on TPR, on the extent to which the structural integrity of the nuclear basket is at all affected upon TPR depletion would be helpful for data interpretation.
- Magnification of representative cells per each condition in Fig. 2E would be welcome.
- Regarding the data in Figs 3 and S3: I am a bit confused about how the obviously decreased NFkappaB nuclear signal (e.g., in Fig. 3D) does not translate into a skewed N/C ratio (e.g., in Fig. 3C)? The western blots indicate that overall NFkappaB levels remain essentially unchanged? Am I missing something?
- Also, along these lines, d8 western blots seem to portray an overall drop in NFkappaB levels. Is this indeed so? Can the authors maybe quantify their blots' replicates and provide a box plot and statistical testing?
- Regarding the ATAC-seq data from d3, I think it could be mined a bit more. For example, compare to d8 (which the authors have apparently done, but don't present in detail) and discuss which are these early regions that also become accessible by d3 and what kind of genes and motifs are associated with them. Moreover, the focus in Fig. S3E is on ATAC sites shared with d8; how about d3-specific ones? How many of these are there (if any) and how might they be affected?
- I trust that the authors quantified their STING blots for the conclusions they present, but since it is difficult to assess these confidently by eye, again, some quantification plots would be welcome in Figs 4C,D and S4D,E.
- As controls for Fig. 5, it would be interesting to see if active histone readouts also mark CCFs in this system.
- The POM121 channel in Fig. 5C appears to have some small signal foci in the cytoplasm; could these be small CCFs? More generally, the authors focus on these large blobs that only appear in <6% of cells in d3 and d5. Does this increase by d8? What is the effect of TPR knockdown on CCF numbers at that later time point?
- I wonder if there is a simple experiment the authors could do to test if this mechanism is only linked to senescence, specifically oncogene-induced senescence? I don't think this is needed to support the conclusions drawn here, but it could significantly broaden the scope of their discovery of, for example, this was true in other senescence models or during proinflammatory activation in general?
I typically disclose my identity to the authors: A. Papantonis
Significance
This is a very clearly written and well-controlled study that addresses a gap in knowledge from the previous work on TPR in senescence. It also brings about a perhaps unexpected effect of a nuclear pore component on NFkappaB signaling that might not necessarily be senescence-specific. As such, I think that the study would be of interest to both the senescence community and to researchers studying inflammatory responses, especially those driven by TNFalpha or IL1A/B.
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Referee #1
Evidence, reproducibility and clarity
DNA damage triggers senescence, inducing chromatin reorganization and SASP activation. The authors previously demonstrated that the TPR nucleoprotein at nuclear pores is crucial for both SAHF formation and SASP activation during senescence. Here they also showed that TPR is required for the formation of cytoplasmic chromatin fragments (CCF), which activate cGAS-STING-TBK1-NF-kB signaling to express SASP. While the mechanistic regulation of CCF formation by TPR remains unclear, their study provides compelling evidence of downstream processes involving CCF. This study offers new insights into CCF formation, suggesting a promising direction for further research. I endorse the manuscript; however, there are several concerns that need addressing before acceptance.
i) "Enhancers dependent on TPR during senescence are enriched for binding sites of inflammatory transcription factors".
Proximity to genes does not confirm an enhancer role for that gene, although Tasdemir et al., 2016 suggested this. At that time, HI-C and Hi-CHiP techniques were not well-established. Nowadays, without combining HI-C and H3K27ac ChIP, Hi-ChIP alone cannot definitively identify actual enhancer regions. If we repeatedly use the Tasdemir et al., 2016 map, we risk incorrect mapping of enhancers of SASP. The authors should either use other public Hi-C databases to map the enhancer of SASP or temper their conclusions about enhancers. Otherwise, this could set a precedent for the SASP enhancer region that might not be entirely accurate.
ii) Many of these include putative enhancers located close to key SASP genes, such as IL1B and IL8 (Figure 1D).
I have the same concern as mentioned earlier about enhancers. However, I am interested in knowing the other key SASP genes where DNA is accessible near the genes. A supplementary table listing key SASP genes along with their distances to the TSS and affected by TPR knock-down would be helpful.
iii) "As we previously reported, knockdown of TPR (siTPR) in RAS cells blocks SAHF formation, but it also results in reduced nuclear localisation (decreased nucleocytoplasmic ratio) of NF-κB, consistent with decreased NF-κB activation (Figure 2A and B, Figure S2A)." TPR is required for CCF, SASP, and SAHF. The relationship between CCF and SASP is well established, but the relationship between SAHF and CCF/SASP remains elusive. Both SAHF and CCF are enriched with heterochromatin markers, suggesting that CCF might originate from SAHF. However, this has not been confirmed. Do the authors think that SAHF is a prerequisite for CCF in the OIS model, or is it an independent event?
iv) The authors suggested that "it is plausible that the decrease in CCFs produced during the early phases of OIS upon TPR knockdown may be caused by an increase in the stability of the nuclear periphery due to the heterochromatin that remains there when SAHF are not formed." I do not completely agree with this explanation because CCF starts forming at day 3-4 but culminates at later time points. According to Figure 5A, only 5-6% of cells are positive for CCFs on day 5. What happens on day 8? By day 8, the percentage of CCF-positive cells could be 20-25%, or the number of CCFs per cell might be 0.2-0.3. If TPR is not required for CCF formation at this stage, then linking CCF to SASP at day 8 becomes critical. This suggests that another mechanism might be driving SASP expression and that TPR could be regulating downstream signaling of CCF. It is possible that changes in nuclear pore density affect the localization of cGAS from the nucleus to the cytoplasm.
Significance
The authors previously demonstrated that the TPR nucleoprotein at nuclear pores is crucial for both SAHF formation and SASP activation during senescence. Here they also showed that TPR is required for the formation of cytoplasmic chromatin fragments (CCF), which activate cGAS-STING-TBK1-NF-kB signaling to express SASP. While the mechanistic regulation of CCF formation by TPR remains unclear, their study provides compelling evidence of downstream processes involving CCF. This study offers new insights into CCF formation, suggesting a promising direction for further research.
However, there are some limitations to this study. The enhancer mapping for SASP is outdated, as advancements in Hi-C have significantly developed this area. Therefore, the claimed enhancers of SASP may not be accurate. Additionally, the authors did not address what happens in the later stages of CCF formation in the absence of TPR. If TPR is not required for CCF formation at later stages, it fails to explain the downstream processes at these time points adequately. This suggests that TPR may also have another mechanism of SASP regulation independent of CCF formation.
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Reply to the reviewers
*Reviewer #1 *
1. The authors conclude that RFP-Ac expression is restricted to emerging SOPs and surroundings cells at 18h APF, indicating that Ac is activated later than Sc. Can the authors provide images for RFP-Ac expression at 10h and 16h APF similar to GFP-Sc as shown in their figures. Do the SOPs that contain high levels of both Ac and Sc (as some SOPs have Sc expression but not Ac) undergo fate divergence and SB faster than the SOPs containing higher levels of only Sc?
We are now showing the expression pattern of GFP-SC and RFP-Ac/GFP-Ac in fixed samples stained also for E-cad at 13h, 16h and 18h APF (Fig 1I-K' and Fig S1E-G'). Ac and Sc were found to be activated around the same time. However, Ac appeared to accumulate at lower levels than Sc prior to SOP selection in the central domain of the ADHN (Fig 1J-K'). We also confirmed that Ac was more strongly expressed in SOPs. Additionally, SOPs appeared to accumulate both Ac and Sc, i.e. SOPs with high levels of GFP-Sc also showed a strong RFP-Ac signal (Fig S1H-H'). Finally, since RFP-Ac was not detectable in living pupae, possibly due to the rapid turn-over of Ac and the slow maturation of RFP, we could not study more precisely the relative dynamics of Ac and Sc. For the same reason, we could not address whether the rate of fate divergence (measured using GFP-Sc) varied with the level of Ac.
2. It would be interesting to see the spatial and temporal dynamics of Ac and Sc in Notch mutants or even Notch dynamics in Sc and Ac mutants to better understand the progression fate divergence and its effect on lateral inhibition in real time.
Following the reviewer's suggestion, we examined the expression pattern of NRE-deGFP, a Notch activity reporter, in ac sc double mutant pupae at 16h and 24h APF (Fig S3A-D). This showed that the initial pattern of NRE-deGFP at 16h APF (signal detected in posterior ADHN cells as well as in the ADHN) did not depend on Ac and Sc. By contrast, the second phase of NRE-deGFP expression (in cells of the proneural ADHN domain, around emerging SOPs) was found to depend on the activity of Ac and Sc. Thus, strong Notch activation observed in cells surrounding emerging SOPs was found to depend on the activity of Ac-Sc, presumably because Ac and Sc are required for SOP specification and SOPs produce Delta, serving as the local source to activate Notch (see also our response to reviewer 3, point #6). Thus, since NRE-deGFP was not up-regulated in the proneural ADHN domain of sc10-1 ac3 mutant pupae, a quantitative analysis of the dynamics of NRE-deGFP may not be informative.
The reviewer also suggested us to study the dynamics of GFP-Sc in Notch mutants. One can easily predict that most Notch mutant cells would accumulate GFP-Sc, as observed in the notum (PMID: 28386027). Therefore, analysis of fate symmetry breaking is unlikely to be useful in that context. Likewise, a FDI analysis would not be relevant. From a technical point of view, live imaging of GFP-Sc would have to be performed in Notch mutant clones. This is because RNAi against Notch (strong 10xUAS-Notch hp2 construct, PMID: 19487563) driven by escargot-Gal4 to knock down Notch in larval histoblasts only led to a partial loss of Notch function (our unpublished data). Generation of Notch mutant clones in the abdomen would require constructing appropriate GFP-Sc Notch FRT recombinant chromosome as well as generating a new FRT GFP-Sc chromosome with an infrared marker (not currently available) to compare the relative dynamics of GFP-Sc in wild-type and mutant cells. In sum, this proposed experiment would take a significant amount of time and is unlikely to shed new light. Given that this experiment is not essential to support the claims of the paper and that it is not clear to us what would be learnt from this experiment, we opted for not performing this experiment.
Minor comments * 1. In figure 1F and F', the authors mention GFP-Sc is not expressed prior to 14h, however, there is still GFP signal detected in their imaging. Can the authors comment what would be the cause of this GFP signal or was it due to non-specific background signal during their imaging analysis?*
We thank the referee for raising this issue. Yes, a strong autofluorescence signal was detected prior to the onset of GFP-Sc expression. We provide below the results of our analysis of the autofluorescence signal (Fig R1) relative to the nuclear signal (Fig R2), and how normalization of the signal was used to measure the specific GFP-Sc signal.
Analysis of the autofluorescence signal over time
To estimate the autofluorescence signal, we measured the average intensity of the signal acquired in the GFP channel for each frame and plotted these values over time. The results are shown in Fig R1 below:
*Fig R1: temporal profile of the autofluorescence signal *
Each measurement corresponds to the average intensity measured in the GFP channel over the entire field at each z-section and for each time point. Mean and SD values of measured are shown over time in black and grey, respectively. Time is in frame number (dt is 2.5 min). The data shown above corresponds to movie 1 (see also Fig 2).
This plot indicates that the autofluorescence signal was progressively bleached. We therefore excluded from our analysis the first 50 time points when the autofluorescence signal was initially strong. No nuclear GFP-Sc signal was detectable in these first 50 frames in the cells of the central area of the ADHN which are studied here (see Fig 2A', t=1:12, time frame #29).
While revising the manuscript, we realized that t=0 corresponded to two distinct time points in the first version of our manuscript: it corresponded to the onset of imaging in Fig 2A-D', and to t=2:08 (time frame #51) in all other figures showing data following removal of the first 50 time points. We have now fixed this issue and are presenting all data with t=0 corresponding to the onset of imaging.
Analysis of the nuclear fluorescence signal over time
To detect the nuclear GFP-Sc signal, we measured the average intensity of the signal acquired in the GFP channel (raw intensity values corresponding to the sum of the GFP-Sc and autofluorescence signals) in segmented nuclei (in 3D, within the entire z-stack). These values were plotted over time (pink curve in Fig R2 below; the autofluorescence is plotted in black, as in Fig R1, for the sake of comparison). This showed that the intensity of the signal measured in nuclei was initially identical to the mean intensity measured across the entire field of view, indicative of autofluorescence only. A specific increase in signal intensity in nuclei (relative to the entire field of view) was detectable after 2h of imaging (time frame 48 in Fig R1; dt is 2.5 min). Importantly, mean intensity values of the autofluorescence signal appeared to be approximately 10-fold stronger than the mean intensity associated with the nuclear GFP-Sc signal.
Fig R2: temporal profile of the GFP-Sc signal
*The plot in pink corresponds to the average intensity in the GFP channel (raw intensity values corresponding to the GFP-Sc and/or autofluorescence signals) per nucleus (within the entire z-stack) for each time point. Mean and SD values measured in each nucleus are shown over time (in pink; these data correspond to movie 1; shown also in Fig 3). This plot (pink) should be compared with the plot shown in Fig R1 (also in black in Fig R2). The intensity difference between the pink and black curves was attributed to the specific GFP-Sc signal. *
Signal normalization and analysis of the GFP-Sc signal
In our study, we normalized the GFP-Sc signal by dividing the averaged value measured in each single nucleus (data corresponding to the pink curve in Fig R2) by the mean value of the signal measured at the same time point in the same channel in the entire image stack (data corresponding to the black curve in Fig R1/R2). Given the low intensity of the GFP-Sc signal, and the small number of pixels corresponding to Scute-expressing nuclei over the entire field of view, this value should closely reflect the autofluorescence noise. Thus, the background autofluorescence signal should be close to 1. This was experimentally verified by measuring the normalized intensity values of the PDHN nuclei that did not express Scute: a mean intensity value of 0.96 +/- 0.10 was measured (at time frame #51; see Fig R1 below). In contrast, the normalized GFP-Sc values measured several hours before SB were found to be close to 1.1 (see Fig 3D). Whether these values reflect very low levels of nuclear GFP-Sc that cannot be detected visually or result from imperfect normalization of the signal remain unclear. Given the intensity and non-uniformity of the autofluorescence signal, we cannot exclude the latter. For this reason, we chose to not over-interpret the initial low intensity values of GFP-Sc.
In the materials and methods, the authors mention that prior to imaging the larvae and pupae are grown at 18, 21 or 25{degree sign}C. Is there a reason why the larvae and pupae are grown at different temperatures for different experiments? Can the authors specify (i.e. in the figure legends) in which experiments different temperatures were used?
Larvae and pupae were grown at different temperatures for convenience, i.e. to adapt the time interval between staging at 0h APF and mounting for live imaging. Indeed, it is much easier to obtain 10-14h APF pupae by collecting staged pupae at 0h APF the day before and incubating them overnight at lower temperature to slow-down development. However, all live imaging experiments were performed at 23-25{degree sign}C, and we have no reason to think that this prior incubation would affect the process studied here.
The citations need to have a better format as they show up as each citation within a single bracket which makes it a little hard to read when multiple references are cited in a single sentence. fixed
In the abstract, the sentence 'Unexpectedly, we observed at low frequency (10%) pairs of cells that are in direct contact at the time of SB'. SB should be replaced with "Symmetry breaking", as it appeared for the first time in the manuscript and should be written out in full. fixed
Throughout the manuscript there are instances where the abbreviations are written in full with the abbreviation in brackets after they have already been introduced in the introduction which can be changed to just the abbreviation itself. fixed
In the discussion on page 11, 'our observation...', our needs to be changed to Our. fixed
7. It would be nice to have arrow heads or dotted lines around the cells or areas on interest in both, all the figures and movies, so that it will be easier to follow the results. The videos have a lot of background due to fragmented apoptotic nuclei, etc. as mentioned by the authors, hence arrow heads or dotted lines would bring viewers focus on the areas of interest.
fixed (see for instance Fig 1D, Fig 2A, Fig 5B, Fig 7A, Fig S3D, etc...)
8. It would be helpful to have anterior - posterior axis (i.e. with an arrow) shown on top of all the figures.
In our earlier version, we indicated that 'In this and all other figures, dorsal is up and anterior is left' in the legend of Fig 1B. We have now moved this sentence at the end of Fig 1 to have it more apparent. Additionally, the AP axis is now clearly indicated in Fig 1C. We believe that it is not necessary to repeat this orientation in all figures.
Scale bars are missing in all figures, videos, and figure legends. Added
Only movies 1 and 3 are referenced in the text. All movies are now referenced in the text
Keeping the colors in the movies and figures consistent and same would be helpful. For example, Movie 2 Histone3.3-mIFP marker is in blue but in figure 3 it is in magenta. fixed (H3.3-mIFP in magenta in this movie, now numbered 3)
As mentioned above, it would be helpful if the authors have arrow heads or dotted lines around the cells or areas of interest in both the figures and movies for better representation of their data. For example, movie 1 shows a larger area of imaging than shown in figure 2A, which makes it hard to follow the cells of interest in the movie.
An additional movie corresponding to the SOP shown in Fig 2A is now provided (new movie 2).
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Reviewer #2
- Despite "symmetry breaking" being the main focus of the paper, in the Introduction, the authors do not explain what this term means and do not provide any description of this process. This is a critical point that makes understanding of the goals of the paper difficult. Therefore, the authors are encouraged to provide more information and a clear description of this term/phenomenon. We thank the reviewer for this suggestion, we are now stating in the introduction what symmetry breaking means in the context of lateral inhibition: 'To describe and study the process of SOP selection, we studied fate SB. The latter refers to the transition point when one cell, the future SOP, starts to stably accumulate a higher level of GFP-Sc relative to its immediate neighbors.'
The role of Achaete in the story is not clear. Even though both factors are required for SOP determination, the authors mainly focus on Scute, so it is not very clear what the role of Achaete in this process is, if there is any. As shown in the paper, Achaete is expressed later when heterogeneity is promoting cell fate divergence. Is Achaete maybe contributing to cell heterogeneity/ cell fate divergence?
We thank the reviewer for raising this point. We now show in Fig S1A-D that abdominal bristles develop in a protein null allele of sc (scM6 ) as well as in an ac mutant corresponding to a 45 kb deletion that removes ac but not sc (PMID: 16216235)). Together with our analysis of sc10-1 ac3 __mutant flies, we can now conclude that __Sc and Ac act redundantly for SOP specification in the pupal abdomen. We have also further studied the expression of Ac relative to Sc and E(spl)HLH-m3 (see our response above to point #1 of reviewer 1). We fully agree with the reviewer that cell-to-cell variations in Ac expression might contribute to proneural heterogeneity and SB. This is now briefly discussed.
Minor points: * * 1. Symmetry Breaking (SB) should be abbreviated in the Abstract. The authors initially use the full term without abbreviation, and only on page 5, the abbreviation is finally defined; however, it should be introduced much earlier.
fixed
The second-to-last sentence in the abstract, "These lateral inhibition defects were correlated via cellular rearrangements," is unclear regarding what defects the authors are referring to.
This sentence was rewritten: 'Live imaging showed that these patterning defects were corrected via cellular rearrangements associated with global tissue fluidity, not via cell fate change.'
For clarity, being more specific in the text in regards to description of the figure panels would be beneficial (e.g. page 3 Fig 1C-E); referring to C-E together makes it hard to understand what does each panel shows.
fixed
In many instances, the movies are not properly referenced (e.g. on page 5, third row simply states "movies"), making it difficult to discern which movie should be checked. On page 8, when authors refer to movie 3, they likely meant movie 5.
fixed
Figure S1 requires some corrections.
We thank the reviewer for helping us improve the presentation of our results.
The authors use the short name "scute" initially and then switch to the shortened version "sc'.
fixed
Additionally, the nlsRFP (blue) is difficult to see; adjusting the levels or changing colors/showing separate channels may improve visibility.
The authors mention clone borders, but none are shown. It would greatly help to outline the borders in all figures.
The ubiquitous nlsRFP marker is now shown in magenta in Fig S1I that now shows only 2 channels to outline the ADHN (white dotted line) and the clones (yellow dotted lines).
We also outlined the clone borders in Fig 4C,C'.
Genotypes of the samples should be indicated, and clarification is needed regarding what "n" represents (number of cells, clones, or flies).
The genotype studied in Fig S1 and Fig 4 (which is the only complex genotype studied here) is now indicated in the Methods section. We have clarified what the different 'n' meant, in Fig 4 (see text) and elsewhere (see legend of Fig S2 for instance).
What do the arrows in the panel B show?
Thanks for pointing this out. The arrows in Fig S1I' indicate Cut/Hnt-positive cells (SOPs) within the clones (as now explained in the legend).
It is also recommended to display important channels as separate black and white images.
Separate channels are now shown in Fig S1 and S3.
Additionally, the use of RNAi against GFP instead of RNAi against scute should be justified; using RNAi GFP as the genotype on the graph could be interpreted as a control genotype rather than downregulation of scute.
A RNAi construct against GFP was used because this construct was known to very efficient and specific. Indeed, a strong knock-down of GFP-Sc was obtained by this approach (see Fig 4C'). We did not test sc RNAi constructs in the context of GFP-Sc. To avoid confusion, we are now indicating Sc downregulation (gfp RNAi) in Fig 4C'.
In the Figure 2 Legend, the authors use "std" as an abbreviation to define standard deviation. Typically, this is abbreviated as SD.
fixed
In Figure 4E, the authors do not explain on why there are points on the x-axis that correspond to a decimal number of cells.
Since heterogeneity was calculated over a 20 min interval, we likewise calculated the number of neighbors over the same time interval. Thus, the number of neighbors for each SOP corresponds to an averaged value calculated over this time interval. This is now explained in the legend.
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Reviewer #3
- First and foremost, the authors should state in the first paragraph of the Results that scGFP is a CRISPR knockin and thus it's the only source of Sc protein in the animals imaged (this is stated only in the Methods section). Thanks for this comment, we agree that this is one of the strengths of our work that we should emphasize. We now state in the results section: 'GFP-Sc is produced from the endogenous locus such that all Sc molecules produced in these pupae are GFP-tagged'
The magnitude of the Sc increase should be commented on. Based on the intensity and FDI plots in Fig. 3B-E, an increase of 15-17% in the amount of Sc is suggested (the FDI plateaus at 0.08, which gives 1.08/0.92 = 1.17x the level of Sc in the SOP vs the surrounding cells). However, in the stills shown in Fig. 2BCD and in Fig. 3A, the intensity differential between SOPs and neighbors seems at least 100% (ie at least double the intensity, which would yield an FDI of >1/3 =0.33). Why is this high contrast never seen in the quantitative measurements?
Thanks for asking about the fold change of GFP-Sc levels in SOPs, from SB to its plateau. This fold change can be seen in Fig 3D: the normalized value of GFP-Sc is 1.12 at SB, and 1.26 three hours after SB (when the FDI plateaus), indicative of a 2.2x fold increase of GFP-Sc in SOPs (0.26/0.12= 2.2, following background subtraction; see our detailed response to reviewer #1, minor point 1, about background signal analysis and normalization of the signal). This fold-change value is now indicated in the legend of Fig 3D. Obviously, this fold-change value is highly sensitive to signal normalization. Since the autofluorescence signal was stronger than the GFP-Sc signal (see Fig R2 above) and varied over time (due to bleaching; see Figs R1 and R2 above), we feel that this fold-change value should be taken with a grain of salt.
From Fig. 2A-D it appears that the ScGFP fluorescence intensity is at the same level or weaker than nearby autofluorescence. Please state (1) how you confirmed that the histoblast nest has lower autofluorescence than the larval epidermis and (2) how you corrected for histoblast nest autofluorescence in your quantifications.
As detailed above (our response to reviewer #1, minor point 1), the specific GFP-Sc signal is ten-times lower than the autofluorescence signal. We did not compare the autofluorescence signal produce by larval and imaginal cells (but note that larval epidermal cells had a stronger autofluorescence signal; see the yellow dots in Fig 2A). Normalization of the signal to correct for autofluorescence was explained in the Methods (and is also detailed above in our response to reviewer #1, minor point 1).
The paradoxical result of Fig. S1B should be discussed. On the one hand it is stated that "Ac and Sc specify the fate of the Sensory Organ Precursor cells (SOPs)" (p.2) and on the other S1B shows SOP specification in the absence of Sc. Are the SOPs shown in Fig S1B rare exceptions? Do the authors believe that these rare exceptions are there because of inefficient RNAi (since in comparison with S1A, in the null condition almost no SOPs should be formed)? Or they are the SOPs in RNAi clones as rare as the occasional bristles in S1A?
We do not see the result of Fig S1B as paradoxical but interpreted this result assuming that Ac and Sc were redundant for SOP determination. We now provide clear genetic evidence in support of this view (see our response above to reviewer #2, point 2). Otherwise, we found that RNAi is efficient (see loss of the GFP signal in clone in Fig. 4C'). In adult males, the density of bristles appeared to be quite normal over clonal patches of gfp RNAi cells (not shown), consistent with Ac being redundant with Sc
One figure that is not straightforward to interpret is Fig. 4E. It plots ScGFP heterogeneity vs. number of RNAi neighbors. Each point in the plot must be an individual SOP (165 total). Therefore, its neighbors (the x-axis) should take integral (not decimal) values. How can a single SOP have a decimal number of RNAi neighbors, especially since heterogeneity was sampled over a 10min time-window, when not much cell rearrangement can take place? Please explain.
Since heterogeneity was calculated over a 20 min interval, we likewise calculated the number of neighbors over the same time interval. Thus, the number of neighbors for each SOP corresponds to an averaged value calculated over this time interval. This is now explained in the legend: 'Note that the number of neighbors was likewise calculated over this time interval, and the resulting number of neighbors may not take an integral value.'
I found the discussion of the Notch reporter dynamics (Fig. 7) confusing in several places. * * (6a) Whereas it's clear that there is plenty of Notch signaling going on before SBN, the authors repeatedly imply that Notch signaling starts after SBN. For example, in the Results (p.9) they state "Thus, this quantitative approach failed to detect a phase of reciprocal Notch signaling during which proneural cluster cells would both send and receive a Delta-Notch signal prior to SOP emergence." The fact that the NRE-deGFP gave a robust signal before the start of the movies clearly means that mutual inhibition was going on for quite some time before SB. In fact, an FDI of 0 for >4h prior to SBN (Fig. 7G) means exactly this: that the level of Notch response among the cluster cells is equivalent ("mutual inhibition" lasts for at least 4h before SBN). (6b) In the first paragraph of this section (p.8) they comment that the pre-existence of Notch signaling is unexpected - why? I interpret it to simply be mutual inhibition (see above). Then they go on to quantitate the average Notch response intensity over the entire posterior ADHN (please define the borders the "posterior" ADHN). I question the informational value of this analysis (averaging over a large region), when Notch signaling is known to have intense local cell-to-cell variability (also evident in the stills shown in Fig. 7A,B,C).
We apologize for not describing well enough the data shown in Fig 7E, and for not explaining clearly our interpretation of the NRE-deGFP signal.
While the observation of a strong NRE-deGFP signal indeed indicates that Notch signaling had been active prior to the time of observation (in this sense, Notch is indeed active long before SBN), this does not necessarily imply that Notch is still active at that time. This is because the deGFP protein produced by the NRE-deGFP reporter is stable relative to the time scale of the studied process. Its measured half-life in S2 cells cultured at 25{degree sign}C is 2h (PMID: 31140975). Based on this data, the NRE-deGFP signal is likely to remain detectable several hours after Notch signaling has been switched off. If the rate of production of deGFP is lower than its rate of degradation, then the NRE-deGFP signal is expected to progressively decay over time. We believe that this is what we observed in our movies: while a strong signal was detected over the posterior half of the ADHN at 14-15h APF, this signal decreased over time (Fig 7D). To interpret the temporal dynamics of NRE-deGFP signal in terms of instantaneous Notch activity, we examined the Rate of Change (ROC): an increase of the NRE-deGFP signal over time (positive ROC) would indicate that Notch activity is increasing (more precisely, the production rate of deGFP is higher than its rate of degradation), whereas a decrease (negative ROC) indicates that Notch becomes less active (or inactive if the rate of decrease approximates the decay rate of the deGFP protein). Our data shown in Fig 7D showed that the NRE-deGFP signal (measured in the area indicated with a dotted line in Fig 7A,B; this area was defined by the initial pattern of NRE-deGFP) decreased over time (negative ROC) between t=1 and t=6.5h. We therefore conclude that Notch signaling is decreasing to reach a minimum at t=~3.5h, indicating that the level of Notch activity is at its lowest around the time of SB. At this minimum, the decay rate corresponds to a protein half-life of 4.4h, which is not so different from the measured half-time of deGFP in S2 cells (particularly if one assumes a 1.4x difference between the decay rates measured at 22 and 25{degree sign}C, based on the known temperature-dependent speed of development). This is why we conclude that Notch signaling is very low at this stage. Additionally, no NRE-deGFP signal was detected before t=4:30h (movie 7) in the initially NRE-deGFP negative cells (located anterior to the area indicated with a dotted line in Fig 7A). This indicated that Notch was activated late in this area. Together, our observations are not consistent with the view that Notch mediates a strong mutual inhibition signal over a prolonged time interval prior to SB.
To further study the pattern of Notch activity, we have monitored over time the accumulation pattern of GFP-tagged E(spl)m3-HLH (GFP-m3) (PMID: 31375669) in fixed sample (Fig S3F-G'). This confirmed that Notch was active in posterior ADHN cells and in the PDHN prior to 14h APF, i.e. prior to the onset of Ac and Sc, and that Notch activation extended to the central ADHN domain at 17-18h APF (Fig S3E-E' and G-G', and Fig 7I-I''), coinciding with SOP emergence.
Otherwise, the reviewer is correct when stating that a FDI value close to 0 indicates that the level of measured fluorescence among the different cells of the considered cluster is similar. Such a FDI value would be measured if cells did not express NRE-deGFP or had decreasing but similar levels of NRE-deGFP. This FDI value does not, per se, imply that Notch is active.
And then they move on to a (much more informative) cell-by-cell analysis, without even changing paragraphs, making it hard for the reader to follow. (6c) The conclusion at the end of the second paragraph (p. 9) "It also showed that SB was detected soon after the onset of Notch-mediated inhibitory signaling." is nowhere supported by data. If I understand well, SB refers to Sc and "the onset of Notch-mediated inhibitory signaling" refers to SBN (which is the onset of ASYMMETRY in Notch signaling, not the onset of Notch signaling, which has been going on for hours earlier). I don't see any data comparing SB with SBN. In fact, this is an important question to address (see below - comment 10).
We apologize for the lack of clarity in our writing, we meant: "It also showed that SBN was detected soon after the onset of Notch-mediated inhibitory signaling."
Yes, SBN refers to the onset of asymmetry in Notch signaling, as measured using NRE-deGFP. As explained above (but see also our response to point #7 below), our data do not provide evidence for a detectable Notch signal prior to SBN.
We agree that comparing SB and SBN would be nice. Unfortunately, our current tools do not permit a detailed comparison (see our detailed response below, point #10).
Mutual inhibition amongst neighboring cells has been proposed to involve (besides mutual Notch signaling) an increase in Sc levels in 2-3 cells in a cluster before the singularization of a single SOP. The authors seem rather biased against such a transient Sc hike based on their results in Fig. 2D, where the neighboring cells stay at rather constant basal Sc levels for several hours, while the Sc SB event happens. However, looking at an individual SOP in Fig. 2B, I do detect a mild hike in the pink curve right around SB in the blue curve. Could the average result from 160 SOPs (in Fig 2D) simply blur such transient Sc hikes, if they happen with different kinetics for different SOPs? Couldn't the 10% of SOP twins (shown in Fig. 6) represent a special case of this transient "subcluster" Sc hike? I would appreciate some discussion on this point. [Whether Sc is transiently upregulated or not, however, does not change my firm conclusion - from the data presented - that Notch-mediated mutual inhibition has been going on long before SBN.]
First, our data are consistent with the notion that a few proneural cells progressively accumulate higher level of Scute prior to SB (as proposed above). Indeed, the moderate increase in both GFP-Sc levels and coefficient of variation values (GFP-Sc heterogeneity) seen prior to SB correspond to what the reviewer has in mind (higher levels of GFP-Sc in a few proneural cluster cells). We also appreciate the reviewer's comment about the plot shown in Fig 2D. However, we strongly feel that our quantitative analysis of a large dataset is a strength. Thus, we do not find useful to discretize a continuous process by introducing the notion of 'subclusters' of 2-3 cells. Likewise, we believe that it is more informative to focus our analysis on the entire dataset using average and SD values and do not wish to base our interpretation of the process based on selected tracks (the one shown in Fig 2B only served as an illustration of how we performed our analysis and has no interpretation value).
The reviewer also states that "mutual inhibition amongst neighboring cells has been proposed to involve an increase in Sc levels in 2-3 cells in a cluster before the singularization of a single SOP". Since there is no published description of the pattern of accumulation of Scute in abdominal histoblats (to our best knowledge), we hypothesize that this statement applies to the proneural clusters in the developing wing disc. This is because the accumulation pattern of Sc has been studied in detail in that context by the Modollel and Carroll labs (PMID: 2044965, PMID: 2044964). However, their description of the accumulation pattern of Scute (in fixed samples, using anti-Sc antibodies) did not refer to sub-clusters of 2-3 cells. We would appreciate if the reviewer could direct us to the relevant published observation.
Finally, we are not sure to follow the reviewer when she/he firmly concluded from our data that Notch-mediated mutual inhibition has been going on long before SBN. Instead, our data clearly showed that the ADHN region that produced SOPs exhibited two distinct NRE-deGFP patterns, with Notch signaling being active prior to imaging (i.e. prior to 14h APF) and decreasing to reach a minimum of Notch activation around 17h APF (i.e. around the time of SB, as determined by GFP-Sc imaging) in the posterior area of the ADHN.
Thus, our data do not show that mutual inhibition does not take place in this tissue but rather imply that the phase of mutual inhibition (or competition) must be relatively short, or transient, and that competition amongst proneural cluster cells operate at low Notch and Sc levels (probably contrary to what many people have in mind).
Some minor points: * * 8. Please change Cad-GFP to Ecad-GFP or shg-GFP, as Cad misdirects to caudal.
Thanks, changed into Ecad-GFP and Ecad-mKate
What is c in "(x,y,z,c,t) movies"? (a fifth independent variable?)
c stands for channel. This is relatively standard nomenclature.
The authors show that Sc displays a SB event leading to FDI of 0.08 and the Notch reporter displays another SB (SBN) leading to a much more pronounced FDI of -0.2. Are these two events (the hike of Sc levels and the plummeting of Notch signal) contemporaneous or does one precede the other? Having both tagged with GFP makes it impossible to image simultaneously, but the authors could register each reporter's dynamics relative to the time of SOP division (as done in Fig. 5C) to get a sense of their relative order.
We do agree with the reviewer that it would be nice to be able to align in time these two data sets. Unfortunately, the temporal correlation between SB and the SOP division is too variable (4.7 +/- 1.1) to confidently align these two datasets using this event as a time reference. New tools are needed (see our response to point #11 below).
Where in the above timeline is the SOP fate definitively adopted? neur-nlsGFP, Ac-RFP, m3Cherry and Sens detection in Figs. 1 and 7 give us a rough idea that these other markers appear around the time of Sc FDI peaking, around 3h after the initial SB. But this is not presented in an organized fashion - the reader collects this information sporadically. A reanalysis of the already existing data attempting to place these various markers in an integrated timeline would be of great importance in understanding the details of this cell fate specification process. Which is the earliest SB event? sc, neur or Notch? How long does it take from that early SB until definitive SOP markers (Sens) first appear?
We agree with the reviewer, it would be interesting to extend the approach reported here for Scute to characterize SB and rate of FDI for other key factors governing the selection of SOPs. As pointed out by the reviewer (point #10 above), it would also be important to register in time these various events. Unfortunately, the maturation time of RFP, mCherry, FP670, etc... appeared to be too slow relative to the rapid turnover of the Ac, Sc and E(spl)-HLH factors prevented us from performing two-color imaging. Hence, current tools do not permit to determine which is the earliest SB event.
More genetic perturbations could be performed to solidify the model of cell-cell communication during lateral inhibition. Two obvious ones come to mind: (a) How would the Sc-GFP dynamics change in a Notch-RNAi background? (b) How would the NRE-deGFP dynamics change in a sc-RNAi background?
See our detailed response to reviewer #1, major point #2.
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Referee #3
Evidence, reproducibility and clarity
Phan et al present their work on the dynamics of Sc accumulation and Notch signaling in the dorsal abdomen of Drosophila. They use live imaging to provide more detailed knowledge on a well-studied phenomenon of lateral inhibition, where proneural proteins (like Sc) promote a certain fate (like SOP) in groups of equivalent cells. The selection of individual SOPs among these equivalent cells depends on Delta-Notch signaling, which allows neighboring cells to communicate. Cells that accumulate higher levels of Sc acquire an increased ability to inhibit their neighbors and will ultimately become SOPs. This paradigm seems to be used time and again in arthropods and vertebrates alike and its central importance is witnessed by the fact that it has been under intense study over the last 3 decades. The present manuscript adds the temporal dimension to one instance that deploys this proneural-Notch interplay. The authors show that the timecourse from equivalence to singularized SOPs takes 3-8 hours (from 13 to 21 h APF) and is visible as an increase in Sc-GFP levels in one cell of the cluster. They calculated precisely the onset of this increase (which they term "symmetry breaking") and showed that Sc levels plateau approx. 3 h later, although some SOPs are faster than others in reaching that plateau. Sc increase is accompanied by Notch reporter decrease. The apical area of the cells does not seem to bias the level of Notch signaling/ Sc accumulation. What does seem to speed up the process is pre-existing heterogeneity in Sc (and Notch?) levels. Interestingly, when this process of lateral inhibition fails to singularize a single cell (resulting in two adjacent cells with high Sc levels), these two cells move apart by cellular rearrangements. During the 3h that the SOP upregulates its Sc levels after SB, its neighboring cells stay at relatively constant baseline Sc levels and only afterwards do they start reducing their own Sc-GFP. The authors have taken the trouble to collect live data for >100 SOPs in different experimental settings, so there is no doubt about their reproducibility and statistical robustness. In general, the figures are clear and self-explanatory. I found it hard to follow the text, however, and I have some suggestions for its improvement.
- First and foremost, the authors should state in the first paragraph of the Results that scGFP is a CRISPR knockin and thus it's the only source of Sc protein in the animals imaged (this is stated only in the Methods section).
- The magnitude of the Sc increase should be commented on. Based on the intensity and FDI plots in Fig. 3B-E, an increase of 15-17% in the amount of Sc is suggested (the FDI plateaus at 0.08, which gives 1.08/0.92 = 1.17x the level of Sc in the SOP vs the surrounding cells). However, in the stills shown in Fig. 2BCD and in Fig. 3A, the intensity differential between SOPs and neighbors seems at least 100% (ie at least double the intensity, which would yield an FDI of >1/3 =0.33). Why is this high contrast never seen in the quantitative measurements?
- From Fig. 2A-D it appears that the ScGFP fluorescence intensity is at the same level or weaker than nearby autofluorescence. Please state (1) how you confirmed that the histoblast nest has lower autofluorescence than the larval epidermis and (2) how you corrected for histoblast nest autofluorescence in your quantifications.
- The paradoxical result of Fig. S1B should be discussed. On the one hand it is stated that "Ac and Sc specify the fate of the Sensory Organ Precursor cells (SOPs)" (p.2) and on the other S1B shows SOP specification in the absence of Sc. Are the SOPs shown in Fig S1B rare exceptions? Do the authors believe that these rare exceptions are there because of inefficient RNAi (since in comparison with S1A, in the null condition almost no SOPs should be formed)? Or they are the SOPs in RNAi clones as rare as the occasional bristles in S1A?
- One figure that is not straightforward to interpret is Fig. 4E. It plots ScGFP heterogeneity vs. number of RNAi neighbors. Each point in the plot must be an individual SOP (165 total). Therefore, its neighbors (the x-axis) should take integral (not decimal) values. How can a single SOP have a decimal number of RNAi neighbors, especially since heterogeneity was sampled over a 10min time-window, when not much cell rearrangement can take place? Please explain.
- I found the discussion of the Notch reporter dynamics (Fig. 7) confusing in several places. (6a) Whereas it's clear that there is plenty of Notch signaling going on before SBN, the authors repeatedly imply that Notch signaling starts after SBN. For example, in the Results (p.9) they state "Thus, this quantitative approach failed to detect a phase of reciprocal Notch signaling during which proneural cluster cells would both send and receive a Delta-Notch signal prior to SOP emergence." The fact that the NRE-deGFP gave a robust signal before the start of the movies clearly means that mutual inhibition was going on for quite some time before SB. In fact, an FDI of 0 for >4h prior to SBN (Fig. 7G) means exactly this: that the level of Notch response among the cluster cells is equivalent ("mutual inhibition" lasts for at least 4h before SBN). (6b) In the first paragraph of this section (p.8) they comment that the pre-existence of Notch signaling is unexpected - why? I interpret it to simply be mutual inhibition (see above). Then they go on to quantitate the average Notch response intensity over the entire posterior ADHN (please define the borders the "posterior" ADHN). I question the informational value of this analysis (averaging over a large region), when Notch signaling is known to have intense local cell-to-cell variability (also evident in the stills shown in Fig. 7A,B,C). And then they move on to a (much more informative) cell-by-cell analysis, without even changing paragraphs, making it hard for the reader to follow. (6c) The conclusion at the end of the second paragraph (p. 9) "It also showed that SB was detected soon after the onset of Notch-mediated inhibitory signaling." is nowhere supported by data. If I understand well, SB refers to Sc and "the onset of Notch-mediated inhibitory signaling" refers to SBN (which is the onset of ASYMMETRY in Notch signaling, not the onset of Notch signaling, which has been going on for hours earlier). I don't see any data comparing SB with SBN. In fact, this is an important question to address (see below - comment 10).
- Mutual inhibition amongst neighboring cells has been proposed to involve (besides mutual Notch signaling) an increase in Sc levels in 2-3 cells in a cluster before the singularization of a single SOP. The authors seem rather biased against such a transient Sc hike based on their results in Fig. 2D, where the neighboring cells stay at rather constant basal Sc levels for several hours, while the Sc SB event happens. However, looking at an individual SOP in Fig. 2B, I do detect a mild hike in the pink curve right around SB in the blue curve. Could the average result from 160 SOPs (in Fig 2D) simply blur such transient Sc hikes, if they happen with different kinetics for different SOPs? Couldn't the 10% of SOP twins (shown in Fig. 6) represent a special case of this transient "subcluster" Sc hike? I would appreciate some discussion on this point. [Whether Sc is transiently upregulated or not, however, does not change my firm conclusion - from the data presented - that Notch-mediated mutual inhibition has been going on long before SBN.] Some minor points:
- Please change Cad-GFP to Ecad-GFP or shg-GFP, as Cad misdirects to caudal.
- What is c in "(x,y,z,c,t) movies"? (a fifth independent variable?)
Significance
The mechanism of proneural-Notch interplay is evolutionarily conserved, so this study of its temporal dynamics is valuable and will interest a broad audience in the field of animal developmental biology. The rich data collected by the authors should contain enough information to make a big contribution to the field, but the presentation in the manuscript stops a little short of that. The fact that Sc expression is highly dynamic was already known - now we have quantitative measurements of this variability. Same holds for Notch signaling. The authors should try to integrate their data better to make a complete timeline of events that leads to SOP specification, using the panoply of fluorescent markers at their disposal.
- The authors show that Sc displays a SB event leading to FDI of 0.08 and the Notch reporter displays another SB (SBN) leading to a much more pronounced FDI of -0.2. Are these two events (the hike of Sc levels and the plummeting of Notch signal) contemporaneous or does one precede the other? Having both tagged with GFP makes it impossible to image simultaneously, but the authors could register each reporter's dynamics relative to the time of SOP division (as done in Fig. 5C) to get a sense of their relative order.
- Where in the above timeline is the SOP fate definitively adopted? neur-nlsGFP, Ac-RFP, m3Cherry and Sens detection in Figs. 1 and 7 give us a rough idea that these other markers appear around the time of Sc FDI peaking, around 3h after the initial SB. But this is not presented in an organized fashion - the reader collects this information sporadically. A reanalysis of the already existing data attempting to place these various markers in an integrated timeline would be of great importance in understanding the details of this cell fate specification process. Which is the earliest SB event? sc, neur or Notch? How long does it take from that early SB until definitive SOP markers (Sens) first appear?
- More genetic perturbations could be performed to solidify the model of cell-cell communication during lateral inhibition. Two obvious ones come to mind: (a) How would the Sc-GFP dynamics change in a Notch-RNAi background? (b) How would the NRE-deGFP dynamics change in a sc-RNAi background?
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Referee #2
Evidence, reproducibility and clarity
Understanding cell fate is crucial for various biological processes, including development, tissue regeneration, and disease progression. In this manuscript, colleagues in the team of François Schweisguth provide high-quality live cell observations demonstrating how SOP (Sensory Organ Precursor) cells are determined from a cluster of proneuronal cells via symmetry breaking (SB), which is key in the process of lateral inhibition. Although the process of lateral inhibition has been proposed long ago, no studies have been conducted to demonstrate it using live imaging. Using Drosophila abdominal epidermis as a model, the authors showed how the levels of the main neuronal determinants are expressed during SOP cell determination. The team uses a tagged version of one of the cell fate determinants, Scute, to follow the dynamics of this process, which is then further supported by genetic experiments showing that cell-to-cell variations in Scute expression levels promote fate divergence and patterning. This paper provides a new perspective on how dynamic expression of proneural factors determines cell fate acquisition from the equivalent population of cells. The data is presented very clearly, and the methods are adequately detailed, with suitable experiments and statistical analysis, as well as convincing key conclusions.
Major points:
- Despite "symmetry breaking" being the main focus of the paper, in the Introduction, the authors do not explain what this term means and do not provide any description of this process. This is a critical point that makes understanding of the goals of the paper difficult. Therefore, the authors are encouraged to provide more information and a clear description of this term/phenomenon.
- The role of Achaete in the story is not clear. Even though both factors are required for SOP determination, the authors mainly focus on Scute, so it is not very clear what the role of Achaete in this process is, if there is any. As shown in the paper, Achaete is expressed later when heterogeneity is promoting cell fate divergence. Is Achaete maybe contributing to cell heterogeneity/ cell fate divergence? Minor points:
- Symmetry Breaking (SB) should be abbreviated in the Abstract. The authors initially use the full term without abbreviation, and only on page 5, the abbreviation is finally defined; however, it should be introduced much earlier.
- The second-to-last sentence in the abstract, "These lateral inhibition defects were correlated via cellular rearrangements," is unclear regarding what defects the authors are referring to.
- For clarity, being more specific in the text in regards to description of the figure panels would be beneficial (e.g. page 3 Fig 1C-E); referring to C-E together makes it hard to understand what does each panel shows.
- In many instances, the movies are not properly referenced (e.g. on page 5, third row simply states "movies"), making it difficult to discern which movie should be checked. On page 8, when authors refer to movie 3, they likely meant movie 5.
- Figure S1 requires some corrections. The authors use the short name "scute" initially and then switch to the shortened version "sc'. Additionally, the nlsRFP (blue) is difficult to see; adjusting the levels or changing colors/showing separate channels may improve visibility. The authors mention clone borders, but none are shown. It would greatly help to outline the borders in all figures. Genotypes of the samples should be indicated, and clarification is needed regarding what "n" represents (number of cells, clones, or flies). What do the arrows in the panel B show? It is also recommended to display important channels as separate black and white images. Additionally, the use of RNAi against GFP instead of RNAi against scute should be justified; using RNAi GFP as the genotype on the graph could be interpreted as a control genotype rather than downregulation of scute.
- In the Figure 2 Legend, the authors use "std" as an abbreviation to define standard deviation. Typically, this is abbreviated as SD.
- In Figure 4E, the authors do not explain on why there are points on the x-axis that correspond to a decimal number of cells.
Reviewer Cross-Commenting
I fully agree with the comments provided by the other reviewers, most of which were complementary and overlapping with mine. Their comments are well-reasoned and highlight certain aspects that I overlooked. I agree that conducting additional genetic analyses would enhance our understanding of the progression fate divergence and its impact on lateral inhibition in real-time. Specifically, exploring the spatial and temporal dynamics of Ac and Sc in Notch mutants, as well as Notch dynamics in Sc and Ac mutants, could strengthen the proposed model of cell-cell communication during lateral inhibition.
Significance
Understanding cell fate is crucial for various biological processes, including development, tissue regeneration, and disease progression. In this manuscript, colleagues in the team of François Schweisguth provide high-quality live cell observations demonstrating how SOP (Sensory Organ Precursor) cells are determined from a cluster of proneuronal cells via symmetry breaking (SB), which is key in the process of lateral inhibition. Although the process of lateral inhibition has been proposed long ago, no studies have been conducted to demonstrate it using live imaging. Using Drosophila abdominal epidermis as a model, the authors showed how the levels of the main neuronal determinants are expressed during SOP cell determination. The team uses a tagged version of one of the cell fate determinants, Scute, to follow the dynamics of this process, which is then further supported by genetic experiments showing that cell-to-cell variations in Scute expression levels promote fate divergence and patterning. This paper provides a new perspective on how dynamic expression of proneural factors determines cell fate acquisition from the equivalent population of cells. The data is presented very clearly, and the methods are adequately detailed, with suitable experiments and statistical analysis, as well as convincing key conclusions.
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Referee #1
Evidence, reproducibility and clarity
Summary
In this manuscript, the authors addressed when and how fate symmetry breaking occurs during lateral inhibition using live imaging of Drosophila pupal abdomen as a model system. Using quantitative live imaging of a GFP tagged version of the proneural gene scute (sc), the authors demonstrated that GFP-Sc expression appears first along the posterior margin of the anterior dorsal histoblast nest (ADHN) and later in the central region of ADHN, suggesting that posterior cues regulate early proneural expression in this region, which is consistent with previous findings. By tracking the temporal expression of GFP-Sc in the sensory organ precursor cells (SOPs), the authors further showed that SOPs emerge within a 2 hour time frame around 17h APF in the ADHN. Moreover, the presumptive SOP and its surrounding histoblasts showed a weak and slowly increasing GFP-Sc signal until the presumptive SOP showed a rapid increase in GFP-Sc accumulation, whereas GFP-Sc levels remained relatively constant in non-selected histoblasts. Interestingly, using symmetry breaking as a reference point, the authors found that the onset of fate divergence took place at low levels of Sc, soon after the onset of proneural gene expression and was not preceded by a prolonged phase of Sc accumulation. The authors also demonstrated that lateral inhibition in the pupal abdomen failed to single out SOPs in around 10% of the cell clusters that are in direct contact with each other during symmetry breaking and that pattern refinement involving cellular rearrangements were required to correct these defects. By manipulating the heterogeneity of Sc in genetically mosaic pupae, the authors successfully showed that increasing the heterogeneity of Sc positively correlated with an increased rate of fate divergence, suggesting that cell-to-cell variations in Sc levels promote fate divergence during lateral inhibition. Although earlier modelling suggested that differences in apical cell area may serve as a possible source of bias for Notch-based decisions, the authors found no significant difference in the apical area of the presumptive SOP compared to the mean area of its neighbouring cells, suggesting that apical cell shape does not bias Notch-mediated cell fate decisions in this developmental context. Finally, examination of Notch activity dynamics using a destabilized GFP expressed downstream of a Notch-Responsive Element as well as analysing the expression of the E(spl)m3 Notch target gene, the authors demonstrate that Notch activity was minimal around the time of SOP emergence as E(spl)m3 was detected after the onset of Sc expression but not prior to SOP emergence, indicating that SOP selection in the abdominal epidermis took place at low levels of Notch signalling.
Major comments:
- Are the key conclusions convincing?
Yes, the key conclusions are convincing with quantitative live imaging and proper explanations on how the analyses were carried out. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
No.
- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.
- The authors conclude that RFP-Ac expression is restricted to emerging SOPs and surroundings cells at 18h APF, indicating that Ac is activated later than Sc. Can the authors provide images for RFP-Ac expression at 10h and 16h APF similar to GFP-Sc as shown in their figures. Do the SOPs that contain high levels of both Ac and Sc (as some SOPs have Sc expression but not Ac) undergo fate divergence and SB faster than the SOPs containing higher levels of only Sc?
- It would be interesting to see the spatial and temporal dynamics of Ac and Sc in Notch mutants or even Notch dynamics in Sc and Ac mutants to better understand the progression fate divergence and its effect on lateral inhibition in real time.
- 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, I believe the suggested experiments are realistic in terms of time and resources, with an estimation of 3-4 months to complete the experiments. - 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?
The experiments are straight forward and were performed with a good number of n values for their live imaging and supported by quantifications.
Minor comments:
- Specific experimental issues that are easily addressable.
- In figure 1F and F', the authors mention GFP-Sc is not expressed prior to 14h, however, there is still GFP signal detected in their imaging. Can the authors comment what would be the cause of this GFP signal or was it due to non-specific background signal during their imaging analysis?
- In the materials and methods, the authors mention that prior to imaging the larvae and pupae are grown at 18, 21 or 25C. Is there a reason why the larvae and pupae are grown at different temperatures for different experiments? Can the authors specify (i.e. in the figure legends) in which experiments different temperatures were used?
- The citations need to have a better format as they show up as each citation within a single bracket which makes it a little hard to read when multiple references are cited in a single sentence.
- In the abstract, the sentence 'Unexpectedly, we observed at low frequency (10%) pairs of cells that are in direct contact at the time of SB'. SB should be replaced with "Symmetry breaking", as it appeared for the first time in the manuscript and should be written out in full.
- Throughout the manuscript there are instances where the abbreviations are written in full with the abbreviation in brackets after they have already been introduced in the introduction which can be changed to just the abbreviation itself.
- In the discussion on page 11, 'our observation...', our needs to be changed to Our.
- Are prior studies referenced appropriately?
Yes. - Are the text and figures clear and accurate? 1. It would be nice to have arrow heads or dotted lines around the cells or areas on interest in both, all the figures and movies, so that it will be easier to follow the results. The videos have a lot of background due to fragmented apoptotic nuclei, etc. as mentioned by the authors, hence arrow heads or dotted lines would bring viewers focus on the areas of interest. 2. It would be helpful to have anterior - posterior axis (i.e. with an arrow) shown on top of all the figures. 3. Scale bars are missing in all figures, videos, and figure legends. 4. Only movies 1 and 3 are referenced in the text. 5. Keeping the colors in the movies and figures consistent and same would be helpful. For example, Movie 2 Histone3.3-mIFP marker is in blue but in figure 3 it is in magenta. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
As mentioned above, it would be helpful if the authors have arrow heads or dotted lines around the cells or areas of interest in both the figures and movies for better representation of their data. For example, movie 1 shows a larger area of imaging than shown in figure 2A, which makes it hard to follow the cells of interest in the movie.
Significance
Since the dynamics of fate acquisition has mostly been studied in fixed samples in Drosophila, this is an interesting study to understand the spatial and temporal dynamics of Notch signalling as well as proneural activity during lateral inhibition using Drosophila pupal abdomen as a model. Using quantitative live imaging the authors provide key evidence on how and when SB occurs during lateral inhibition, providing experimental support for the intracellular feedback model. Most importantly, they show that fate selection occurred early and was not preceded by a detectable phase of mutual inhibition, but instead, the initial bias in Sc expression and heterogeneity might play a significant role in in SB.
- Place the work in the context of the existing literature (provide references, where appropriate).
So far, most work in this field has focused on the dynamics of fate acquisition using fixed samples and mathematical remodelling, with some live imaging analysis (Skeath and Carol, 1991; Collier et al, 1996; Castro et al, 2005; Corson et al, 2017; Couturier et al, 2019; Troost et al, 2023). Here, considering previous literature, the authors move one step forward, using quantitative live imaging of proneural factors Scute to determine fate SB and monitor fate divergence during lateral inhibition. This study though not entirely conceptually novel provides important new insights into SB and fate divergence in the pupal abdomen, wherein symmetry breaking occurred at low Sc levels and that fate divergence was not preceded by a prolonged phase of low or intermediate level of Sc accumulation. Furthermore, the relative size of the apical area did not influence this fate choice but cell-to-cell variations in Sc levels promoted fate divergence, thereby providing experimental support for the intercellular negative feedback loop model. - State what audience might be interested in and influenced by the reported findings.
Developmental and cell biologists. - 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.
Stem cells, developmental biology.
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Response to reviewers
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Hazari et al reports on an important new function of the UPR IRE1 in liver fibrosis. The results are clearly and logically described. It shows that the genetic ablation of IRE1 in mice prevents liver fibrosis through retention of collagen in the ER and its degradation hence preventing secretion and accumulation in liver parenchyma. This effect was reversed was P4HB (a multifunctional enzyme that belongs to the protein disulfide isomerase) showing that IRE1 control P4HB.
As such the paper is both scientifically and medically relevant.
There is in my opinion a conceptual issue the paper does not directly address nor resolve.
A clear functional distinction between IRE1 and XBP1 is not made nor attempted. This comes across as an unresolved issue. In the Introduction the authors cite ref 29 that provided evidence for a role of RIDD in alleviating hepatic cytotoxicity. The paper is based on chronic liver toxicity by CCL4 to identify the protective role of IRE1 and acute CCL4 toxicity to identify P4HB. IRE1 KO demonstrates that IRE1 controls collagen metabolism (degradation vs. secretion). However, many considerations involve XBP1 (see Fig. 8 as a example). Yet the paper concludes by saying "we propose that pharmacological inhibition of IRE1 activation..." Granted that IRE1 inhibition would definitely cause an attenuation of XBP1 splicing, I see a clear distinction between IRE1 and XBP1 still necessary to back the conclusion that inhibition of IRE1 and not XBP1 is the therapeutic modality one should develop.
It is clear that IRE1 controls XBP1, but it also controls RIDD. Both independently control the fate of multiple downstream genes and also miRNAs in the case of RIDD. Because RIDD may offer some advantages in attenuating liver pathology, forfeiting some benefits IRE1 can offer via RIDD in order to blunt XBP1 may not be the optimal solution. Therefore, I suggest to complement the present study with experiments that target XBP1 specifically bypassing IRE1. Experiments of deletion (siRNA or Cre) as well as specific activation of XBP1 for which there exist commercially available molecules (e.g., IAX4 is a direct activator of XBP1 without UPR that also does not induce XBP1s-independent IRE1 signaling such as RIDD or JNK phosphorylation) will permit to better differentiate the role of XBP1 from IRE1 in P4HB regulation and collagen degradation vs. secretion in hepatic stellate cells and deposition in liver.
Throughout the paper the authors refer to XBP1 and even center the Discussion on XBP1 more than on IRE1. Since determining the precise mechanism in this particular instance is critical to future treatments to prevent liver fibrosis these additional experiments should be performed.
Minor point
Fig. 4D. The label must have been erroneously copied and pasted. There is not way to distinguish what is different in lane 1-2 from lane 3-4. The legend is not helpful.
Fig. S4B. Same problem.
Significance
This is an excellent paper with profound new medical implications with conceptual advances for the treatment of NASH.
The limitations have been underscored in comments to authors.
The audience remains specialized for the time being.
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Referee #1
Evidence, reproducibility and clarity
The current study aimed to investigate the possible contribution of the unfolded protein response (UPR), the main adaptive pathway that monitors and adjusts the protein production capacity at the endoplasmic reticulum (ER), to collagen biogenesis and liver disease. The authors targeted the ER sensor, inositol requiring transmembrane kinase/endoribonuclease 1 alpha (IRE1), using the IRE1Lox/Lox, Mx1Cre/- mouse strain where Cre is induced with 3 poly I:C injections. After confirmation of depleting IRE1 in the liver, they challenged mice with a high dose of carbon tetrachloride (CCl4). Phenotype analysis revealed the deleterious role of IRE1 in the liver in acute liver damage. Then, the authors determined the biological consequence of IRE1 deletion on the progression of experimental liver fibrosis. Consistently, IRE1 plays a promotive role in the CCl4-induced liver fibrosis mouse model since the results suggest that IRE1 knockout mice have reduced collagen deposition and AST/ALT activity (two major liver damage enzymes). To determine the potential mechanisms underlying the protective effects of IRE1 deficiency, the authors did mass spectrometry-based quantitative proteomics of liver samples from the experimental mice. Through bioinformatic analysis, they determined that protein and mRNA expression levels of P4HB decreased in IRE1 deficiency mice. Besides, in vitro and ex vivo studies suggested that IRE1 deficiency downregulated collagen generation. They further investigated the impact of IRE1 deficiency in liver steatosis in a mouse model fed a high-fat diet (HFD). Similarly, IRE1 deficiency suppressed liver steatosis and fibrosis in this mouse model. To clarify how IRE1 modulates collagen expression, the authors generated IRE1 knockout cell (KO) lines. They found that IRE1 KO cells may cause misfolding and intracellular accumulation of collagen inside the cells, reducing its secretion. Finally, they suggested the correlation of IRE1 associated gene (XBP1) and PH4B but not PH4A1 in human patients with non-alcoholic steatohepatitis (NASH). Overall, the current study may provide a potential molecular linkage between IRE1 and collagen in chronic liver disease progression. However, the inconsistent data presentation, poor data quality, and lack of suitable animal models make the current manuscript's impact on the field of chronic liver disease low.
Major concern:
- The authors suggested that P450 levels are not changed in the liver samples of IRE1 KO mice compared to the wildtype (WT) ones. It has been demonstrated that IRE1 activation reduced P450 expression levels (PMID: 22291093). Can the authors explain the inconsistent findings?
- Why did the authors challenge mice with CCl4 for 12 weeks? The CCl4-induced liver fibrosis model would have a severe fibrotic liver phenotype at 8 weeks. Did the author check if IRE1 deficient mice have less liver injury than wildtype (WT) mice at early time points? Also, could you check the liver fibrosis phenotype using another typical liver fibrosis mouse model? The HFD-feeding mouse model would induce liver fibrosis after challenging mice for 50 weeks (PMID: 37270060).
- What are the mouse numbers, age, and sex in the CCl4 studies? In Fig. 2, several figures have inconsistent mouse numbers for the data presentation.
- There are many downregulated and upregulated target genes. What is the rationale for focusing on downregulated P4HB in IRE1 deficient mice, given that IRE1 has an RNase domain functioning in posttranscriptional regulation? In this case, how does IRE1 depletion upregulate P4HB?
- The author suggested that TGFb1 would promote collagen generation, but targeting IRE1 would reduce P4HB and the TGFb1-mediated collagen generation. The Fig. 4D does not support the statement. However, the authors' data demonstrated that TGFb1 cannot promote IRE1 and P4HB expression (Fig. 4G). If TGFb1 cannot promote collagen generation through activating IRE1 and P4HB, how can targeting IRE1 reduce TGFb1-induced collagen (Fig. 4H)?
- The authors stated that hepatic Ern1 deficiency suppresses the progression of liver steatosis. In fact, it has been reproducibly demonstrated that hepatic IRE1 deletion promotes hepatic steatosis progression (PMID: 21407177 and PMID: 29764990), contradicting the authors' findings. Besides, the authors did not deplete IRE1 specifically in the liver. Therefore, they made a misleading conclusion without solid evidence. The authors basically depleted IRE1 in the whole body of these experimental mice upon poly I:C injection. The authors must not conclude that hepatic Ern1 deficiency suppresses the progression of liver steatosis without considering the contributions of other organs in the phenotypes that you observed. Also, the HFD-treated mice would develop liver fibrosis 50 weeks post-feeding (PMID: 37270060). It is unclear what other treatments the authors used to accelerate the fibrotic liver phenotype, as shown in Fig. 5D. The authors should show body weight and liver weight over body weight in the result section. Besides, hepatic cholesterol, serum triglyceride, and cholesterol need to be measured in these mice to clarify how deleting IRE1 in the whole body can suppress hepatic steatosis, but liver-specific deletion of IRE1 promotes fatty liver. Without clarifying this issue, it is unclear how hepatic IRE1 deficiency can reduce steatosis and liver fibrosis.
- The authors suggested that ablation of IRE1 expression increased the levels of intracellular GFP-collagen as compared with control cells (Fig. 6C). How did the authors quantify the results? It is not clear if KO really increased the intracellular collagen levels. As the authors showed in Fig. 6C, WT-NT, and WT-GFP-collagen-untreated have no overlap of green fluorescence. However, KO-NT and KO-GFP-collagen-untreated still have an overlap of green fluorescence, indicating that some cells are not GFP-positive. In this case, how could authors conclude that IRE1-KO cells have a more than 2-fold increase of green fluorescence change compared to WT? Besides, Fig. 6F suggested that secreted collagens increased in KO cells, contradicting the authors' previous data in Fig. 2, 4, and 5. Why did you use U2OS, Hepa1-6, and Huh7 in these studies? Should the collagen be secreted by hepatic stellate cells?
- In Fig. 7, the authors suggested that IRE1 KO promotes the levels of collagen inside cells using the whole cell lysate. Interestingly, they indicated that IRE1 deficiency suppressed TGFb1-induced collagen production using whole cell lysates (Fig. 4D). It is really confusing if IRE1 KO promotes or suppresses collagen production or secretion. Also, Fig. 7C did not support that IRE1-KO reduced collagen secretion. Besides, what cells did the authors use for these studies? Are they hepatic stellate cells?
- It is interesting to see the positive correlation between XBP1 and P4HB mRNA expression. However, it is still unclear if IRE1 deficiency could downregulate P4HB mRNA expression, given its RNase function. Thus, it would be essential to determine how IRE1 regulates P4HB expression before analyzing the correlation using human datasets. Besides, Fig. 8D did not suggest that XBP1 expression levels are really correlated with chronic liver disease progression, given that its correlation scores with AST and ALT are 0 and -0.01, respectively.
Significance
The current study aimed to investigate the possible contribution of the unfolded protein response (UPR), the main adaptive pathway that monitors and adjusts the protein production capacity at the endoplasmic reticulum (ER), to collagen biogenesis and liver disease. The authors targeted the ER sensor, inositol requiring transmembrane kinase/endoribonuclease 1 alpha (IRE1), using the IRE1Lox/Lox, Mx1Cre/- mouse strain where Cre is induced with 3 poly I:C injections. After confirmation of depleting IRE1 in the liver, they challenged mice with a high dose of carbon tetrachloride (CCl4). Phenotype analysis revealed the deleterious role of IRE1 in the liver in acute liver damage. Then, the authors determined the biological consequence of IRE1 deletion on the progression of experimental liver fibrosis. Consistently, IRE1 plays a promotive role in the CCl4-induced liver fibrosis mouse model since the results suggest that IRE1 knockout mice have reduced collagen deposition and AST/ALT activity (two major liver damage enzymes). To determine the potential mechanisms underlying the protective effects of IRE1 deficiency, the authors did mass spectrometry-based quantitative proteomics of liver samples from the experimental mice. Through bioinformatic analysis, they determined that protein and mRNA expression levels of P4HB decreased in IRE1 deficiency mice. Besides, in vitro and ex vivo studies suggested that IRE1 deficiency downregulated collagen. They further investigated the impact of IRE1 deficiency in liver steatosis in a mouse model fed a high-fat diet (HFD). Similarly, IRE1 deficiency suppressed liver steatosis and fibrosis in this mouse model. To clarify how IRE1 modulates collagen expression, the authors generated IRE1 knockout cell (KO) lines. They found that IRE1 KO cells may cause misfolding and intracellular accumulation of collagen inside the cells, reducing its secretion. Finally, they suggested the correlation of IRE1 associated gene (XBP1) and PH4B but not PH4A1 in human patients with non-alcoholic steatohepatitis (NASH). Overall, the current study may provide a potential molecular linkage between IRE1 and collagen in chronic liver disease progression. However, the inconsistent data presentation, poor data quality, and lack of suitable animal models make the current manuscript's impact on the field of chronic liver disease low.
Major concern:
- The authors suggested that P450 levels are not changed in the liver samples of IRE1 KO mice compared to the wildtype (WT) ones. It has been demonstrated that IRE1 activation reduced P450 expression levels (PMID: 22291093). Can the authors explain the inconsistent findings?
- Why did the authors challenge mice with CCl4 for 12 weeks? The CCl4-induced liver fibrosis model would have a severe fibrotic liver phenotype post the 8-week CCl4 challenge. Did the author check if IRE1 deficient mice have less liver injury than wildtype (WT) mice at early time points? Also, could you check the liver fibrosis phenotype using another typical liver fibrosis mouse model? The HFD-feeding mouse model would induce liver fibrosis after challenging mice for 50 weeks (PMID: 37270060).
- What are the mouse numbers, age, and sex in the CCl4 studies? In Fig. 2, several figures have inconsistent mouse numbers for the data presentation.
- There are many downregulated and upregulated target genes. What is the rationale for focusing on downregulated P4HB in IRE1 deficient mice, given that IRE1 has an RNase domain functioning in posttranscriptional regulation? In this case, how does IRE1 depletion upregulate P4HB mRNA expression?
- The author suggested that TGFb1 would promote collagen generation, but targeting IRE1 would reduce P4HB and the TGFb1-mediated collagen generation. The Fig. 4D does not support the statement. However, the authors' data demonstrated that TGFb1 cannot promote IRE1 and P4HB expression (Fig. 4G). If TGFb1 cannot promote collagen generation through activating IRE1 and P4HB, how can targeting IRE1 reduce TGFb1-induced collagen (Fig. 4H)?
- The authors stated that hepatic Ern1 deficiency suppresses the progression of liver steatosis. In fact, it has been reproducibly demonstrated that hepatic IRE1 deletion promotes hepatic steatosis progression (PMID: 21407177 and PMID: 29764990), contradicting the authors' findings. Besides, the authors did not deplete IRE1 specifically in the liver. Therefore, they made a misleading conclusion without solid evidence. The authors basically depleted IRE1 in the whole body of these experimental mice upon poly I:C injection. The authors must not conclude that hepatic Ern1 deficiency suppresses the progression of liver steatosis without considering the contributions of other organs in the phenotypes that they observed. Also, the HFD-treated mice would develop liver fibrosis 50 weeks post-feeding (PMID: 37270060). It is unclear what other treatments the authors used to accelerate the fibrotic liver phenotype, as shown in Fig. 5D. The authors should show body weight and liver weight over body weight in the result section. Besides, hepatic cholesterol, serum triglyceride, and cholesterol need to be measured in these mice to clarify how deleting IRE1 in the whole body can suppress hepatic steatosis, but liver-specific deletion of IRE1 promotes fatty liver. Without clarifying this issue, it is unclear how hepatic IRE1 deficiency can reduce steatosis and liver fibrosis.
- The authors suggested that ablation of IRE1 expression increased the levels of intracellular GFP-collagen as compared with control cells (Fig. 6C). How did the authors quantify the results? It is not clear if KO really increased the intracellular collagen levels. As the authors showed in Fig. 6C, WT-NT and WT-GFP-collagen-untreated have no overlap of green fluorescence. However, KO-NT and KO-GFP-collagen-untreated still have an overlap of green fluorescence, indicating that some cells are not GFP-positive. In this case, how could authors conclude that IRE1-KO cells have a more than 2-fold increase of green fluorescence change compared to WT? Besides, Fig. 6F suggested that secreted collagens increased in KO cells, contradicting the authors' previous data in Fig. 2, 4, and 5. Why did you use U2OS, Hepa1-6, and Huh7 in these studies? Should the collagen be secreted by hepatic stellate cells?
- In Fig. 7, the authors suggested that IRE1 KO promotes the levels of collagen inside cells using the whole cell lysate. Interestingly, they indicated that IRE1 deficiency suppressed TGFb1-induced collagen production using whole cell lysates (Fig. 4D). It is really confusing if IRE1 KO promotes or suppresses collagen production or secretion. Also, Fig. 7C did not support that IRE1-KO reduced collagen secretion. Besides, what cells did the authors use for these studies? Are they hepatic stellate cells?
- It is interesting to see the positive correlation between XBP1 and P4HB mRNA expression. However, it is still unclear if IRE1 deficiency could downregulate P4HB mRNA expression, given its RNase function. Thus, it would be essential to determine how IRE1 regulates P4HB expression before analyzing the correlation using human datasets. Besides, Fig. 8D did not suggest that XBP1 expression levels are correlated with chronic liver disease progression, given that its correlation scores with AST and ALT are 0 and -0.01, respectively.
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Reply to the reviewers
We thank the reviewers for taking the time to read and comprehensively evaluate our manuscript. We are pleased that, overall, they recognize the quality of our data and that it supports our conclusions. We are grateful for their comments, insights and advice and have revised the manuscript accordingly as described in the point-by-point response below. We believe that the revised manuscript is substantially improved by some experimental additions, additional replicates, improved analysis and increased clarity. Some key enhancements are as follows:
Previously we had found increased expression of the WNT pathway following CHRDL2 treatment, using RNA seq. We have now demonstrated this experimentally using the cellular levels and localisation of β-catenin. Previously we had shown that overexpression of CHRDL2 increased resistance to common chemotherapy treatments, as well as irradiation in colorectal cell lines. We have now shown that cells surviving treatment show a further reduction SMAD1/5/8 phosphorylation indicating a selection for CHRLD2 high cells during the treatment. We have also demonstrated a decrease in chemotherapy sensitivity in intestinal organoids treated with secreted forms of CHRDL2.
1. Point-by-point description of the revisions
This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *
Reviewer #1
__Evidence, reproducibility and clarity __
Clarkson and Lewis present data suggesting that overexpression of Chordin like 2 (CHRDL2) can affect colorectal cancer cell responses to chemotherapy agents, possibly by modulating stem-cell like pathways. I have the following comments:
- Fig. 1J-it is standard to show the images of cell migration-this is important here, given the modest effect of CHRDL2 overexpression here.
We have now included 3 replicate control and CHRDL2 overexpressing cell images in this figure panel to support the quantification in the graph.
Fig. 2A-the very small error bars for most of the data on the curves suggests these are n=1 experiments with multiple technical replicates to generate the error bars. Please clarify. The legend says n=3 with ANOVA analysis but no significance detected. Please clarify.
All experiments in this figure were done with 5 technical replicates per experiment, this was replicated at least three times to give n=3 biological replicates. The error bars represent the standard error of the mean of these 3 biological replicates as stated in the legend. Some data points showed very little data variation, hence the small error bars. Raw data is available if requested.
- Fig. 2B-given the overlapping error bars here, how can there be a pWe have removed this representation of the data as it combined many different experiments with variable cell types and chemotherapeutics and it was difficult to carry out meaningful statistics. An overview of the data can be better seen in table form as shown in the revised figure 2B.
Fig. 2G-did the authors try to estimate the concentration of CHRDL2 in the conditioned medium? Which cell line was used to generate this CM?
Conditioned media was harvested from the matching transgenic cell lines with inducible CHRDL2. eg RKO cells were treated with media collected from doxycycline induced transgenic RKO cells whereas CaCO2 cells were treated with media from CaCO2 cells. The concentration of doxycycline was represented by ++ for 10ug/ml, the same notation we have used for directly induced cells treated with 10ug/ml dox.
We did not try to quantify the absolute concentration of CHRDL2 but we have shown the relative amount on a Western blot normalised with a ponceau stain (quantification now included in supplementary figure 1).
We have clarified our description of this experiment, inserting the following statement, "Conditioned media was harvested from corresponding cell lines with the inducible CHRDL2 transgene and the parental control cells. Induction of CHRDL2 to generate conditioned media was carried out using the same concentration and duration of doxycycline treatment as the cells in figure 2A. "
Fig. 5-what is the potential mechanism for gene expression changes in response to CHRDL2 overexpression? Is it all due to BMP inhibition? More mechanistic detail would be welcome here.
We have suggested other pathways involved in these functional effects based on our RNA seq data but at the moment it is not possible to say whether any changes are independent of BMP signaling. CHRDL2 is relatively understudied and as yet there is not much literature supporting BMP independent actions of CHRDL2. However, we have added some discussion and reference to an article suggesting interactions between CHRLD2 and YAP (Wang et al., 2022) including the following statement on page 17: "While the changes in BMP and WNT signaling shown in our GSEA analysis suggest that the effects of CHRDL2 in our system work directly through inhibition of BMP, it is not possible to rule out that some pathways are affected by BMP independent actions of CHRLD2. Indeed, Wang et al, suggest that CHRDL2 can directly alter phosphorylation and activity of YAP in gastric cancer cell lines, which merits further exploration (Wang et al., 2022)"
Significance
Unclear whether genetically engineered inducible overexpression has any real physiological significance but we all use cell models so this is OK.
Reviewer #2
__Evidence, reproducibility and clarity __
Summary: In the manuscript entitled "BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer" the authors demonstrated that Chordin-like 2 (CHDRL2), a secreted BMP antagonist, promotes a chemo-resistant colorectal cancer stem cell phenotype through the inhibition of BMP signaling. The authors took advantage of both 2D engineered colorectal cancer (CRC) cells and healthy murine 3D organoid systems. Specifically, the authors showed a decreased proliferation rate and reduced clonogenic capability upon overexpression of CHRDL2 in established human colon cancer cell lines. Subsequently, they identified a chemo-resistant phenotype upon standard therapies (5FU, Oxaliplatin and Irinotecan) in CHDRL2 overexpressing cells by performing MTS assay. The authors showed that this chemo-resistant phenotype is associated with ATM and RAD21 activation, supporting an induction of DNA damage signaling pathway. Of note, the authors assessed that the exposure of 3D murine organoid to CHRDL2 resulted in a stem-like phenotype induction accompanied by a reduction of the differentiated counterpart. From RNA-seq data analysis emerged the upregulation of genes associated to stemness and DNA repair pathways in CHRDL2 overexpressing cells.
Major comments: 1. In the first paragraph of the result section authors assessed that "Colorectal adenocarcinoma cell lines were deliberately chosen to encompass a range of CHRDL2 expression levels and genetic mutations", without showing qRT-PCR or WB data on the differential expression levels of CHRDL2 in a panel of immortalized CRC cell lines. Authors should include these data to better support their choice.
*We have now included some qRT-PCR in supplementary figure 1 alongside a table of some of the key driver mutations in each cell line. Western blotting of these cells shows only a very low concentration of CHRDL2 protein. As shown in figure 1B in the control columns, no significant protein expression is observed in any line. *
In Figure 1F, authors described a reduction of cell proliferation in CRC cell lines expressing high levels of CHRDL2 only under low glucose conditions. Why did the authors perform the assay under these conditions? They should better argue this aspect and validated the role of CHRDL2 in metabolism rewiring by performing additional in vitro assays.
We have removed this aspect of the paper as it does not add significantly to our overall conclusions and we can clearly see the effects of CHRDL2 overexpression under standard growth conditions (Figure 1G).
The authors should evaluate the role of CHRDL2 in promoting a stem-like phenotype in human colon cancer stem cells freshly isolated from patients and characterized.
We would very much like to do experiments such as this but it is beyond the scope of this study and will be included in upcoming grant proposals.
In order to confirm the data obtained on 3D murine organoids system obtained from normal Intestinal Stem Cells, authors should investigate the stemness induction, driven by CHRDL2, also in human intestinal organoids.
Experiments using human intestinal organoids are currently planned and ethical approval applications and grant proposals are underway for future experiments of this nature.
The authors should evaluate the oncogenic role of CHRDL2, through the maintenance of stemness, by performing orthotopic or subcutaneous experiments in vivo model.
Similarly, this is not possible for this manuscript but is planned for the future alongside a transgenic mouse model of inducible CHRDL2 overexpression in the intestine.
BMPs proteins are part of a very broad protein family. In the introduction section, authors should indicate the specific BMP protein on which CHRDL2 exerts its inhibitory function. Moreover, they should have assessed BMP protein levels in CACO2, LS180, COLO320 and RKO cell lines.
We have clarified the interactions between CHRDL2 and specific BMPs in the introduction. We have not specifically assessed the BMP protein levels in our cells however we have now included an analysis of expression data from the Cancer Cell Line Encyclopedia in supplementary figure 1 C.
In first panel, the authors should quantify the secreted levels of CHRDL2 in the media of overexpressing CHRDL2 cell lines.
We have done this using the ponceau staining as a loading control and the results are displayed (supplementary figure 1).
In Figure 2D the authors should use the appropriate controls and describe this with more details in results section.
In this figure we have used Hoechst staining followed by FACs analysis to identify the cell cycle profile of our CHRDL2 treated cells. We have improved the description of this in the methods section. Appropriate controls for staining, both negative and positive, are used when setting up the analysis for this experiment. The cell cycle profile is calculated using the Novocyte in house software. We have now included the histogram plots in the main figure to clarify these data in figure 2D.
In Figure 3A, the authors should have performed the assay by choosing IC50.
*We attempted these experiments with the IC50 levels, however the high amount of cell death and frequency of apoptotic cells meant that clear images were difficult to obtain. We therefore reduced the concentrations and still had very measurable effects. *
In Supplementary Fig. 4A-B. the results are unclear. The control cell lines are already chemo resistant.
Again, we used IC25 levels of the drugs so that our cells were damaged but still live throughout the experiment. This has been explained on page 10.
The authors should review and add statistical analysis in both main and supplementary figures.
*We have now added additional details about statistical analysis throughout the figures, legend and main text, showing all significance levels as well as non-significance for each data set. * Minor comments: 1. The quality of immunofluorescence and WB images should be implemented, and in the immunofluorescence panels scale bars should be added.
We have added or improved scale bars on each immunofluorescence image. Western blot images have been improved.
In the graphical abstract authors reported that CHRDL2 overexpression increase WNT and EMT pathways, without performing any specific assay to demonstrate this. Authors should correct and graphically improve the graphical abstract.
*This is a good point and we have now carried out Beta-catenin immunofluorescence as a measure of WNT signaling on both our cancer cell lines - showing an increase in nuclear beta-catenin (figure 1J and K), and our organoids - showing an increase in overall levels and cytoplasmic staining (Figure 4 F). In terms of EMT markers we have carried out immunofluorescence on IQGAP1 (Figure 1K). IQGAP1 is significantly upregulated in CHRDL2 cells, reflecting its role in reduced cell adhesion and increased migration. This correlates with our data showing increased cellular migration as well as the increase in EMT related transcription in our RNAseq data. *
The term "significantly" in the discussion section is inappropriately referred to data showed in the histogram in Figure 1J. Moreover, in Figure 1Jthe authors should delete from the y-axis the term "corrected".
We have changed significantly to substantially
The term "significant" in discussion is inappropriately referred to BMI1 expression level if compared to the histogram in Figure 4G.
We have changed significantly to "a trend to increase"
In Figure 2C the authors should add the unit of measurement (fold over control) in the table.
We have done this
In Figure 4E the authors should add the figure legend reporting OLFM4 protein.
We have done this
The authors should include few sentences summarizing the findings at the end of each paragraph.
We have added short summaries at the start or end of each section to improve the flow of the results section.
Significance
General assessment: Overall, the work is aimed to elucidate the role of CHRDL2 already considered a poor prognosis biomarker involved in the promotion of CRC (PMID: 28009989), in promoting stem-like properties. The authors elucidated new additional insights into the molecular mechanisms regulating stemness phenotype induced by the BMP antagonist CHRDL2 in CRC. The authors include in the study a large amount of data, which only partially support their hypothesis. However, this manuscript lacks organization and coherence, making it challenging to follow and read. Numerous concerns need to be addressed, along with some sentences to rephrase in the result and discussion sections.
Advance: The manuscript reported some functional insights on the role of CHRDL2 in colorectal cancer, but additional data should be added to support authors 'conclusions.
Audience: The manuscript is suggested for basic research scientists.
__Reviewer #3 (Evidence, reproducibility and clarity (Required)):____ __ Summary BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer Eloise Clarkson et al. The manuscript explored the function of CHRDL2, a BMP antagonist, on colorectal cancer (CRC). The authors found that CHRDL2 overexpression can enhance the survival of CRC cells during chemotherapy and irradiation treatment with elevated levels of stem-cell markers and reduced differentiation. Further RNA-seq analysis revealed that CHRDL2 increased the expression of stem-cell markers, WNT signaling and other well-established cancer-associated pathways. Overall, the manuscript is well-written and presented. I have some suggestions:
Major points:
- The authors assert BMP antagonism was demonstrated by assessing levels of phosphorylated SMAD1/5 (Figure 1G). However, the immunoblotting assay only depicted P-SMAD 1/5 levels and B-ACTIN as internal control. It's suggested to include total-SMAD1/5 immunoblotting as an internal control to further support the claim of BMP antagonism.
The reviewer is correct that this is the best control. Western blotting has now been performed with total SMAD1 protein expression used as an internal control and this is shown in Figure 1D and Supplementary figure 1F
The authors argue that CHRDL2 overexpression reduced the proliferation of CRC cell lines, as evidenced by cell proliferation assays. However, from Figure 1E, the reduction in proliferation appears insignificant. It would be beneficial to perform one-way ANOVA tests on each time point for CHRDL2+ and CHRDL2++ with Control in Figure 1E to ascertain significance.
*We now have repeated this experiment to reduce variability and have also provided two-way ANOVA analysis between Control and CHRDL2+ and Control and CHRDL2++. One-way ANOVA at timepoint 96hr also provided with details in the figure legend. *
The findings indicating that overexpressing CHRDL2 can confer resistance to chemotherapy in CRC cells (Figure 2A-C) are noteworthy. To deepen the understanding of BMP signaling in cancer stemness and the molecular underpinning of CHRDL2 antagonism, additional western blot assays on P-SMAD1/5 with CHRDL2 overexpression and drug treatment are recommended.
*Western blotting of P-SMAD1/5 upon cells treated with IC50 5FU has now been performed in figure 2C (in the same experiment as the revised panels in figure 1D). The data suggest that CHRDL2 overexpressing cells able to survive chemotherapy have higher levels of P-SMAD1/5 reduction compared to that of untreated cells, strongly suggesting that chemotherapy treatment acts to select the cells with the highest CHRDL2 expression. We thank reviewer 3 for this suggested experiment and have included further discussion on this on page 9. *
The assertion that extrinsic CHRDL2 addition diminishes differentiation and enhances stem-cell numbers in an intestinal organoid model is intriguing. As BMP signaling inhibition contributes to intestinal cell stemness, incorporating additional layers for BMP antagonism of CHRDL2 on intestinal organoids through immunoblotting or real-time quantitative PCR for treated organoids would augment the conclusions.
As stated in the response to reviewer 2, we have investigated Beta-catenin in our organoids following CHRDL2 treatment using immunofluorescence and find that the levels are increased with the staining shifting from the membrane to the cytoplasm and nucleus (Figure 4F).
The authors claim CHRDL2 overexpression can decrease BMP signaling based on GSEA analysis (Figure 5E). However, the GSEA results did not demonstrate the downregulation of BMP signaling. Reanalysis of this GSEA analysis is warranted.
*We agree with this point and have changed the description of this result since the gene set covers both positive and negative regulators of the BMP pathway. We cannot conclusively say from this RNAseq data set that BMP signaling is "downregulated", however since SMAD phosphorylation is increased and nuclear beta-catenin is increased, overall we suggest that the changes we see are likely to represent the effects of decreased BMP signaling along with increased WNT signaling. *
Minor Points:
6.Provide the threshold/cutoff values chosen for differential expressed genes (DEGs) in CHRDL2+ and CHRDL2++ RNA-seq compared with control cells. Explain the minimal overlap between CHRDL2 LOW and CHRDL2 HIGH DEGs. Consider presenting all DEGs in CHRDL2 LOW and CHRDL2 HIGH compared with control cells in one gene expression heatmap for better visualization.
We have now provided the cutoff values for the DEGs in the legend for figure 5 (PThe minimal overlap of DEGs in the low and high expressing cells is an interesting point. We hypothesize that this may be related to the different effects of intermediate vs high levels of WNT signaling that occurs in colon cancer cells, frequently discussed in the literature as the "Just right hypothesis" (Lamlum et al. 1999, Albuquerque et al., 2002, Lewis et al., 2010). However, we haven't included this in the discussion as it merits further exploration. However, we have mainly focused on specific genes that are modified in both data sets, which are more likely to be the direct result of CHRDL2 modification. *
After DEGs analysis, perform Gene Ontology (GO) analysis on these DEGs to further investigate possible gene functions rather than selectively discussing some genes, enhancing understanding of CHRDL2 functions in CRC cells.
We have carried out this analysis using a variety of tools and have now included a Gene Ontology Panther analysis as supplementary figure 7. We have included a comment on this in the text on page 14 saying "Gene ontology analysis supports these findings with enrichment in biological processes such as cellular adhesion, apoptosis and differentiation. "
Conduct similar experiments in both 2D culture and organoid systems, if feasible, to provide more comprehensive insights into CHRDL2's oncogenic roles in CRC tumor progression.
*We have now performed chemotherapy treatment on our organoid systems, and have found that organoids with extrinsic CHRDL2 addition have a higher survival rate after chemotherapy compared to a control (Figure 4H and I). *
Label significance (*, **, ***, and n.s.) for every CRC cell line treated with CHRDL2 in Figure 2D, 2F, 2J, 4G, 5D, and 5F.
We have done this
Label the antibodies with different colors used for immunofluorescence in the figure text in Figure 4E.
We have done this
* * Include replicate dots for the Control group in the bar plots in Figure 1F and 2B.
We have done this
* * Add scale bars in Figure 3A and correct similar issues in other figures if applicable.
We have done this
* *13.Correct grammar and punctuation mistakes throughout the manuscript. For example:
We have done this and further proofread our revised manuscript
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P *We have now added additional details about statistical analysis throughout the figures, legend and main text, showing all significance levels as well as non-significance for each data set. * Reviewer #3 (Significance (Required)):
The current study presents compelling evidence demonstrating that BMP signaling antagonist CHRDL2 enhances colon stem cell survival in colorectal cancer cell lines and organoid models. Further validation through CRC mouse models could offer invaluable insights into the clinical relevance and therapeutic implications of CHRDL2 in colorectal cancer.
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Referee #3
Evidence, reproducibility and clarity
Summary
BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer Eloise Clarkson et al. The manuscript explored the function of CHRDL2, a BMP antagonist, on colorectal cancer (CRC). The authors found that CHRDL2 overexpression can enhance the survival of CRC cells during chemotherapy and irradiation treatment with elevated levels of stem-cell markers and reduced differentiation. Further RNA-seq analysis revealed that CHRDL2 increased the expression of stem-cell markers, WNT signaling and other well-established cancer-associated pathways. Overall, the manuscript is well-written and presented. I have some suggestions:
Major points:
- The authors assert BMP antagonism was demonstrated by assessing levels of phosphorylated SMAD1/5 (Figure 1G). However, the immunoblotting assay only depicted P-SMAD 1/5 levels and B-ACTIN as internal control. It's suggested to include total-SMAD1/5 immunoblotting as an internal control to further support the claim of BMP antagonism.
- The authors argue that CHRDL2 overexpression reduced the proliferation of CRC cell lines, as evidenced by cell proliferation assays. However, from Figure 1E, the reduction in proliferation appears insignificant. It would be beneficial to perform one-way ANOVA tests on each time point for CHRDL2+ and CHRDL2++ with Control in Figure 1E to ascertain significance.
- The findings indicating that overexpressing CHRDL2 can confer resistance to chemotherapy in CRC cells (Figure 2A-C) are noteworthy. To deepen the understanding of BMP signaling in cancer stemness and the molecular underpinning of CHRDL2 antagonism, additional western blot assays on P-SMAD1/5 with CHRDL2 overexpression and drug treatment are recommended.
- The assertion that extrinsic CHRDL2 addition diminishes differentiation and enhances stem-cell numbers in an intestinal organoid model is intriguing. As BMP signaling inhibition contributes to intestinal cell stemness, incorporating additional layers for BMP antagonism of CHRDL2 on intestinal organoids through immunoblotting or real-time quantitative PCR for treated organoids would augment the conclusions.
- The authors claim CHRDL2 overexpression can decrease BMP signaling based on GSEA analysis (Figure 5E). However, the GSEA results did not demonstrate the downregulation of BMP signaling. Reanalysis of this GSEA analysis is warranted.
Minor Points:
6.Provide the threshold/cutoff values chosen for differential expressed genes (DEGs) in CHRDL2+ and CHRDL2++ RNA-seq compared with control cells. Explain the minimal overlap between CHRDL2 LOW and CHRDL2 HIGH DEGs. Consider presenting all DEGs in CHRDL2 LOW and CHRDL2 HIGH compared with control cells in one gene expression heatmap for better visualization. 7. After DEGs analysis, perform Gene Ontology (GO) analysis on these DEGs to further investigate possible gene functions rather than selectively discussing some genes, enhancing understanding of CHRDL2 functions in CRC cells. 8. Conduct similar experiments in both 2D culture and organoid systems, if feasible, to provide more comprehensive insights into CHRDL2's oncogenic roles in CRC tumor progression. 9. Label significance (, , **, and n.s.) for every CRC cell line treated with CHRDL2 in Figure 2D, 2F, 2J, 4G, 5D, and 5F. 10. Label the antibodies with different colors used for immunofluorescence in the figure text in Figure 4E. 11. Include replicate dots for the Control group in the bar plots in Figure 1F and 2B. 12. Add scale bars in Figure 3A and correct similar issues in other figures if applicable. 13.Correct grammar and punctuation mistakes throughout the manuscript. For example:
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P
Page 7: "As seen in Figure 1J, CHRDL2 overexpression significantly increased the number of migrated cells (P < 0.0449)," suggesting increased migratory ability, a hallmark of cancer stem cells."
Page 8: "CHRDL2 overexpression resulted in an approximate twofold increase in IC50 values compared to control cells (P < 0.001)."
Page 10: "As seen in Figure 4B, upon the" should be corrected to "Figure 4B."
- Specify the statistical methods or estimates used for determining statistical significance.
Significance
The current study presents compelling evidence demonstrating that BMP signaling antagonist CHRDL2 enhances colon stem cell survival in colorectal cancer cell lines and organoid models. Further validation through CRC mouse models could offer invaluable insights into the clinical relevance and therapeutic implications of CHRDL2 in colorectal cancer.
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Referee #2
Evidence, reproducibility and clarity
Summary:
In the manuscript entitled "BMP antagonist CHRDL2 enhances the cancer stem-cell phenotype and increases chemotherapy resistance in Colorectal Cancer" the authors demonstrated that Chordin-like 2 (CHDRL2), a secreted BMP antagonist, promotes a chemo-resistant colorectal cancer stem cell phenotype through the inhibition of BMP signaling. The authors took advantage of both 2D engineered colorectal cancer (CRC) cells and healthy murine 3D organoid systems. Specifically, the authors showed a decreased proliferation rate and reduced clonogenic capability upon overexpression of CHRDL2 in established human colon cancer cell lines. Subsequently, they identified a chemo-resistant phenotype upon standard therapies (5FU, Oxaliplatin and Irinotecan) in CHDRL2 overexpressing cells by performing MTS assay. The authors showed that this chemo-resistant phenotype is associated with ATM and RAD21 activation, supporting an induction of DNA damage signaling pathway. Of note, the authors assessed that the exposure of 3D murine organoid to CHRDL2 resulted in a stem-like phenotype induction accompanied by a reduction of the differentiated counterpart. From RNA-seq data analysis emerged the upregulation of genes associated to stemness and DNA repair pathways in CHRDL2 overexpressing cells.
Major comments:
- In the first paragraph of the result section authors assessed that "Colorectal adenocarcinoma cell lines were deliberately chosen to encompass a range of CHRDL2 expression levels and genetic mutations", without showing qRT-PCR or WB data on the differential expression levels of CHRDL2 in a panel of immortalized CRC cell lines. Authors should include these data to better support their choice.
- In Figure 1F, authors described a reduction of cell proliferation in CRC cell lines expressing high levels of CHRDL2 only under low glucose conditions. Why did the authors perform the assay under these conditions? They should better argue this aspect and validated the role of CHRDL2 in metabolism rewiring by performing additional in vitro assays.
- The authors should evaluate the role of CHRDL2 in promoting a stem-like phenotype in human colon cancer stem cells freshly isolated from patients and characterized.
- In order to confirm the data obtained on 3D murine organoids system obtained from normal Intestinal Stem Cells, authors should investigate the stemness induction, driven by CHRDL2, also in human intestinal organoids.
- The authors should evaluate the oncogenic role of CHRDL2, through the maintenance of stemness, by performing orthotopic or subcutaneous experiments in vivo model.
- BMPs proteins are part of a very broad protein family. In the introduction section, authors should indicate the specific BMP protein on which CHRDL2 exerts its inhibitory function. Moreover, they should have assessed BMP protein levels in CACO2, LS180, COLO320 and RKO cell lines.
- In first panel, the authors should quantify the secreted levels of CHRDL2 in the media of overexpressing CHRDL2 cell lines.
- In Figure 2D the authors should use the appropriate controls and describe this with more details in results section.
- In Figure 3A, the authors should have performed the assay by choosing IC50.
- In Supplementary Fig. 4A-B. the results are unclear. The control cell lines are already chemoresistant.
- The authors should review and add statistical analysis in both main and supplementary figures.
Minor comments:
- The quality of immunofluorescence and WB images should be implemented, and in the immunofluorescence panels scale bars should be added.
- In the graphical abstract authors reported that CHRDL2 overexpression increase WNT and EMT pathways, without performing any specific assay to demonstrate this. Authors should correct and graphically improve the graphical abstract.
- The term "significantly" in the discussion section is inappropriately referred to data showed in the histogram in Figure 1J. Moreover, in Figure 1Jthe authors should delete from the y-axis the term "corrected".
- The term "significant" in discussion is inappropriately referred to BMI1 expression level if compared to the histogram in Figure 4G.
- In Figure 2C the authors should add the unit of measurement (fold over control) in the table.
- In Figure 4E the authors should add the figure legend reporting OLFM4 protein.
- The authors should include few sentences summarizing the findings at the end of each paragraph.
Significance
General assessment:
Overall, the work is aimed to elucidate the role of CHRDL2 already considered a poor prognosis biomarker involved in the promotion of CRC (PMID: 28009989), in promoting stem-like properties. The authors elucidated new additional insights into the molecular mechanisms regulating stemness phenotype induced by the BMP antagonist CHRDL2 in CRC. The authors include in the study a large amount of data, which only partially support their hypothesis. However, this manuscript lacks organization and coherence, making it challenging to follow and read. Numerous concerns need to be addressed, along with some sentences to rephrase in the result and discussion sections.
Advance: The manuscript reported some functional insights on the role of CHRDL2 in colorectal cancer, but additional data should be added to support authors 'conclusions.
Audience: The manuscript is suggested for basic research scientists.
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Referee #1
Evidence, reproducibility and clarity
Clarkson and Lewis present data suggesting that overexpression of Chordin like 2 (CHRDL2) can affect colorectal cancer cell responses to chemotherapy agents, possibly by modulating stem-cell like pathways. I have the following comments:
- Fig. 1J-it is standard to show the images of cell migration-this is important here, given the modest effect of CHRDL2 overexpression here.
- Fig. 2A-the very small error bars for most of the data on the curves suggests these are n=1 experiments with multiple technical replicates to generate the error bars. Please clarify. The legend says n=3 with ANOVA analysis but no significance detected. Please clarify.
- Fig. 2B-given the overlapping error bars here, how can there be a p<0.01 between the groups?
- Fig. 2G-did the authors try to estimate the concentration of CHRDL2 in the conditioned medium? Which cell line was used to generate this CM?
- Fig. 5-what is the potential mechanism for gene expression changes in response to CHRDL2 overexpression? Is it all due to BMP inhibition? More mechanistic detail would be welcome here.
Significance
Unclear whether genetically engineered inducible overexpression has any real physiological significance but we all use cell models so this is OK.
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Reply to the reviewers
The authors do not wish to provide a response at this time.
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Referee #3
Evidence, reproducibility and clarity
The authors investigate the role of the AMP-activated protein kinase (AMPK) in mitochondrial dysfunction. Using HEK293T cells as model system they induce expression of either the wild-type or a dominant-negative variant of the mitochondrial DNA polymerase, which results in depletion of mtDNA and a decreased mitochondrial membrane potential. Using different time points of Pol induction they correlate the mitochondrial defects with activation of AMPK and make the interesting observation that only the mitochondrial associated fraction of AMPK becomes activated at an early stage of mitochondrial dysfunction. The authors then apply a known AMP activator (A-769662) and assess its impact on mtDNA levels and respiratory chain subunit steady state levels. Finally, they compare the findings using the HEK cells with patient derived fibroblasts, which show the same response to the activator.
Regarding the so far provided data I have the following concerns:
- I do not agree with the statement that the mechanisms in the HEK and patient cells are different. First of all, there is no analysis of the mechanism in the HEK cells nor the patient fibroblasts. Secondly, the control cell line (Pol WT overexpression) is also showing a decrease in mitochondrial membrane potential, but no change in mtDNA - which is fully reflecting the observation of the patient cells.
- Figure 1H-J: the authors claim that CIV activity is decreasing. However, CIV is virtually absent in these samples and therefore the statement that CIV activity is decreased is not correct.
- It is not clear why the authors used the dominant negative D1135A variant in the HEK system and not the most common dominant patient mutations (of which they in the end use the patient fibroblasts).
- Supplemental Figure S1A/B: Which time point of induction is shown here?
- The rho zero cell line and the control UV treatment are not described in the materials and methods section.
Significance
The observation that mitochondrial-associated AMPK reacts much earlier than the global AMPK pool to the mitochondrial dysfunction is interesting. The other observations described in the manuscript were rather to be expected given previous publications. Overall, the study primarily provides descriptive findings, which, in my view, seem preliminary at this stage and requires significant revisions for it to be truly valuable to the scientific community.
At least some molecular insight into the regulation of the different cellular AMPK pools or detailed analysis on how the activator impacts on mtDNA or the general mechanism that results in stabilization of the mitochondrial membrane potential are necessary to provide sufficient novel findings for publication. This additional analysis would be necessary to strengthen the study's conclusions and broaden its relevance to a larger readership.
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Referee #2
Evidence, reproducibility and clarity
The authors aimed to investigate the functional consequences of AMPK agonist, A-769662, in improving the cellular energetic response to mitochondrial DNA depletion. AMP kinase plays a crucial role in switching the metabolic programming in the cells upon energetic stresses. It drives the activation of transcription factors of the mitochondrial genome as well as nuclear genes important for mitochondrial biogenesis (Herzig and Shaw., 2018, Bonekamp et al., 2021). Previous studies have shown that triggering the AMPK cascade has positive outcomes in mitochondrial disease models (Peralta et a., 2016, Moore et al., 2020). However, the mechanistic basis of their impact on mitochondrial function, especially mtDNA is not known.
Brief Summary:
In this study, the authors characterize the dynamics of mitochondrial dysfunction in response to severe mtDNA depletion, using a cell model and report that A-769662, a non-AMP mimetic AMPK agonist maintains cellular energy homeostasis by stimulating the AMPK cascade to restore membrane potential in mtDNA depleted cells. The positive effect was observed only in patient-derived, mtDNA-depleted cells and absent in control cells, suggesting that A-769662 mediates mitochondrial activity and cellular function via partially different mechanisms.
Major Results:
- The authors used a previously established inducible cell line to transiently express a dominant negative mutant of the mitochondrial DNA polymerase, Poly, as a model system for mtDNA depletion. Compared to the wildtype cells, mtDNA copy number, membrane potential, assembly and levels of respiratory chain complexes I, III and IV were severely reduced in the PolyD1135A cells (Figure 1A-K).
- The authors checked the relative ratio of AMP/ADP and ATP in the PolyD1135A cells and found that ADP levels were elevated, as expected (Figure 2A). Consequently, the levels of phosphorylated AMPK in/at (?) the mitochondria were also increased, in good correlation with the depletion of mtDNA levels and respiratory complexes after 3 days of induction (Figure 2B-E). Activation of the pAMPK pathway was further confirmed by the increased levels of AMPK substrate ACC. However, metabolite levels and cell cycle profiles are mildly altered in the PolyD1135A cells, suggesting that the activation of mitochondrial AMPK is an early response to mtDNA dysfunction.
- Chemical activation of AMPK by agonist A-769662 had a sustained positive effect on the membrane potential in both induced and uninduced cells. This was specific to AMPK signalling as evidenced by no change in membrane potential in cells transfected with AMPK siRNA (Figure 3A-C). However, the A-769662 treatment was insufficient to rescue the proliferation defects in the PolyD1135A mutant cells (Figure 3D), suggesting that the growth defect is independent of AMPK activation.
- A-769662 treatment for 48 or 72h in PolyD1135A improved mtDNA copy number and respiratory complex subunit expression, while having no effect on control cells (Figure 4A-G). Upon A-769662 treatment, patient-derived mutant cell lines showed no change in mtDNA levels (Figure 4H). Interestingly, membrane potential was enhanced in both control and patient cell lines (Figure 4I), suggesting an overall activation of mitochondrial function and not a specific response to restoring mtDNA.
Taken together the manuscript by Carvalho et al., proposes a stimulatory effect of AMP agonist, A-769662 on mtDNA depletion. However, since the model is not consistent in different model systems, the authors should provide stronger evidence for the utility of A-769662 as a therapeutic possibility for mtDNA disorders. Moreover, some mechanistical molecular insights into these largely descriptive results must be presented in a revised version. What drives AMPK localization to mitochondria? Is this kinase imported? Is it just attached? What regulates the distribution of AMPK between their different locations?
Significance
Major points:
- The effect of A-769662 on mtDNA levels in the FlipIn-TRex cell line, harboring a severe mutation and clinically-relevant patient-derived cell lines are not comparable (Figure4A and 4H), suggesting that the amelioration of mitochondrial defect is probably dependent on the extent of mtDNA damage. The FlipIn system shows a loss of almost 85% of mtDNA on day 3 of induction (Figure 1A) whereas the patient-derived cells retain almost 60% of the mtDNA (Figure 4H). The authors argue that the two systems are not comparable. A good control would be to check an inducible FlipIn-TRex cell line with the same patient-derived mutations or alter the induction system, with a shorter induction time or reduced concentration of doxycycline, to have comparable levels of mtDNA depletion.
- A more thorough investigation of A-769662 in different cell models of mtDNA dysfunction, possibly different disease-specific mutations in Poly, or cell types which contain AMPK complexes with -subunits (irresponsive to A-769662 stimulation) will be needed to claim its therapeutic merit.
- To substantiate the claim that the downstream effect of A-769662 treatment is dependent on the metabolic context of the cell, it would be necessary to test the levels of crucial metabolites like mtDNA transcripts, ATP, NADH, in addition to dNTPs tested in the study. It would also help to compare the levels of these metabolites in of PolyD1135A cells, grown in galactose medium.
- In Figure 2C, TFAM is nearly absent in the mitochondrial fraction of PolyD1135A cells, since Poly dysfunction triggers a reduced expression of the transcription machinery. Considering the mtDNA level upregulation is mild after if A-769662 treatment (about 8%, Figure 4A), it would be worthwhile to check if A-769662 could alter transcript levels of mtDNA and/or expression of mitochondrial transcription factors TFAM and TF2B.
Minor points:
- The data from Figure 1 conclusively show defects in mtDNA. It would be necessary to compare OCR, ROS and cellular ATP levels to demonstrate the extent of mitochondrial dysfunction in this model due to mtDNA depletion.
- To further delineate the differences in mitochondrial bioenergetics in PolyD1135A , cells should be grown in galactose media and probed for respiratory fitness.
- The study uses 100uM of A-769662 in the cell assays (Figures 3 and 4) and 200uM to test the activation of AMPK substrate ACC (Figure S2A-C). The authors should explain if a dose-dependent study was performed and how the concentration of A-769662 to be used was determined.
A-769662 is a known AMPK activator with possible therapeutic effects in metabolic disorders, type II diabetes (Cool et al., 2006, Görransson et al., 2007): However, due to the wide range of effects it may have, it would be necessary to get to the molecular basis of how A-769662 targets mtDNA depletion. This study is a nice starting point to further probe into the benefits of A-769662, however it is not (yet) conclusive and definitely needs the clarification of the underlying molecular mechanisms.
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Carvalho and colleagues addresses how an increase in AMPK signaling, triggered by the agonist A769662, contributes to ameliorate cellular phenotypes caused by mtDNA depletion. The authors build on previously established cellular models of mtDNA depletion, and use subcellular fractions in an attempt to distinguish pools of AMPK associated with different organelles. The data is very clearly presented, and the western blot data shown is of high quality. The authors also use cells from two patients with mtDNA depletion, in which they stimulate AMPK using A769662. The overall conclusion is that AMPK stimulation in the cell models with mtDNA depletion is advantageous to minimize the disease-related phenotypes.
There is one fundamental weakness: there are several intracellular AMPK pools described, the major ones being in the cytoplasm, or associated with mitochondria, or with lysosomes, or in the nucleus. However, and importantly, while the authors convincingly show that there is no cytoplasm in their mitochondrial fractions, they do not control for the presence of lysosomal proteins. For the conclusions to be valid, it is absolutely essential to distinguish the effects of mitochondria-associated AMPK from lysosome-associated AMPK, because they may have different effectors and because they are activated by different mechanisms. Furthermore, the authors do not show what happens to AMPK in the patient cells, and this would be very informative. Finally, it would be important to put these findings in the context of other studies on AMPK signaling in response to other mitochondrial perturbations and that find AMPK to be down-regulated in chronic mitochondrial respiratory chain deficiency, as well as to more carefully reference the different intracellular AMPK pools. These studies might help to strengthen the discussion, given that the authors find AMPK signaling to be increased but show that increasing it pharmacologically has benefits - is A769662 activating different pools of AMPK?
Significance
The manuscript addresses an important question: how does the metabolic hub AMPK contribute to the phenotypes observed in chronic mitochondrial malfunction. Most of the studies on AMPK and mitochondrial malfunction focus on acute effects, which are not a model for mitochondrial diseases. Therefore, focusing on chronic effects is important to understand the long term consequences on AMPK signaling and its downstream signaling. Furthermore, if AMPK reactivation is beneficial (as this study proposes and other studies have also shown in other models of chronic mitochondrial malfunction), then this can become a new important therapeutic strategy.
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Reply to the reviewers
1. General Statements
*We thank the reviewers for the overwhelmingly positive feedback on our initial submission. *
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Reviewer 1: “Overall this is a very simple, although extensive and excellent, study analyzing a wide range of data form (sic) patients with bronchoalveolar lavage and epithelial cell samples, human epithelial cell cultures after infection with a range of respiratory viruses as well as the development of a 3D in silico analysis of potential protease and serpin interactions.”
Reviewer 2: “Overall, this paper will be interesting to a specialized audience that is interested in SERPIN function. The SERPIN expression data during viral infection, discovery of CTSL as a target of PAI-1, and evidence that PAI-1 can inhibit SARS-CoV-2 replication, will move that field forward.”
No new experiments were requested, but some were either suggested or explicitly marked optional. We thus focused the initial 4-week-revision on performing new experiments aimed to enhance our study’s significance and impact by validating the heart of our study: the data from the in-silico docking screen.
Reviewer 1: “Further analysis of the detected proteases that are reported here to bind to PAI-1 would be of great interest.”
Reviewer 2: “It is exciting that they predicted CTSL as a target of PAI-1, but it is not obvious that this is a generalizable approach without further hypothesis testing.”
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Thus, we performed additional protease activity assays to validate SERPIN-protease pairs from the in-silico-screen. The results elevate our study above the proof-of-principle state. Beyond their described roles in infectious disease, the two SERPINs that are now tested in more detail (SERPINB2, plasminogen activator inhibitor 2 and SERPINE1, plasminogen activator inhibitor 1) also play critical roles in cancer, neurodegeneration, aging, and cardiovascular disease. (Bouton et al., EMBO Mol Med 2023 Vol. 15 No. 6; Zhang et al., EMBO Mol Med 2023 Vol. 15 No. 9; Bode et al., EMBO Journal 1986 Vol. 5 No. 10; Uhl et al., EMBO Mol Med 2021 Vol. 13 No. 6). Given these multifaceted roles, we anticipate that our discovery of new SERPIN-protease binders and non-binders will advance various areas of human disease driven by SERPIN biology.
2. Description of the planned revisions
*We believe that the planned revisions outlined below can be finalized within 1-2 months. *
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Reviewer 1: “Further analysis of the detected proteases that are reported here to bind to PAI-1 would be of great interest.”
*At the time of the 30-day revision, recombinant SERPINB1 (LEI) and SERPING1 (C1-INH) were still backordered with an estimated shipping date the week of resubmission. Once delivered, we will perform protease activity assays with LEI or C1-INH and uPA, TMPRSS2, Cathepsin L, and Cathepsin B to bring up the number of validated SERPIN:protease interactions from 8 to 16. *
Reviewer 2, major points:
9) Further, to strengthen the conclusions of this data the authors should include additional controls. One would be to use trixplanin as they did in previous panels to show that PAI-1 is necessary. Further, if the authors generate mutant PAI-1 that is unable to inhibit TMPRSS2 (see comment 11 below), they could also use this as a control to show the necessity of functional PAI-1.
*We agree that these optional experiments would increase rigor. We generated plasmids containing mutated PAI-1 that we can use in spike cleavage assays as suggested and can perform this experiment. *
*We can unfortunately not use triplaxinin on cells, as our preliminary data show that it is quite cytotoxic at the concentrations required to inhibit PAI-1. *
10) For Figures 4I-J, is it possible to also blot for S1 cleavage? If possible, this optional data would be helpful to understand whether the entire cleavage process is disrupted or only S2 to S2' especially given that visually it appears as if the full length is more depleted in the condition with PAI-1 suggesting that it is cleaving spike better into S1 and S2. Could also suggest that the dynamics of cleavage are shifted rather than impaired?
*S1 cleavage is shown indirectly in (now) Figure 5f,g – the main product of S1 cleavage is the fragment annotated as S2. Due to high levels of endogenous furin in BHK cells, this cleavage always occurs in this experimental setting. It is true that we have not shown the effects of PAI-1 inhibits on S1 cleavage– we can include that control in the above optional experiment (point 9). We do not expect PAI-1 to have an effect on S1 cleavage, as it is well-established that it does not inhibit furin. *
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Reviewer 2, minor points
7) As a supplemental figure, can the authors show a complex blot (similar to Figure 4F) for CTSB to show that is does not complex with PAI-1.
*Purified active CTSB is not commercially available, but we can attempt to perform gel shift analysis on the samples from the in vitro protease assay. Due to the presence of proteinaceous substrate in these samples, we have previously observed lot of background on the gel, but we can attempt it and include it in a revised manuscript if reviewer/editor find it useful. *
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3. Description of the revisions that have already been incorporated in the transferred manuscript
Reviewer 1:
Summary:
The authors do not reference the prior work which has examined cross class serpins, viral and mammalian, - this should be noted as alternative protease targets are known.
*Thank you – please see our response in point 1 below. *
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The bronchiolar lavage analysis is excellent but cannot differentiate epithelial cell and associated immune cells and their roles in the response.
We apologize for not making this clear – scRNAseq can indeed differentiate between different cell types using cell-type specific expression markers for each individual cell. This is how we were able to retrieve expression data specific for individual cell types. The reviewer is correct in that an expression analysis cannot show the role of individual cell types in the antiviral response. However, as epithelial cells are the primary cell type infected by SARS-CoV-2 gene expression patterns in these epithelial cells may show us cell-intrinsic effectors that are upregulated in response to viral infection. We now revised language in this paragraph to make this clearer (lines 156-162).
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PAI-1 does not seem to be present in the bronchoalveolar lavage samples.
We do not know if PAI-1 is present, as we did not analyze protein levels in these samples. The gene expression data suggests that SERPINE1, the gene encoding PAI-1, is expressed at low levels in the epithelial cell subset at baseline, and expressed at slightly higher levels in individuals with severe COVID-19 (Figure 1c). This is consistent with previously published data on SERPINE1 gene expression upon viral infection (Dittmann et al., Cell, 2015).
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Further discussion of prior work with cross class serpins and also the limitations of the in-silico analyses and the lavage specimens should be provided.
Prior work evidencing other cross class serpin protease targets as well as limitations related to the analyses as discussed in the critiques above should be noted and the abstract and title could better describe and define the studies as performed.
*Thank you for raising these important points. For cross-class SERPINs, please see our response to point 1. The limitations of in silico analyses are discussed in-depth in a paragraph of the discussion (lines 608-631). We also discuss discrepancies observed between SERPIN expression in lavage specimens and in HAEC – please advise whether this is sufficient or needs bolstering (lines 546-564). We revised both title and abstract to better describe and define the studies as performed. *
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These correlations between changes in serpin and protease expression with viral infections and potential new interactions for serpins with previously non identified proteases is of clear interest. This shows an excellent correlation but as with big data sets this does not provide a true cause and effect - rather providing new potential directions for analysis of these interactions in viral infections in lung epithelium and this is valuable as a basis for ongoing studies.
*We are in agreement with the lack of cause and effect –to our knowledge, we make no such claim from the gene expression data. We state that we used the expression data to guide the selection of SERPINs for our in-silico screen (lines 317-319). We then validated select data from our in-silico screen in vitro, which provides true cause and effect (Figures 4 and 5). *
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Major points:
- Cross class serpin interactions are known and have been reported for at least two viral serpins Serp-1 and CrmA - both of which bind cysteine proteases as well as serine proteases as well as the mammalian SCCA serpins. *Thank you for bringing these two examples to our attention – we added them to the discussion (lines 648-652). We now also emphasized throughout the manuscript that the novelty of our findings is in PAI-1 cross-class inhibition, specifically, which has not been previously reported despite PAI-1 being an extremely well-studied SERPIN. *
*We also would like to mention that in our opinion the scientific advance provided by our in-silico screen is not limited to the identification of new PAI-1 targets, but also provides a birds-eye view on SERPIN selectivity in a specific proteolytic landscape. For example, to our knowledge, it was unknown that SERPINB1 is promiscuous and that SERPINC1 is more selective, which our docking predicted. It was unknown that most TMPRSSs are unlikely SERPIN targets and that those that are SERPIN targets need to be in their active state to bind. The unsupervised clustering in Figure 4b (both on the SERPIN and on the protease side) predicts such unrecognized patterns in SERPIN selectivity. *
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The protease targets are reported to vary when interacting with glycosaminoglycans such as heparan sulfate - PAI-1 inhibits thrombin in the presence of heparin - thus while a canonical serpin suicide inhibition is considered specific - it can vary. This is noted in the discussion Yes, we agree (lines 608-610).
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What is the potential impact of the noted interactions of PAI-1 with other proteases such as cathepsin - PAI-1 is considered to have predominately extracellular functions, but prior work indicates internalization of PAI-1 when bound to the uPA/uPAR complex with alterations in intra cellular activation This is correct and PAI-1 internalization is cited and mentioned in discussion (lines 620-624). We now also added data on SARS-CoV-2 variant Omicron BA.1, which predominantly uses CTSL for maturation, and we show is also inhibited by PAI-1 (new Figure 5).
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This is supported by basic in vivo and in vitro serpin and protease interactions that are demonstrated confirming in silico analyses, eg. gel shift analyses or even Mass spectrometry analysis particularly for PAI-1 Yes, this is the data shown in Figure 4. We now also added protease activity assays for other SERPIN-protease pairs, thereby elevating our study above the proof-of-principle state. *This was also a suggestion raised by reviewer 2. *
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Per the authors "To date, three SERPINs have been studied in the context of innate antiviral defense: PAI- 1 (encoded by SERPINE1) against influenza viruses encoding hemagglutinin H1 and SARS-CoV-2, by impeding the proteolytic maturation of H1 or spike, respectively19,20; alpha-1-antitrypsin (encoded by SERPINA1) and antithrombin (encoded by SERPINC1) against SARS-CoV-2, likely through the inhibition of TMPRSS2, by reducing maturation of spike, although direct inhibition of TMPRSS2 by either SERPIN was not shown". This is partially complete however other serpins such as C1Inh and one virus derived serpin that have been analyzed for efficacy in treating SARS Thank you for mentioning this, we added the information to the introduction *(lines 106-111). *
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While TMPRSS2 is indeed a serine protease - Beneficial effects of some serpins may be due to modulation of the immune response as opposed to selective anti-viral responses. The immune / cytokine storm and coagulopathies (with clotting and even hemorrhage) seen in the excess inflammatory response that causes respiratory vascular leak and severe viral sepsis. PAI-1 targets tPA and uPA - uPA has marked proinflammatory actions when bound to the uPA receptor (uPAR) and can activate growth factors and MMPs which can enhance immune cell invasion - PAI-1 binds to the uPA / uPAR complex which can thus also alter inflammatory cell responses and cell activation when internalized. Thank you for bringing up this point. The role of SERPINs in inflammation and anti-viral immune responses is indeed well-established. While our study focuses on cell-intrinsic antiviral roles of SERPINs by shutting down pro-viral proteases, which is much less established, we now added this to the results section for clarification (line 153-156).
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The RCL does in general incorporate P4 to P4' but can vary from this specific P4 to P4' sequence *Yes, we agree. *
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How accurately does in silico protease serpin analysis predict real interactions? - this should be discussed as HADDOCK may have some limitations - This is outside my field of expertise We added an in-depth paragraph on how HADDOCK operates to the results section to help readers not familiar with the technique (lines 248-290). We discuss the limitations of HADDOCK in depth in the discussion section *(lines 608-631)– please advise whether this needs additional information. *
*We argue that, with the limitations stated in the discussion, our in-silico method predicts interactions well, as shown by the correct prediction of known binders and non-binders, as well as of new binders (PAI-1 to *active* TMPRSS2 and CTSL) and a new non-binder (CTSB). *
*As with any screening method, results require validation via another method, which we performed for select SERPINs and proteases. In fact, the revised manuscript now features in vitro validation of 8 SERPIN-protease pairs (Figure 4a, b), with 8 additional planned (see “planned revisions” section). *
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The data from a published study examining bronchoalveolar lavage fluid single cell transcriptional analysis from patients with and without COVID - mild and severe - and with comparison to patients without COVID does demonstrate altered protease and serpin activity - but does not indicate specific interactions *We agree with this statement partially. We disagree in that the data does not demonstrate altered protease and SERPIN activity; it instead demonstrates changes in gene expression levels. We agree in that this does indeed not indicate specific interactions. *
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What is the significance for changes in gene expression in epithelial cells versus macrophage T and B cells looks - This looks like a small change like a small change in the mean values Figure 1b *We performed additional statistical analyses on the Figure 1 data – please refer to Reviewer 2 point 1. *
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Of interest - is the brocholaveolar lavage fluid likely to contain both epithelial cells as well as immune response macrophage, T cells and NK cells etc - one assumes single cells were identified and isolated- Is this defined? Apologies if this was unclear. Yes, the BALF contains all of these cell types. We now added some sentences to the results section explaining scRNAseq and analyses in more detail *(lines 147-162). *
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The known previously reported target proteases for PAI-1 should be noted Agreed; it is noted in the results section where we first speak about PAI-1 target specificity (line 379-382).
SERPINE1 is not noted in figure 1 - this is PAI-1 - but is seen in the HAEC infection model data
SERPINE1 is indeed not significantly upregulated in Figure 1, but is significantly upregulated in HAEC upon infection with Reovirus and parainfluenzavirus 3, and upon IFN stimulation (new Supplemental Tables S1 and S2). The possible reasons for discrepancies between the BALF and HAEC data are discussed in lines 546-564.
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“To overcome this limitation, we developed a computational method to predict 3D interactions between SERPINs and proteases, simulating the binding process depicted in Supplemental Figure 1a. Specifically, we employed High Ambiguity Driven protein- protein Docking (HADDOCK), a tool that predicts complex structures, integrating experimental and computational data35,36." This analysis looks to be extensive however this is a correlation - not a true analysis of cause and effect. We agree on the first point – to our knowledge, our study provides the most extensive SERPIN target discovery process (testing 480 SERPIN-protease interactions). We disagree on the point that our results provide a mere correlation. If you will, we performed a computer-modeled interaction experiment that yields predicted binding energies between each SERPIN with each tested protease. We added a paragraph on how HADDOCK operates to the results section to help readers unfamiliar with the technique. As with any screening method, results need to be validated with another method, which we did for select SERPINs and proteases (Figure 4a, b). This does however have the potential to identify significant interactions We certainly agree on this point. * In future it might be of interest to assess PAI-1 given to infected cultures to assess viral replication and titers or perhaps examine a knock out cell model? We did exactly the former in Figure 4 (now 5). *
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As PAI-1 was identified as having new cathepsin protease binding in addition to TMPRSS2 - the authors did demonstrate inhibition of the new targets on fluorometric analysis and also demonstrated interaction by gel shift - This is excellent *Thank you. *
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The title and the abstract could be better written and more clearly indicate the extent of the analyses performed and the discovery of alternate protease targets for PAI-1 We modified both title and abstract.
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Was the SARS CoV2 lung epithelial cell culture analysis performed in BSL3? Yes. All SARS-CoV-2 infection experiments were performed in a BSL3 environment. We added this information throughout the Methods section, and also generated a new Methods section on Biohazards (lines 779-797).
__Minor critiques __
1) Results section heading "SERPINs are differentially expressed individuals with COVID-19 and in response to respiratory virus infection in a model of the human airway epithelium." The word in needs to be inserted between expressed and individuals *Thank you for catching this – we fixed the sentence (lines 128-129). *
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Reviewer 2:
Major points:
1) The rigor of the results presented in Figure 1 are unclear. For the COVID-19 analyses (Figure 1), only one dataset is used, and no statistical analyses are performed to determine to what degree any of the changes they observe are significant relative to variation in the dataset. This makes it difficult to determine how much can be extrapolated from these data.
We agree that performing statistics on the BALF dataset would be ideal. However, the BALF contains only two non-infected individuals (intubated gun-shot victims), limiting our possibilities for statistical analysis.
*For Figure 1b, we overcame this limitation by adding statistical analysis of upregulated expression values between cell types (i.e. by analyzing differences of upregulation of given SERPIN in epithelial cells compared to macrophages; Supplemental Table S1). We also performed statistical analysis on upregulation for individual SERPINs compared to housekeeping gene B2M (Supplemental Table S1). This revealed that SERPINs statistically significantly upregulated in severe COVID-19 in most cell types, including epithelial cells, in which SERPIN function has not been broadly studied. Upregulation was not statistically significant in mild COVID-19 samples, likely due to the n=3 (as compared to n=6 in the severe COVID-19 group). *
*As for analysis of Figure 1c, we could theoretically perform analysis of differential levels between mild and severe COVID-19, but this is not the question we are trying to answer. The question is whether epithelial cells express SERPINs and proteases, and whether there is an upregulation of either in infected individuals. We now state the limitation of lacking statistical power in the figure legend and the text (lines 176-177). *
2) Similarly, the qPCR data presented in Figure 2 are presented with no statistical analyses. Results should not only be presented with fold change but also p-values that are adjusted for multiple testing.
*We now present p-values in Supplemental Table S2. Of note, data obtained with the experimental system of polarized airway epithelial cultures, differentiated over several weeks, tends to be noisier than that obtained with cell lines. Despite this, a number of SERPINs reach statistical significance. *
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3) How is the dotted line drawn in Figure 3C and D? It would appear there is very little in terms of HADDOCK score to distinguish a predicted "binder" from "non-binder". Also, they later show that CTSB is non inhibited, and yet in Figure 3C it is below the dotted line. Can the authors more clearly delineate how one might use their dataset shown in Figure 3B to accurately predict targets of SERPINs?
This is a valid point. We added a more in-depth description to the results section on how we define “binders” and “non-binders” *(lines 324-331 and Figure 3 legend). We added raw data graph with the thresholds in Supplemental Figure 3d. We further added and defined a threshold line to the PAI-1:CTSs graph (Figure 3c). It is now evident that CTSL, A, F, K score as high-confidence “binders”, while CTSB and others do not. We also added the normalization process and the visual assessment of top-scoring complexes to the in silico docking screen schematic in Figure 3a and the respective figure legend to guide readers. *
4) Based on this, it would be preferable for the authors to tone down their claims about the broad applicability of this approach to predict SERPIN-protease interactions. It is true that they have used it to accurately predict PAI-1-CTSL interactions, but to make such a broad claim about the generalizable nature of this approach would require testing several more SERPIN-protease pairs (both binders and non-binders) to clearly define the scores and parameters that can used to robustly predict interactions.
We thank the reviewer for this criticism. We now address this in the text as outlined in our response to point 3 above. As with any screening method, the results require to be validated via an alternative approach, which we did in the initial submission for TMPRSS2 and CTSL as binders and CTSB as a non-binder. The revised manuscript now features additional in vitro validation of binders and non-binders for a total of 8 SERPIN-protease combinations (Figure 4a, b), which were all correctly predicted by our in-silico method. Two more SERPINs will be added in the final revision (see “planned revisions” section). Our study provides ample data for future studies validating additional predicted pairs and characterizing their biological function, in infectious disease and beyond.
5) In Figure 3D, the authors mutate all eight modeled RCL residues to alanine to create a LOF mutant that has a higher HADDOCK score. Single residue mutations would be more convincing for their model, and would be more informative in terms of their predicted models of interactions.
*We now performed the docking with the single mutant, please see new Figure 3c. *
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7) Further, in Figure 4G lanes 2-4, the PAI-1 band at ~38kDa is not present. Can the authors explain this?
*This is likely because CTSL digests PAI-1 working at its optimum pH (aka “the protease wins”). We removed the panel from the manuscript. *
9) In Figure 4I, the authors claim that the addition of PAI-1 is inhibiting cleavage of the SARS-CoV-2 spike protein (S2) based on densitometry quantifications. However, it is unclear how the authors are normalizing their data, nor whether the experiments (and therefore quantification) are from a single experiment or multiple replicates. Could the authors explain the quantification further and provide replicate information (including statistical support) if those experiments were performed?
Thank you for pointing this out. An explanation has now been added to the Figure 5 legend.
__Minor comments: __
1) The authors speculate about SERPINA1 regulation during viral infection, suggesting an active process of "viral evasion". However, it would appear that even upon interferon treatment in Figure 2C, SERPINA1 expression is decreased. Based on that, the authors should soften their claims about the cause of downregulation of SERPINA1.
Thank you for pointing this out – we softened the language on this point (*lines 225-228). *
2) In Figure 2C, do the authors have an explanation or hypothesis for why SERPINE1 is less upregulated at 72hrs when compared to 24hr infection of SARS-CoV-2?
*We can only speculate on this point. It is possible that one or several of the SARS-CoV-2 accessory proteins modulate SERPINE1 expression in a time-dependent manner. *
3) Can the authors demonstrate how the docking structure of the TMPRSS2 zymogen differs from the active version (especially zooming in on the interface of PAI-1 and the protease)? This could be supplemental data but can the authors show a panel like that in Figure 3F to show how the interface between PAI-1 and TMPRSS2 zymogen looks. Does the inactive TMPRSS2 not interface well with the RCL? Or what is leading to the decreased HADDOCK score?
We added an extensive paragraph on how HADDOCK operates to the results section to introduce how the HADDOCK score is calculated *(lines 248-290). We also added a visual of the top-scoring docking complex of PAI-1 and the TMPRSS2 zymogen (Figure 3d) to illustrate the differences in binding. *
4) In methods, uPA fluorometric protease assay information is missing. Please add this information.
Thank you for catching this – we added the information (line 890).
5) It is a bit confusing that Figure 4K is the quantification of assays shown in Figure 4A-C, rather than quantification of any of the intervening figure panels. It might be clearer to move this quantification next to 4A-C so that it is clearer.
*Thank you for the suggestion – Figure 4 has been restructured. *
6) In Figure 4H, the authors show that addition of recombinant PAI-1 decreases the number of SARS-CoV-2 nucleoprotein positive cells. Have the authors examined whether this decreases the viral titers as well?
*Yes, this is now part of the (new) Figure 5. *
8) In the text, the authors suggest that PAI-1 inhibition of CTSL is surprising/novel. The authors should reconsider phrasing this since there are several other SERPINs that have been shown to inhibit other cathepsins, making this appear less surprising than the authors are suggesting.
*Thank you for pointing this out. We have now clarified throughout the manuscript that while other SERPINs indeed are known to inhibit cathepsins, this had not been previously shown for the extremely well-studied SERPIN PAI-1 with over 15,000 pubmed entries. We also added the implications of this PAI-1-specific finding to the discussion section. *
__Significance: __
The claim of novelty about TMPRSS2 is confusing. In their previous paper (reference 19) they show that PAI-1 inhibits TMPRSS2 activity. These data are clearly shown in Figure 4C & 4D of that paper and are summarized in their sentence in the discussion: "Here, we find three new PAI-1 protease targets: human tryptase (tryptase Clara; club cell secretory protein), HAT, and TMPRSS2 ...". In this current paper, although they characterize the PAI-1-TMPRSS2 interaction in more detail than in their previous paper, they have truly only discovered one new target for PAI-1, which is CTSL.
Thank you for pointing this out – we softened language on the novelty of TMPRSS2 as a PAI-1 target *throughout the manuscript. We further clarify that the novelty is that TMPRSS2 has to be in its active form to be inhibited by PAI-1, which was previously unknown (lines 392, 432). The revised manuscript now also provides validation of total 8 predicted binders and non-binders for 2 (Figure 4 b,c), with 8 more pending (see “planned revisions” section). As those two (future four) SERPINs have various roles in cancer, cardiovascular disease, neurodegeneration, and immunity, our findings have impact beyond their antiviral potential, thereby increasing the overall significance of the manuscript. *
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4. Description of analyses that authors prefer not to carry out
Reviewer 1 major point:
The more common names for the SERPINS as detected in COVID alveolar lavage samples would be helpful in figure 1 - and specifically labelling PAI-1 as this is a focus for this study - together with the known SERPIN nomenclature or under abbreviations - For example SERPINB2 is PAI-2 and SERPING1 is C1INH and SERPINA1 is alpha 1 antitrypsin *Thank you for this suggestion. We tried to keep the SERPIN nomenclature consistent throughout the manuscript, in that the SERPIN genes are referred to by their gene name (i.e., SERPINE1), while the proteins are referred to by their protein name (i.e., PAI-1). Editor and/or Reviewer 1, please advise whether this is acceptable or should be changed. We also added the protein corresponding names in the figure legend. *
Why does supplemental figure 2 show SERPINB1 and not PAI-1. *We performed this computer-modeled experiment (docking SERPINs to known binders and known non-binders) for each SERPIN tested in the study. This was needed to obtain thresholds to define likely binders and likely non-binders. We chose to show SERPINB1 in this supplemental figure because it is well-described with regards to binders and non-binders (the latter, as “negative result”, is not always published for a given SERPIN). We also did not want to narrow the study immediately to PAI-1, as we believe our screen is a generalizable method and our findings are valid beyond PAI-1. We can easily show any other SERPIN here - editor and/or Reviewer 1, please advise. *
Reviewer 2 major point:
6) Figures 4F and 4G are rather confusing. First, in Figure 4F, amount of PAI-1 in lane 1 is not the same as in the lanes with CTSL. The biggest concern with this is that there is a second, higher MW band that is present in lane 1 (also in Figure 4G lane 1) that runs near the band in lanes 2&3 that is marked as the PAI-1-CTSL complex. Although it does appear that the band in lane 1 and lanes 2&3 are slightly different sizes, it is hard to say that conclusively when the amounts of PAI-1 are different. Can the authors repeat this assay to load consistent amounts PAI-1 across all conditions and even potentially separate the top bands to more convincingly show that the band in lanes 2&3 is not in the PAI-1 alone control?
*The upper band is an impurity that disappears upon addition of a protease to the reaction. We confirmed that this band is neither PAI-1 nor CTSL via western blot with PAI-1- or CTSL-specific antibodies. Should reviewer 2 and/or the editor feel that we should repeat the experiment with more loading in the first lane, we can certainly do so. Please advise. *
8) The authors show that exogenous PAI-1 can inhibit SARS-CoV-2 in a multicycle infection in Figure 4H. However, this could be acting at multiple points during the viral infection cycle. A clearer virology experiment to support their model would be to perform single-cycle infections to show that the virus fails to productively infect the cell. For instance, have the authors attempted a high MOI, single-cycle infection to see whether they can detect uncleaved spike protein to show inhibition of cleavage? Or show that no early products of viral infection are produced? While this type of experiment is optional in that it is not required to support the claim that PAI-1 inhibits multicycle SARS-CoV-2 infection, it would support the conclusion that PAI-1 is inhibiting viral entry.
*We agree with the reviewer. We did expand on the virology by using now two strains of SARS-CoV-2 with different proteolytic needs, ancestral WA-1 and Omicron BA.1. We also performed titer analysis (all in Figure 5). *
*However, the other suggested experiments would represent a substantial amount of work in a BSL3 environment. We thus would prefer not do these experiments (as the reviewer states, it is optional), and instead tone down the manuscript to make clear we make no claims on viral entry. *
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Reviewer 2 minor point:
11) One (optional) way to extend these data and support their molecular model would be to mutate residues in PAI-1 that they predict are important for protease inhibition. As their source of PAI-1 currently is commercial, this would require purification of WT and variant PAI-1, which is clearly an undertaking. However, these data would strongly support their modeling and the importance of these residues in engaging with the proteases and springing the mousetrap for their in-vitro/in-vivo experiments (as suggested by data shown in Figure 3F and explained in text). Further, the authors can use these mutants to do some of the functional experiments in Figure 4 as a negative control, and potentially even separate the role of PAI-1 in inhibition of CTSL and TMPRSS2 in terms of SARS-CoV-2 inhibition.
*We agree that these (optional) experiments would be beautiful and are indeed part of future studies on the subject. We feel that they exceed the scope of this current manuscript. *
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Referee #2
Evidence, reproducibility and clarity
Summary:
Rodriguez Galvan et al. use a combined computational and functional approach to identify a novel target for the protease inhibitor, PAI-1 (SERPINE1), and show that exogenous PAI-1 can inhibit SARS-CoV-2 replication. They first use a COVID-19 dataset to identify SERPINs that are differentially expression in individuals with mild and severe COVID-19. They further use experimental infections of a model of human airway epithelium to identify SERPINs that are upregulated in response to several viruses, as well as treatment with interferon. Using this panel of SERPINs and a panel of host proteases, they use computational docking to predict SERPINs that may inhibit human proteases that may be relevant for viral infection. Using these predictions, they show that PAI-1 inhibits TMPRSS2 (previously shown) and CTSL (newly shown in this study), two proteases with relevance for SARS-CoV-2 infection. They finally show that extracellular addition of PAI-1 inhibits multicycle replication of SARS-CoV-2.
Major comments:
1) The rigor of the results presented in Figure 1 are unclear. For the COVID-19 analyses (Figure 1), only one dataset is used, and no statistical analyses are performed to determine to what degree any of the changes they observe are significant relative to variation in the dataset. This makes it difficult to determine how much can be extrapolated from these data. 2) Similarly, the qPCR data presented in Figure 2 are presented with no statistical analyses. Results should not only be presented with fold change but also p-values that are adjusted for multiple testing. 3) How is the dotted line drawn in Figure 3C and D? It would appear there is very little in terms of HADDOCK score to distinguish a predicted "binder" from "non-binder". Also, they later show that CTSB is non inhibited, and yet in Figure 3C it is below the dotted line. Can the authors more clearly delineate how one might use their dataset shown in Figure 3B to accurately predict targets of SERPINs? 4) Based on this, it would be preferable for the authors to tone down their claims about the broad applicability of this approach to predict SERPIN-protease interactions. It is true that they have used it to accurately predict PAI-1-CTSL interactions, but to make such a broad claim about the generalizable nature of this approach would require testing several more SERPIN-protease pairs (both binders and non-binders) to clearly define the scores and parameters that can used to robustly predict interactions. 5) In Figure 3D, the authors mutate all eight modeled RCL residues to alanine to create a LOF mutant that has a higher HADDOCK score. Single residue mutations would be more convincing for their model, and would be more informative in terms of their predicted models of interactions. 6) Figures 4F and 4G are rather confusing. First, in Figure 4F, amount of PAI-1 in lane 1 is not the same as in the lanes with CTSL. The biggest concern with this is that there is a second, higher MW band that is present in lane 1 (also in Figure 4G lane 1) that runs near the band in lanes 2&3 that is marked as the PAI-1-CTSL complex. Although it does appear that the band in lane 1 and lanes 2&3 are slightly different sizes, it is hard to say that conclusively when the amounts of PAI-1 are different. Can the authors repeat this assay to load consistent amounts PAI-1 across all conditions and even potentially separate the top bands to more convincingly show that the band in lanes 2&3 is not in the PAI-1 alone control? 7) Further, in Figure 4G lanes 2-4, the PAI-1 band at ~38kDa is not present. Can the authors explain this? 8) The authors show that exogenous PAI-1 can inhibit SARS-CoV-2 in a multicycle infection in Figure 4H. However, this could be acting at multiple points during the viral infection cycle. A clearer virology experiment to support their model would be to perform single-cycle infections to show that the virus fails to productively infect the cell. For instance, have the authors attempted a high MOI, single-cycle infection to see whether they can detect uncleaved spike protein to show inhibition of cleavage? Or show that no early products of viral infection are produced? While this type of experiment is optional in that it is not required to support the claim that PAI-1 inhibits multicycle SARS-CoV-2 infection, it would support the conclusion that PAI-1 is inhibiting viral entry. 9) In Figure 4I, the authors claim that the addition of PAI-1 is inhibiting cleavage of the SARS-CoV-2 spike protein (S2) based on densitometry quantifications. However, it is unclear how the authors are normalizing their data, nor whether the experiments (and therefore quantification) are from a single experiment or multiple replicates. Could the authors explain the quantification further and provide replicate information (including statistical support) if those experiments were performed? Further, to strengthen the conclusions of this data the authors should include additional controls. One would be to use trixplanin as they did in previous panels to show that PAI-1 is necessary. Further, if the authors generate mutant PAI-1 that is unable to inhibit TMPRSS2 (see comment 11 below), they could also use this as a control to show the necessity of functional PAI-1. 10) For Figures 4I-J, is it possible to also blot for S1 cleavage? If possible, this optional data would be helpful to understand whether the entire cleavage process is disrupted or only S2 to S2' especially given that visually it appears as if the full length is more depleted in the condition with PAI-1 suggesting that it is cleaving spike better into S1 and S2. Could also suggest that the dynamics of cleavage are shifted rather than impaired? 11) One (optional) way to extend these data and support their molecular model would be to mutate residues in PAI-1 that they predict are important for protease inhibition. As their source of PAI-1 currently is commercial, this would require purification of WT and variant PAI-1, which is clearly an undertaking. However, these data would strongly support their modeling and the importance of these residues in engaging with the proteases and springing the mousetrap for their in-vitro/in-vivo experiments (as suggested by data shown in Figure 3F and explained in text). Further, the authors can use these mutants to do some of the functional experiments in Figure 4 as a negative control, and potentially even separate the role of PAI-1 in inhibition of CTSL and TMPRSS2 in terms of SARS-CoV-2 inhibition.
Minor comments:
1) The authors speculate about SERPINA1 regulation during viral infection, suggesting an active process of "viral evasion". However, it would appear that even upon interferon treatment in Figure 2C, SERPINA1 expression is decreased. Based on that, the authors should soften their claims about the cause of downregulation of SERPINA1. 2) In Figure 2C, do the authors have an explanation or hypothesis for why SERPINE1 is less upregulated at 72hrs when compared to 24hr infection of SARS-CoV-2? 3) Can the authors demonstrate how the docking structure of the TMPRSS2 zymogen differs from the active version (especially zooming in on the interface of PAI-1 and the protease)? This could be supplemental data but can the authors show a panel like that in Figure 3F to show how the interface between PAI-1 and TMPRSS2 zymogen looks. Does the inactive TMPRSS2 not interface well with the RCL? Or what is leading to the decreased HADDOCK score? 4) In methods, uPA fluorometric protease assay information is missing. Please add this information. 5) It is a bit confusing that Figure 4K is the quantification of assays shown in Figure 4A-C, rather than quantification of any of the intervening figure panels. It might be clearer to move this quantification next to 4A-C so that it is clearer. 6) In Figure 4H, the authors show that addition of recombinant PAI-1 decreases the number of SARS-CoV-2 nucleoprotein positive cells. Have the authors examined whether this decreases the viral titers as well? 7) As a supplemental figure, can the authors show a complex blot (similar to Figure 4F) for CTSB to show that is does not complex with PAI-1. 8) In the text, the authors suggest that PAI-1 inhibition of CTSL is surprising/novel. The authors should reconsider phrasing this since there are several other SERPINs that have been shown to inhibit other cathepsins, making this appear less surprising than the authors are suggesting.
Significance
Assessment and impact:
This paper brings attention to the potential role of SERPINs in viral pathogenesis. The datasets shown in Figure 1 and 2, with the statistical caveats described above, are interesting demonstrations of the regulation of SERPINS during viral infection. In particular, the comparison of different viruses, and viruses compared to interferon alone, in Figure 2B is intriguing. These data are the strongest points of the paper.
The impact of the computational modeling is difficult to assess. While they have used this dataset to predict one novel interaction (CTSL) with PAI-1, the generalizable nature of this approach to broadly predict SERPIN-protease interactions is unclear since they have not tested or validated any other SERPIN-protease pairs. One major concern is the one raised in Major comments 3&4 above, which is that the score difference between a "non-binder" (CTSB) and a "binder" (uPa) is very small. It is exciting that they predicted CTSL as a target of PAI-1, but it is not obvious that this is a generalizable approach without further hypothesis testing.
The claim of novelty about TMPRSS2 is confusing. In their previous paper (reference 19) they show that PAI-1 inhibits TMPRSS2 activity. These data are clearly shown in Figure 4C & 4D of that paper and are summarized in their sentence in the discussion: "Here, we find three new PAI-1 protease targets: human tryptase (tryptase Clara; club cell secretory protein), HAT, and TMPRSS2 ...". In this current paper, although they characterize the PAI-1-TMPRSS2 interaction in more detail than in their previous paper, they have truly only discovered one new target for PAI-1, which is CTSL.
Finally, the data on SARS-CoV-2 are intriguing and contribute to an emerging field on antiviral SERPINs. This reveals an additional virus that is inhibited by PAI-1, to add to their previous discoveries (reference 19) of influenza virus and Sendai virus inhibition by PAI-1. Future virology experiments, and experiments with mutants that ideally separate the ability of PAI-1 to inhibit TMPRSS2 versus CTSL, will further reveal the step of viral replication that is inhibited, and reveal the contribution of inhibition of TMPRSS2, CTSL, or any other PAI-1 targets, on SARS-CoV-2 replication.
Audience: Overall, this paper will be interesting to a specialized audience that is interested in SERPIN function. The SERPIN expression data during viral infection, discovery of CTSL as a target of PAI-1, and evidence that PAI-1 can inhibit SARS-CoV-2 replication, will move that field forward.
Field of expertise: Biochemistry, host-virus interactions
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Referee #1
Evidence, reproducibility and clarity
Title - In-silico docking platform with serine protease inhibitor (SERPIN) structures identifies host cysteine protease targets with significance for SARS-CoV-2
Authors - Joaquín J Rodriguez Galvan, Maren de Vries, Shiraz Belblidia, Ashley Fisher, Rachel A Prescott, Keaton M Crosse, Walter F. Mangel, Ralf Duerr, Meike Dittmann
Summary
The finding that PAI-1 has cross class serpin functions is of definite interest given the roles of PAI-1 in regulation of physiological processes as well as in driving pathology. PAI-1 is generally considered to be a key regulator of thrombolysis and thus an effect on other pathways and even intracellular pathways is of interest. Examining airway epithelial proteases and serpins is of definite interest in respiratory viral infections. Broadening the targets for serpins is also of very definite interest. This study ranges from an overview of prior published work on bronchoalveolar lavage samples and serpin expression, a tissue culture analysis of lung epithelial cells and expression of proteases and serpins is assessed. In addition selective changes in serpin expression and protease targets are assessed by in silico analysis as well as proof of concept via Western blot and fluorometric analysis. This is an extensive study and of definite interest.
There are some limitations as with any study, albeit the study overall is excellent. The authors do not reference the prior work which has examined cross class serpins, viral and mammalian, - this should be noted as alternative protease targets are known. They do mention the modulation of protease targets by glycosaminoglycans in the discussion. Further, serpins are inhibitors, thus while the RCL provides a target for a protease, but the response may not be fully selective in vivo as the protease has to be present and active to complete the serpin protease interaction. The bronchiolar lavage analysis is excellent but cannot differentiate epithelial cell and associated immune cells and their roles in the response. PAI-1 does not seem to be present in the bronchoalveolar lavage samoles. Further discussion of prior work with cross class serpins and also the limitations of the in silico analyses and the lavage specimens should be provided. Further analysis of the detected proteases that are reported here to bind to PAI-1 would be of great interest. The data from the bronchoalveolar lavage is published.
Overall this is a very simple, although extensive and excellent, study analyzing a wide range of data form patients with bronchoalveolar lavage and epithelial cell samples, human epithelial cell cultures after infection with a range of respiratory viruses as well as the development of a 3D in silico analysis of potential protease and serpin interactions. These correlations between changes in serpin and protease expression with viral infections and potential new interactions for serpins with previously non identified proteases is of clear interest. This shows an excellent correlation but as with big data sets this does not provide a true cause and effect - rather providing new potential directions for analysis of these interactions in viral infections in lung epithelium and this is valuable as a basis for ongoing studies. Prior work evidencing other cross class serpin protease targets as well as limitations related to the analyses as discussed in the critiques above should be noted and the abstract and title could better describe and define the studies as performed.
Critique
Major
- Cross class serpin interactions are known and have been reported for at least two viral serpins Serp-1 and CrmA - both of which bind cysteine proteases as well as serine proteases as well as the mammalian SCCA serpins
- The protease targets are reported to vary when interacting with glycosaminoglycans such as heparan sulfate - PAI-1 inhibits thrombin in the presence of heparin - thus while a canonical serpin suicide inhibition is considered specific - it can vary. This is noted in the discussion
- What is the potential impact of the noted interactions of PAI-1 with other proteases such as cathepsin - PAI-1 is considered to have predominately extracellular functions, but prior work indicates internalization of PAI-1 when bound to the uPA/uPAR complex with alterations in intra cellular activation
- This is supported by basic in vivo and in vitro serpin and protease interactions that are demonstrated confirming in silico analyses, eg. gel shift analyses or even Mass spectrometry analysis particularly for PAI-1
- Per the authors "To date, three SERPINs have been studied in the context of innate antiviral defense: PAI- 1 (encoded by SERPINE1) against influenza viruses encoding hemagglutinin H1 and SARS-CoV-2, by impeding the proteolytic maturation of H1 or spike, respectively19,20; alpha-1-antitrypsin (encoded by SERPINA1) and antithrombin (encoded by SERPINC1) against SARS-CoV-2, likely through the inhibition of TMPRSS2, by reducing maturation of spike, although direct inhibition of TMPRSS2 by either SERPIN was not shown". This is partially complete however other serpins such as C1Inh and one virus derived serpin that have been analyzed for efficacy in treating SARS
- While TMPRSS2 is indeed a serine protease - Beneficial effects of some serpins may be due to modulation of the immune response as opposed to selective anti-viral responses. The immune / cytokine storm and coagulopathies (with clotting and even hemorrhage) seen in the excess inflammatory response that causes respiratory vascular leak and severe viral sepsis. PAI-1 targets tPA and uPA - uPA has marked proinflammatory actions when bound to the uPA receptor (uPAR) and can activate growth factors and MMPs which can enhance immune cell invasion - PAI-1 binds to the uPA / uPAR complex which can thus also alter inflammatory cell responses and cell activation when internalized.
- The RCL does in general incorporate P4 to P4' but can vary from this specific P4 to P4' sequence
- How accurately does in silico protease serpin analysis predict real interactions? - this should be discussed as HADDOCK may have some limitations - This is outside my field of expertise
- The data from a published study examining bronchoalveolar lavage fluid single cell transcriptional analysis from patients with and without COVID - mild and severe - and with comparison to patients without COVID does demonstrate altered protease and serpin activity - but does not indicate specific interactions
- What is the significance for changes in gene expression in epithelial cells versus macrophage T and B cells looks - This looks like a small change like a small change in the mean values Figure 1b
- The more common names for the SERPINS as detected in COVID alveolar lavage samples would be helpful in figure 1 - and specifically labelling PAI-1 as this is a focus for this study - together with the known SERPIN nomenclature or under abbreviations - For example SERPINB2 is PAI-2 and SERPING1 is C1INH and SERPINA1 is alpha 1 antitrypsin
- Of interest - is the brocholaveolar lavage fluid likely to contain both epithelial cells as well as immune response macrophage, T cells and NK cells etc - one assumes single cells were identified and isolated- Is this defined?
- The known previously reported target proteases for PAI-1 should be noted
- SERPINE1 is not noted in figure 1 - this is PAI-1 - but is seen in the HAEC infection model data
- "To overcome this limitation, we developed a computational method to predict 3D interactions between SERPINs and proteases, simulating the binding process depicted in Supplemental Figure 1a. Specifically, we employed High Ambiguity Driven protein- protein Docking (HADDOCK), a tool that predicts complex structures, integrating experimental and computational data35,36." This analysis looks to be extensive however this is a correlation - not a true analysis of cause and effect This does however have the potential to identify significant interactions - In future it might be of interest to assess PAI-1 given to infected cultures to assess viral replication and titers or perhaps examine a knock out cell model?
- Why does supplemental figure 2 show SERPINB1 and not PAI-1
- As PAI-1 was identified as having new cathepsin protease binding in addition to TMPRSS2 - the authors did demonstrate inhibition of the new targets on fluorometric analysis and also demonstrated interaction by gel shift - This is excellent
- The title and the abstract could be better written and more clearly indicate the extent of the analyses performed and the discovery of alternate protease targets for PAI-1
- Was the SARS CoV2 lung epithelial cell culture analysis performed in BSL3?
Minor critiques
- Results section heading "SERPINs are differentially expressed individuals with COVID-19 and in response to respiratory virus infection in a model of the human airway epithelium." The word in needs to be inserted between expressed and individuals
Significance
Overall this is a very simple, although extensive and excellent, study analyzing a wide range of data form patients with bronchoalveolar lavage and epithelial cell samples, human epithelial cell cultures after infection with a range of respiratory viruses as well as the development of a 3D in silico analysis of potential protease and serpin interactions. These correlations between changes in serpin and protease expression with viral infections and potential new interactions for serpins with previously non identified proteases is of clear interest. This shows an excellent correlation but as with big data sets this does not provide a true cause and effect - rather providing new potential directions for analysis of these interactions in viral infections in lung epithelium and this is valuable as a basis for ongoing studies. Prior work evidencing other cross class serpin protease targets as well as limitations related to the analyses as discussed in the critiques above should be noted and the abstract and title could better describe and define the studies as performed.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: Authors performed a metatranscriptomic analysis from publicly-available datasets of whole blood from 3 places in Indonesia. Their goal was to explore which pathogens were present on the blood of those 117 healthy individuals. It was interesting that reads from Flaviviridae and Plasmodium were detected in asymptomatic subjects.
Major comments: 1) How did the authors assess and correct batch-effects between different datasets?
Our response: We have sequencing batch information for the Indonesian dataset and saw no clear clustering based on batches in the first 8 PCs. We recognize that sampling variations may exist between islands, though the taxa matrix we acquired from the unmapped reads are very scarce that such variations did not have a strong enough effect to introduce batch effects in our microbiome analyses, and that the signals were driven by pathogenic reads. For our comparative analyses between datasets, we made sure that all three datasets shared similar processing (collected using Tempus Blood RNA Tubes and went through globin depletion method) and have trimmed both Indonesian and Malian reads to match the length of the UK reads (75BP).
2) Did the RNA-seq capture poly-A mRNAs? If so... these reads that did not map the human genome were captured because of internal priming. Can they find internal poly A sequences in the genome of Flaviviridae and Plasmodium pathogens? I would like to know that to understand the source of the reads and which other pathogens may be missing (due to the lack of internal priming).
__Our response: __No, our dataset did not capture poly-A mRNAs. We performed ribosomal RNA (rRNA) and globin mRNA depletion.
3) Principal coordinates analysis (PCoA) is often utilized in metagenomics analysis. Although they are equivalent, is there a reason for using PCA?
Our response: Since we used CLR transformation, the resulting matrix lies in Euclidian space. PCA is just a form of PCoA in Euclidian space.
Minor comments: 1) "Indonesia is a country with large numbers of endemic and emerging infectious diseases [16], making it a crucially important location to monitor and understand the effects of pathogens on human hosts." Is there any epidemiological data that shows differences in infectious diseases across these 3 places? Can the authors provide a map and better explanation about the importance in comparing these 3 areas?
__Our response: __We have added references to malaria infection being more prevalent in the eastern side of Indonesia in the discussion section.
2) Why is it so hard to try to identify (only for Flaviviridae reads) reads that map to very relevant viruses, such as Zika, Dengue, and Yellow Fever? Why did the authors state that they "were unable to refine this assignment further" if this is one of the most interesting finding?
__Our response: __Our reanalysis showed a small percentage of the Flaviviridae reads to be assigned to the Pegivirus genus. As more diverse microbial genomes are added to reference databases and identical regions become more common between them, it becomes harder for the classifer to further define reads to species level (https://link.springer.com/article/10.1186/s13059-018-1554-6). Flaviviridae has distinct species spread across six different genera (https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=11050). In comparison, despite Plasmodiidae having more species recorded compared to Flaviviridae, an overwhelming majority of the species is part of the Plasmodium genus, hence we were able to refine them down to species-level (https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id=1639119).
3) Is the script available at https://gitlab.unimelb.edu.au/igr-lab/Epi_Study ? This reviewer could not access it. __Our response: __We thank Reviewer 1 for pointing this out and have amended the link, now accessible here: https://gitlab.svi.edu.au/muhamad.fachrul/indo_blood_microbiome
Reviewer #1 (Significance (Required)):
Interesting paper that enable to extract additional knowledge from whole blood RNA-seq data. There are already several papers that do this and I think authors could go one step forward (for instance, PCR validation of additional individuals). I don't think this can be used for surveillance if it cannot identify species, it is more expensive than running targeted assays, and that may be many false negative pathogens in the samples.
__Our response: __We thank Reviewer 1 for their comments. We have updated our manuscript to reflect our updated analyses which minimizes false positive taxa and the project’s significance not as a mainline surveillance tool, but a retrospective one.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary:
Bobowik and colleagues perform a computational analysis of whole blood RNA-seq datasets from healthy individuals of three different regions of Indonesia. Their goal is to identify infecting pathogens and other microbes and correlate their abundances to host gene expression patterns or health characteristics in these populations. They find a broad range of bacterial, viral and microeukaryote taxa. When comparing the three Indonesian populations, they find that the Korowai population is the most diverse and different from the other two, possibly driven by the higher prevalence and abundance of Plasmodium (Apicomplexa) in this population.
Then, the authors conduct a statistical decomposition of human gene expression in these samples in independent factors using ICA, and correlate each of these factors to the abundances of the microbial taxa detected. This analysis allows researchers to associate specific patterns of gene expression, such as immune-related pathways, to the presence of members of the Apicomplexa and Kitrinoviricota phyla.
Lastly, the authors use previously published data from other two cohorts (from Mali and the UK) to contextualize their blood microbiome findings. They find microbial reads in all datasets. The Mali cohort is characterized by a large abundance of archaea, not found in the other two populations, while the UK cohort has the lower diversity. Altogether, the authors propose the use of RNA-seq data from human whole blood as a way to study the blood microbiome and establish potential associations between blood resident microbes and host gene expression
Major comments:
1) The methodology to filter and remove reads from potential contaminants needs to be more stringent to ensure the results do not contain spurious contaminants and that the conclusions are correct. It has been described that genomic databases are heavily contaminated with human sequences (Steinegger and Salzberg, 2020), and in this manuscript, even after a two-pass alignment with STAR, reads mapping to helminths also corresponded to the human genome. Additionally, ad-hoc removal of specific taxa (Metazoa and Viridiplantae) was only performed after suspicion of contamination. However, this ad-hoc removal cannot be performed with microbial (bacterial, viral, etc.) contaminants as there is a risk of removing actual bacteria from the samples. But it has been confirmed that many microbial assemblies also suffer from human contamination. Possible actions to take are the following: a.Perform the human mapping with more lenient parameters to avoid human reads to map to other (likely contaminated) genomes in genome databases. b.Remove common contaminants that have been documented, for instance in blood (Chrisman et al., 2022). c.Run a tool to detect contaminated contigs in the database used to map reads to microbes and remove these problematic contigs from further analysis.
Our response: We thank Reviewer 2 for the suggestions, especially to address contaminants. We have reanalyzed our data which resulted in much fewer taxa yet still retained the main pathogenic findings.
2) In line with the above, removing singletons (as I have understood these are taxa that are represented by a single read), is a way to minimize the risk of contamination. To take advantage of the functional profiling of RNA-seq, a measure to ensure that microbes found in blood are active would be to include in the analysis only taxa for which expression of more than a few genes is detected. This type of filtering has been previously applied in studies where very low microbial loads are expected (Lloréns-Rico et al., 2021). In this study, it has only been applied to the specific case of the archaeal taxon Methanocaldococcaceae. However, I would expect cleaner results if applied consistently to all taxa detected.
__Our response: __We have reanalyzed the data and applied this to all taxa detected.
3) The specificity of Methanocaldococcaceae in the samples from Mali is very striking. I am highly suspicious that this only occurs due to a batch effect, even though the authors were highly selective in their cohorts to avoid these. In fact, I extracted the genes spanning the regions highlighted in Supplementary Figure 9 of the Methanocaldococcus jannaschii genome. A BLAST search of these sequences returned, among Methanocaldococcus hits, hits from the ERCC synthetic spike-in sequences, used as internal controls in many RNA-seq experiments. ERCC synthetic spike-in hits appeared for all 4 regions in the genome of M. jannaschii highlighted in this figure. In the original publications of this dataset, there is no reference to the use of these ERCC controls, but given the observed matches, I suggest the authors to perform an extra step in their filtering pipeline to remove all reads mapping to these ERCC standards in all their three cohorts to prevent these sort of batch effects.
__Our response: __We thank Reviewer 2 for pointing this out. Our reanalysis, which now used proper 2-pass mapping and further downstream classification with both pairs of the reads, no longer detected any archaea.
4) I am puzzled by the inconsistencies shown between forward and reverse reads when mapping paired-end data. I expect these inconsistencies at lower taxonomic ranks (species or genus level) due to incomplete genomes, but not at higher taxonomic ranks. I wonder if, by performing more stringent filtering of contaminants as suggested above, the consistency between forward and reverse reads increases and both mates can be used, making the mapping more reliable.
__Our response: __We have reanalyzed the data using both pairs of the reads for classification, resulting in less detected taxa. We believe the new results are more robust as it no longer includes taxa that are not typically found in humans (such as the archae Methanocaldococcus and other environmental bacteria).
In summary, my main concerns regarding this manuscript involve the possibility that contaminants in the sequencing data may be the cause of some of the results presented, and I tried to propose ways of dealing with these contaminants. While some of the results may not be affected by detection of contaminants (i.e. the association between Apicomplexa and some ICs), others such as the diversity measures or the comparison across cohorts may be severely affected. I will consider these results highly preliminary until a more thorough and stringent approach for contaminant removal is applied.
Our response: We thank Reviewer 2 for the suggestions and have updated our manuscript with results updated analyses that are more stringent towards contaminants, as can be seen from our updated findings.
Minor comments:
1) I would appreciate some of the analyses done at lower taxonomic levels if the sparsity of the data allows it, after removing contaminants. Given that the CLR transformation does not allow for zeros, other alternatives such as GMPR (Chen et al., 2018) or adding a pseudocount would allow these analyses?
__Our response: __After our reanalysis, we ended up with even sparser data and therefore could not perform the analyses at lower taxonomic levels.
2) In the PCA shown in figure 1, does the number of microbial reads detected correlate with any of the first two components?
__Our response: __Yes Plamosdiidae correlates well with PCs 1 and 2 (0.66 & 0.73) and Flaviviridae correlates very strongly with PC1 (0.917). We have added this detail in the results section.
3) In Figure 1C, the x axis is wrongly named PC2.
__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.
4) There is a typo in the legend of Figure 1A ("showeing")
__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.
5) In the alpha diversity estimates comparison across the three different cohorts, after subsampling each population to achieve similar sample size in each cohort, it is stated that "after subsampling, each population had similar diversity estimates". However, the numbers shown afterwards corresponding to the mean values of alpha diversity, without confidence intervals or a boxplot/violin plot together with an accompanying statistical test, are not enough to assess similarity. I would appreciate a figure (similar to Figure 3E and F) or a test accompanying these mean values.
__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.
6) In the volcano plots (Figure 3A, B and others throughout the manuscript) it would help the reader to add lines for the thresholds chosen for the effect size and -log10(p-value) to separate significant results.
__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.
7) In Figure 3E and F, I would appreciate having bars for the statistically significant comparisons.
__Our response: __We thank Reviewer 2 for pointing this out and have amended this detail.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Bobowik and colleagues perform a computational analysis of whole blood RNA-seq datasets from healthy individuals of three different regions of Indonesia. Their goal is to identify infecting pathogens and other microbes and correlate their abundances to host gene expression patterns or health characteristics in these populations. They find a broad range of bacterial, viral and microeukaryote taxa. When comparing the three Indonesian populations, they find that the Korowai population is the most diverse and different from the other two, possibly driven by the higher prevalence and abundance of Plasmodium (Apicomplexa) in this population.
Then, the authors conduct a statistical decomposition of human gene expression in these samples in independent factors using ICA, and correlate each of these factors to the abundances of the microbial taxa detected. This analysis allows researchers to associate specific patterns of gene expression, such as immune-related pathways, to the presence of members of the Apicomplexa and Kitrinoviricota phyla.
Lastly, the authors use previously published data from other two cohorts (from Mali and the UK) to contextualize their blood microbiome findings. They find microbial reads in all datasets. The Mali cohort is characterized by a large abundance of archaea, not found in the other two populations, while the UK cohort has the lower diversity. Altogether, the authors propose the use of RNA-seq data from human whole blood as a way to study the blood microbiome and establish potential associations between blood resident microbes and host gene expression
Major comments:
- The methodology to filter and remove reads from potential contaminants needs to be more stringent to ensure the results do not contain spurious contaminants and that the conclusions are correct. It has been described that genomic databases are heavily contaminated with human sequences (Steinegger and Salzberg, 2020), and in this manuscript, even after a two-pass alignment with STAR, reads mapping to helminths also corresponded to the human genome. Additionally, ad-hoc removal of specific taxa (Metazoa and Viridiplantae) was only performed after suspicion of contamination. However, this ad-hoc removal cannot be performed with microbial (bacterial, viral, etc.) contaminants as there is a risk of removing actual bacteria from the samples. But it has been confirmed that many microbial assemblies also suffer from human contamination. Possible actions to take are the following:
- a.Perform the human mapping with more lenient parameters to avoid human reads to map to other (likely contaminated) genomes in genome databases.
- b.Remove common contaminants that have been documented, for instance in blood (Chrisman et al., 2022).
- c.Run a tool to detect contaminated contigs in the database used to map reads to microbes and remove these problematic contigs from further analysis.
- In line with the above, removing singletons (as I have understood these are taxa that are represented by a single read), is a way to minimize the risk of contamination. To take advantage of the functional profiling of RNA-seq, a measure to ensure that microbes found in blood are active would be to include in the analysis only taxa for which expression of more than a few genes is detected. This type of filtering has been previously applied in studies where very low microbial loads are expected (Lloréns-Rico et al., 2021). In this study, it has only been applied to the specific case of the archaeal taxon Methanocaldococcaceae. However, I would expect cleaner results if applied consistently to all taxa detected.
- The specificity of Methanocaldococcaceae in the samples from Mali is very striking. I am highly suspicious that this only occurs due to a batch effect, even though the authors were highly selective in their cohorts to avoid these. In fact, I extracted the genes spanning the regions highlighted in Supplementary Figure 9 of the Methanocaldococcus jannaschii genome. A BLAST search of these sequences returned, among Methanocaldococcus hits, hits from the ERCC synthetic spike-in sequences, used as internal controls in many RNA-seq experiments. ERCC synthetic spike-in hits appeared for all 4 regions in the genome of M. jannaschii highlighted in this figure. In the original publications of this dataset, there is no reference to the use of these ERCC controls, but given the observed matches, I suggest the authors to perform an extra step in their filtering pipeline to remove all reads mapping to these ERCC standards in all their three cohorts to prevent these sort of batch effects.
- I am puzzled by the inconsistencies shown between forward and reverse reads when mapping paired-end data. I expect these inconsistencies at lower taxonomic ranks (species or genus level) due to incomplete genomes, but not at higher taxonomic ranks. I wonder if, by performing more stringent filtering of contaminants as suggested above, the consistency between forward and reverse reads increases and both mates can be used, making the mapping more reliable.
In summary, my main concerns regarding this manuscript involve the possibility that contaminants in the sequencing data may be the cause of some of the results presented, and I tried to propose ways of dealing with these contaminants. While some of the results may not be affected by detection of contaminants (i.e. the association between Apicomplexa and some ICs), others such as the diversity measures or the comparison across cohorts may be severely affected. I will consider these results highly preliminary until a more thorough and stringent approach for contaminant removal is applied.
Minor comments:
- I would appreciate some of the analyses done at lower taxonomic levels if the sparsity of the data allows it, after removing contaminants. Given that the CLR transformation does not allow for zeros, other alternatives such as GMPR (Chen et al., 2018) or adding a pseudocount would allow these analyses?
- In the PCA shown in figure 1, does the number of microbial reads detected correlate with any of the first two components?
- In Figure 1C, the x axis is wrongly named PC2.
- There is a typo in the legend of Figure 1A ("showeing")
- In the alpha diversity estimates comparison across the three different cohorts, after subsampling each population to achieve similar sample size in each cohort, it is stated that "after subsampling, each population had similar diversity estimates". However, the numbers shown afterwards corresponding to the mean values of alpha diversity, without confidence intervals or a boxplot/violin plot together with an accompanying statistical test, are not enough to assess similarity. I would appreciate a figure (similar to Figure 3E and F) or a test accompanying these mean values.
- In the volcano plots (Figure 3A, B and others throughout the manuscript) it would help the reader to add lines for the thresholds chosen for the effect size and -log10(p-value) to separate significant results.
- In Figure 3E and F, I would appreciate having bars for the statistically significant comparisons.
References:
Chen, L., Reeve, J., Zhang, L., Huang, S., Wang, X., and Chen, J. (2018). GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ 6, e4600. https://doi.org/10.7717/peerj.4600.
Chrisman, B., He, C., Jung, J.-Y., Stockham, N., Paskov, K., Washington, P., and Wall, D.P. (2022). The human "contaminome": bacterial, viral, and computational contamination in whole genome sequences from 1000 families. Sci Rep 12, 9863. https://doi.org/10.1038/s41598-022-13269-z.
Lloréns-Rico, V., Gregory, A.C., Van Weyenbergh, J., Jansen, S., Van Buyten, T., Qian, J., Braz, M., Menezes, S.M., Van Mol, P., Vanderbeke, L., et al. (2021). Clinical practices underlie COVID-19 patient respiratory microbiome composition and its interactions with the host. Nat Commun 12, 6243. https://doi.org/10.1038/s41467-021-26500-8.
Steinegger, M., and Salzberg, S.L. (2020). Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in GenBank. Genome Biol 21, 115. https://doi.org/10.1186/s13059-020-02023-1.
Significance
The research reported in this manuscript may have both technical and clinical significance, once the concerns raised above are adequately addressed. At the technical level, once contamination can be ruled out or securely minimized, this work can provide guidelines for microbial identification from whole blood RNA-seq data, applicable to both prospective studies as well as to retrospective studies using previously generated datasets. From this perspective, this work would add to the existing body of bioinformatics pipelines aimed at detecting microbes from host RNA-seq data (Simon et al., 2018). From a clinical perspective, it can provide an additional means of pathogen and disease surveillance without the need of microbial culturing or pathogen-specific tests. However, the requirement of blood samples may still hamper use in rural or underdeveloped areas. Lastly, another advantage is the possibility to directly link microbial abundances to gene expression patterns in the host.
Field of expertise: bacterial transcriptomics, metatranscriptomics, low-biomass microbiome analyses.
Limitations in my expertise: I cannot evaluate the clinical implications of the associations between host gene expression patterns and microbial abundances. Also, I am not familiar with the ICA methodology.
Reference:
Simon, L.M., Karg, S., Westermann, A.J., Engel, M., Elbehery, A.H.A., Hense, B., Heinig, M., Deng, L., and Theis, F.J. (2018). MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data. GigaScience 7, giy070. https://doi.org/10.1093/gigascience/giy070.
- The methodology to filter and remove reads from potential contaminants needs to be more stringent to ensure the results do not contain spurious contaminants and that the conclusions are correct. It has been described that genomic databases are heavily contaminated with human sequences (Steinegger and Salzberg, 2020), and in this manuscript, even after a two-pass alignment with STAR, reads mapping to helminths also corresponded to the human genome. Additionally, ad-hoc removal of specific taxa (Metazoa and Viridiplantae) was only performed after suspicion of contamination. However, this ad-hoc removal cannot be performed with microbial (bacterial, viral, etc.) contaminants as there is a risk of removing actual bacteria from the samples. But it has been confirmed that many microbial assemblies also suffer from human contamination. Possible actions to take are the following:
-
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Referee #1
Evidence, reproducibility and clarity
Summary:
Authors performed a metatranscriptomic analysis from publicly-available datasets of whole blood from 3 places in Indonesia. Their goal was to explore which pathogens were present on the blood of those 117 healthy individuals. It was interesting that reads from Flaviviridae and Plasmodium were detected in asymptomatic subjects.
Major comments:
- How did the authors assess and correct batch-effects between different datasets?
- Did the RNA-seq capture poly-A mRNAs? If so... these reads that did not map the human genome were captured because of internal priming. Can they find internal poly A sequences in the genome of Flaviviridae and Plasmodium pathogens? I would like to know that to understand the source of the reads and which other pathogens may be missing (due to the lack of internal priming).
- Principal coordinates analysis (PCoA) is often utilized in metagenomics analysis. Although they are equivalent, is there a reason for using PCA?
Minor comments:
- "Indonesia is a country with large numbers of endemic and emerging infectious diseases [16], making it a crucially important location to monitor and understand the effects of pathogens on human hosts." Is there any epidemiological data that shows differences in infectious diseases across these 3 places? Can the authors provide a map and better explanation about the importance in comparing these 3 areas?
- Why is it so hard to try to identify (only for Flaviviridae reads) reads that map to very relevant viruses, such as Zika, Dengue, and Yellow Fever? Why did the authors state that they "were unable to refine this assignment further" if this is one of the most interesting finding?
- Is the script available at https://gitlab.unimelb.edu.au/igr-lab/Epi_Study ? This reviewer could not access it.
Significance
Interesting paper that enable to extract additional knowledge from whole blood RNA-seq data. There are already several papers that do this and I think authors could go one step forward (for instance, PCR validation of additional individuals). I don't think this can be used for surveillance if it cannot identify species, it is more expensive than running targeted assays, and that may be many false negative pathogens in the samples.
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Reply to the reviewers
Response to Reviewer 1
__Glycosaminoglycan (GAG)-binding proteins regulating essential processes such as cell growth and migration are essential for cell homeostasis. It is reported that the GAG has the ability to bind to Herpin sulfate. As both GAGs and the LPS lipid A disaccharide core of gram-negative bacteria contain negatively charged disaccharide units, the researchers proposed that heparin-binding peptides might have cryptic antimicrobial peptide motifs. To prove the hypothesis, they have synthesized five candidates [HBP1-5], which showed a binding affinity towards heparin and LPS binding. By using various methods, they showed that these molecules have antimicrobial activity. The key finding in this study is the finding of the CPC domain, where C is a cationic amino acid and P is a polar amino acid. __
Major comments
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__ Even though the Authors propose here that CPC' clip motif is needed for antimicrobial activity. However, various studies have demonstrated that the mere presence of cationic amino or hydrophobic amino acids does not give the activity, the location of these amino acids at the strategic position is critically needed. The major issue in this work, the authors have not presented, whether there was a single CPC motif or multiple in the 5 peptides they have synthesized. Further, they need to demonstrate how are the charged and hydrophobic amino acids distributed in the peptides. these things will clearly explain the difference in the activity as well spectrum of the peptides. The authors should make an extra figure or add information highlighting this unique characteristic for better understanding to the reader.__
We thank the reviewer for his/her comments and suggestions. We concur that the distribution of amino acids is crucial for the antimicrobial activity of the peptides and their ability to bind heparin. We also agree with the suggestion of illustrating the location of the CPC' motifs of HBPs in the context of the parental proteins and have accordingly done so in the new Supplementary Figure 1. In all cases, only one CPC' motif was identified in the antimicrobial region, as highlighted in the figure, and the inter-residue distances measured are consistent with the CPC' motif definition. Thus, we demonstrate that a CPC' motif exists in all five HBPs, which explains how they recognize and bind heparin.
To illustrate the distribution of charged and hydrophobic amino acids in HBPs, we have also prepared new Supplementary Figure 2, displaying electrostatic potentials in the predicted HBP structures, and showing how the distribution of charged residues creates hydrophobic and cationic patches on the surface of the peptides. Our analysis reveals cationic patches to be surrounded by hydrophobic residues, which may explain the ability of the peptides to disrupt membranes and exert antimicrobial activity.
__ It is strange to observe that there are quite a number of reports showing that the peptides derived from the Herprin binding proteins have antimicrobial activity, but no one has reported their efficacy in the in vivo mouse model. if possible, the authors could add their observations if in vivo studies were done. or as a future line of study.__
We thank the reviewer for his/her comment on the observation of antimicrobial activity in peptides derived from heparin-binding proteins. Indeed, a few such studies have appeared in the literature, some with moderate success [1]. It is possible that a lack of understanding on how to identify heparin-binding regions in proteins and AMPs underlies their relative paucity. In this context, we believe our results will spur further efforts, specifically by providing a rationale on how to identify CPC' motifs hence heparin-binding regions in protein sequences.
Regarding the suggestion of assessing the in vivo efficacy of HBPs, we would agree that it would be helpful for better understanding their potential therapeutic applications. However, we feel that such experiments are beyond the scope of our manuscript, which offers ample, compelling in vitro and in silico evidence of how heparin-binding proteins can be a source of AMPs. We have done this by showing that CPC' motifs embedded in such proteins can be unveiled, accurately defined in structural terms, and experimentally shown to possess antimicrobial activity. Furthermore, we have shown that heparin binding correlates with LPS binding, allowing us to propose a mechanistic explanation for how heparin binding can be related to antimicrobial activity.
Translating these results to animal models is possibly premature at this stage as, from a classical medicinal chemistry perspective, it would require previous structural elaboration in terms of, e.g., optimized serum half-life or serum protein binding, both of which can modulate activity in in vivo studies regardless of heparin affinity or bactericidal activity per se. Ongoing work in our laboratories is focused in these directions and will be reported in due time.
*Referees cross-commenting**
Minor comments
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__ The presence of Cryptic antimicrobial domain in various heparin-binding proteins like laminin isoforms, von Willebrand factor, vitronectin, protein C inhibitor, matrix glycoproteins thrombospondin, proline arginine-rich end leucine-rich repeat protein and fibronectin, have been reported previous. It is not clear why the authors did not refer to that work. The authors should refer to the works. (same as reviewer 3)__
We were aware of other prior studies on heparin-binding proteins and did indeed cite some of them, though not exhaustively for conciseness' sake. However, as encouraged by reviewers 1 and 3 we have cited the following studies:
Malmström E, Mörgelin M, Malmsten M, Johansson L, Norrby-Teglund A, Shannon O, Schmidtchen A, Meijers JC, Herwald H. Protein C inhibitor--a novel antimicrobial agent. PLoS Pathog. 2009 Dec;5(12):e1000698. doi: 10.1371/journal.ppat.1000698. Epub 2009 Dec 18. PMID: 20019810; PMCID: PMC2788422.
Ishihara, J., Ishihara, A., Fukunaga, K. et al. Laminin heparin-binding peptides bind to several growth factors and enhance diabetic wound healing. Nat Commun 9, 2163 (2018). https://doi.org/10.1038/s41467-018-04525-w
Chillakuri Chandramouli R, Jones Céline and Mardon Helen J(2010), Heparin binding domain in vitronectin is required for oligomerization and thus enhances integrin mediated cell adhesion and spreading, FEBS Letters, 584, doi: 10.1016/j.febslet.2010.06.023
Papareddy P, Kasetty G, Kalle M, Bhongir RK, Mörgelin M, Schmidtchen A, Malmsten M. NLF20: an antimicrobial peptide with therapeutic potential against invasive Pseudomonas aeruginosa infection. J Antimicrob Chemother. 2016 Jan;71(1):170-80. doi: 10.1093/jac/dkv322. Epub 2015 Oct 26. PMID: 26503666.
All the earlier studies related to the antimicrobial activity of the peptides derived from the Heparin-binding protein reported a consensus Cardin and Weintraub motifs i.e, XBBBXXBX or XBBXBX, where X represents hydrophobic or uncharged amino acids, and B represents basic amino acids. However, in this work, the researchers report about the presence of the new CPC motif. So, this is unique and a novelty in the study.
We thank the reviewers for these observations. Indeed, our quest to unveil CPC' motifs in antimicrobial regions of heparin-binding proteins is the key point of our investigation, and what distinguishes it from previous studies on consensus motifs such as XBBBXXBX or XBBXBX. We believe our definition of CPC' motifs in simple, structure-based, and experimentally verifiable terms is not only a significant departure but also a step forward from earlier views, highlighting the importance of a structural perspective in defining heparin-binding regions. In point of fact, we show that our peptides, even without consensus Cardin-Weintraub motifs, bind heparin with high affinity. The presence of the CPC' motif is crucial for such binding, as well as for LPS binding, and the new experiments performed at editor/reviewer's request, where the CPC motif in HBP5 is abolished, with predictable impact, fully support our view, see new section "Insights into the CPC' motif of HBP-5 and its implication on the antibacterial mechanism" and new Table 3 in the revised manuscript.
__ Even though the researchers report on the role of the CPC motif in the antimicrobial activity and binding to the heprin, the authors did not show any data or draw the conclusions related to the CPC domain when it comes to differences in the activity. this is the weakness of the manuscript. (same as reviewer 2)__
We welcome the reviewer's observation. To address it, we made and tested three HBP-5 mutants aimed at showing how alterations in the CPC' motif might influence interaction with heparin and LPS, as well as antimicrobial properties. The first two mutants involved replacing positively charged R10 and R14 residues with glutamine, similar in size and polarity but uncharged. As shown in the new section "Insights into the CPC' motif of HBP-5 and its implication on the antibacterial mechanism" and on the new Table 3 of the revised manuscript, the changes reduced heparin binding, i.e., shorter retention times on affinity chromatography, as well as LPS binding, i.e., a decrease in EC50 in the cadaverine assay (Table 3). The modifications had a lesser impact on antimicrobial activity, most likely due to the low resolution of MIC assays.
In a further step to assess the effect of the CPC' motif on antimicrobial activity, we deleted it in full by replacing residues H9, R10 and R14 of HBP-5 by alanine. As expected, this DCPC' peptide showed a sharp reduction in both heparin and LPS binding (Table 3) and, most importantly, a significant and asymmetric change in antimicrobial activity, with substantial impact on Gram-negatives yet practically no effect on Gram-positives, suggesting that LPS plays a key role in this selective response. Altogether, these observations align with our hypothesis that heparin-binding proteins might exploit their intrinsic affinity for heparin as an opportunity to developing antimicrobial properties by leveraging structural similarities between glycosaminoglycans and LPS.
__ It is strange to observe that there are quite a number of reports showing that the peptides derived from the Herprin (sic) binding proteins have antimicrobial activity, but no one has reported their efficacy in the in vivo mouse model. if possible, the authors could add their observations if in vivo studies were done. or as a future line of study. (Same as reviewer 2)__
We would kindly direct attention to #2 in the response to reviewer 1 above.
__ There are more than 20 different AMP databases or prediction software. however, not all of them are 100 % current, their success rate varies from 30-50% only. It needs to be investigated if adding this search in the hit peptides might increase the success rate of the extra in silico-based AMPs prediction software.__
If we understand the question correctly, the reviewer wonders whether including a CPC' motif predictor would increase the accuracy of AMP search algorithms. In our view, this strategy has two main limitations to be considered: (i) locating a CPC' motif in a peptide sequence typically requires a known 3D structure. Unfortunately, this is not always the case, and for proteins lacking reliable 3D data it can be a challenging and resource-intensive process; (ii) while CPC' motifs may predispose proteins to evolve antimicrobial properties, it is unclear if this is a required feature for all AMPs. Imposing the presence of a CPC' motif may not be applicable to all AMPs, although it might help identifying peptides with specific activity against gram-negative strains.
In summary, while the query of including a CPC' motif search tool in AMP predictors is intriguing and worthy of exploration for its potential bearing on antimicrobial research, it is technically complicated and beyond the scope of our manuscript.
__Reviewer #1 (Significance (Required)): __
__All the earlier studies related to the antimicrobial activity of the peptides derived from the Heparin-binding protein reported a consensus Cardin and Weintraub motifs i.e, XBBBXXBX or XBBXBX, where X represents hydrophobic or uncharged amino acids, and B represents basic amino acids. However, in this work, the researchers report about the presence of the new CPC motif. So this is unique and a novelty in the study. __
Even though the researchers report on the role of the CPC motif in the antimicrobial activity and binding to the heparin, the authors did not show any data or draw conclusions related to the CPC domain when it comes to differences in the activity. This is the weakness of the manuscript.
We would direct reviewer's attention to #1 in the Referee's cross-commenting section above.
Response to Reviewer 2
This is a very nice paper by the Andreu and Torrent groups that report the antimicrobial and heparin-binding of several encrypted peptides. Overall, this study presents an intriguing exploration into the potential dual functionality of glycosaminoglycan (GAG)-binding proteins, specifically heparin-binding proteins (HBPs), in recognizing lipopolysaccharide (LPS) and exhibiting antimicrobial properties. The findings, particularly the identification and characterization of novel encrypted peptides, such as HBP-5, are promising and contribute to our understanding of the intricate interplay between GAG-binding proteins and immunity. The data provided and methodology are thorough and well described. In sum, this is a very nice work. Please see below my minor comments.
Minor comments:
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__ Fig. 1 legend does not show antimicrobial activity. Please remove from the figure legend title.__
As pointed out by the reviewer, the legend was incorrect and has been corrected accordingly and now reads "Figure 1. Structural and bioinformatics analysis of HBPs".
__ Discussion section: the authors should expand this section a bit to discuss recent work in the encrypted/cryptic peptide area. There are some recent relevant papers published in the past 3 years that should be discussed.__
We agree with the reviewer's suggestion to expand the discussion section to address recent work in the field of encrypted/cryptic peptides. We have carefully reviewed the recent literature and added several references in this topic:
Torres MDT, Melo MCR, Flowers L, Crescenzi O, Notomista E, de la Fuente-Nunez C. Mining for encrypted peptide antibiotics in the human proteome. Nat Biomed Eng. 2022 Jan;6(1):67-75. doi: 10.1038/s41551-021-00801-1. Epub 2021 Nov 4. Erratum in: Nat Biomed Eng. 2022 Dec;6(12):1451. PMID: 34737399.
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Santos MFDS, Freitas CS, Verissimo da Costa GC, Pereira PR, Paschoalin VMF. Identification of Antibacterial Peptide Candidates Encrypted in Stress-Related and Metabolic Saccharomyces cerevisiae Proteins. Pharmaceuticals (Basel). 2022 Jan 28;15(2):163. doi: 10.3390/ph15020163. PMID: 35215278; PMCID: PMC8877035.
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Boaro A, Ageitos L, Torres MT, Blasco EB, Oztekin S, de la Fuente-Nunez C. Structure-function-guided design of synthetic peptides with anti-infective activity derived from wasp venom. Cell Rep Phys Sci. 2023 Jul 19;4(7):101459. doi: 10.1016/j.xcrp.2023.101459. PMID: 38239869; PMCID: PMC10795512.
__ References provided are a bit outdated and do not accurately reflect the latest in the field (see comment above).__
We thank the reviewer for this comment. Older references were updated as suggested.
__ Gram should be capitalized throughout the text.__
Gram has been capitalized as suggested by the reviewer.
__ Can the authors comment on the potential translatability of HBP-5? Please also comment on the potential advantages of having peptides that 1) bind to heparin; and 2) kill bacteria.__
We appreciate the reviewer's interest in the potential of HBP-5. Indeed, we believe it has promise for clinical applications due to its unique attributes, but further studies, including in vivo experiments and pharmacokinetic assessments, are needed to fully evaluate its potential. The advantages of peptides that bind to heparin and kill bacteria include targeted delivery or localization of therapeutic agents, enhanced efficacy, and minimized off-target effects. HBP-5's ability to perturb outer membrane LPS, a crucial aspect of its antibacterial activity, makes it a promising approach to combat Gram-negative bacterial infections, which are often challenging to treat. By disrupting the outer membrane integrity, HBP-5 may also enhance the susceptibility of Gram-negative bacteria to other antimicrobial agents or host immune responses, underscoring its translational potential for treating bacterial infections.
__ More details on the computational tools and methods used to mine the peptides are needed.__
We have updated the Methods section to provide more details on the computational tools used for defining AMPs. Briefly, from the library of heparin-binding proteins obtained from previous studies [2] and AMP scanning for all these proteins was performed using the AMPA tool. The predicted antibacterial segments were located in the 3D structure of their respective proteins. Then, the CPC' motifs were searched in each segment following the criteria previously reported in [3, 4]. The motif involves two cationic residues (Arg or Lys) and a polar residue (preferentially Asn, Gln, Thr, Tyr or Ser), with fairly conserved distances between the carbons and the side chain center of gravity, defining a clip-like structure where heparin would be lodged. This structural motif is highly conserved and can be found in many proteins with reported heparin binding capacity. Finally, for all these regions, docking with a heparin disaccharide was performed using AutoDock Vina to evaluate the potential binding energy.
Response to Reviewer 3
__Summary: This manuscript has identified and investigated antimicrobial peptides from GAG binding proteins. Authors hypothesized that due to physiochemical similarity between GAG and LPS, fragments of GAG binding proteins might exert antimicrobial activity particularly against G- bacteria. Authors have identified few such AMPs that demonstrate LPS binding and displayed antibacterial activity. They have also solved NMR structure of the potent peptide and mode of action. __
Major comments: AMPs are promising molecules that can serve as lead for the development of therapeutics against MDR bacteria. In particular, currently therapeutic options to treat MDR Gram negative pathogens are limited. The current study is interesting and provides new non-toxic AMPs. Conclusions drawn from the works are largely valid. However, authors should address following comments:
- __ The design and characterization of the peptide YI12WF is not described. Previous studies had shown design of β-boomerang peptides (Bhattacharjya and coworkers) that target LPS.__
We thank the reviewer for this comment. YI12WF (YVLWKRKRFIFI-amide) has been previously reported [4, 5] and shown to bind LPS with high affinity. YI12WF also contains a CPC' motif that, if deleted, reduces heparin binding [4]. References have been added in the text.
__ Mutations or substitution of the key residues peptide 5 might improve the novelty of the work.__
We thank the reviewer for this comment and agree that targeted substitutions in HBP-5 might shed light on the importance of the CPC' motif. As this point was also raised by reviewer 1, we would direct the reviewer's attention to #2 in the *Referees cross-commenting** section above.
__ How these peptides disrupt LPS permeability is not investigated. As LPS is the major target.__
We thank the reviewer for this suggestion and have accordingly evaluated the outer membrane (OM) permeability of the peptides by the 1-N-phenyl-naphthylamine (NPN) assay, a widely used method to assess OM integrity in Gram-negative bacteria. NPN is typically unable to cross the intact outer membrane; however, when the membrane is damaged or disrupted, it can penetrate and interact with lipids and proteins inside the cell, leading to an increase in fluorescence which is directly correlated with the degree of OM permeability and serves as an indicator of membrane damage.
Our results, illustrated in the new Figure 2D, show that all peptides are able to disrupt the OM of Gram-negative bacteria comparably to the LL-37 positive control, except for HBP2. Notably, HBP-5 exhibits the highest activity against OM, consistent with findings elsewhere in the manuscript and altogether confirming the ability of HBPs to bind to and disrupt the LPS structure.
__ Are the D-enantiomers of the peptides active against bacteria?__
We tested the antibacterial activity of the D-enantiomer of HBP5 (dHBP-and 5) and found it to be even higher than that of all-L HBP-5 against both Gram-negative and -positive bacteria, probably due to increased proteolytic stability as found in many AMP studies [6, 7]. As for LPS and heparin affinity, L- and D-HBP-5 behaved similarly (Table R1). As expected, the CD signatures of L- and D-HBP-5 were mirror images (Figure R1). These results suggest that the conformation of the CPC' motif is preserved in dHBP5, in tune with all previous results.
Antibacterial Activity
ID
E. Coli
P. Aeruginosa
A. Baumannii
S. Aureus
E. Faecium
L. monocytognes
HPB-5
0.4
0.8
0.2
6.3
25
1.6
dHBP-5
0.1
0.2
0.2
1.6
0.4
0.2
Binding Affinity
LPS (EC50, µM)
Heparin (% Elution buffer)
HPB-5
0.9 {plus minus} 0.7
98.0
dHBP-5
1.1 {plus minus} 0.8
97.2
Table R1. Antimicrobial activity of HBP-5 and dHBP-5
Figure R1. CD spectra of HBP-5 (red line) and dHBP-5 (green line) in LPS (left panel) and heparin (right panel).
__ 3D structure of peptide 5 is solved in DPC micelle which is a mimic for eukaryotic cells. Authors should attempt to determine structure in LPS as shown in several recent studies with potent AMPs thanatin, MSI etc.__
We appreciate the suggestion and have indeed attempted to obtain NMR spectra of HBP-5 in LPS micelles. However, we've been hindered by peptide precipitation and, despite considerable efforts, have not been able to obtain satisfactory results thus far. In contrast, we have succeeded in obtaining CD spectra of HBP5 in LPS micelles, showing an a-helix conformation similar to the one in SDS micelles, hence suggesting similar conformation in both environments.
Minor comments: There are examples of AMPs derived from human proteins. Authors should highlight such works.
Other studies have been cited according to the reviewers' comments:
Malmström E, Mörgelin M, Malmsten M, Johansson L, Norrby-Teglund A, Shannon O, Schmidtchen A, Meijers JC, Herwald H. Protein C inhibitor--a novel antimicrobial agent. PLoS Pathog. 2009 Dec;5(12):e1000698. doi: 10.1371/journal.ppat.1000698. Epub 2009 Dec 18. PMID: 20019810; PMCID: PMC2788422.
Ishihara, J., Ishihara, A., Fukunaga, K. et al. Laminin heparin-binding peptides bind to several growth factors and enhance diabetic wound healing. Nat Commun 9, 2163 (2018). https://doi.org/10.1038/s41467-018-04525-w
Chillakuri Chandramouli R, Jones Céline and Mardon Helen J(2010), Heparin binding domain in vitronectin is required for oligomerization and thus enhances integrin mediated cell adhesion and spreading, FEBS Letters, 584, doi: 10.1016/j.febslet.2010.06.023
Papareddy P, Kasetty G, Kalle M, Bhongir RK, Mörgelin M, Schmidtchen A, Malmsten M. NLF20: an antimicrobial peptide with therapeutic potential against invasive Pseudomonas aeruginosa infection. J Antimicrob Chemother. 2016 Jan;71(1):170-80. doi: 10.1093/jac/dkv322. Epub 2015 Oct 26. PMID: 26503666.
References
- Papareddy, P., et al., An antimicrobial helix A-derived peptide of heparin cofactor II blocks endotoxin responses in vivo. Biochimica et Biophysica Acta (BBA) - Biomembranes, 2014. 1838(5): p. 1225-1234.
- Ori, A., M.C. Wilkinson, and D.G. Fernig, A systems biology approach for the investigation of the heparin/heparan sulfate interactome. J Biol Chem, 2011. 286(22): p. 19892-904.
- Torrent, M., et al., The "CPC Clip Motif": A Conserved Structural Signature for Heparin-Binding Proteins.PLOS ONE, 2012. 7(8): p. e42692.
- Pulido, D., et al., Structural similarities in the CPC clip motif explain peptide-binding promiscuity between glycosaminoglycans and lipopolysaccharides. J R Soc Interface, 2017. 14(136).
- Bhunia, A., et al., Designed beta-boomerang antiendotoxic and antimicrobial peptides: structures and activities in lipopolysaccharide. J Biol Chem, 2009. 284(33): p. 21991-22004.
- Varponi, I., et al., Fighting Pseudomonas aeruginosa Infections: Antibacterial and Antibiofilm Activity of D-Q53 CecB, a Synthetic Analog of a Silkworm Natural Cecropin B Variant. Int J Mol Sci, 2023. 24(15).
- Chen, Y., et al., Comparison of Biophysical and Biologic Properties of α-Helical Enantiomeric Antimicrobial Peptides. Chemical Biology & Drug Design, 2006. 67(2): p. 162-173.
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #3
Evidence, reproducibility and clarity
Summary: This manuscript has identified and investigated antimicrobial peptides from GAG binding proteins. Authors hypothesized that due to physiochemical similarity between GAG and LPS, fragments of GAG binding proteins might exert antimicrobial activity particularly against G- bacteria. Authors have identified few such AMPs that demonstrate LPS binding and displayed antibacterial activity. They have also solved NMR structure of the potent peptide and mode of action.
Major comments: AMPs are promising molecules that can serve as lead for the development of therapeutics against MDR bacteria. In particular, currently therapeutic options to treat MDR Gram negative pathogens are limited. The current study is interesting and provides new non-toxic AMPs. Conclusions drawn from the works are largely valid. However, authors should address following comments
- The design and characterization of the peptide YI12WF is not described. Previous studies had shown design of b-boomerang peptides (Bhattacharjya and coworkers) that target LPS.
- Mutations or substitution of the key residues peptide 5 might improve the novelty of the work.
- How these peptides disrupt LPS permeability is not investigated. As LPS is the major target.
- Are the D-enantiomers of the peptides active against bacteria?
- 3-D structure of peptide 5 is solved in DPC micelle which is a mimic for eukaryotic cells. Authors should attempt to determine structure in LPS as shown in several recent studies with potent AMPs thanatin, MSI etc,
Minor comments: There are examples of AMPs derived from human proteins. Authors should highlight such works.
Significance
The work described in the manuscript is novel and hold promises to develop antimicrobials in future.
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Referee #2
Evidence, reproducibility and clarity
This is a very nice paper by the Andreu and Torrent groups that report the antimicrobial and heparin-binding of several encrypted peptides. Overall, this study presents an intriguing exploration into the potential dual functionality of glycosaminoglycan (GAG)-binding proteins, specifically heparin-binding proteins (HBPs), in recognizing lipopolysaccharide (LPS) and exhibiting antimicrobial properties. The findings, particularly the identification and characterization of novel encrypted peptides, such as HBP-5, are promising and contribute to our understanding of the intricate interplay between GAG-binding proteins and immunity. The data provided and methodology are thorough and well described. In sum, this is a very nice work. Please see below my minor comments.
Minor comments:
- Fig. 1 legend does not show antimicrobial activity. Please remove from the figure legend title.
- Discussion section: the authors should expand this section a bit to discuss recent work in the encrypted/cryptic peptide area. There are some recent relevant papers published in the past 3 years that should be discussed.
- References provided are a bit outdated and do not accurately reflect the latest in the field (see comment above).
- Gram should be capitalized throughout the text.
- Can the authors comment on the potential translatability of HBP-5? Please also comment on the potential advantages of having peptides that 1) bind to heparin; and 2) kill bacteria.
- More details on the computational tools and methods used to mine the peptides are needed.
Significance
The data provided and methodology are thorough and well described. In sum, this is a very nice work.
-
Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #1
Evidence, reproducibility and clarity
Glycosaminoglycan (GAG)-binding proteins regulating essential processes such as cell growth and migration are essential for cell homeostasis. It is reported that the GAG has the ability to bind to Herpin sulfate. As both GAGs and the LPS lipid A disaccharide core of gram-negative bacteria contain negatively charged disaccharide units, the researchers proposed that heparin-binding peptides might have cryptic antimicrobial peptide motifs. To prove the hypothesis, they have synthesized five candidates [HBP1-5], which showed a binding affinity towards heparin and LPS binding. By using various methods, they showed that these molecules have antimicrobial activity. The key finding in this study is the finding of the CPC domain, where C is a cationic amino acid and P is a polar amino acid.
Major comments
- Even though the Authors propose here that CPC' clip motif is needed for antimicrobial activity. However, various studies have demonstrated that the mere presence of cationic amino or hydrophobic amino acids does not give the activity, the location of these amino acids at the strategic position is critically needed. The major issue in this work, the authors have not presented, whether there was a single CPC motif or multiple in the 5 peptides they have synthesised. Further, they need to demonstrate how are the charged and hydrophobic amino acids distributed in the peptides. these things will clearly explain the difference in the activity as well spectrum of the peptides. The authors should make an extra figure or add information highlighting this unique characteristic for better understanding to the reader.
- It is strange to observe that there are quite a number of reports showing that the peptides derived from the Herprin binding proteins have antimicrobial activity, but no one has reported their efficacy in the in vivo mouse model. if possible, the authors could add their observations if in vivo studies were done. or as a future line of study.
Minor comments
- The presence of Cryptic antimicrobial domain in various heparin-binding proteins like laminin isoforms, von Willebrand factor, vitronectin, pro-tein C inhibitor, matrix glycoproteins thrombospondin, proline arginine-rich end leucine-rich repeat protein and fibronectin, have been reported previous. It is not clear why the authors did not refer to that work. the authors should refer to the works.
Referees cross-commenting
Minor comments
- The presence of Cryptic antimicrobial domain in various heparin-binding proteins like laminin isoforms, von Willebrand factor, vitronectin, pro-tein C inhibitor, matrix glycoproteins thrombospondin, proline arginine-rich end leucine-rich repeat protein and fibronectin, have been reported previous. It is not clear why the authors did not refer to that work. the authors should refer to the works. (same as reviewer 3)
All the earlier studies related to the antimicrobial activity of the peptides derived from the Heparin-binding protein reported a consensus Cardin and Weintraub motifs i.e, XBBBXXBX or XBBXBX, where X represents hydrophobic or uncharged amino acids, and B represents basic amino acids. However, in this work, the researchers report about the presence of the new CPC motif. So this is unique and a novelty in the study.
Even though the researchers report on the role of the CPC motif in the antimicrobial activity and binding to the heprin, the authors did not show any data or draw the conclusions related to the CPC domain when it comes to differences in the activity. this is the weakness of the manuscript. (same as reviwer 2) 2. It is strange to observe that there are quite a number of reports showing that the peptides derived from the Herprin binding proteins have antimicrobial activity, but no one has reported their efficacy in the in vivo mouse model. if possible, the authors could add their observations if in vivo studies were done. or as a future line of study.(Same as reviewer 2)
Significance
All the earlier studies related to the antimicrobial activity of the peptides derived from the Heparin-binding protein reported a consensus Cardin and Weintraub motifs i.e, XBBBXXBX or XBBXBX, where X represents hydrophobic or uncharged amino acids, and B represents basic amino acids. However, in this work, the researchers report about the presence of the new CPC motif. So this is unique and a novelty in the study.
Even though the researchers report on the role of the CPC motif in the antimicrobial activity and binding to the heprin, the authors did not show any data or draw the conclusions related to the CPC domain when it comes to differences in the activity. this is the weakness of the manuscript.
There are more than 20 different AMP databases or prediction software. however, not all of them are 100 % current, their success rate varies from 30-50% only. It needs to be investigated if adding this search in the hit peptides might increase the success rate of the extra in silico-based AMPs prediction software
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Reply to the reviewers
Reviewer 1:
Although HEK cells are effective for studying molecular mechanisms and post-translational modifications through siRNA and variant overexpression manipulations, they lack functional relevance in a neuronal context. Consequently, the connection between molecular findings and observed phenotypes in mice is tenuous. It is suggested that the authors attempt to replicate these results (Figures 4 and 5) using a neuronal differentiation model employing ESCs or iPSCs.
We have previously attempted to generate DDX3XSer584Ala knock-in ESCs via CRISPR-Cas9 because, as the reviewer points out, this would facilitate investigating the role of DDX3X O-GlcNAcylation in a neuronal differentiation model. However, clones derived after puromycin selection stop proliferating and perish during clonal outgrowth - we will include a statement to this effect in the revised manuscript. A similar phenomenon has been reported previously in Neuro2a cells by Lennox et al. (2020), who reported that installation of DDX3X patient variants is potentially toxic in certain cell lines. Therefore, the HEK293 KD/overexpression approach, also used for the study of clinically relevant DDX3X variants in other studies, while sub-optimal, is the best possible currently accessible model.
While employing variant overexpression following siRNA-mediated reduction of the endogenous protein is a direct method to illustrate the effects of mutated DDX3X variants, the authors stress a connection between this regulatory mechanism and neurodevelopmental defects. Therefore, it would be justifiable for the authors to create cell lines by editing the endogenous DDX3X gene and demonstrate the effects of O-GlcNAc, disruption of DDX3X target levels, and cell cycle regulation. Combining these approaches (from points 1 and 2), the authors could generate iPSC/ESC lines containing the DDX3X mutations and examine their effects within a neuronal differentiation context. Such an approach would significantly enhance the impact of this study.
As mentioned above for point 1, we have previously tried to edit the endogenous DDX3X gene with a Ser584Ala point mutation for this purpose. However, after trying this approach in both ESCs and HEK293T cells, we consistently observed stalled proliferation and cell death during clonal outgrowth. Therefore, as desirable as this experiment is, we are limited to siRNA-mediated reduction of DDX3X and rescue via over-expression, as also extensively used in other studies.
Given the points raised by reviewers 1 and 3 about the appropriateness of model system and the links drawn between DDX3X O-GlcNAcylation and neurodevelopmental defects, we will revise the manuscript to highlight the correlative nature of this link. In addition we will add further data from OGT-CDG mouse models that strengthens this possible link (see response to point 4 by reviewer 1).
The finding of diminished DDX3X levels in OGT mutant mice and the consequent reduction in O-GlcNAc represents a pivotal connection to the observed neurodevelopmental defects in OGT-CDG. However, this aspect of the research remains somewhat unclear, as it has not been definitively demonstrated that O-GlcNAc levels of DDX3X in OGT mutant mice are indeed decreased. Without this confirmation, the causal relationship between OGT malfunction, O-GlcNAc, and reduced DDX3X levels cannot be firmly established. There is a possibility of indirect effects, and merely observing correlation does not suffice to draw the robust conclusions presented in this paper. To address the uncertainty surrounding Figure 6D, attributed to the antibody's declared lack of specificity, the authors should conduct additional experiments.
The aim of this study is not to confirm whether there is a causal link between OGT catalytic deficiency and DDX3X, but rather to report the function of DDX3X O-GlcNAcylation and propose a possible link between OGT catalytic deficiency, DDX3X loss of activity, and neurodevelopmental defects. However, we do agree that further investigation is required to determine whether there is a correlative link between OGT catalytic deficiency and DDX3X levels (and O-GlcNAcylation) in the mouse brain. Towards this end, we will repeat the immunoprecipitation of DDX3X from mouse brain lysate of wild type and OGT-CDG mice and blot for O-GlcNAc using different pan-specific O-GlcNAc antibodies/ far western techniques (CTD110.6, GST-CpOGAD298N). We will also incorporate an internal negative control (competition with free GlcNAc) to verify the specificity of the O-GlcNAc signals.
The study places significant emphasis on this phenotype and seeks to elucidate it, at least partially, through the O-GlcNAcylation of DDX3X. However, a precise description or depiction of this phenotype is absent. Understanding the phenotype of the OGT-CDG mice necessitates consulting existing literature. The authors ought to contemplate providing brain sections with relevant staining to (i) showcase the microcephaly phenotype and (ii) bolster their assertion regarding the dysregulated cell cycle by utilising appropriate marker stainings for the progenitor cells during embryonic development.
We have recently published a manuscript reporting a mouse model of OGT-CDG (OGTC921Y). OGTC921Y mice display microcephaly as determined by brain weight and skull length. An additional mouse model of the N648Y OGT-CDG variant also displays microcephaly (Authier et al., 2024 reports the C921Y mouse; the N648Y mice line is on BioRxiv: https://doi.org/10.1101/2023.08.23.554427 and subject to peer review elsewhere). As part of the revisions for this manuscript, we will report a micro CT-based analysis of reduced skull/brain volume that supports the microcephaly phenotype. O-GlcNAc, OGT, OGA, DDX3X and cyclin E1 western blots for three brain regions from these mouse models will also be provided. Furthermore, we will include NeuN staining of cortical brain sections to establish whether cortical density is reduced in OGTN648Y mouse brains. The proposed staining of progenitor cells during embryonic corticogenesis is a very good suggestion for future investigation, but is a time-consuming experiment (est. 1 year) that falls beyond the scope of this study and would delay sharing of our current findings.
The proposed staining of progenitor cells during embryonic corticogenesis is a time consuming experiment (est. 1 year) that falls beyond the scope of this study.
Reviewer 2:
Figure 6 D - text last paragraph on page 16, and in supplement where you use RL2 - You need to do the control to show that RL2 staining goes away in the presence of free GlcNAc or when you galactosylate the protein. This control would indicate that you are detecting the sugar, not the protein backbone.
We agree with the reviewer that an internal negative control will help validate the specificity of the RL2 signal. We will repeat the immunoprecipitation/ O-GlcNAc western blot with a free GlcNAc control.
- Reviewer 3:*
Lack of Appropriate Model System: Although the authors initially address the role of O-GlcNAcylation (OGT/OGA) and DDX3X in cerebral development, given that mutations in these enzymes are causative for neurological malformations and disabilities, the experiments addressing the consequences of DDX3X or OGT silencing are predominantly performed in HEK293T cells rather than a nervous system model. This limits the relevance of the findings.
As discussed above (see Reviewer 1, points 1 and 2), we have previously tried to generate ESC knock-ins of the DDX3XSer584Alain ESCs to establish a neuronal differentiation model. However, clones perish during outgrowth, and thus these experiments are not possible. We have however performed preliminary biochemical analysis of DDX3X levels in the brains of OGT-CDG mice. It is important to emphasise that whether there is a causal link between OGT catalytic deficiency, DDX3X and cerebral development, lies beyond the remit of this manuscript. This manuscript aims to highlight DDX3X loss of activity as a candidate conveyor of neurodevelopmental defects in the mouse brain.
Inconclusive Cell Cycle Analysis: The authors' analysis regarding cell cycle characterization is not sufficiently conclusive. First, they need to accurately define the link between DDX3X and cyclin E1. The study they refer to (Lai et al., 2010) is rather superficial, and requires a more in-depth analysis, in order to appreciate the existing link between the two given molecules. Indeed, the current experiments do not clarify whether cells are stuck in G1 due to cyclin E1 downregulation or if cyclin E1 is downregulated because cells are blocked in G1. A suggested approach would be to perform rescue experiments with cyclin E1 overexpression, by using Quantitative Image-Based Cytometry (QIBC) or flow cytometry (EdU incorporation + Hoecsht staining) to monitor cell cycle changes and define the interplay between these molecules mechanistically. However, this alone cannot exclude the presence of alternative substrates of DDX3X regulation influencing cell cycle phase transition. A more holistic approach, such as an interactome analysis through mass spectrometry, may be helpful. Additionally, mild mitotic stress often results in cell cycle arrest in the subsequent G0/G1 phase, which can resemble the G1/S transition impairment described by the authors. Consequently, the statement "Here we identify Ser584 O-GlcNAcylation of DDX3X (...) as a key regulator of G1/S transition" is not well-supported (to do so, the authors should define the temporal pattern of such post-translational modification). It would also be interesting to determine whether the reduction in cell viability is due to a simple slowdown of the cell cycle or apoptotic induction.
We agree that performing cyclin E1 over-expression could provide mechanistic insights into the link between DDX3X, cyclin E1, and the cell cycle. We will therefore repeat the cell cycle analysis by flow cytometry shown in Fig. 5B with the addition of cyclin E1 over-expression in cells co-transfected with siRNA against DDX3X and siRNA-resistant DDX3XSer584Ala, to investigate whether cyclin E1 rescues the observed accumulation of cells in G1.
We acknowledge the reviewer's point that quiescence (G0 entry) and G1/S stalling can provide similar cell cycle profiles to that observed in Fig. 5B. We will therefore re-write the necessary sections of the manuscript to emphasise that we cannot be certain whether the observed cell cycle defects resulting from loss of DDX3X Ser584 O-GlcNAcylation stem from G1/S phase stalling or mitotic stress followed by quiescence.
Regarding the possibility that our observed reductions in the number of viable cells stems from stalled cell cycle progress or apoptosis, we will knock-down DDX3X and blot for cleaved caspase-3 as a marker for apoptosis to investigate the latter.
The proposed interactome analysis of DDX3X is a tangential experiment. Given that DDX3X regulates cell cycle progression through its RNA helicase and transcriptional co-regulator functions, interactome analysis would not provide direct (or indeed, useful) readouts of how loss of DDX3X Ser584 O-GlcNAcylation affects cell cycle progression (i.e. it is possible there are significant effects on DDX3X activity without affecting the interactome).
Weak Correlative Link: While the link between OGT/OGA and the cell cycle is well-established (seereviewSaunders et al., 2023) due to the multitude of targets subjected to this post-translational regulation, the correlative link between DDX3X mutations and cell cycle effects is further weakened by the fact that cyclin E1 knockout mice are indistinguishable from their wild-type littermates.
DDX3X controls the transcription and translation of hundreds of genes, not just cyclin E1. For example, DDX3X controls Klf4 transcription which, in certain cell lines, regulates S-phase entry (Canizarro et al., FEBS Lett. 2018). Thus, it is possible for one candidate conveyor of the observed cell cycle defects to not produce significant phenotypes in a KO mouse. In the interests of emphasising this point, we will add a discussion point regarding the multiple pathways through which loss of DDX3X O-GlcNAcylation may affect the cell cycle in the discussion.
Throughout the text, the authors refer to figures out of alphanumerical order, making the reading experience extremely difficult. To enhance readability, it is essential to present figures in a logical, sequential manner. Additionally, for further comments I would suggest to implement the text with line numbers.
We will correct the text to ensure all figures are referenced and presented in a sequential manner, and line numbers will be added.
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Referee #3
Evidence, reproducibility and clarity
Summary:
In the manuscript, Mitchell et al. explore the interplay between O-GlcNAcylation and the cell cycle, with a particular focus on how DDX3X, an RNA helicase of the DEAD box family, influences the G1/S phase transition. The authors begin with a bioinformatics strategy to identify patterns of correlation between cell cycle regulators and the enzymes OGT and OGA in a dataset of temporal transcriptomics of the human prefrontal cortex. They then narrow their analysis to DDX3X, due to its strong potential correlation with the cell cycle and its candidacy as a substrate for O-GlcNAcylation. Subsequently, the authors biochemically investigate how O-GlcNAcylation regulates DDX3X. Finally, given that DDX3X has been previously shown to regulate cyclin E1, the authors assess the effect of DDX3X depletion on the cell cycle in vitro.
Major comments:
Despite the authors' interesting identification of a novel substrate for O-GlcNAcylation, most conclusions drawn from the study are correlative. The manuscript suffers from three major drawbacks:
- Lack of Appropriate Model System: Although the authors initially address the role of O-GlcNAcylation (OGT/OGA) and DDX3X in cerebral development, given that mutations in these enzymes are causative for neurological malformations and disabilities, the experiments addressing the consequences of DDX3X or OGT silencing are predominantly performed in HEK293T cells rather than a nervous system model. This limits the relevance of the findings.
- Inconclusive Cell Cycle Analysis: The authors' analysis regarding cell cycle characterization is not sufficiently conclusive. First, they need to accurately define the link between DDX3X and cyclin E1. The study they refer to (Lai et al., 2010) is rather superficial, and requires a more in-depth analysis, in order to appreciate the existing link between the two given molecules. Indeed, the current experiments do not clarify whether cells are stuck in G1 due to cyclin E1 downregulation or if cyclin E1 is downregulated because cells are blocked in G1. A suggested approach would be to perform rescue experiments with cyclin E1 overexpression, by using Quantitative Image-Based Cytometry (QIBC) or flow cytometry (EdU incorporation + Hoecsht staining) to monitor cell cycle changes and define the interplay between these molecules mechanistically. However, this alone cannot exclude the presence of alternative substrates of DDX3X regulation influencing cell cycle phase transition. A more holistic approach, such as an interactome analysis through mass spectrometry, may be helpful. Additionally, mild mitotic stress often results in cell cycle arrest in the subsequent G0/G1 phase, which can resemble the G1/S transition impairment described by the authors. Consequently, the statement "Here we identify Ser584 O-GlcNAcylation of DDX3X (...) as a key regulator of G1/S transition" is not well-supported (to do so, the authors should define the temporal pattern of such post-translational modification). It would also be interesting to determine whether the reduction in cell viability is due to a simple slowdown of the cell cycle or apoptotic induction.
- Weak Correlative Link: While the link between OGT/OGA and the cell cycle is well-established (see review Saunders et al., 2023) due to the multitude of targets subjected to this post-translational regulation, the correlative link between DDX3X mutations and cell cycle effects is further weakened by the fact that cyclin E1 knockout mice are indistinguishable from their wild-type littermates.
Minor comments:
Throughout the text, the authors refer to figures out of alphanumerical order, making the reading experience extremely difficult. To enhance readability, it is essential to present figures in a logical, sequential manner. Additionally, for further comments I would suggest to implement the text with line numbers.
Significance
Overall, the major novelty of the manuscript is the interesting link between DDX3X, its O-GlcNAcylation, and cell cycle regulation. However, this section requires the most intense revision to ensure robustness and clarity of the findings.
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Referee #2
Evidence, reproducibility and clarity
Missense mutations in OGT are causative in X-linked intellectual disability disorders, termed OGT-CDG. This paper identifies O-GlcNAcylation (OGN) at Ser584 of DDX3X, a known intellectual disability and microcephaly associated protein, as a regulator of G1/S transition, inhibiting the proteasome degradation of DDX3X. This OGN site controls the degradation of DDX3X. Lack of OGN at Ser 584 results in more rapid degradation of the protein, which affects the cell cycle. The study shows that dysregulation of DDX3X-dependent translation and concomitant impairments in cortical neurogenesis as a pathway disrupted in OGT-CDG. Overall, a well written paper. The data support the author's conclusions.
Figure 6 D - text last paragraph on page 16, and in supplement where you use RL2 - You need to do the control to show that RL2 staining goes away in the presence of free GlcNAc or when you galactosylate the protein. This control would indicate that you are detecting the sugar, not the protein backbone.
Significance
This well prepared paper is highly significant. 1) It provides mechanistic insights into a cause of human intellectual disability; 2) It helps elucidate the role of O-GlcNAcylation in nutrient regulation of the cell cycle. 3) It provides more data on the roles of DDX3X.
The paper is of significant general interest.
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Referee #1
Evidence, reproducibility and clarity
O-GlcNAcylation is a post-translational modification and plays a crucial role in neurodevelopment. Variants of O-GlcNAc transferase (OGT) are linked to OGT Congenital Disorder of Glycosylation (OGT-CDG), a syndrome causing intellectual disability. Microcephaly in OGT-CDG patients suggests the involvement of cell cycle dysregulation and abnormal neurogenesis. Mitchell et al. identify Ser584 O-GlcNAcylation of DDX3X, a protein associated with intellectual disability and microcephaly, as a regulator of G1/S-phase transition. They show that this PTM of DDX3X stabilises and prevents the targeting of DDX3X to the proteasome and therefore its degradation. Reduced DDX3X levels in an OGT-CDG mouse model and decreased expression of DDX3X-target gene cyclin E1 suggest impaired cell cycle control and cortical neurogenesis as pathways affected in OGT-CDG.
This study introduces DDX3X as a novel target for O-GlcNAcylation, which enhances its stability and ensures proper regulation of the cell cycle, particularly through its target cyclin E1. While the findings regarding DDX3X O-GlcNAcylation and the identification of the modified residue are compelling, addressing several issues regarding the link between O-GlcNAc of DDX3X-cell cycle regulation and neurodevelopmental impact would enhance the study's robustness.
Major points requiring attention:
- Although HEK cells are effective for studying molecular mechanisms and post-translational modifications through siRNA and variant overexpression manipulations, they lack functional relevance in a neuronal context. Consequently, the connection between molecular findings and observed phenotypes in mice is tenuous. It is suggested that the authors attempt to replicate these results (Figures 4 and 5) using a neuronal differentiation model employing ESCs or iPSCs.
- While employing variant overexpression following siRNA-mediated reduction of the endogenous protein is a direct method to illustrate the effects of mutated DDX3X variants, the authors stress a connection between this regulatory mechanism and neurodevelopmental defects. Therefore, it would be justifiable for the authors to create cell lines by editing the endogenous DDX3X gene and demonstrate the effects of O-GlcNAc, disruption of DDX3X target levels, and cell cycle regulation. Combining these approaches (from points 1 and 2), the authors could generate iPSC/ESC lines containing the DDX3X mutations and examine their effects within a neuronal differentiation context. Such an approach would significantly enhance the impact of this study.
- The finding of diminished DDX3X levels in OGT mutant mice and the consequent reduction in O-GlcNAc represents a pivotal connection to the observed neurodevelopmental defects in OGT-CDG. However, this aspect of the research remains somewhat unclear, as it has not been definitively demonstrated that O-GlcNAc levels of DDX3X in OGT mutant mice are indeed decreased. Without this confirmation, the causal relationship between OGT malfunction, O-GlcNAc, and reduced DDX3X levels cannot be firmly established. There is a possibility of indirect effects, and merely observing correlation does not suffice to draw the robust conclusions presented in this paper. To address the uncertainty surrounding Figure 6D, attributed to the antibody's declared lack of specificity, the authors should conduct additional experiments.
- The study places significant emphasis on this phenotype and seeks to elucidate it, at least partially, through the O-GlcNAcylation of DDX3X. However, a precise description or depiction of this phenotype is absent. Understanding the phenotype of the OGT-CDG mice necessitates consulting existing literature. The authors ought to contemplate providing brain sections with relevant staining to (i) showcase the microcephaly phenotype and (ii) bolster their assertion regarding the dysregulated cell cycle by utilising appropriate marker stainings for the progenitor cells during embryonic development.
Minor issue:
Consider replacing in cellulo with in vitro
Significance
The study provides an in-depth biochemical analysis of the O-GlcNAcylation of DDX3X, identifying it as a novel target. However, limitations stem from the absence of a robust causal connection between OGT-DDX3X and neurodevelopmental effects, as well as the utilization of HEK cells rather than a neuronal model. Moreover, the study would gain from exploring endogenous proteins instead of solely relying on siRNA and OE methods to investigate the cellular and functional impacts of DDX3X O-GlcNAcylation. Overall, the study provides a mechanistic advance regarding O-GlcNAc PTM and the targets of OGT. This work could be of interest to audiences interested in neurodevelopment, as well as PTM of non-histone proteins.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This manuscript uses C. elegans as a model to interrogate the effects of autism-associated variants of previously unknown function in the RNA-binding protein RBM-26/RBM27.
Despite its potential impact, there are several concerns related to the technical rigor and specificity of the observed effects.
Major concerns: 1. The effects on PLM are interesting, but why was this neuron selected for study? Was this a lucky guess or are other axons also affected? It is important to clarify whether the effects of RBM-26 are specific to this neuron or act pleiotropically across many or all neurons. According to CeNGEN, rbm-26 is strongly expressed in the well-characterized neurons ASE, PVD, and HSN. Are there morphological defects in these neurons, or others? As a note, there are also functional assays for these neurons (salt sensing, touch response, and egg laying, respectively).
We have added new data to the supplemental materials showing that loss of rbm-26 function also causes the beading phenotype in the axons and dendrites of the PVD neuron (Figure S4 and lines 196-199). We have focused on the PLM neuron because our preliminary studies indicated that it had a higher penetrance of axon defects relative to the PVD neuron. Moreover, we observed expression of endogenously tagged RBM-26 in the PLM neuron (Figure 3A-C and lines 210-215).
Similarly, the choice of the MALSU homolog seemed like a shot in the dark. It is ranked 46th (out of 63 genes) for fold-enrichment following RBM-26 pull-down, and 9th for p-value. Were any of the mRNAs with greater fold-enrichment or smaller p-values examined further? It is important to determine whether many or all of these interacting genes are overexpressed in the absence of RBM-26 and whether they are also required for the phenotypic effects of RBM-26 mutants, or if the MALSU homolog is special.
We have clarified our reasoning for selecting the MALS-1 ortholog of MALSU1 for further study (see lines 283-284 and Table S2). Amongst binding partners with human orthologs, MALS-1 was by far the top ranked candidate. The adjusted p-value for MALS-1 was 0.0008. The next smallest adjusted p-value was two orders of magnitude larger (0.028 for dpy-4). Moreover, the log2fold fold enrichment for MALS-1 was 1.98, about the same as the largest (ACADS with 2.13). Nonetheless, we agree that some of the other interactors may also be of interest and have thus included them in the supplemental table S2. Although these other potential binding partners are outside the scope of this study, we expect that future studies by ourselves or others may focus on the roles of these other binding partners.
In addition to the specificity controls mentioned above, positive and negative controls are needed throughout the results. While each of these may be relatively minor by itself, as a group they raise questions about the technical rigor of the study. Briefly these include: Fig 1C. Missing loading controls and negative control (rbm-26 null allele). Additional exposures should be included to show whether RBM-26(P80L) protein or the lower band for RBM-26(L13V) are present at all, relative to the null allele.
We have added no-stain loading controls to figure 1C. We have also switched to using ECL detection, which is much more sensitive and reveals faint bands for RBM-26(P80L) and additional faint bands for RBM-26(L13V). In addition, we have included a longer exposure for the blot (Figure S1). We are unable to test the null, as we can only produce a limited number of small maternally rescued progeny, thereby precluding western blot analysis.
Fig 2. Controls to distinguish overextension of PLM axon from posterior mispositioning of ALM cell body are needed. Quantification of PLM axon lengths in microns (or normalized to body size) with standard deviation, not error of proportion, should be shown. Measurement of "beading phenotype" should be more rigorous, see for example the approach in Rawson et al. Curr. Biol. 2017 https://doi.org/10.1016/j.cub.2014.02.025 . The developmental stage examined, and the reason for choosing that stage, should be described for this and all figures.
We have added new data that shows PLM axon length relative to body length for each of the RBM-26 mutants (Figure S2 and lines 183-185). These results indicate that the PLM axon has a larger axon length to body length ration, suggesting that the PLM/ALM overlap phenotype is a result of PLM axon overextension. For most experiments, we retain penetrance, as this has been standard practice in the field and allows for a much larger sample size (see examples listed below). We have also added examples of how the beading phenotype was measured (Figure S3). Moreover, we have now analyzed this phenotype and others at multiple developmental stages (Figures 2D-H and Table S1). In general, we have conducted experiments at the L3 stage because the rbm-26(null) mutants don't survive past this stage. However, for many of our experiments we have also included additional stages as well. We have added this explanation to the methods section of phenotype analysis and also at various locations throughout the text. We have also labeled all graphs to clearly indicate the developmental stages and included.
10.1038/s41467-019-12804-3 Article by laboratory of Brock Grill
10.1371/journal.pgen.1002513 Article by laboratory of Ian Chin-Sang
doi.org/10.1073/pnas.1410263111 Article by laboratory of Chun-Liang Pan
10.1016/j.neuron.2007.07.009 Article by laboratory of Yishi Jin
doi.org/10.1523/JNEUROSCI.5536-07.2008 Article by laboratory of William Wadsworth
Fig 3. Controls without auxin and with neuronal TIR1 expression alone should be included. Controls demonstrating successful RBM-26 depletion, in larvae as well as in embryos at the time of PLM extension, should be included (weak embryonic depletion might explain why the overextension phenotype is only 14% instead of 40% as in the null). According to CeNGEN, rbm-26 expression in PLM is barely detected, thus depletion with a PLM-specific TIR1 should also be tested. To confirm the authors' identification of the cell marked "N" as the PLM cell body, co-expression of rbm-26 and a PLM-specific marker should be added. Rescue of the rbm-26 mutants with neuronal (and PLM-only) expression should be included to test sufficiency in PLM, and as a further control for potential artifacts of the AID system.
We have added new data showing that an endogenously tagged RBM-26::Scarlet protein is expressed in the PLM neuron (Figure 3A-C). Moreover, we have added rescue experiments, showing that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype and the PLM/ALM overlap phenotype (Figure 3 F-G). We have also added controls without auxin (Figure S7) __and without the rbm-26::scarlet::aid gene (Figure S8). We have added a new figure showing auxin-mediated depletion of RBM-26::Scarlet::AID in the PLM neuron (Figure S10)__. We examined auxin-mediated depletion at the L3 stage for consistency with our auxin-mediated phenotypic experiments. Moreover, these were done at the L3 stage for consistency with other experiments that included the rbm-26(null) mutants, which don't survive past this stage.
In general, auxin-mediated knockdown tends to be hypomorphic in neurons. This is likely due to the fact that the neuronal TIR1 driver is expressed at much lower levels relative to the other drivers. In addition, the lower penetrance observed in auxin-mediated PLM/ALM overlap phenotype could reflect the fact that this phenotype resolves by the L4 stage in the hypomorphic mutants. For example, in P80L mutants at the L3 stage we see only about a 20% penetrance of the PLM/ALM overlap phenotype (relative to about 15% in auxin-mediated knockdown).
Fig 4. More rigorous quantification of the distribution of mitochondria along the axon should be included, not only total number, and it should be clarified what region of the axon the images are taken from. Including the AID-depletion strain with and without auxin would further add to the sense of rigor. For the mitoTimer experiments, why is RBM-26(L13V) not included and why do wild-type values differ ~5-fold between experiments (despite error bars being almost non-existent)? A more rigorous approach to standardizing imaging conditions may be needed. Positive controls using compounds that affect oxidation should be included. Measurements of individual mitochondria with standard deviations should be shown, rather than aggregate averages with error of proportion.
We have changed our methodology for measuring mitochondria, so that we now report the density of mitochondria in the axon (number per 100µm), (Figure 4E-F). We agree that this method is much better than counting the total number of mitochondria per axon, as it corrects for differences in body length and axon length). We also now include data for the whole axon (Figure 4E), proximal axon (Figure 4G), and distal axon (Figure 4H). These data suggest that the mitochondrial density defects occur in the proximal axon but not in the distal axon. Using the null allele, we have also examined the timing of mitochondria defects in the axon and report that the defects begin in the L1 stage and continue throughout larval development (Figure 4F). Individual datapoints have been added for all graphs in Figure 4.
For the mitoTimer experiments (Figure 5), we have added data for L13V and have added the individual datapoints to the graph. In the prior version, the values did not differ 5-fold between experiments with the same stage, rather the different graphs were from different stages (as noted in the figure legends/main text) and the L4 stage has much more oxidation than the L2 stage. To clear this up, we have added labels to the graphs to indicate the stages for each experiment. We have also added new data, so that we now show results for the L2, L3, and L4 stages for all three rbm-26 mutants (see Figure 5C-E). We didn't test the L1 stage because the signal was not sufficient for accurate quantitation.
Fig 5. Additional positive and negative controls should be added, including additional rbm-26 alleles, the AID-tagged strain with and without auxin, and a rescued mutant.
The old Figure 5 has become Figure 6 in the new version. We have added the rbm-26(L13V) allele to each experiment, (Figure 6B-D). We have also added the loading controls for the western blot along with quantification for 3 biological replicates of the western blot analysis (Figure 6D). We agree that these additions significantly strengthen the data because they show that two independent alleles of rbm-26 cause very substantial increase in the expression of mals-1 at both the mRNA and protein levels. We did not do these experiments with the rescuing transgene or with the AID-tagged strain because these experiments are done on whole worm lysates, whereas the AID-tagged and rescuing transgene are neuron-specific.
Fig 6. Controls showing whether the Scarlet-tagged protein is functional are needed, to rule out dominant negative or toxicity-related effects.
This is Figure 7 in the new version. For this experiment, we are showing that overexpression of MALS-1 does cause defects. The idea is that excessive amounts of MALS-1 causes deleterious effects to the mitochondria. In fact, these defects could be considered as dominant negative or toxic. We considered the possibility of crossing the Pmec-7::mals-1::scarlet transgene with rbm-26; mals-1 double mutants. However, this does not seem workable, because the single copy Pmec-7::mals-1::scarlet transgene produces the phenotypes at penetrances that are similar to what we observe in rbm-26; mals-1 double mutants. We concede that the results of the overexpression experiments in Figure 7 are limited when considered in isolation. However, we think that they are meaningful when considered in combination with the results on the mals-1;rbm-26 double mutants in Figure 8.
Fig 8. Controls for other mitochondrial components need to be included. It is important to determine if the decrease in ribosomes is specific or reflects a general decrease in mitochondria. If there are fewer mitochondria as suggested in Fig. 4, then of course mitochondrial ribosomal protein levels are also reduced. Additional rbm-26 alleles should be included here as well. Is this effect dependent on the MALSU homolog?
This is Figure 8D-E in the new version. We have added new data showing that the decrease in MRPL-58 expression that is caused by the rbm-26(P80L) mutation is dependent on MALS-1. We concede that these experiments cannot be used to determine anything about the mitoribosomes per se, but rather serve as an alternative way of testing the effect of rbm-26 on mitochondria. We have revised the text accordingly (lines 355-357). Given these limitations we have elected not to try additional mitochondrial markers and have also not included additional rbm-26 alleles for this experiment.
Finally the authors should address concerns about image manipulation, which amplify the concerns about technical rigor outlined above. The image in Fig. 2A appears to have a black box placed over the lower-right portion of the field to hide some features. Black boxes also appear to have been placed over the tops of images in Fig. 4B and 4D and at the left of Fig. 6A, 6B, and 6C. While these manipulations probably do not affect the conclusions, they further undermine confidence in data integrity and experimental rigor.
We have corrected all of these image processing errors. The box in 2A was for the purpose of squaring off a corner that was clipped during image rotation. The boxes in Figures 4 and 6 (of the prior version) were added to give space for labels (without obscuring image features). We have now used alternative methods to accomplish the same goals. For example, in Figures 4-D we have placed the labels outside of the images.
Minor points. 1. C. elegans nomenclature conventions should be followed: - C. elegans gene names have three or four letters, thus the MALSU homolog cannot be named "malsu-1". Please have new gene names approved by WormBase BEFORE submitting for publication http://tazendra.caltech.edu/~azurebrd/cgi-bin/forms/gene_name.cgi
We have changed malsu-1 to mals-1. In addition, both mals-1 and mrpl-58 have now been approved by wormbase and will be listed on the website upon its next update.
- If two sequential CRISPR edits are made on the same gene then they should be listed as a compound allele, such as rbm-26(cue22cue25)
We have updated our gene names to reflect this convention.
- Genes on the same chromosome should not be separated with a semicolon, for example rbm-26(cue40) K12H4.2(syb6330)
We have updated our gene names to reflect this convention.
Describing the defects as "neurodevelopmental" is misleading in the case of axon beading or degeneration. Similarly, there is no evidence for an "axon targeting" defect as stated in the abstract.
We have revised such that instead of referring to degeneration phenotypes as neurodevelopmental, we now refer to axon degeneration phenotypes that occur during development. For example, in the abstract we now say, "These observations reveal a mechanism that regulates expression of a mitoribosomal assembly factor to protect against axon degeneration during neurodevelopment.
Regarding targeting defects, this was meant to refer to the misplacement of the PLM axon tip (which contains electrical synapses). However, our subsequent analysis has revealed that these defects are transient in P80L and L13V mutants, as they resolve by the L4 stage. The rbm-26 null axon development defects do not resolve, though these mutant die prior to the L4 stage. Given these findings, we have decided not to use the term of targeting defects. Instead, we now refer to this as an axon tiling defect or PLM/ALM overlap phenotype.
In Fig. 5A, the symbol that appears to correspond to F59C6.15 (lowest p-value) is a different size than the others and is colored as ncRNA, whereas WormBase annotates this gene as snoRNA.
This error has been corrected.
In the Introduction, the last sentences of the first two paragraphs should be varied ("However, little is known about the [...] mechanisms that protect [...] during neurodevelopment.")
This has been done.
Why is RBM-26 protein running as a doublet at both sizes?
We have improved our western blotting methodology by using 12% gel, allowing for better resolution. We have also switched from colorimetric detection to ECL detection, allowing for greater sensitivity. In our new blots, we identify 6 different RBM-26 protein bands. We don't know the reason for these bands, but speculate that they are the result of post-translational processing (148-150).
When showing the RBM-26 expression pattern (Fig. 3) please include a lower-magnification image of the entire animal.
This has been done (Figure S6)
It is confusing to refer to the RNA IP experiments as an "unbiased screen", which in C. elegans typically refers to a genetic screen.
We now refer to this as a "biochemical screen".
The relationship between axon overextension, beading, and mitochondrial localization is not clear. What causal connection between these is being proposed? The causal connections between these phenotypes, if any, should be clarified experimentally. For example, if the axon extension defects develop before mitochondrial localization defects, then it is unlikely that mitochondrial defects cause axon overextension.
We have added new data showing that the reduction in mitochondrial density within the axon begins during the L1 stage and increases throughout larval development (Figure 4F). We have also added additional data showing that the increase in mitochondrial oxidation is weak in the L2 stage and surges in the L3 stage (Figure 5C-E), coincident with the beginning of the axon degeneration phenotypes. We propose (lines 383-391) that a low level of mitochondrial defects is present in L1 larvae, giving rise to the axon tiling defects. In the L3 stage there is a surge in excessive mitochondrial oxidation, giving rise to the axon degeneration phenotypes. We have added a new section to the discussion that addresses the relationship between defects in axon development and axon degeneration (lines 375-405).
Please explain how to interpret the difference in axon beading in the two deletion alleles of the MALSU homolog (axon beading defects in tm12122 but not in syb6330). Is syb6330 not a null allele? Or are the defects in tm12122 due to other mutations in this strain background?
One likely reason for this difference is that tm12122 is predicted to cause a partial deletion of the mals-1 coding sequence, whereas the syb6330 is a full deletion. Thus, the tm12122 could be acting as a dominant negative. In fact, prior work on the MALSU1 ortholog has indicated that this protein is subject to interference by a dominant negative construct (see Rorbach et al, Nucleic Acids Res 2012). Nonetheless, we cannot rule out the possibility of a linked second mutation in tm12122. However, since we have found similar phenotypes and genetic interactions with both alleles, we can conclude that these phenotypes and interactions are due to loss of MALS-1, rather than a second mutation.
Are mitochondria reduced in number or mislocalized? If they are reduced in number, is this due to altered balance of fission/fusion?
We have adjusted our methods for quantifying mitochondria and have also analyzed the proximal vs distal axon (Figure 4). We find that the density of mitochondria is decreased in the proximal axon, but not in the distal axon. We speculate that this might reflect a higher demand on mitochondria in the proximal axon, due to a higher amount of trafficking activity in the proximal axon (lines 255-257). We propose that the loss of RBM-26 causes dysfunction in mitochondria. Since fission and fusion are mechanisms that can help to repair damaged mitochondria, it is likely that they would be involved in the phenotypes that we observe.
In Fig. 3A-D, please keep the labels in the same position in all panels and do not alter brightness settings between single-color and merged panels.
These images have been moved to the supplemental data section (Figure S5). We have adjusted the labels as suggested. We have not changed the brightness settings, as they were already the same in all panels. However, the blue signal in the merged panel does obscure some of the red signal, giving an appearance of an alteration in color balance.
The claim that rbm-26 acts cell-autonomously requires PLM-specific depletion and rescue experiments.
We have added new data indicating that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype (Figure 3F-G).
**Referees cross-commenting** I appreciate the use of the consultation session to resolve differences between reviewers, but in this case I fully agree with the content and tone of all the comments from the other reviewer -- I think our remarks are very well aligned!
Reviewer #1 (Significance (Required)):
The study engineers autism-associated variants in conserved residues of RBM27 into the C. elegans homolog RBM-26 and identifies neuronal phenotypes potentially relevant to autism and a potential molecular mechanism involving regulation of mitochondrial ribosome assembly.
The key claims of the study are 1} that autism-associated variants in RBM-26 decrease its protein expression; 2} that impaired RBM-26 function leads to a variety of defects in development and maintenance of a single neuron called PLM, including altered axonal localization of mitochondria; 3} that RBM-26 normally binds the mRNA for the C. elegans homolog of MALSU, a mitochondrial ribosomal assembly factor; 4} that loss of RBM-26 leads to overexpression of the MALSU homolog; and 5} that MALSU is required for some of the deleterious effects on the PLM neuron seen in RBM-26 mutants.
This study will be of interest to the autism research community because it bolsters the idea that variants in RBM27 are likely to disrupt gene function and to affect neuronal health. It will also be of interest to the broader cell biology community because it suggests an interesting potential nucleus-to-mitochondria signaling mechanism, in which a nuclear RNA-binding protein might regulate assembly of mitochondrial ribosomes.
My field of expertise is developmental biology in C. elegans.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary In this manuscript, the authors studied an ASD-associated gene, rbm-26 in neuronal morphology using the touch receptor neuron PLM in C. elegans, and found that loss-of-function rbp-27 causes overextension and the formation of bulb-like structures in the axon. Using UV-crosslinking RNA immunoprecipitation and RNA-Seq, they identify malsu-1 as a target of rbm-26. Genetic analyses suggest malsu-1 likely functions downstream of rbm-26 in controlling the PLM morphology. Major comments:
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The authors describe RBM27 is associated with ASD and ID while they only cite SFARI paper that describes a weak association of RBM27 to ASD. The appropriate referenced that show link between RBM27 and ID should be provided. The link with ID was an error. We had meant to say "ASD or other neurodevelopmental disorders." This has been corrected.
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SFARI database only has three (P79L, R190Q, G348D) mutations listed as ASD-associated. Where are other mutations L13V and R455H, particularly L13V that the authors used to generate the C. elegans mutant come from? Are they associated with intellectual disabilities? The others came from the devovo-DB. We have added a reference for this database and have also added the primary source references for each of the five de novo variants (see line 121).
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The authors should be very careful when describing 'gene X causes Y diseases'. Many (if not all) of the examples described in this manuscript are disease-associated genes without validation to be causal genes. We have revised accordingly. For example on lines 433-435, we now say," For example, mutations in the EXOSC3, EXOSC8 and EXOSC9 are thought to cause syndromes that include defects in brain development such as hypoplasia of the cerebellum and the corpus callosum". We have decided to use the phrase "thought to cause" because three of the five referenced articles on these genes use titles that indicate causation.
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The authors refer PLM axon beading and overextension phenotypes to 'axon degeneration and targeting defects'. The authors must provide additional evidence of axon degeneration (see below). Also the term 'targeting defects' is misleading as the authors did not examine if overextension of the PLM axon causes targeting defects. At least they should examine some synaptic markers. To provide more evidence of degeneration we have analyzed several additional phenotypes at multiple developmental stages (Figure 2 and Table S1). Regarding targeting defects, this was meant to refer to the misplacement of the PLM axon tip (which contains electrical synapses). However, our subsequent analysis has revealed that these defects are transient in P80L and L13V mutants, as they resolve by the L4 stage. The rbm-26 null axon development defects do not resolve, though these mutant die prior to the L4 stage. Given these findings, we have decided not to use the term of targeting defects. Instead, we now refer to this as an axon tiling defect or PLM/ALM overlap phenotype.
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Neuronal phenotypes (axon overextension and beading) should be examined at different developmental timepoints (larval, young adult, and aged animals) to test if these phenotypes are indeed degenerative instead of developmental defects. We have included new data to observe all of these phenotypes at multiple developmental time points (Figure 2 and Table S1).
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The authors use the blebbing (beading) phenotype in the axon as the sole evidence of neurodegenerative properties of the PLM neuron. A more thorough analysis of this phenotype as done by others (Pan PNAS 2006) must be provided to support the authors' claim that this phenotype represents neurodegeneration. We have included new data on multiple degenerative phenotypes in axons including: blebbing, beading, waviness and breaks (Table S1).
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The number of beads per axon should be quantified to better represent the severity of rbm-26 mutant. Individual samples should be plotted in the quantification instead of showing the percentage of animals. We have added data on the density of beads in rbm-26(null), rbm-26(P80L), and rbm-26(L13V) mutants (Figure S3). For most experiments we have decided to use penetrance to measure axon degeneration because this is a standard in the field and allows for a larger sample size. For examples please see:
10.1523/JNEUROSCI.1494-11.2012 (Toth et al, 2012)
https://doi.org/10.1016/j.cub.2014.02.025 (Rawson et al, 2014)
10.1073/pnas.1011711108 (Pan et al, 2012)
https://doi.org/10.7554/eLife.80856 (Czech et al, 2023)
https://doi.org/10.1016/j.celrep.2016.01.050 (Nichols et al, 2016)
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Based on the single gel image in Fig. 1C with no loading control, the P80L mutant appears to have no protein expression. How is the P80L viable while the null mutant is lethal? The authors should quantify the protein expression levels from multiple blots with proper loading controls. If P80L mutation is introduced into RBM-26::mScarlet strain can it cause depletion of the signal in vivo? We have added new data showing that the RBM-26::Scarlet signal is diminished by the P80L mutation in vivo (Figure 1E-F). We have also added quantification from 3 biological replicate blots (Figure 1D). Finally, we have improved the sensitivity of our blots by using ECL detection and also show various exposures to highlight the fainter bands (Figures 1C and S1). Therefore, we are now able to detect low level expression of RBM-26(P80L) mutant protein. It is likely that the low level of RBM-26(P80L) and RBM-26(L13V) seen on western blots is sufficient to prevent the lethal phenotype.
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'Moreover, loss of either the SPTBN1 or ADD1 genes causes a neurodevelopmental syndrome that includes autism and ADHD' References are missing, and as described above, be extra careful when indicating causality. Very few genes are known to cause ASD and ADHD. We have added the citations for this work (line 81). We also note that the titles for both of the cited articles indicate causation. To be on the safe side we have revised this line to say, "Moreover, loss of either the SPTBN1 or ADD1 genes are thought to cause a neurodevelopmental syndrome that includes autism and ADHD"
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Fig. 3E F, the authors should use the strains that express TIR1 specifically in the touch receptor neurons to argue cell autonomous function of RBM-26. Alternatively, the authors may conduct PLM neuron-specific rescue experiments to test the sufficiency. We have added new data indicating that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype and the PLM/ALM overlap phenotype (see Figure 3F-G).
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'Loss of RBM-26 causes mitochondria dysfunction in axons.' The authors did not examine mitochondria function in axons. They only examined the number of mitochondria, and ROS production in the soma. The authors should provide additional evidence to support the idea that elevated ROS production in the soma is due to mitochondrial dysfunction in axons. Also, the authors should use both P80L and L13V for this experiment, and indicate individual datapoint as dots. Here, they quantified at the L4 stage, which the authors should justify. We have added the L13V data to this experiment and now show the individual data points. In addition, we have now conducted this analysis at the L2, L3 and L4 stages (Figure 5C-E). We have also revised the text to indicate that loss of rbm-26 function causes mitochondrial dysfunction in the cell body which could potentially cause a reduction of mitochondria in the axon (see lines 100-101 and 268-270). We speculate that mitochondria in the axon are also dysfunctional. However, the mitoTimer signal is not bright enough in axons to allow for quantification.
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Figure 5B and C: the authors should also use L13V to quantify malsu-1 mRNA and protein level, and include quantifications in panel C (from multiple blots). This is Figure 6 in the new version. We have added new data for expression of mals-1 mRNA and protein in rbm-26(L13V) mutants (Figure 6B-D). We have also included quantifications from 3 biological replicates (Figure 6D).
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In the rbm-26 mutant, the number of mitochondria is reduced, while the amount of MALSU-1 protein is increased. If MALSU-1 is specifically localized at mitochondria in wild type, where does the excessive MALSU-1 go in the rbm-26 mutants? Quantification of MALSU-1 signal intensity should be provided. Our Pmec-7::mals-1::scarlet transgene uses the tbb-2 3'UTR and causes an overexpression phenotype. To address the question posed by the reviewer, we would need to express MALS-1 at endogenous levels. Given that endogenous levels of MALS-1 are very low, it is unlikely that we would be able to visualize its expression. Nonetheless, as a way to address this question we have attempted to create a single copy Pmec-7::mals-1::scarlet transgene that utilizes the mals-1 endogenous 3'UTR. We have tried multiple approaches for generating this construct, but all have failed, likely due to sequence complexities within the mals-1 3'UTR. While we cannot say where the extra MALS-1 protein goes, we think that it is likely overloaded into the remaining mitochondria and could also be in the cytosol as well.
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Figure 7C: malsu-1 knockout mutants exhibit PLM overextension phenotype, which is not consistent with their model. The authors should discuss this in detail. We have added a paragraph to the discussion explaining that mitochondria function could be disrupted by either MALS-1 overexpression or by MALS-1 loss of function (lines 471-480).
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'To validate these findings, we also repeated these experiments with an independent allele of malsu-1, malsu-1(tm12122) and found similar results (Fig. 7A-C).' The malsu-1(tm12122) exhibits beading phenotype and more severe overextension phenotype which the authors must describe and discuss more carefully. One likely reason for this difference is that tm12122 is predicted to cause a partial deletion of the mals-1 coding sequence, whereas the syb6330 is a full deletion. Thus, the tm12122 could be acting as a dominant negative. In fact, prior work on the MALSU1 ortholog has indicated that this protein is subject to interference by a dominant negative construct (see Rorbach et al, Nucleic Acids Res 2012). Nonetheless, we cannot rule out the possibility of a linked second mutation in tm12122. However, since we have found similar phenotypes and genetic interactions with both alleles, we can conclude that these phenotypes and interactions are due to loss of MALS-1, rather than a second mutation (albeit at a slightly different penetrance). We have added these considerations to the results section (lines 342-244).
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Figure 8: The authors should include data from L13V, malsu-1 and rbm-26; malsu-1 mutants. Quantification from multiple blots should be provided. This is Figure 8D in the new version. We have added the malsu-1 and rbm-26;malsu-1 double mutants to this experiment. We have also added quantification from multiple biological replicate blots. As pointed out by the other reviewer, we think that this experiment does not give specific information about mitoribosomes, but is an alternative approach to looking at the reduction in mitochondria. Given this limitation and considering that we have added L13V data to the mitochondria experiment in Figure 8B, we have elected not to add additional data on L13V to the western blot experiment in Figure 8D
Minor comments: • 'Consistent with a role for mitochondria in neurodevelopmental disorders, some of these disorders include a neurodegenerative phenotype.' Why is it consistent to have neurodegenerative phenotypes if mitochondria is associated with neurodevelopmental disorders? A better explanation would help.
We have changed this sentence to, "Some neurodevelopmental syndromes feature neurodegenerative phenotypes that occur during neuronal development."
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L13V is generally more severe in axon overextension phenotype than P80L while protein level is more abundant. The authors should discuss about this. We have also added a time course for the PLM/ALM overlap phenotype mutants (Figure 2D). This new data shows that the PLM/ALM overlap is quite similar overall between the P80L and L13V mutants. Both of these mutations cause an increase in PLM/ALM overlap in early larval development that is resolved by the L4 stage. The P80L phenotype resolves slightly sooner for reasons that are unknown. This could reflect differences in expression within the PLM that are not reflected in the whole worm lysate. This could also be due to a slight difference in the genetic background or other stochastic factors. The key point is that these two independent alleles cause similar phenotype overall, indicating that this phenotype is the result of loss in RBM-26 function.
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Fig. 2E, F: 'Beading refers to focal enlargement or bubble-like lesions which were at least twice the diameter of the axon in size.' How are the diameters of axons measured? A more detailed quantification method, and examples of measurement should be provided. We have added example measurements to the supplemental section (Figure S3). Additional detail on the measurements are in the Methods section (lines 517-518).
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Figure 3: The authors should also include low-magnification images to show where RBM-26 is expressed. The current image does now allow identifying cells. The transgene that labels the nuclei of hypodermis should be indicated in the manuscript. Specifically, the expression of the RBM-26 in the PLM should be shown. We have added a low magnification image (Figure S6) and have also added images of endogenously tagged RBM-26:Scarlet in the PLM (Figure 3A-C). The transgenic label for the hypodermis has been added to the legend of Figure S5.
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Figure 3: 'Tissue specific degradation of RBM-26::SCARLET::AID was achieved due to cell-type specific TIR-1 driver lines (see methods for details).' This information is not provided in the method section. This information has been added to methods section, "Auxin proteindegredation"
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Fig. 4 E. Values from individual samples should be indicated as dots. Representative images of P80L and L13V should be included. Conduct quantifications at adult stage as the authors use in other quantifications, or justify use of specific developmental stage (L3) they used. Figure 4 has become Figures 4 and 5 in the revised version. We have updated the graphs to include dots for individual data points. We have added quantifications of the mitoTImer experiments for the L2, L3 and L4 stages (Figure 5C-E). We note that our other experiments were done at the L1, L2, L3 and L4 and adult stages. The mitoTimer signal is not sufficient at the L1 stage for quantification. At the adult stage, the red signal becomes saturated. We have added representative images for mitoTimer in P80L and L13V mutants (Figure S9).
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The genes malsu-1 and mrpl-58 are not listed on wormbase. If the authors would like to designate names to these gene, they should clearly indicate that along with the sequence name. We have changed malsu-1 to mals-1. In addition, both mals-1 and mrpl-58 have now been approved by wormbase and will be listed on the website upon its next update.
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The authors found that MRPL-58 amount is reduced in rbm-26 mutants (which require additional verifications). This can be explained by the fact that axonal mitochondria number is reduced in the rbm-26 mutants. How did the authors confirm that the reduction in MRPL-58 level is due to the disruption of mitoribosome assembly? This is Figure 8D-E in the new version. We have added new data showing that the decrease in MRPL-58 expression that is caused by the rbm-26(P80L) mutation is dependent on MALS-1. We concede that these experiments cannot be used to determine anything about the mitoribosomes per se, but rather serve as an alternative way of testing the effect of rbm-26 on mitochondria. We have revised the text accordingly (lines 355-357).
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'MALSU-1 is a mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module [37-39].' I don't think these are known for C. elegans MALSU-1. We have revised to, "MALS-1 is an ortholog of the MALSU1 mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module"
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'Moreover, our results also suggest that disruption of this process can give rise to neurodevelopmental disorders.' I feel this is a quite a bit of stretch.
This has been replaced with, "Therefore, we speculate that human RBM26/27 could function with the RNA exosome complex to protect against neurodevelopmental defects and axon degeneration in infants." (lines 371-373)
**Referees cross-commenting** Yes, many of our comments overlap, and I fully agree with all comments from the other reviewer too. Reviewer #2 (Significance (Required)):
I found the manuscript interesting particularly the use of innovative techniques in identifying the target of RBM-26, The genetic analyses of rbm-26 and malsu-1 generally support the authors main conclusions that rbm-26 inhibits malsu-1 and be of potential interest to basic neuroscientists and cell biologists. However, the current manuscript looked premature which made my reading experience less pleasant. The phenotypic analyses is superficial compared to works similar to this work, which are insufficient to support the authors' claim of 'axon degeneration and targeting defects'. A number of issues listed above should be addressed before this manuscript is published. The reviewer's expertise: neurodevelopment in model organisms.
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Referee #2
Evidence, reproducibility and clarity
Summary
In this manuscript, the authors studied an ASD-associated gene, rbm-26 in neuronal morphology using the touch receptor neuron PLM in C. elegans, and found that loss-of-function rbp-27 causes overextension and the formation of bulb-like structures in the axon. Using UV-crosslinking RNA immunoprecipitation and RNA-Seq, they identify malsu-1 as a target of rbm-26. Genetic analyses suggest malsu-1 likely functions downstream of rbm-26 in controlling the PLM morphology.
Major comments:
- The authors describe RBM27 is associated with ASD and ID while they only cite SFARI paper that describes a weak association of RBM27 to ASD. The appropriate referenced that show link between RBM27 and ID should be provided.
- SFARI database only has three (P79L, R190Q, G348D) mutations listed as ASD-associated. Where are other mutations L13V and R455H, particularly L13V that the authors used to generate the C. elegans mutant come from? Are they associated with intellectual disabilities?
- The authors should be very careful when describing 'gene X causes Y diseases'. Many (if not all) of the examples described in this manuscript are disease-associated genes without validation to be causal genes.
- The authors refer PLM axon beading and overextension phenotypes to 'axon degeneration and targeting defects'. The authors must provide additional evidence of axon degeneration (see below). Also the term 'targeting defects' is misleading as the authors did not examine if overextension of the PLM axon causes targeting defects. At least they should examine some synaptic markers.
- Neuronal phenotypes (axon overextension and beading) should be examined at different developmental timepoints (larval, young adult, and aged animals) to test if these phenotypes are indeed degenerative instead of developmental defects.
- The authors use the blebbing (beading) phenotype in the axon as the sole evidence of neurodegenerative properties of the PLM neuron. A more thorough analysis of this phenotype as done by others (Pan PNAS 2006) must be provided to support the authors' claim that this phenotype represents neurodegeneration.
- The number of beads per axon should be quantified to better represent the severity of rbm-26 mutant. Individual samples should be plotted in the quantification instead of showing the percentage of animals.
- Based on the single gel image in Fig. 1C with no loading control, the P80L mutant appears to have no protein expression. How is the P80L viable while the null mutant is lethal? The authors should quantify the protein expression levels from multiple blots with proper loading controls. If P80L mutation is introduced into RBM-26::mScarlet strain can it cause depletion of the signal in vivo?
- 'Moreover, loss of either the SPTBN1 or ADD1 genes causes a neurodevelopmental syndrome that includes autism and ADHD' References are missing, and as described above, be extra careful when indicating causality. Very few genes are known to cause ASD and ADHD.
- Fig. 3E F, the authors should use the strains that express TIR1 specifically in the touch receptor neurons to argue cell autonomous function of RBM-26. Alternatively, the authors may conduct PLM neuron-specific rescue experiments to test the sufficiency.
- 'Loss of RBM-26 causes mitochondria dysfunction in axons.' The authors did not examine mitochondria function in axons. They only examined the number of mitochondria, and ROS production in the soma. The authors should provide additional evidence to support the idea that elevated ROS production in the soma is due to mitochondrial dysfunction in axons. Also, the authors should use both P80L and L13V for this experiment, and indicate individual datapoint as dots. Here, they quantified at the L4 stage, which the authors should justify.
- Figure 5B and C: the authors should also use L13V to quantify malsu-1 mRNA and protein level, and include quantifications in panel C (from multiple blots).
- In the rbm-26 mutant, the number of mitochondria is reduced, while the amount of MALSU-1 protein is increased. If MALSU-1 is specifically localized at mitochondria in wild type, where does the excessive MALSU-1 go in the rbm-26 mutants? Quantification of MALSU-1 signal intensity should be provided.
- Figure 7C: malsu-1 knockout mutants exhibit PLM overextension phenotype, which is not consistent with their model. The authors should discuss this in detail.
- 'To validate these findings, we also repeated these experiments with an independent allele of malsu-1, malsu-1(tm12122) and found similar results (Fig. 7A-C).' The malsu-1(tm12122) exhibits beading phenotype and more severe overextension phenotype which the authors must describe and discuss more carefully.
- Figure 8: The authors should include data from L13V, malsu-1 and rbm-26; malsu-1 mutants. Quantification from multiple blots should be provided.
Minor comments:
- 'Consistent with a role for mitochondria in neurodevelopmental disorders, some of these disorders include a neurodegenerative phenotype.' Why is it consistent to have neurodegenerative phenotypes if mitochondria is associated with neurodevelopmental disorders? A better explanation would help.
- L13V is generally more severe in axon overextension phenotype than P80L while protein level is more abundant. The authors should discuss about this.
- Fig. 2E, F: 'Beading refers to focal enlargement or bubble-like lesions which were at least twice the diameter of the axon in size.' How are the diameters of axons measured? A more detailed quantification method, and examples of measurement should be provided.
- Figure 3: The authors should also include low-magnification images to show where RBM-26 is expressed. The current image does now allow identifying cells. The transgene that labels the nuclei of hypodermis should be indicated in the manuscript. Specifically, the expression of the RBM-26 in the PLM should be shown.
- Figure 3: 'Tissue specific degradation of RBM-26::SCARLET::AID was achieved due to cell-type specific TIR-1 driver lines (see methods for details).' This information is not provided in the method section.
- Fig. 4 E. Values from individual samples should be indicated as dots. Representative images of P80L and L13V should be included. Conduct quantifications at adult stage as the authors use in other quantifications, or justify use of specific developmental stage (L3) they used.
- The genes malsu-1 and mrpl-58 are not listed on wormbase. If the authors would like to designate names to these gene, they should clearly indicate that along with the sequence name.
- The authors found that MRPL-58 amount is reduced in rbm-26 mutants (which require additional verifications). This can be explained by the fact that axonal mitochondria number is reduced in the rbm-26 mutants. How did the authors confirm that the reduction in MRPL-58 level is due to the disruption of mitoribosome assembly?
- 'MALSU-1 is a mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module [37-39].' I don't think these are known for C. elegans MALSU-1.
- 'Moreover, our results also suggest that disruption of this process can give rise to neurodevelopmental disorders.' I feel this is a quite a bit of stretch.
Referees cross-commenting Yes, many of our comments overlap, and I fully agree with all comments from the other reviewer too.
Significance
I found the manuscript interesting particularly the use of innovative techniques in identifying the target of RBM-26, The genetic analyses of rbm-26 and malsu-1 generally support the authors main conclusions that rbm-26 inhibits malsu-1 and be of potential interest to basic neuroscientists and cell biologists. However, the current manuscript looked premature which made my reading experience less pleasant. The phenotypic analyses is superficial compared to works similar to this work, which are insufficient to support the authors' claim of 'axon degeneration and targeting defects'. A number of issues listed above should be addressed before this manuscript is published.
The reviewer's expertise: neurodevelopment in model organisms.
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Referee #1
Evidence, reproducibility and clarity
This manuscript uses C. elegans as a model to interrogate the effects of autism-associated variants of previously unknown function in the RNA-binding protein RBM-26/RBM27.
Despite its potential impact, there are several concerns related to the technical rigor and specificity of the observed effects.
Major concerns:
- The effects on PLM are interesting, but why was this neuron selected for study? Was this a lucky guess or are other axons also affected? It is important to clarify whether the effects of RBM-26 are specific to this neuron or act pleiotropically across many or all neurons. According to CeNGEN, rbm-26 is strongly expressed in the well-characterized neurons ASE, PVD, and HSN. Are there morphological defects in these neurons, or others? As a note, there are also functional assays for these neurons (salt sensing, touch response, and egg laying, respectively).
- Similarly, the choice of the MALSU homolog seemed like a shot in the dark. It is ranked 46th (out of 63 genes) for fold-enrichment following RBM-26 pull-down, and 9th for p-value. Were any of the mRNAs with greater fold-enrichment or smaller p-values examined further? It is important to determine whether many or all of these interacting genes are overexpressed in the absence of RBM-26 and whether they are also required for the phenotypic effects of RBM-26 mutants, or if the MALSU homolog is special.
- In addition to the specificity controls mentioned above, positive and negative controls are needed throughout the results. While each of these may be relatively minor by itself, as a group they raise questions about the technical rigor of the study. Briefly these include:
Fig 1C. Missing loading controls and negative control (rbm-26 null allele). Additional exposures should be included to show whether RBM-26(P80L) protein or the lower band for RBM-26(L13V) are present at all, relative to the null allele.
Fig 2. Controls to distinguish overextension of PLM axon from posterior mispositioning of ALM cell body are needed. Quantification of PLM axon lengths in microns (or normalized to body size) with standard deviation, not error of proportion, should be shown. Measurement of "beading phenotype" should be more rigorous, see for example the approach in Rawson et al. Curr. Biol. 2017 https://doi.org/10.1016/j.cub.2014.02.025 . The developmental stage examined, and the reason for choosing that stage, should be described for this and all figures.
Fig 3. Controls without auxin and with neuronal TIR1 expression alone should be included. Controls demonstrating successful RBM-26 depletion, in larvae as well as in embryos at the time of PLM extension, should be included (weak embryonic depletion might explain why the overextension phenotype is only 14% instead of 40% as in the null). According to CeNGEN, rbm-26 expression in PLM is barely detected, thus depletion with a PLM-specific TIR1 should also be tested. To confirm the authors' identification of the cell marked "N" as the PLM cell body, co-expression of rbm-26 and a PLM-specific marker should be added. Rescue of the rbm-26 mutants with neuronal (and PLM-only) expression should be included to test sufficiency in PLM, and as a further control for potential artifacts of the AID system.
Fig 4. More rigorous quantification of the distribution of mitochondria along the axon should be included, not only total number, and it should be clarified what region of the axon the images are taken from. Including the AID-depletion strain with and without auxin would further add to the sense of rigor. For the mitoTimer experiments, why is RBM-26(L13V) not included and why do wild-type values differ ~5-fold between experiments (despite error bars being almost non-existent)? A more rigorous approach to standardizing imaging conditions may be needed. Positive controls using compounds that affect oxidation should be included. Measurements of individual mitochondria with standard deviations should be shown, rather than aggregate averages with error of proportion.
Fig 5. Additional positive and negative controls should be added, including additional rbm-26 alleles, the AID-tagged strain with and without auxin, and a rescued mutant.
Fig 6. Controls showing whether the Scarlet-tagged protein is functional are needed, to rule out dominant negative or toxicity-related effects.
Fig 8. Controls for other mitochondrial components need to be included. It is important to determine if the decrease in ribosomes is specific or reflects a general decrease in mitochondria. If there are fewer mitochondria as suggested in Fig. 4, then of course mitochondrial ribosomal protein levels are also reduced. Additional rbm-26 alleles should be included here as well. Is this effect dependent on the MALSU homolog? 4. Finally the authors should address concerns about image manipulation, which amplify the concerns about technical rigor outlined above. The image in Fig. 2A appears to have a black box placed over the lower-right portion of the field to hide some features. Black boxes also appear to have been placed over the tops of images in Fig. 4B and 4D and at the left of Fig. 6A, 6B, and 6C. While these manipulations probably do not affect the conclusions, they further undermine confidence in data integrity and experimental rigor.
Minor points.
- C. elegans nomenclature conventions should be followed:
- C. elegans gene names have three or four letters, thus the MALSU homolog cannot be named "malsu-1". Please have new gene names approved by WormBase BEFORE submitting for publication http://tazendra.caltech.edu/~azurebrd/cgi-bin/forms/gene_name.cgi
- If two sequential CRISPR edits are made on the same gene then they should be listed as a compound allele, such as rbm-26(cue22cue25)
- Genes on the same chromosome should not be separated with a semicolon, for example rbm-26(cue40) K12H4.2(syb6330)
- Describing the defects as "neurodevelopmental" is misleading in the case of axon beading or degeneration. Similarly, there is no evidence for an "axon targeting" defect as stated in the abstract.
- In Fig. 5A, the symbol that appears to correspond to F59C6.15 (lowest p-value) is a different size than the others and is colored as ncRNA, whereas WormBase annotates this gene as snoRNA.
- In the Introduction, the last sentences of the first two paragraphs should be varied ("However, little is known about the [...] mechanisms that protect [...] during neurodevelopment.")
- Why is RBM-26 protein running as a doublet at both sizes?
- When showing the RBM-26 expression pattern (Fig. 3) please include a lower-magnification image of the entire animal.
- It is confusing to refer to the RNA IP experiments as an "unbiased screen", which in C. elegans typically refers to a genetic screen.
- The relationship between axon overextension, beading, and mitochondrial localization is not clear. What causal connection between these is being proposed? The causal connections between these phenotypes, if any, should be clarified experimentally. For example, if the axon extension defects develop before mitochondrial localization defects, then it is unlikely that mitochondrial defects cause axon overextension.
- Please explain how to interpret the difference in axon beading in the two deletion alleles of the MALSU homolog (axon beading defects in tm12122 but not in syb6330). Is syb6330 not a null allele? Or are the defects in tm12122 due to other mutations in this strain background?
- Are mitochondria reduced in number or mislocalized? If they are reduced in number, is this due to altered balance of fission/fusion?
- In Fig. 3A-D, please keep the labels in the same position in all panels and do not alter brightness settings between single-color and merged panels.
- The claim that rbm-26 acts cell-autonomously requires PLM-specific depletion and rescue experiments.
Referees cross-commenting I appreciate the use of the consultation session to resolve differences between reviewers, but in this case I fully agree with the content and tone of all the comments from the other reviewer -- I think our remarks are very well aligned!
Significance
The study engineers autism-associated variants in conserved residues of RBM27 into the C. elegans homolog RBM-26 and identifies neuronal phenotypes potentially relevant to autism and a potential molecular mechanism involving regulation of mitochondrial ribosome assembly.
The key claims of the study are 1} that autism-associated variants in RBM-26 decrease its protein expression; 2} that impaired RBM-26 function leads to a variety of defects in development and maintenance of a single neuron called PLM, including altered axonal localization of mitochondria; 3} that RBM-26 normally binds the mRNA for the C. elegans homolog of MALSU, a mitochondrial ribosomal assembly factor; 4} that loss of RBM-26 leads to overexpression of the MALSU homolog; and 5} that MALSU is required for some of the deleterious effects on the PLM neuron seen in RBM-26 mutants.
This study will be of interest to the autism research community because it bolsters the idea that variants in RBM27 are likely to disrupt gene function and to affect neuronal health. It will also be of interest to the broader cell biology community because it suggests an interesting potential nucleus-to-mitochondria signaling mechanism, in which a nuclear RNA-binding protein might regulate assembly of mitochondrial ribosomes.
My field of expertise is developmental biology in C. elegans.
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Reviewer 1
The paper is overall convincing. However, a little more attention to data presentation and possibly the addition of at least another technique (see below) would greatly strengthen the findings.
As we hope to demonstrate below, we have taken steps to improve our manuscript on both fronts (data presentation and experimental evidence).
The absence of statistics catches immediately the eye. I am sure that the shown differences are statistically significant (thanks to the number of analyzed cells), but reporting the result of some statistical test would help the reader in identify the relevant data in a plot. This is somehow necessary considering that sometimes in the text something is deemed to be "significant" or "not significant", and I felt that I really needed that when looking at the plot in Fig. 3D.
To facilitate the interpretation of figures that contain data from multiple strains (such as the one mentioned by the reviewer), we have carried out a nonparametric single-step multiple comparison test (Games-Howell) to identify mutants whose means differ significantly from each other. To avoid overcrowding the figures, we have graphically summarized the p-values of all pairwise comparisons in a small matrix within the corresponding panel, and provided 99% confidence intervals and p-values of all differences in the Supplement.
Related to the previous point: for every N/C distribution analysis, a number of analyzed cells is reported. By the way it is written, it seems that the replication relies solely by the cells in that specific population, i.e.: each cell is treated as a replicate. At least I could not find if that is not the case in the legends or in the methods. I wonder what the results would be (and their significance) if each replicate would be a new assay on another population.
Cell populations exhibit significant variability in their phenotypic characteristics. Consequently, the quantification of a specific feature (e.g., the Sfp1 nuclear/cytoplasmic ratio) across a sample of cells from a given population results in a distribution rather than a single fixed value. For each quantification, we report the number of cells that were used to construct the corresponding distribution, i.e. the sample size. To compare samples from different populations (e.g., different Sfp1 mutant strains), we run them in parallel during microscopy experiments and compare their means, as described above. Throughout our study, we have tried to ensure that we quantify a sufficiently large number of cells to overcome cell-to-cell variability and enhance the reliability of our results.
In this context, the question of the reviewer is not entirely clear to us, as individual measurements of a sample are not replicates. However, one can replicate the entire experiment on a different day by re-growing the different strains, running microscopy, quantifying the new movies etc. In this sense, the experiments shown in the manuscript consist of single replicates, i.e. experiments that were carried out on the same day, with all the relevant mutants and controls quantified together. However, we have monitored many of our mutants multiple times over the course of our work. For example, Fig. 1 below shows replicates of the Sfp1 N/C ratio distributions at steady-state in the analog-sensitive (A) and wild-type (B) background, which were quantified several times across various experiments. While day-to-day variability in the empirical distributions of the same mutant exists to a small extent, it is quite small.
The scale of x axes in N/C ratio plots. Besides not being consistent throughout the figures, it originates from 1, visually enhancing the differences.
We believe the reviewer was referring to the y-axes, as the x-axes represent time. Summarizing the N/C ratio dynamics of different Sfp1 mutants has been challenging. First, the average N/C ratios at steady-state vary considerably across different mutants, as shown in the panels that summarize steady-state N/C ratios. To compare the magnitude and features of their responses, normalization is necessary. We chose to normalize the time series of each mutant to have a mean of 1 prior to the onset of a perturbation. This allows the normalized time series to represent the percentage-wise changes in the Sfp1 N/C ratio upon perturbation.
Using a common y-axis scale for all plots of N/C ratio dynamics not ideal, as some responses are subtler than others. Additionally, we do not believe that N/C dynamics across different figures need to (or should) be compared to each other. However, within a figure, panels that require comparison are placed in the same row and share the same y-axis scale. We believe that this approach optimizes data visualization and facilitates important visual comparisons.
Related to the previous point: it is evident from the plots that the N/C ratio is always positive, even in the most deficient of the analyzed mutants. This implies that a relevant fraction of Sfp1 is still nuclear. I thus wonder what the impact of these mutations would be on the actual function of Sfp1. For this reason, I feel that qPCR evaluation of transcripts of Sfp1 target genes is particularly needed. Since lack of Sfp1 is known to yield some of the smallest cells possible, it would also be cool to have an estimate of the size of mutants where Sfp1 is less nuclear. These analyses could confer phenotypical relevance to the data, but would also help in assessing a currently unexplored possibility, that phosphorylation events by PKA influence Sfp1 function besides its localization, i.e.: the still somehow nuclear fraction is not as functional as wt Sfp1 in promoting transcription.
It is indeed the case that the recorded N/C ratios are larger than 1 in all strains that we have monitored. We have never observed an N/C ratio smaller than 1 using widefield microscopy for two main reasons: first, out-of-focus light from the cytosol above and below the nucleus is added to the nuclear signal, causing the nuclear signal to always be non-zero, even for predominantly cytosolic proteins. Second, both in- and out of focus vacuoles are devoid of the fluorescent protein fusions that we quantify, which reduces the average brightness of the cytosol. For these reasons, even when a protein is largely cytosolic, the average N/C ratio over a cell population is no lower than around 1.5. Keeping these points in mind, one can observe that our most delocalized Sfp1 mutants have an N/C ratio that is around 1.6-1.7, which is very close to the lower limit. This means that these Sfp1 mutants are largely cytosolic, and the nuclear fraction (if non-zero) is quite small.
We agree that assessing the phenotypic relevance of Sfp1 mutations is of interest. However, this was impossible with our original strains, as we introduced each Sfp1 mutant as an extra copy in the HO locus while leaving the endogenous Sfp1 locus intact. This was done in order to avoid any phenotypic changes that might result from changes in Sfp1 activity.
To address the suggestion of the reviewer, we therefore deleted the endogenous Sfp1 copy in strains carrying sfp1PKA2A, sfp1PKA2D and sfp113A, leaving only the mutated Sfp1 copy at the HO locus. Surprisingly, the growth rate and drug sensitivity (determined by halo assays) of these single-copy mutants did not differ much in comparison to the mutants carrying the functional Sfp1 copy and from the wild-type (Supp. Figs. 4J and 7). This observation aligns with findings for the single-copy sfp1-1 mutant in [Lempiäinen et al. 2009], which corresponds to sfp1TOR7A in our work. [Lempiäinen et al. 2009] had suggested that Sch9 compensates for the loss of Sfp1 activity via a feedback mechanism, which could explain our results as well. If this is the case, acute depletion of wild-type Sfp1 could unveil transient changes in cell growth, before the compensatory effect of Sch9 was established. Unfortunately, we were unable to efficiently degrade wild-type Sfp1 carrying a C-terminal auxin-inducible degron. Instead, we followed the same approach with [Lempiäinen et al. 2009] and deleted SCH9.
As we describe in the last section of Results, the difference was dramatic for sfp113A __mutants, which were extremely slow-growing in the absence of Sch9 (doubling time was around 4 hours, but it was hard to estimate because we could not grow the cells consistently). Interestingly, SCH9 deletion had a negative impact on sfp1__PKA2D __but not sfp1__PKA2A __cells (__Supp. Fig. 7). Overall, these results demonstrate that Sch9 can compensate for loss of Sfp1 activity, which makes it challenging to study the impact of Sfp1 mutations on cellular phenotypes.
To further understand to what extent Sch9 compensates for loss of Sfp1 phosphorylation, we carried out RNA-seq on WT and cells carrying a single copy of sfp113A (with the endogenous SFP1 copy removed). Despite the fact that sfp113A __grow as well as WT, RNA-seq picked up several differentially expressed genes related to amino acid biosynthesis. This surprising finding is presented in the last section of Results, and in __Supplementary Figures 8, 9 and 10. We explore the relevance of these results and their connection with past literature on Sfp1 and Sch9 in the Discussion section.
I found some typos here and there, and it would greatly help to report them if in the manuscript line numbers were included.
We apologize for the typos. We have tried to eliminate them, and we have also added line numbers to the manuscript.
Reviewer 2
There is no biochemical evidence presented that the putative PKA sites (S105 and S136) are genuinely phosphorylated by PKA. The fact that they match the PKA consensus motif, alone, does not guarantee this. In order to claim that they are looking at the effect of PKA by mutagenizing these residues, the authors have to demonstrate the PKA-dependency of S105 and S136 phosphorylation by, for example, mass spec experiments or western blotting with phospho-specific antibodies (Cell Signaling Technology #9624 for example). Also, does the band-shift caused by PKA inhibition (Fig 3C) is canceled by the S105A/S136A mutation?
We took several actions to demonstrate that the putative PKA sites are indeed phosphorylated by PKA. We first tried to detect Sfp1 phosphorylation using the antibody mentioned by the reviewer, but failed as the sensitivity of this antibody appears to be quite low. On the other hand, mass spectrometry did not produce the right fragments to detect the sites of interest. We therefore resorted to an in vitro kinase assay using [γ-32P]ATP together with purified PKA and Sfp1. Unfortunately, bacterial overexpression of MBP-tagged Tpk1, Tpk2 and Tpk3 (the catalytic subunits of PKA) was quite challenging and we were unable to produce soluble protein. We therefore resorted to commercially available bovine PKA (bPKA, PKA catalytic subunit, Sigma-Aldrich 539576), which shows high homology to the yeast Tpk kinases [Toda et al. 1987]. Moreover 87% of bPKA substrates have been shown to also be Tpk1 substrates [Ptacek et al. 2005], and bPKA has been used to identify new Tpk substrates in budding yeast [Budovskaya et al. 2005__]. As we show in the revised manuscript, bovine PKA does phosphorylate Sfp1. Moreover, phosphorylation is reduced by 50% in the double S105A, S136A mutant (Fig.1F), and becomes undetectable in the 13A mutant__ (Supp Fig. 6). Together with the rapid response of Sfp1 localization to acute PKA inhibition which we had already reported, we believe that these results provide strong evidence that Sfp1 is a direct PKA substrate, and that the two phosphosites that we identified are functional.
As the above in vivo experiments do not exclude S105/S136 phosphorylation by other kinases downstream of PKA, in order to claim the direct phosphorylation, the authors need in vitro PKA kinase assay. These biochemical experiments are not trivial, but I think absolutely necessary for this story.
One cannot exclude that S105/S136 are also phosphorylated by other kinases of the AGC family (note that [Lempiäinen et al. 2009] has already excluded Sch9). However, as we hope to have shown, PKA indeed phosphorylates Sfp1. Examining if other kinases besides PKA and TORC1 target Sfp1 is a very interesting question that should be addressed in future work.
The authors only look at the localization of Sfp1. To assess its functionality and so physiological impact, it would be informative to measure the mRNA level of target ribosomal genes in various Sfp1 mutants they created.
As we described in our response to Reviewer 1 above, we did perform RNA-seq on WT and cells carrying a single copy of sfp113A. We observed a notable absence of differentially expressed ribosomal genes and ribosome-related categories in the GO analysis (Supp. Figs. 8, 9 and 10). Together with our observations on SCH9 deletion (Supp. Fig. 7), these results suggest that Sch9 can largely compensate for the loss of Sfp1 activity. On the other hand, the emergence of differentially expressed amino acid biosynthesis genes is a finding that merits further investigation, as it connects with previous observations made with Sch9 deletion mutants and the [ISP+] prion form of Sfp1 (cf. Discussion).
In the experiments using analog-sensitive PKA (Fig 1D and E for example), they directly compare wildtype-PKA versus analog sensitive-PKA, or with 1-NM-PP1 versus without 1-NM-PP1. This makes interpretation difficult, particularly because 1-NM-PP1 itself has a significant impact even in the wild PKA strain. The real question is the difference between wild-type Sfp1 versus mutant Sfp1. In the current form, they compare Fig 1D versus 1E, these two do not look like a single, side-by-side experiment. They should compare wild-type Sfp1 versus mutant Sfp1 side-by-side.
Figure 1D shows that 1-NM-PP1 has a transient off-target effect on Sfp1 localization in WT cells, which could also affect Sfp1 mutants. This observation prompted us to use wild-type PKA as a control when testing the effect of 1-NM-PP1 on sfp1PKA2D in cells carrying PKAas (Figure 1E). As Fig. 1E shows, the effect of 1-NM-PP1 on sfp1PKA2D localization in PKAas cells is quite similar to the off-target effect in cells carrying sfp1__PKA2D __and wild-type PKA. This behavior of sfp1__PKA2D __is clearly different from the response of wild-type Sfp1 to PKAas inhibition, which results in sustained delocalization. We have made the latter observation repeatedly, both in this study and our previously published work [Guerra et al. 2021].
In Figure 3, the argument around the additive effects of PKA and TORC1 is confusing. The authors say they are additive referring Figure 3E, but say they are not additive referring Figure 3B. Which is true? In fact, Figure 3B appears to show an additive effect as well.
We did not use the word "additive" in the text, because we find it difficult to interpret. Instead, we state that PKA and TORC1 appear to control Sfp1 phosphorylation independently of each other. PKA and TORC1 phosphorylation converges to the same response, affecting Sfp1 localization. It appears that loss of either kinase delocalizes Sfp1, while loss of both kinases may only have a small additional effect.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors investigated how Sfp1, a transcription factor for ribosomal genes, integrates signals from TORC1 and PKA pathways. They did so by analyzing the nuclear localization of the GFP-tagged Sfp1 variants harboring unphosphorylatable or phosphomimetic mutations on either TORC1 target sites, putative PKA target sites, or a combination of both. This approach was complemented by examining the effect of pharmacological inhibition of either pathway on Sfp1 localization. The obtained results support that TORC1 and PKA independently promote nuclear localization of Snf1, provided that the putative PKA sites are genuinely PKA sites (see Major point). In course of their investigation, the authors made two novel findings about the regulatory mechanism of Sfp1 localization. First, they identified the 98-106aa region as a nuclear export signal (NES). Because this region overlaps with a putative PKA site, it is conceivable that PKA regulates Sfp1 localization via altering the functionality of NES. In addition, they found that the nuclear localization of Snf1 requires its C-terminal zinc fingers, although this domain appears to work independently from TORC1- and PKA-dependent regulations.
Major points:
- There is no biochemical evidence presented that the putative PKA sites (S105 and S136) are genuinely phosphorylated by PKA. The fact that they match the PKA consensus motif, alone, does not guarantee this. In order to claim that they are looking at the effect of PKA by mutagenizing these residues, the authors have to demonstrate the PKA-dependency of S105 and S136 phosphorylation by, for example, mass spec experiments or western blotting with phospho-specific antibodies (Cell Signaling Technology #9624 for example). Also, does the band-shift caused by PKA inhibition (Fig 3C) is canceled by the S105A/S136A mutation?
- As the above in vivo experiments do not exclude S105/S136 phosphorylation by other kinases downstream of PKA, in order to claim the direct phosphorylation, the authors need in vitro PKA kinase assay. These biochemical experiments are not trivial, but I think absolutely necessary for this story.
Minor points:
- The authors only look at the localization of Sfp1. To assess its functionality and so physiological impact, it would be informative to measure the mRNA level of target ribosomal genes in various Sfp1 mutants they created.
- In the experiments using analog-sensitive PKA (Fig 1D and E for example), they directly compare wildtype-PKA versus analog sensitive-PKA, or with 1-NM-PP1 versus without 1-NM-PP1. This makes interpretation difficult, particularly because 1-NM-PP1 itself has a significant impact even in the wild PKA strain. The real question is the difference between wild-type Sfp1 versus mutant Sfp1. In the current form, they compare Fig 1D versus 1E, these two do not look like a single, side-by-side experiment. They should compare wild-type Sfp1 versus mutant Sfp1 side-by-side.
- In Figure 3, the argument around the additive effects of PKA and TORC1 is confusing. The authors say they are additive referring Figure 3E, but say they are not additive referring Figure 3B. Which is true? In fact, Figure 3B appears to show an additive effect as well.
Significance
TORC1 and PKA are major pro-growth signaling pathways widely conserved in eukaryotes, that often converge on the same target proteins. How the information from the two pathways is integrated is an interesting question, which the authors directly and meticulously address here with yeast Sfp1 as an example. Provided that they can demonstrate that the putative PKA sites are the real ones (this is really important- TORC1 sites are already known, what is new here is PKA sites), their data and conclusion should be of interest to the signal transduction field.
Their additional discovery of NES and the role of zinc fingers in Sfp1 localization should be of interest to those working on Sfp1, or transcriptional regulation of ribosomal genes in general.
My area of expertise: yeast TOR
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Referee #1
Evidence, reproducibility and clarity
Summary:
In this paper, Vuillemenot and Milias-Argeitis investigate in budding yeast the role of Protein Kinase A (PKA) in regulating through phosphorylation the subcellular localization of the transcription factor Sfp1, known for controlling transcription of RP genes. Sfp1 is very well known for being regulated by another signaling pathway, centered on the kinase TORC1. Thus, regulation of Sfp1 by PKA raises the intriguing possibility of a downstream crosstalk between the two pathways. Indeed, the authors find that Sfp1 is regulated by PKA independently from TORC1. In the study, the authors employ mainly single-cell microscopy to monitor the nucleo/cytosolic localization of Sfp1 mutants, an experimental set-up they established in a previous paper, with some contribution by PhosTag bandshift assays.
Major comments:
The paper is overall convincing. However, a little more attention to data presentation and possibly the addition of at least another technique (see below) would greatly strengthen the findings. Summarizing my major concerns: - The absence of statistics catches immediately the eye. I am sure that the shown differences are statistically significant (thanks to the number of analyzed cells), but reporting the result of some statistical test would help the reader in identify the relevant data in a plot. This is somehow necessary considering that sometimes in the text something is deemed to be "significant" or "not significant", and I felt that I really needed that when looking at the blot in Fig. 3D. - Related to the previous point: for every N/C distribution analysis, a number of analyzed cells is reported. By the way it is written, it seems that the replication relies solely by the cells in that specific population, i.e.: each cell is treated as a replicate. At least I could not find if that is not the case in the legends or in the methods. I wonder what the results would be (and their significance) if each replicate would be a new assay on another population. - The scale of x axes in N/C ratio plots. Besides not being consistent throughout the figures, it originates from 1, visually enhancing the differences. - Related to the previous point: it is evident from the plots that the N/C ratio is always positive, even in the most deficient of the analyzed mutants. This implies that a relevant fraction of Sfp1 is still nuclear. I thus wonder what the impact of these mutations would be on the actual function of Sfp1. For this reason, I feel that qPCR evaluation of transcripts of Sfp1 target genes is particularly needed. Since lack of Sfp1 is known to yield some of the smallest cells possible, it would also be cool to have an estimate of the size of mutants where Sfp1 is less nuclear. These analyses could confer phenotypical relevance to the data, but would also help in assessing a currently unexplored possibility, that phosphorylation events by PKA influence Sfp1 function besides its localization, i.e.: the still somehow nuclear fraction is not as functional as wt Sfp1 in promoting transcription.
Minor comments:
Experimental issues and suggestions on data presentation are reported in the major comments section, since I felt those were major issues.
Just a side remark: I found some typos here and there, and it would greatly help to report them if in the manuscript line numbers were included.
Significance
The finding that both PKA and TORC1 impinge on Sfp1, and therefore presumably on protein synthesis, is a valuable conceptual addition to the field of cell signaling. The audience potentially interested by the findings of the study include not only yeast cell biologists, but also computational biologists interested in modeling crucial cellular processes. One example is the regulation of cell size, where TORC1, PKA and Sfp1 are already know to play a role, but were potentially missing a crosstalk link.
As requested by Review Commons, I specify that my expertise is on TORC1/AMPK/PKA pathways, on their crosstalk and their regulation by metabolic intermediates.
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Reply to the reviewers
*Reviewer #1 (Evidence, reproducibility and clarity (Required)): ** Summary
The nucleus is recognised as a core component of mechanotransduction with many mechano-sensitive proteins shuttling between the nucleus and cytoplasm in response to mechanical stimuli. In this work, Granero-Moya et al characterise a live florescent marker of nucleocytoplasmic transport (NCT) and how it responds to a variety of cues. This work follows on from the authors previous study (Andreu 2022) where they examined the response of passive and active NCT to mechanical signalling using a series of artificial constructs. One of these constructs (here named Sencyt) showed a differential localisation depending on substrate stiffness, accumulating in the nucleus on stiffer substrates (which the authors previously showed was due to differences in mechano-sensitivity of passive versus facilitated NCT). Here the authors use Sencyt as a tool to probe how different cues affect NCT and thus nuclear force-sensing in two different cell lines (one epithelial, one mesenchymal). *
They have established a 3D image segmentation pipeline to measure both the nuclear/cytoplasmic ratio of Sencyt and 3D nuclear shape parameters. As a proof-of -principle, they show that hypoosmotic shock (which inflates the nucleus and would be expected to increase nuclear tension) and hyper-osmotic shock (which shrinks and deforms the nucleus) alter Sencyt nuclear-cytoplasmic ration as expected. They then show that inhibiting acto-myosin, which would be expected to block force transduction to the nucleus, reduces NCT, although interestingly this is without any changes to nuclear morphology. They then examine how cell density affects NCT and show that Sencyt localisation correlates only weakly with density but much more strongly with nuclear deformation (especially as measured by solidity). This is surprising considering that mechano-sensitive transcription factors such as YAP have been shown to exit the nucleus at high cell densities. Therefore, the authors directly compare Sencyt and Yap nucleo/cytoplasmic localisation and show that Sencyt behaves differently to YAP with YAP localisation correlating strongly with cell density. This reveals an added layer of complexity in YAP regulation beyond pure changes to NCT.* Major points *
The data presented throughout this work are high quality and rigorous. The controls used are appropriate (including the use of a freely diffusing mCherry to illustrate the specificity of the Sencyt probe in osmotic shock experiments - figure S2). Experiments are properly replicated and the statistical analysis is appropriate. The data are beautifully presented in figures and the manuscript is well written and very clear. Overall this is a high quality work.
We thank the reviewer for the positive assessment of the manuscript.
* The discussion is careful and the conclusions are supported by the data. My only small concern is that the authors place too much emphasis on how this work is in 'multicellular systems' as opposed to their previous work in single cells (for example "Here, we demonstrate that mechanics also plays a role in multicellular systems, in response to both hypo and hyper-osmotic shocks, and to cell contractility. L212). Cell density is only controlled in figures 3 and 4 and in some of the earlier experiments, cells look quite sparse (eg Figure 2). It's also debatable how far a monolayer of cancer cells, which lack contact inhibition of growth, is a multicellular system. Furthermore, the authors don't specifically look at cell/cell adhesion or observe major differences between the epithelial or mesenchymal lines. For this reason, the authors should tone down this discussion before publication. *
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We agree with the reviewer that properly assessing cell-cell adhesion is important in the context of the work. To this end, we have stained for E-cadherin in both cell lines. As expected and as described previously, the results confirm that MCF7 cells do have clear cadherin-mediated cell-cell adhesions, with a cadherin staining localized specifically in cell-cell junctions. Also as expected, C26 cells show much lower cadherin expression, without a clear pattern. Further confirming this difference, MCF7 cells show clearly distinct actin organizations in their apical and basal sides, whereas C26 cells do not. Thus, we believe that the two cell models do represent a reasonable assessment of epithelial versus mesenchymal phenotypes, in a multicellular context. The data are presented in new supplementary fig. 1, and discussed in page 3 of the manuscript (first paragraph). We have also included a paragraph in the discussion to comment on the differences between cell types (page 7, 2nd paragraph).
* Optional experimental suggestions: For me, the most compelling finding is that nuclear deformation has a greater correlation with NCT than cell density and that this is different from the behaviour of YAP. To cement the importance of nuclear deformation, the authors could induce deformation in single cells, for example by culture on very thin micropatterned lines and assess the localisation of Sencyt and YAP. It would also be interesting to assess the role of force transduction in this context or in different densities by removing actin, which affects NCT without inducing nuclear shape changes. These functional experiments would allow the authors to draw stronger conclusions about the role of nuclear shape and deformation but they aren't necessary for publication. *
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This is a very interesting suggestion. Following the reviewer's advice, we have now carried out experiments in which we have seeded cells on micropatterns of different sizes, and measured both sencyt and YAP ratios. In C26 cells, we have found as expected that increasing spreading leads to progressive nuclear deformation (as measured through nuclear solidity) and progressive increase in both sencyt and YAP ratios. Interestingly, cell spreading in MCF7 did not affect nuclear solidity, sencyt ratios, or YAP ratios. This further confirms the relationship between nuclear deformation and nucleocytoplasmic transport, and shows as well that different cell lines have different sensitivities. The lack of response of MCF7 cells is consistent with the lower sencyt response, and lower sencyt/nuclear shape correlation measured in fig. 4. It suggests that MCF7 cells may have mechanisms to shield the nucleus from deformation, something which we have reported in a different context (Kechagia et al., Nat. Mater. 2023). The new results are reported in new fig. 3, and supplementary fig. 8, and discussed in pages 5 (1st paragraph) and 6 (1st paragraph) of the manuscript results.
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Minor points
* - I'd like to see better examples of 3D reconstructions of nuclei (ie fig 1C but bigger) in different conditions. This is especially important in figure 3 where it would be helpful to see examples of nuclei with high or low solidity. The differences in oblateness are clear to see from the images in 3a and 3f but solidity could be better illustrated. *
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We have now added 3D reconstructions as requested, which illustrate the nuclear shape changes that take place. This is shown in figs. 1, 4 (which corresponds to figure 3 in the previous version of the manuscript), s3, and s7.
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- Where Sencyt index is plotted, it would be clearer to add labels to at least figure 1 which indicate whether it is more cytoplasmic or nuclear. *
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We have done this as requested in figure 1.
* Reviewer #1 (Significance (Required)): *
* In this work, Granero-Moya et al characterise a new tool for measuring NCT and show that it is mechanically regulated. Given the importance of NCT in mechano-transduction, this tool will be a great asset to the mechano-biology community and will likely be adopted by multiple groups in the future. The findings about the effects of cell density on NCT and differences from YAP are interesting but could be further fleshed out. This work is likely to be of greatest interest to a specialised audience working in the fields of mechano-biology and nuclear transport. *
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We thank the reviewer for the positive assessment.
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*Reviewer #2 (Evidence, reproducibility and clarity (Required)): *
* The study conducted by Granero Moya and colleagues describes the application of a synthetic protein which is observed to enter the nucleus in response to mechanical strains, rather than being influenced by cell density. However, the novelty of this work is minimal since the conceptual framework and the utilization of this identical or similar tool have been previously reported by the same team in earlier publications. *
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We respectfully disagree with the assessment of the reviewer. Please see below for a detailed response regarding novelty.
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*In their experiments, they employ this GFP-based sensor, referred to as Sencyt, in cells subjected to osmotic shocks. These shocks are highly stressful and impact a range of cellular processes, including stress response pathways MAPK and others; Osmoregulatory pathways; cell cycle regulations, autophagy and death pathway; ion channel regulations and others. The second findings are on cells treated with a combo of drugs affecting the actin cytoskeleton. The justification for using a combination of two specific drugs remains unclear, as the study does not adequately explain the rationale behind this choice. Additionally, there is a lack of information regarding the full range of targets these drugs affect. This raises questions about the comprehensiveness and applicability of the findings, as understanding the complete scope of the drugs' targets is crucial for interpreting the results within a minimal frame of physiological context. *
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The two drugs used are paranitroblebbistatin (a photostable version of blebbistatin) and Ck666. We apologize for not explaining in more detail the action of these drugs, both of which have been characterized and used extensively in the literature. Paranitroblebbistatin binds to myosin, preventing its ATPase activity and therefore impairing actomyosin contractility (https://doi.org/10.1002/anie.201403540). It acts on different myosin isoforms, including non-muscle myosin II, the main type of myosin responsible for actomyosin contractility in non-muscle cells. CK666 binds to and inhibits arp2/3, a protein responsible for nucleating branched actin (https://doi.org/10.1016/j.chembiol.2013.03.019). This impairs lamellipodial formation and therefore cell spreading (see for instance https://doi.org/10.1371/journal.pone.0100943).
The rationale for using both drugs in combination was explained in page 4 of the manuscript. In our previous work, we determined that myosin inhibition with blebbistatin is not sufficient to inhibit nuclear mechanotransduction. Indeed, in an epithelial context, we observed that due to reduced contractility, blebbistatin-treated epithelial cells in fact spread more on their substrate. This leads to more deformed (flattened) nuclei, leading to the counterintuitive result that YAP nuclear localization increases rather than decreases. If cell spreading is impaired by interfering with branched actin nucleation, then this spreading is prevented, and the combination of drugs leads to reduced nuclear deformation, and reduced YAP nuclear localization (see supplementary fig. 7 in Kechagia et al, Nat. Mater. 2023, https://doi.org/10.1038/s41563-023-01657-3). Similar results had been published previously by the group of Clare Waterman (https://doi.org/10.1074/jbc.M115.708313).
Thus, the combination of drugs was designed to ensure that we were impairing nuclear mechanotransduction. Of course, we agree with the reviewer that all perturbations have potential side effects. Osmotic shocks will affect a range of cellular processes (as mentioned in the discussion of the manuscript), and any drug treatment can potentially have off-target effects. However, the fact that two orthogonal perturbations with different potential side effects (osmotic shocks versus actomyosin-targeting drugs) lead to the same effects in sencyt strongly suggests that the effect is mediated by mechanics, and not other factors. To reinforce this, we have now added an additional mechanical manipulation: seeding cells on micropatterned islands of different sizes. As spreading increases, cells are known to increase actomyosin contractility, and nuclear deformation (https://doi.org/10.1529/biophysj.107.116863, https://doi.org/10.1073/pnas.0235407100, https://www.nature.com/articles/ncomms1668, https://doi.org/10.1073/pnas.1902035116). As expected, nuclear solidity, sencyt ratios, and Yap ratios all increased with cell spreading. Interestingly, this occurred only for C26 and not MCF7 cells, where no changes were measured in solidity, sencyt, or YAP. The lack of response of MCF7 cells is consistent with the lower sencyt response, and lower sencyt/nuclear shape correlation measured in fig. 4. It suggests that MCF7 cells may have mechanisms to shield the nucleus from deformation, something which we have reported in a different context (Kechagia et al., Nat. Mater. 2023).
The new results are shown in figs. 3 and s8. We have also expanded the explanation of drug treatments in page 4 (3rd paragraph).
* The novelty is on the specificity of this synthetic fusion protein for these manipulations and not on cell density. Yet, the reasons behind this selective response remain unexplained, potentially attributable to the unique characteristics or sensitivity thresholds of their synthetic probe. As comparison, YAP localization and this is sensitive to both inputs, but this is also already published (fig4). The focus is anyway on Sencyt for which they offer simple observations and quantifications. *
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The main novelty of the work lies in the characterization of the role of nucleocytoplasmic transport in mechanotransduction, in the context of multicellular systems. We and others had shown that nucleocytoplasmic transport responds to mechanical force in the context of single cells (see for instance Andreu et al. 2022 from our group, but also https://doi.org/10.1126/science.abd9776 from the Martin Beck group). However, to what extent this applies to multicellular systems was unknown. It is true that in multicellular systems, the response of YAP and other mechanosensitive transcription factors has been characterized (such as in our Elosegui-Artola 2017 paper, mostly done at the single cell level but including one figure panel on epithelial cell monolayers). The reviewer argues here and in the consultation comments with other reviewers (see below) that this demonstrated the role of nucleocytoplasmic transport in multicellular systems. However, we respectfully disagree. As also noted by reviewer 3 in the consultation, the response of YAP, and of any transcription factor, may include effects on nucleocytoplasmic transport, but will also likely include effects caused by the complex biochemical signalling pathways that regulate them. Disentangling such effects requires a sensor that only responds to nucleocytoplasmic transport, and this is precisely what Sencyt provides.
The reviewer also states that our manuscript does not explain why sencyt responds to mechanics and not cell density. We disagree: sencyt responds to mechanics for the reasons explained in our previous work (Andreu et al., Nat. Cell Biol. 2022), and there is no reason to expect a specific response to cell density. In this regard, we don't think there are any sensitivity thresholds to detect cell density, as the probe is not designed to sense this parameter in the first place. The fact that YAP responds to both mechanics and cell density shows that the response to density cannot be merely explained by mechanics, and is rather due to signalling through other means. Of course, we agree that we do not explain the mechanism by which YAP senses cell density, but we think this lies clearly out of the scope of our manuscript.
In terms of novelty, our work also characterizes a tool to assess nucleocytoplasmic transport live in cells. We agree with the reviewer that the specific construct had been reported in our previous paper, but it had not been characterized in detail. This is done here, enabling its use by the community as a tool to measure nucleocytoplasmic transport in any context, be it related to mechanics or not.
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When reviewing the figures presented, I find it challenging to detected marked differences, despite their quantitative data suggesting otherwise.
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We assume here that the reviewer refers to differences in sencyt nuclear localization, that is, the sencyt index. We have now checked the example images showing changes in sencyt index, in figures 1 and 2. In figure 1, the example cells under hypo-osmotic shocks increase their sencyt index from 1.2 to 1.45 (C26). In figure 1, the example cells under hyper-osmotic shocks decrease their sencyt index from 0.9 to 0.3 (MCF7) and from 1.4 to 0.5 (C26). In figure 2, the example cells increase their sencyt index upon drug washout from 0.2 to 1.4 (MCF7) and from 0 to 0.9 (C26). Of course, these individual values don't reflect exactly average values, but they do reflect the reported average trends and their magnitudes faithfully. Here we note that even though sencyt changes with the different treatments, it is always more nuclear than cytosolic (sencyt index >0, as it has an NLS). Thus, to the naked eye, sencyt always seems to show a "bright" nucleus, and it is hard to intuitively see changes in its localization. Further, we also note that osmotic shocks lead to overall changes in fluorescence levels due to volume changes (as GFP molecules get diluted or concentrated in hypo or hyper osmotic shocks, respectively). This does not affect ratiometric quantifications as assessed with our mcherry control, but means that changes in ratios are hard to see by eye. To help in this visualization, we have now changed the images from green to grayscale, which is better perceived by the human eye. We have also specified the issue of fluorescence intensity changes in the legend of the figure.
In addition to this, we have seen that there is indeed a case in which examples were not following average trends. In the case of hypo-osmotic shocks in figure 1, example MCF7 cells were barely changing their sencyt index with treatment. We apologize for choosing this non-representative image for the figure, we have now changed the figure to show more representative cells.
- Furthermore, the study attempts to correlate the behavior of Sencyt with the nuclear geometric parameter of solidity, a connection that seems to lack a clear basis in cell biology and could potentially lead to misconceptions. *
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Mechanical effects on nucleocytoplasmic transport are mediated by mechanical tension application to nuclear pores, which are embedded in the nuclear membrane (nuclear envelope). Whereas nuclear envelope tension is very challenging to measure directly, it can be indirectly related to nuclear shape. Indeed, a tense membrane will tend to even out membrane irregularities and appear rounded, whereas a membrane under low tension will tend to show wrinkles. Nuclear solidity is a geometric parameter that compares actual nuclear volume to the volume of the convex hull (intuitively, the volume of the smallest wrinkle-free object containing all of the nucleus). Thus, it is the geometric parameter that best reflects the presence of wrinkles, folds or irregularities, and as such the one that should best correlate to membrane tension. Of course, this correlation is not perfect, and there could be many situations in which changes in membrane tension may not directly affect nuclear solidity. But we do believe that solidity is the geometrical parameter that should best reflect membrane tension, and this is why we focus on it. Consistent with our hypothesis, solidity is the geometrical parameter that best correlates with sencyt. To further clarify this, we now explain this rationale in detail in page 4 of the manuscript (1st paragraph).
* Reviewer #2 (Significance (Required)): *
* In sum, I think the MS is of interest for a very specialistic audience. There are no clear interpretations. The work is done in one or two cellular model systems in vitro; and the general significance of these observations is of very limited impact and no novelty. *
We strongly disagree. The study is done on two cellular models, one with epithelial and the other with mesenchymal phenotype, and thus highly relevant for multicellular systems. Following suggestions by reviewers 1 and 2, we have now characterized the epithelial/mesenchymal behaviour of the cell types in detail (see supp. fig. 1). The results are novel in that they demonstrate the role of nucleocytoplasmic transport in multicellular systems, something which as argued above had not been done before. The difference with YAP, and the disentanglement between transport and signalling, is also novel. Finally, we believe the manuscript will be impactful because of this novelty, but also because of the availability of sencyt as a tool for the community. In fact, since placing this manuscript in biorxiv, we have received many requests (directly and through addgene) to share sencyt, which is currently being used in several labs across the world.
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*Reviewer #3 (Evidence, reproducibility and clarity (Required)): *
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In this very well-written manuscript, Pere Roca-Cusachs and colleagues investigated the response of nucleocytoplasmic transport (NCT) to mechanical stress and tested whether this response is similar in epithelial and mesenchymal cells using a combination of quantitative approaches. This study builds upon their earlier findings, which elegantly demonstrated that NCT is sensitive to mechanical forces transmitted to the nuclear membrane. Using a similar approach to their recent work, they quantitatively analyzed NCT and compared the two cell types using various treatments that impact nuclear membrane tension. The study is straightforward and experimentally sound, with an adequate number of replicates and independent experiments. While one might consider the limitations given their previous work, none have demonstrated that NCT is mechanosensitive in epithelial cells. Additionally, they provide a simple approach to measure NCT, which should be of interest in the field. However, it is unclear how the authors defined the epithelial phenotype in this work and whether they solely based this characterization on the tissue/cell's origin. Epithelia can be defined ultrastructurally with reference to their apico-basal polarity and specific cell-cell junctions (Alberts et al., 1994; Davies and Garrods, 1997). Changing cell density should affect cell/cell adhesion, but the authors provide no evidence that the cells tested in the study are attached to their neighbors on all sides and form an epithelium. While I recognize that the objective of this study is not to mimic the in vivo behavior of epithelial tissue, the authors should at least ensure that cells form a monolayer by quantitatively assessing cell-cell junctions (or they should adjust their conclusions adequately). This control is specifically important for Figure 3 and 4, whose objective is to test the impact of cell/cell contacts. But it would also be important to provide this essential control for Figure 1 and 2, as it is unclear from the images provided if MCF7 cells are forming an epithelium (and form cell/cell junctions).
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We thank the reviewer for the positive assessment of our work. We fully agree with the reviewer that properly assessing cell-cell adhesion is important in the context of the work. To this end, we have stained for E-cadherin in both cell lines. As expected and as described previously, the results confirm that MCF7 cells do have clear cadherin-mediated cell-cell adhesions, with a cadherin staining localized specifically in cell-cell junctions. Also as expected, C26 cells show much lower cadherin expression, without a clear pattern. Further confirming this difference, MCF7 cells (but not C26 cells) show a clear apico-basal polarization, with distinct actin organizations in their apical and basal sides. Thus, we believe that the two cell models do represent a reasonable assessment of epithelial versus mesenchymal phenotypes, in a multicellular context. The data are presented in new supplementary fig. 1. We have also included a paragraph in the discussion to comment on the differences between cell types (page 7, 2nd paragraph).
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Reviewer #3 (Significance (Required)): *
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The mechanosensitivity of NCT is an important question central to many aspects of cell biology. One might consider the impact of the proposed work limited, given their previous research. However, none have demonstrated that NCT is mechanosensitive in epithelial cells, making it a crucial question that needs to be addressed. Additionally, they provide a simple approach to measure NCT, which should be of interest to a broad audience.
We thank again the reviewer for this positive assessment.
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*Referees cross-commenting *
* Here comments from all 3 reviewers are reported *
* Reviewer 1: *
* I disagree with R2's comment that there is 'no novelty' here. Although this work is going to be of greater interest to a specialised rather than general audience, it characterises in depth a simple tool to measure NCT which will be useful for mechanobiology field. Also, using 'two cellular model systems in vitro' is very standard in the field when assessing subcellular processes like NCT. Using this approach in vivo would be very interesting but challenging and would be an entirely different study . *
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*I agree with R2's comments that the authors should better justify their combination of two actin inhibitors and R3s point on better assessing cell/cell junctions. *
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We thank the reviewer for these comments. Both issues have been addressed, as described in the response to reviewers above.
* Reviewer 2 *
* About Reviewer 3's comments, I believe it's a stretch to highlight the strength and novelty based on "NCT's mechanosensitivity in epithelial cells has not been demonstrated,". There are thousands of papers on the Hippo pathway, that is known to be mechanosensitive, on the regulation of YAP, that enters in the nucleus in Hippo inhibited conditions and exits to the cytoplasm in Hippo induced cells, including downstream of mechanical signals. The phenomenon of nuclear-cytoplasmic shuttling being a common event from neurons to endothelial and multiple types of epithelial, immune, and fibroblast cells is already established through NCT of this and other endogenous proteins. This is simply an accepted fact. Then, The Nature cell Biology 2022 was offering a very general claim. No warning that conclusions could have been cell type specific. In the Artola 2017 Cell paper they also showed NCT in mammary epithelial cells. We should definitively conclude that NCT's mechanosensitivity in epithelial cells has been well demonstrated. *
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We disagree with this assessment, for the same reasons also exposed by reviewer 3 below. Previous work on YAP and other transcription factors cannot be seen as a demonstration of the role of nucleocytoplasmic transport per se. The localization of any transcription factor is highly regulated by complex signalling pathways, and can be affected by many factors. One of them is nucleocytoplasmic transport, but signalling events (for instance through phosphorylation) could change localization by promoting binding to cytosolic or nuclear binding partners, by promoting protein degradation, by masking nuclear localization signals, and others. To isolate the role of nucleocytoplasmic transport, a probe sensitive only to this factor should be designed. This is exactly what sencyt provides. In fact, this has allowed us to answer an important open question: is the sensitivity of YAP to cell density mediated by mechanics and nucleocytoplasmic transport, or is it mediated by some other factor? Our results suggest that some other factor, likely mediated by the Hippo pathway and not necessarily mechanotransduction, explains this sensing of cell density. This is a novel finding, which was not provided in either our Elosegui-Artola 2017 paper or our Andreu 2022 paper.
* About Reviewer 1: I find it challenging to grasp the point made in the comment. On novelty, in their previous study in NBC 2022 Syncet was already shown to undergo NCT. The reviewer states that the study presents "a simple tool to measure nuclear-cytoplasmic transport (NCT) beneficial for the mechanobiology field, and evidence that this demonstrates a novel layer of regulation in hippo signaling (also because this is observational and not a mechanistic study). The tool in question is far from simple. Its application requires transfection into cell cultures, conducting live imaging, etc. If one aims to measure NCT of endogenous proteins, straightforward immunofluorescence or live imaging of endogenous proteins (like GFP-tagged YAP, Twist, Smads, etc.) using the same experimental setup should suffice to demonstrate relevance, without necessitating any additional experiments. What then, is the unique benefit of this proposed tool? Given it's an artificial construct combining NLS-GFP with a bacterial protein, questions arise about the effects of the forced nuclear localization signal (NLS) or the bacterial component. It is an empirical artificial construct and there is no mechanism to explain its behavior.The comparison of Syncet with YAP seems to me questionable and of limited utility. *
As also noted by reviewer 3 below, the use of genetically encoded fluorescent sensors that require transfection is by now absolutely standard in biology, and cannot be considered to be "far from simple". And as stated above, imaging of endogenous transcription factors (which also requires transfection if it is done live) does not isolate the role of nucleocytoplasmic transport. We also disagree that "there is no mechanism to explain its behaviour". Sencyt was developed in our previous andreu et al 2022 paper, where the mechanism is explained in detail.
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*It's unsurprising that an artificial construct only mirrors some aspects of what is considered a genuine mechanosensitive protein. The utility of a synthetic tool lies in its ability to replicate actual phenomena, not in what it fails to do. In comparison to their NBC 2022 study, this manuscript focuses on what their reporter fails to detect. *
We disagree that a synthetic tool is only useful if it replicates the behaviour of endogenous proteins. A synthetic tool, precisely due to its engineered, artificial nature, can be made to respond only to specific factors (in this case, nucleocytoplasmic transport). This can then be used to disentangle the role of such specific factors, as done here.
The osmotic shock was the assay in their 2017 Cell paper. Here they demonstrate that a combination of Blebbistatin+CK (an unclear choice of drugs) is ineffective, as is cell density. Are there other specific peculiarities associated with this construct?
Here, we note that our osmotic shock experiments in our 2017 paper were done for YAP (not nucleocytoplasmic transport in general). Regarding the choice of drugs, please refer to our answer to the reviewer comments above for a full explanation. Also, we want to clarify that this combination is not ineffective, as it leads to clear changes in sencyt. * *
* My other concern is on the minor quantitative changes reported, which seem inconsistent with the provided representative images, where significant differences are difficult to appreciate. For instance, the claim that the transfected sensor differs from an endogenous NCT protein, YAP, after cell density treatment, is hard to detect in their images. In Figure 4, comparing YAP and Syncet in C26 cells, YAP appears uniformly nuclear at high cell density, potentially more nuclear than the synthetic sensor, which is not coherent with their claim.*
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Regarding the concern of the minor changes seen in images, please refer to our full response to the reviewer comments above. Regarding the comparison between sencyt and YAP, we want to clarify that in our manuscript we do not compare the absolute values of nuclear localization between YAP and sencyt. As the reviewer notes, these are two different proteins, so which one is more nuclear does not really provide useful information. So whether YAP is more or less nuclear than sencyt is unrelated to (not incoherent with) our claim. What we state in figure 4 is that YAP responds to cell density, whereas sencyt does not. This is clear from the quantifications and also from the images.
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- From the Hippo perspective, there is really an unusual amount of nuclear YAP left in their cells. This should be almost completely cytoplasmic from prior contact inhibition studies in the Hippo field. Syncet could be simply less sensitive than YAP in these borderline conditions. Although there's a more noticeable cytoplasmic noise in dense cells with YAP compared to Syncet, this could be attributed to several factors, including differences in protein degradation rates, which I suspect to be quicker for a synthetic protein. From a technical perspective it is complex to get strong conclusions after comparing something so unrelated with each other. One is a live GFP detection and the other is a staining by immunofluorescence. the nature of the background is also different and so conclusions from comparisons between unrelated systems is not justified. *
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In conditions of high density, average YAP ratios are close to one (zero in logarithmic scale, as reported in the figures) for MCF10A cells, so there is no nuclear localization. This is similar to what we and others have previously reported in similar conditions (Elosegui Artola et al 2017, Kechagia et al. 2023, for example). In C26 cells, YAP levels at high density are a bit higher. This is likely due to their mesenchymal nature, and therefore diminished cell-cell contact inhibition (as assessed in detail in this revision). This in fact further suggests that the response of YAP to cell-cell contacts is different from a mere mechanical factor, supporting our hypothesis. Regarding the issue of noise, background noise is removed from quantifications, and potential noise coming from non-specificities or autofluorescence is also cancelled by the fact that we compute fluorescence ratios between nucleus and cytoplasm (and not absolute values). Thus, we don't think noise is an issue. Further, we note again that we do not directly compare values between sencyt and yap.
* This suggests caution on what is heralded as the main claim here put forward. *
* Reviewer 1: *
*I do have some sympathy with R2s comments in the consultation. I agree that showing that NCT is mechanosensitive in an epithelium is not new. I also agree that sometimes it is difficult to see the quantitative differences by eye. This second point could be addressed by including more details of the segmentation and analysis in the supplemental material (along with some example images). *
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We thank the reviewer for the suggestions. Regarding the novelty, please see above for a detailed discussion, and also the comments of reviewer 3 below (previous work studied not NCT but transcription factors, affected by many parameters). Regarding quantitative differences, we have now addressed this issue by showing images in grayscale rather than green, and also by replacing one example cell in figure 1 which indeed did not reflect the average measured trends. We now also show examples of 3D rendered images of the nuclei in different conditions. We have also gone through the methods and clarified in detail how ratios are calculated, the segmentation procedure is also explained in detail.
* Regarding novelty, I would be interested to know if R2 thinks that there are experiments that the authors could do to improve the work. Or do they need to simply tone down their claims? It's perfectly acceptable to publish a well characterised tool with a series of observations and it's beneficial to the community to do so.*
- Reviewer 3 *
* Thanks to Reviewers #1 and #2 for using this consultation option; I truly appreciate their feedback on my comments and find it extremely valuable. I agree with Reviewer #1 that the method proposed here is relatively simple. Transfecting cells and conducting live fluorescent imaging can hardly be considered difficult. I believe the construct used/designed by the authors is the main advantage as it provides a specific way to quantitatively assess NCT and not limit the analysis to a single nuclear protein (such as YAP). Reviewer #2 suggests using immunofluorescence staining of YAP or live imaging of fusion fluorescent protein (following transfection) to analyze NCT, but this approach would yield a readout not only based on NCT but also on the many other interacting partners/mechanisms that regulate the candidate localization, resulting in an unspecific readout (and similar transfection/live imaging set-up). *
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We thank the reviewer for this comment, we fully agree and have elaborated on this in our responses above.
* Regarding the impact of the study, I agree that it is certainly not as impactful as previous publications on this topic. Although I find reviewer#2 argument on Yap irrelevant, as YAP is not the main focus of this paper. Some experiments have been done with cells of epithelial origin, but NCT mechanosensitivity has not been clearly tested in epithelial monolayer, which is the main claim of the proposed study here. The 2017 Cell paper focused on YAP transport into the nucleus (and not NCT in general) and they showed a correlation between YAP nuclear localization and traction force in MCF10A. I am not sure if one would say that "NCT mechanosensitivity has been well demonstrated in epithelial cells" based on this single panel. The impact of the proposed study is certainly not outstanding but offering a thorough analysis in epithelial cells (as monolayers and not as individual cells) and presenting a well-defined experimental approach should be of interest in the field. I agree with comments from reviewer#2 that some reported effects in graph are unclear on main images. More experimental details should hopefully clarify this aspect.*
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We fully agree with the reviewer. Regarding quantitative differences, we have now addressed this issue by showing images in grayscale rather than green, and also by replacing one example cell in figure 1 which indeed did not reflect the average measured trends.
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Referee #3
Evidence, reproducibility and clarity
In this very well-written manuscript, Pere Rochas-Cusachs and colleagues investigated the response of nucleocytoplasmic transport (NCT) to mechanical stress and tested whether this response is similar in epithelial and mesenchymal cells using a combination of quantitative approaches. This study builds upon their earlier findings, which elegantly demonstrated that NCT is sensitive to mechanical forces transmitted to the nuclear membrane. Using a similar approach to their recent work, they quantitatively analyzed NCT and compared the two cell types using various treatments that impact nuclear membrane tension. The study is straightforward and experimentally sound, with an adequate number of replicates and independent experiments. While one might consider the limitations given their previous work, none have demonstrated that NCT is mechanosensitive in epithelial cells. Additionally, they provide a simple approach to measure NCT, which should be of interest in the field. However, it is unclear how the authors defined the epithelial phenotype in this work and whether they solely based this characterization on the tissue/cell's origin. Epithelia can be defined ultrastructurally with reference to their apico-basal polarity and specific cell-cell junctions (Alberts et al., 1994; Davies and Garrods, 1997). Changing cell density should affect cell/cell adhesion, but the authors provide no evidence that the cells tested in the study are attached to their neighbors on all sides and form an epithelium. While I recognize that the objective of this study is not to mimic the in vivo behavior of epithelial tissue, the authors should at least ensure that cells form a monolayer by quantitatively assessing cell-cell junctions (or they should adjust their conclusions adequately). This control is specifically important for Figure 3 and 4, whose objective is to test the impact of cell/cell contacts. But it would also be important to provide this essential control for Figure 1 and 2, as it is unclear from the images provided if MCF7 cells are forming an epithelium (and form cell/cell junctions).
Significance
The mechanosensitivity of NCT is an important question central to many aspects of cell biology. One might consider the impact of the proposed work limited, given their previous research. However, none have demonstrated that NCT is mechanosensitive in epithelial cells, making it a crucial question that needs to be addressed. Additionally, they provide a simple approach to measure NCT, which should be of interest to a broad audience.
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Referee #2
Evidence, reproducibility and clarity
The study conducted by Granero Moya and colleagues describes the application of a synthetic protein which is observed to enter the nucleus in response to mechanical strains, rather than being influenced by cell density. However, the novelty of this work is minimal since the conceptual framework and the utilization of this identical or similar tool have been previously reported by the same team in earlier publications. In their experiments, they employ this GFP-based sensor, referred to as Sencyt, in cells subjected to osmotic shocks. These shocks are highly stressful and impact a range of cellular processes, including stress response pathways MAPK and others; Osmoregulatory pathways; cell cycle regulations, autophagy and death pathway; ion channel regulations and others. The second findings are on cells treated with a combo of drugs affecting the actin cytoskeleton. The justification for using a combination of two specific drugs remains unclear, as the study does not adequately explain the rationale behind this choice. Additionally, there is a lack of information regarding the full range of targets these drugs affect. This raises questions about the comprehensiveness and applicability of the findings, as understanding the complete scope of the drugs' targets is crucial for interpreting the results within a minimal frame of physiological context.
The novelty is on the specificity of this synthetic fusion protein for these manipulations and not on cell density. Yet, the reasons behind this selective response remain unexplained, potentially attributable to the unique characteristics or sensitivity thresholds of their synthetic probe. As comparison, YAP localization and this is sensitive to both inputs, but this is also already published (fig4). The focus is anyway on Sencyt for which they offer simple observations and quantifications. When reviewing the figures presented, I find it challenging to detected marked differences, despite their quantitative data suggesting otherwise. Furthermore, the study attempts to correlate the behavior of Sencyt with the nuclear geometric parameter of solidity, a connection that seems to lack a clear basis in cell biology and could potentially lead to misconceptions.
Significance
In sum, I think the MS is of interest for a very specialistic audience. There are no clear interpretations. The work is done in one or two cellular model systems in vitro; and the general significance of these observations is of very limited impact and no novelty.
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Referee #1
Evidence, reproducibility and clarity
Summary
The nucleus is recognised as a core component of mechanotransduction with many mechano-sensitive proteins shuttling between the nucleus and cytoplasm in response to mechanical stimuli. In this work, Granero-Moya et al characterise a live florescent marker of nucleocytoplasmic transport (NCT) and how it responds to a variety of cues. This work follows on from the authors previous study (Andreu 2022) where they examined the response of passive and active NCT to mechanical signalling using a series of artificial constructs. One of these constructs (here named Sencyt) showed a differential localisation depending on substrate stiffness, accumulating in the nucleus on stiffer substrates (which the authors previously showed was due to differences in mechano-sensitivity of passive versus facilitated NCT). Here the authors use Sencyt as a tool to probe how different cues affect NCT and thus nuclear force-sensing in two different cell lines (one epithelial, one mesenchymal).
They have established a 3D image segmentation pipeline to measure both the nuclear/cytoplasmic ration of Sencyt and 3D nuclear shape parameters. As a proof-of -principle, they show that hypoosmotic shock (which inflates the nucleus and would be expected to increase nuclear tension) and hyper-osmotic shock (which shrinks and deforms the nucleus) alter Sencyt nuclear-cytoplasmic ration as expected. They then show that inhibiting acto-myosin, which would be expected to block force transduction to the nucleus, reduces NCT, although interestingly this is without any changes to nuclear morphology. They then examine how cell density affects NCT and show that Sencyt localisation correlates only weakly with density but much more strongly with nuclear deformation (especially as measured by solidity). This is surprising considering that mechano-sensitive transcription factors such as YAP have been shown to exit the nucleus at high cell densities. Therefore, the authors directly compare Sencyt and Yap nucleo/cytoplasmic localisation and show that Sencyt behaves differently to YAP with YAP localisation correlating strongly with cell density. This reveals an added layer of complexity in YAP regulation beyond pure changes to NCT.
Major points
The data presented throughout this work are high quality and rigorous. The controls used are appropriate (including the use of a freely diffusing mCherry to illustrate the specificity of the Sencyt probe in osmotic shock experiments - figure S2). Experiments are properly replicated and the statistical analysis is appropriate. The data are beautifully presented in figures and the manuscript is well written and very clear. Overall this is a high quality work.
The discussion is careful and the conclusions are supported by the data. My only small concern is that the authors place too much emphasis on how this work is in 'multicellular systems' as opposed to their previous work in single cells (for example "Here, we demonstrate that mechanics also plays a role in multicellular systems, in response to both hypo and hyper-osmotic shocks, and to cell contractility. L212). Cell density is only controlled in figures 3 and 4 and in some of the earlier experiments, cells look quite sparse (eg Figure 2). It's also debatable how far a monolayer of cancer cells, which lack contact inhibition of growth, is a multicellular system. Furthermore, the authors don't specifically look at cell/cell adhesion or observe major differences between the epithelial or mesenchymal lines. For this reason, the authors should tone down this discussion before publication.
Optional experimental suggestions: For me, the most compelling finding is that nuclear deformation has a greater correlation with NCT than cell density and that this is different from the behaviour of YAP. To cement the importance of nuclear deformation, the authors could induce deformation in single cells, for example by culture on very thin micropatterned lines and assess the localisation of Sencyt and YAP. It would also be interesting to assess the role of force transduction in this context or in different densities by removing actin, which affects NCT without inducing nuclear shape changes. These functional experiments would allow the authors to draw stronger conclusions about the role of nuclear shape and deformation but they aren't necessary for publication.
Minor points
- I'd like to see better examples of 3D reconstructions of nuclei (ie fig 1C but bigger) in different conditions. This is especially important in figure 3 where it would be helpful to see examples of nuclei with high or low solidity. The differences in oblateness are clear to see from the images in 3a and 3f but solidity could be better illustrated.
- Where Sencyt index is plotted, it would be clearer to add labels to at least figure 1 indicting which indicate whether it is more cytoplasmic or nuclear.
Referees cross-commenting
Here comments from all 3 reviewers are reported
Reviewer 1:
I disagree with R2's comment that there is 'no novelty' here. Although this work is going to be of greater interest to a specialised rather than general audience, it characterises in depth a simple tool to measure NCT which will be useful for mechanobiology field. Also, using 'two cellular model systems in vitro' is very standard in the field when assessing subcellular processes like NCT. Using this approach in vivo would be very interesting but challenging and would be an entirely different study .
I agree with R2's comments that the authors should better justify their combination of two actin inhibitors and R3s point on better assessing cell/cell junctions.
Reviewer 2
About Reviewer 3's comments, I believe it's a stretch to highlight the strength and novelty based on "NCT's mechanosensitivity in epithelial cells has not been demonstrated,". There are thousands of papers on the Hippo pathway, that is known to be mechanosensitive, on the regulation of YAP, that enters in the nucleus in Hippo inhibited conditions and exits to the cytoplasm in Hippo induced cells, including downstream of mechanical signals. The phenomenon of nuclear-cytoplasmic shuttling being a common event from neurons to endothelial and multiple types of epithelial, immune, and fibroblast cells is already established through NCT of this and other endogenous proteins. This is simply an accepted fact. Then, The Nature cell Biology 2022 was offering a very general claim. No warning that conclusions could have been cell type specific. In the Artola 2017 Cell paper they also showed NCT in mammary epithelial cells. We should definitively conclude that NCT's mechanosensitivity in epithelial cells has been well demonstrated.
About Reviewer 1: I find it challenging to grasp the point made in the comment. On novelty, in their previous study in NBC 2022 Syncet was already shown to undergo NCT. The reviewer states that the study presents "a simple tool to measure nuclear-cytoplasmic transport (NCT) beneficial for the mechanobiology field, and evidence that this demonstrates a novel layer of regulation in hippo signaling (also because this is observational and not a mechanistic study). The tool in question is far from simple. Its application requires transfection into cell cultures, conducting live imaging, etc. If one aims to measure NCT of endogenous proteins, straightforward immunofluorescence or live imaging of endogenous proteins (like GFP-tagged YAP, Twist, Smads, etc.) using the same experimental setup should suffice to demonstrate relevance, without necessitating any additional experiments. What then, is the unique benefit of this proposed tool? Given it's an artificial construct combining NLS-GFP with a bacterial protein, questions arise about the effects of the forced nuclear localization signal (NLS) or the bacterial component. It is an empirical artificial construct and there is no mechanism to explain its behavior. The comparison of Syncet with YAP seems to me questionable and of limited utility. It's unsurprising that an artificial construct only mirrors some aspects of what is considered a genuine mechanosensitive protein. The utility of a synthetic tool lies in its ability to replicate actual phenomena, not in what it fails to do. In comparison to their NBC 2022 study, this manuscript focuses on what their reporter fails to detect. The osmotic shock was the assay in their 2017 Cell paper. Here they demonstrate that a combination of Blebbistatin+CK (an unclear choice of drugs) is ineffective, as is cell density. Are there other specific peculiarities associated with this construct?
My other concern is on the minor quantitative changes reported, which seem inconsistent with the provided representative images, where significant differences are difficult to appreciate. For instance, the claim that the transfected sensor differs from an endogenous NCT protein, YAP, after cell density treatment, is hard to detect in their images. In Figure 4, comparing YAP and Syncet in C26 cells, YAP appears uniformly nuclear at high cell density, potentially more nuclear than the synthetic sensor, which is not coherent with their claim. From the Hippo perspective, there is really an unusual amount of nuclear YAP left in their cells. This should be almost completely cytoplasmic from prior contact inhibition studies in the Hippo field. Syncet could be simply less sensitive than YAP in these borderline conditions. Although there's a more noticeable cytoplasmic noise in dense cells with YAP compared to Syncet, this could be attributed to several factors, including differences in protein degradation rates, which I suspect to be quicker for a synthetic protein. From a technical perspective it is complex to get strong conclusions after comparing something so unrelated with each other. One is a live GFP detection and the other is a staining by immunofluorescence. the nature of the background is also different and so conclusions from comparisons between unrelated systems is not justified. This suggests caution on what is heralded as the main claim here put forward.
Reviewer 1: I do have some sympathy with R2s comments in the consultation. I agree that showing that NCT is mechanosensitive in an epithelium is not new. I also agree that sometimes it is difficult to see the quantitative differences by eye. This second point could be addressed by including more details of the segmentation and analysis in the supplemental material (along with some example images).
Regarding novelty, I would be interested to know if R2 thinks that there are experiments that the authors could do to improve the work. Or do they need to simply tone down their claims? It's perfectly acceptable to publish a well characterised tool with a series of observations and it's beneficial to the community to do so.
Reviewer 3
Thanks to Reviewers #1 and #2 for using this consultation option; I truly appreciate their feedback on my comments and find it extremely valuable. I agree with Reviewer #1 that the method proposed here is relatively simple. Transfecting cells and conducting live fluorescent imaging can hardly be considered difficult. I believe the construct used/designed by the authors is the main advantage as it provides a specific way to quantitatively assess NCT and not limit the analysis to a single nuclear protein (such as YAP). Reviewer #2 suggests using immunofluorescence staining of YAP or live imaging of fusion fluorescent protein (following transfection) to analyze NCT, but this approach would yield a readout not only based on NCT but also on the many other interacting partners/mechanisms that regulate the candidate localization, resulting in an unspecific readout (and similar transfection/live imaging set-up). Regarding the impact of the study, I agree that it is certainly not as impactful as previous publications on this topic. Although I find reviewer#2 argument on Yap irrelevant, as YAP is not the main focus of this paper. Some experiments have been done with cells of epithelial origin, but NCT mechanosensitivity has not been clearly tested in epithelial monolayer, which is the main claim of the proposed study here. The 2017 Cell paper focused on YAP transport into the nucleus (and not NCT in general) and they showed a correlation between YAP nuclear localization and traction force in MCF10A. I am not sure if one would say that "NCT mechanosensitivity has been well demonstrated in epithelial cells" based on this single panel. The impact of the proposed study is certainly not outstanding but offering a thorough analysis in epithelial cells (as monolayers and not as individual cells) and presenting a well-defined experimental approach should be of interest in the field. I agree with comments from reviewer#2 that some reported effects in graph are unclear on main images. More experimental details should hopefully clarify this aspect.
Significance
In this work, Granero-Moya et al characterise a new tool for measuring NCT and show that it is mechanically regulated. Given the importance of NCT in mechano-transduction, this tool will be a great asset to the mechano-biology community and will likely be adopted by multiple groups in the future. The findings about the effects of cell density on NCT and differences from YAP are interesting but could be further fleshed out. This work is likely to be of greatest interest to a specialised audience working in the fields of mechano-biology and nuclear transport.
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Reply to the reviewers
Reply to reviewers
First, we would like to extend our gratitude to all reviewers for their supportive and enthusiastic feedback, which acknowledges our study as an interesting, well-executed, and well-documented contribution to the field. We are also pleased that the novelty and significance of our work have been recognized and appreciated.
As highlighted by reviewers 2, 3, and 4, our research represents a substantial advancement in understanding the mechanisms that coordinate the development of different cell types. Our findings have broader implications for developmental biology. We also thank the reviewers for their valuable insights, which have significantly improved the overall readability of our manuscript. We have carefully considered all minor corrections and text modifications they had suggested and made amendments accordingly.
The reviewers proposed several complementary experiments to enhance and clarify our points. We have conducted most of these experiments, with one exception (detailed below), and incorporated the corresponding results into this revised version of the manuscript.
Additionally, reviewers agreed that higher resolution images depicting the interactions between tendon and myoblast membranes would strengthen our manuscript. In response, we are pleased to present new high-resolution images of Ama::EGFP localization with respect to tendon and muscle cells, obtained using Zeiss Airyscan technology. We also provide new images using newly generated flies that allow simultaneous observation of both myoblast and tendon membranes.
We believe these modifications substantially enhance the quality and interest of our results, as already highlighted by the reviewers.
__Referees cross-commenting: __
All reviewers agreed* that "this is an interesting study that is well done and well documented. I agree with reviewer 1 that the study would further benefit from better imaging of the cellular extensions of tendons and myoblasts to see how both cell types interact." *
Reply: We agree with this point. To address it, we analyzed leg discs from Sr-Gal4>UAS-myrGFP line (labelling tendon membranes) crossed with a newly generated line R32D05::CD4TdTomato (myoblast specific expression of membrane tagged Tomato protein). Using confocal Zeiss Airy Scan technology, we generated high resolution images for which both tendon cell extensions and myoblast membranes are simultaneously visualized. These images are included in the new Figure 2 (O, P and O', P'). To be noticed: as we also provide new high resolution images of Ama::EGFP and Nrt localizations (Fig. 2 M, N and M', N'), we removed images of zoom in of 5h APF leg disc.
REVIEWER 1
Moucaud et al carried out single cell sequencing on myoblasts from the developing drosophila leg muscles, focusing on gene expressions overlapping with tendon and muscle cells. This study proposes that neuronal cell adhesion molecules Ama and Nrt interact in myoblast and tendon adhesion to support tendon and in proliferation of muscle progenitors. This study traces Ama and Nrt expression with various drosophila mutant strains and provides evidence to support its claims using single cell sequencing, immuno-fluoresence and in situ hybridisation. *The authors report novel markers to study the interactions between muscle and tendon progenitors in the Drosophila leg provide convincing evidence of their functions in muscle and muscle and tendon formation. *
- The authors report novel markers to study the interactions between muscle and tendon progenitors in the Drosophila leg provide convincing evidence of their functions in muscle and muscle and tendon formation. *
__ Reply:__ We are grateful to the Reviewer 1 for his/her supportive comments on the quality of our work.
Page 2 "cell types*..." might be worth including other cell types such as vascular/endothelial if listing all cell types in the limb, as the sentence is suggesting. *
__ Reply__: "blood vessels" have been added as components of the limb musculoskeletal system.
Reviewer's comment: The authors discuss the interactions between the myoblasts and tendon cells but do not show any cellular resolution of the interaction between the cells and the secreted adhesion proteins. It would enhance the manuscript if the authors could show high resolution images of these cellular interactions with the secreted protein in vivo.
__ Reply__: see reply to referees cross-commenting about newly generated high-resolution images shown in Fig. 2.
Lots of examples of definite article (the) missing throughout the text.
Reply: The text has been edited and missing articles added
Second line of Abstract does not flow: Ama encodes secreted proteins to "Ama encodes a secreted protein"
__ Reply__ the correction has been made accordingly
2nd para Intro- this para is essentially discussing vertebrate limb muscle/tendon precursors, although includes a non-vertebrate citation. It could be helpful to (briefly) compare/contrast the non-vertebrate vs vertebrate literature on this topic.
__ Reply__: Indeed, this paragraph is primarily focused on the development of the musculoskeletal system in vertebrates. The comparison (from a molecular standpoint) with the muscle system of the Drosophila leg appears in the following paragraph. For clarity, we have included a brief, more general description (end of second paragraph) about the muscle/tendon system in Drosophila to highlight certain divergences between vertebrate and invertebrate systems and to introduce the subsequent paragraph.
"in limb of chick embryo add "the limb"
__ Reply:__ the correction has been made accordingly
p6 because these two antibodies were raised in rabbit, as the Twist antibody, needs some additional explanatory text.
__ Reply__: We have modified the text to give a more accurate explanation: "Because these two antibodies were raised in rabbit, as was the Twist antibody, we could not use this latter to visualize the myoblasts"
P9 discussion creeping into results section-with some speculation on Ama forming homophilic adhesions which has not been experimentally tested.
__ Reply:__ Because we chose to submit this work as a short format paper, Results and Discussion sections are indeed combined. However, we agree that homophilic adhesion properties of Ama have been shown only in cell culture and not tested in physiological context. To clarify this point, we have modified the corresponding part of the text and only suggest that Ama could directly bind to membranes through its putative GPI modification as proposed by Seeger et al.
Ama depletion affects both viability and the proliferation rate of leg disc myoblasts (in a Nrt-independent way) Does it have similar role in tendon precursors? Could the authors provide any evidence of apoptosis given proposed role of Ama in glial cells?
__ Reply: __As asked by Reviewer 1, we have tested these two points and included the results in suppl figure 3 (A-C). As expected, the proliferation rate of tendon cells is not affected as we have previously showed that tendon cells are post-mitotic cells (Laurichesse et al. 2021). Moreover, we now show that Ama depletion does not lead to apoptosis of tendon cells. See Supp. Figure 3 (A-C), the main text has also been modified accordingly.
REVIEWER 2:
In this well-written, comprehensive, and interesting manuscript, the authors study the molecular circuitry that supports the coordinated activity of tendon cells and myoblasts during development. As the authors themselves point out in the introduction, the assembly of tissues within the musculoskeletal system provides a particularly attractive system in which to study how different cell types coordinate their behaviours to form higher-order structures. Using single-cell transcriptomics, the authors first identify the cell adhesion molecule Ama and transmembrane protein Nrt as enriched in Drosophila myoblasts and tendon cells. Their transcriptomic data suggest that Nrt is specifically expressed in the tendon cells while Ama is expressed in both. They support these data with a variety of in situ, antibody, and endogenous stainings. Using a series of genetic manipulations, they then convincingly show that Ama controls the total number of myoblasts during the larval stages: Ama knockdown is associated with both decreased proliferation and increased apoptosis of myoblasts. Ama's role in regulating myoblast number is shown to be independent of Nrt and likely under the control of the FGF/RTK pathway. Finally, the authors show that the loss of either Nrt or Ama activity is associated with a loss of adhesion between myoblasts and tendon cells and with the stunted growth of the long tendon. Thus, their data point to Ama playing dual roles in muscle development by regulating both myoblast number and cell adhesion.
* I very much enjoyed reading the paper, which I think makes an important contribution to our understanding of both the developing musculature and inter-cell-type coordination during development more broadly. I have only a handful of grammatical errors to point out.*
__Reply: __We appreciate these enthusiastic and supportive comments, and we would like to thank the Reviewer for highlighting the broader contribution of our work to the understanding of the mechanisms of coordination between different cell types.
*- Grammar: 'This prompted us to use Drosophila model to search' should read 'This prompted us to use the Drosophila model to search' - Grammar: '...identify Neurotactin (Nrt) and its binding partner, Amalgam (Ama) as candidates...' should read '...identify Neurotactin (Nrt) and its binding partner, Amalgam (Ama), as candidates...' - Grammar: 'As tendon precursors in leg disc' should read 'As tendon precursors in the leg disc'. - Grammar : '...we performed a series of in situ hybridization...' should read '...we performed a series of in situ hybridizations...' - Grammar: 'Because these two antibodies were raised in rabbit, as the Twist antibody' should read 'Because these two antibodies were raised in rabbit, as was the Twist antibody' - Grammar: 'Statistical analysis reveals an increase myoblast total number when overexpressing an activated ERK' should read 'Statistical analysis reveals an increase in the total number of myoblasts when overexpressing an activated ERK' - Grammar: The following section header needs rephrasing: 'Ama, potential downstream effector of FGF pathway in the regulation of myoblast number'. Maybe 'Ama is a potential downstream effector of the FGF pathway in the regulation of myoblast number' or Ama: a potential downstream effector of the FGF pathway in the regulation of myoblast number. - Grammar: 'whereas its reduction (UAS-StyRNAi) lead to more myoblasts' should read 'whereas its reduction (UAS-StyRNAi) leads to more myoblasts' - For clarity 'The expression of the constitutively active form of ERK could rescue the phenotype of Ama depletion in glial cells (Ariss et al. 2020)' might read better as 'Previous work has shown that the expression of the constitutively active form of ERK can rescue the phenotype of Ama depletion in glial cells (Ariss et al. 2020). Therefore, we tried...' - Grammar: 'showed a significant higher number of myoblasts compared' should read 'showed a significantly higher number of myoblasts compared' - Grammar: 'Another, not exclusive, possibility' should read Another, non-mutually exclusive, possibility'. - Grammar: 'We measured the length of the tilt relatively to the length' should read '. We measured the length of the tilt relative to the length' *
Reply: All the modifications suggested above by Reviewer 2 are now included in the text.
REVIEWER 3:
*Myoblast and tendon precursors stem from different developmental origins. Hence, they need to find each other to build a functional muscle-skeleton. How they do so is an exciting biological problem, not only for this reviewer who is working on Drosophila muscle development, too. As we currently understand little about how myoblasts communicate with tendons during development, I find this manuscript a generally interesting contribution unravelling a new mechanism of cell-cell communication between these two cell types. It proposes a role for 2 interesting proteins that are little studied. Furthermore, Drosophila leg muscle-tendon development is complex and results in an intricate final architecture. Thus, a better understanding of its molecular mechanisms of development is exciting to this reviewer and to the muscle and tendon fields. *
Reply: We express our gratitude to Reviewer 3 for his/her keen interest in our work and for emphasizing its significance within the field of developmental biology.
*While some Ama mRNA expression in myoblasts was confirmed with in situ hybridisation, it was also shown that Ama mRNA is expressed in other sources including tendon precursors. As the interesting AmaGFP protein overlapping with the developing tendon cells is found at some distance from the myoblasts, the source for this Ama protein population is not entirely clear. To identify if it is secreted from myoblasts I suggest to stain for Ama-GFP in the muscle-specific Ama knock-down discs at 5h APF. This could use the late knock-down condition. *
__ Reply:__ In Suppl Fig.2 in a close-up view of the femur region, we show that at 5h APF Ama is transcribed in addition to myoblasts also in the developing tilt tendon. This tendon associated Ama expression is specific as it is detected after myoblast specific Ama knockdown. Thus, at 5h APF, the Ama-GFP signal detected at the interface of muscle and tendon precursors could in part correspond to Ama secreted by the tilt tendon cells. However, we also observed clear Ama-GFP signal at the interface of myoblasts and tendon precursors at 0h APF when Ama is not yet transcriptionally activated in tilt tendon precursors (not shown). Thus, we are confident that the myoblasts are the main source of secreted Ama protein that ensure close proximity of myoblast and tendon precursor cells. A view supported by the loss of myoblast-tendon cell proximity in myoblast-specific Ama knockdown. However, to clarify this point, we immunostained myoblast-specific Ama knock-down discs for the Ama protein in at 5h APF as suggested. As stated in the text, we were concerned that GFP tag could influence the life-time of the Ama protein, as GFP itself is pretty stable. This is why we used anti-Ama antibody kindly provided by Dr. Silman to determine whether myoblast-specific Ama knockdown (using R32D05-Gal4 driver) would completely abolish Ama protein at 5h APF. We indeed observed a strong reduction of Ama protein at this stage indicating that the contribution of Ama protein from tendon cells is minimal (but cannot be completely excluded), with myoblasts remaining the major source at this stage. This new result is now presented in Suppl Fig. 2M-P. This result is also in accordance with our new result showing that tendon-specific AmaKD has no effect on tendon growth (see reply to the comment below regarding tendon length). In light of this new result, we have modified the text accordingly in the corresponding paragraph (p5-6).
Generally, it might be useful to move the part of Figure 2 that shows the Ama-GFP Nrt co-staining to the later part in the text that addresses the interaction of both cell types and keep the autonomous Ama role in muscle for the start of paper only.
__ Reply: __We have indeed debated extensively about this possibility before submitting this work. While such a presentation would have some logical coherence, it also has the disadvantage of having to resume, at least partially, the expression of Ama, leading to certain redundancies. Additionally, we chose to begin with a comparison of the new myoblast transcriptomic data with pre-established tendon data to highlight the presence of ligand-receptor pairs. In this context, it seemed to us more pertinent to present the expression patterns of Ama and Nrt together in the initial figures.
To quantify the interaction of the myoblast cell membranes and the tendon cells better it would be useful to combine sr>CAAXmCherry with a myoblast membrane maker (possibly Him-CD8-GFP or use R15B03-Gal4 with R79D08-lexA). This could also improve the "mean distance" measurements. As currently presented, it is not so clear how the mean distance was measured. It could be helpful to indicate some examples in zoom-in vies on Figure 5. Does a distance of 4 µm in wild type mean that the myoblast is not touching the tendon precursors, or is only the myoblast nucleus that is Twi positive at this distance?
__ Reply: We are grateful to this reviewer for its relevant suggestion. Thus, as stated above (referees cross-commenting), we provide new high-resolution images with labelled membranes of both tendon cells and myoblasts (fig 2 O-P). As shown here, myoblast membranes are very closed to each other, and nuclei occupy an important part of myoblast volumes. So, we found more accurate to use the myoblast nucleus (stained with Twist antibody) to detect individual myoblasts using Imaris Spot function rather than myoblast membranes. We also believe that the distances between the center of myoblast nuclei and the tendon surface are representative of the distance between these two cell types as nucleus myoblast occupies most of the cell volume. We addressed this point in the new Suppl. Fig.5. __Regarding the distance of 4____ µm: As mentioned in the original text, the 4 µm distance represents the average distance between myoblasts and the tendon surface in wild type discs. We do not perceive this distance as indicative of a threshold distinguishing myoblasts that interact physically with tendons from those that do not. We rather use this mean distance to quantify the distribution of myoblasts around the tendon and their dispersion/mis-distribution in Ama and Nrt knockdown leg discs. To clarify this point, we have modified the corresponding paragraph: "This result indicates that AmaKD leads to myoblasts mis-distribution around the tilt, suggesting that the reduction of Ama level could affect myoblast-tendon adhesion".
For a better understanding of how the mean distance was measured, we added a new Supplementary Figure 5 (rather than a zoom-in in the main figure as suggested by this reviewer), with a corresponding description of how this distance was measured in addition to the explanations in the material and method section.
Is the tendon elongation phenotype seen after Ama RNAi in muscle and in Nrt mutants due to the fact that myoblasts are further away from tendons or is it an Ama/Nrt role that is autonomous to tendons? This could be tested by assaying tendon elongation after tendon-specific Ama knock-down as shown in Figure S2.
__ Reply: __As asked by this reviewer, we have performed this experiment using Sr-Gal4 driver to induce tendon-specific Ama knockdown and assessed tendon elongation using R79D08-lexA>lexAop-GFP marker. Overall statistical analysis is now included in Fig 5F and G graphs. This analysis shows that tendon-specific Ama knockdown does not affect tendon elongation. This is in concordance with the fact that Ama knockdown in myoblasts leads to tendon defects similar to that of Nrt loss of function in tendon clearly indicating that the observed phenotypes are due to Ama's role in myoblasts. This does not exclude an additional subsequent Ama function in growing tendon precursors in later development.
Minor : Is Figure 2Q a zoom-in from Figure 2P? If yes, it would be helpful to indicate the rough position of it in the lower magnification image.
Reply: Figure 2Q was not a zoom-in from Figure 2P in the previous version of the paper. As stated above Fig. 2 has now been modified.
Minor page 6 - w[1118] is with small "w".
Reply: modifications have been made accordingly in main and figure texts.
REVIEWER 4:
*The manuscript is well-organized, with clear descriptions of methods and results. The use of transcriptomic datasets and gene expression analyses provides insights into the molecular mechanisms underlying the interaction between muscle and tendon precursors. *
*The immunostaining and in situ hybridization experiments well illustrate the expression patterns of Ama and Nrt in muscle and tendon cells during leg disc development in Drosophila. *
*The functional analyses, including knockdown experiments, support the conclusion that Ama plays a crucial role in maintaining the pool of leg muscle precursor cells and coordinating tendon and muscle precursor growth. *
The manuscript significantly enhances our understanding of cell-cell interactions in the musculoskeletal system of Drosophila. The findings have broader implications for the field of developmental biology. In general, this manuscript provides valuable insights into the molecular processes governing leg muscle and tendon development.
Reply: We are indebted to Reviewer 4 for highlighting that our manuscript is well-organized and well-illustrated. We are also grateful to Reviewer 4 for highlighting the valuable insights of our work.
some aspects in the manuscript, for example how Ama regulates myoblast number and its interaction with the FGFR pathway, could be further explored or clarified.
__ Reply:__ Regarding Ama's contribution for maintaining the myoblast pool through its interaction with the FGF pathway, we demonstrate here that, contrary to what has been proposed for glial cells, Ama acts downstream of this pathway, although we emphasize that there is a synergistic effect with the MAPK pathway inhibitor, Sprouty. These findings thus reveal complex and variable regulatory mechanisms between Ama and the FGF pathway that would require specific investigation, the entirety of which appears challenging to integrate into this same publication.
the organization of the abstract could be improved to provide a clearer and more comprehensive overview of the study. The abstract currently lacks a structured presentation of essential components such as methods, results, and conclusions. It would greatly benefit from a more systematic arrangement.
__ Reply:__ We have made modifications to propose a more structured abstract.
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Referee #4
Evidence, reproducibility and clarity
The manuscript by Moucaud et al. titled "Amalgam plays a dual role in controlling the number of leg muscle progenitors and regulating their interactions with developing tendon" presents a comprehensive investigation of cell-cell interaction between skeletal muscle and tendon precursor cells. The authors use the Drosophila leg development model to findr candidates involved in early interactions between muscle and tendon precursors. The manuscript focuses on the role of Amalgam (Ama) and Neurotactin (Nrt) in these two cell populations communication during musculoskeletal system development .
The results demonstrate that Ama and Nrt are selectively expressed in skeletal muscle and tendon precursors, respectively. Moreover, Ama and Nrt are required to keep myogenic and tendom precursors closely associated, thus is essential for leg muscles development. Furthermore, Ama plays also important role in maintaining the pool of leg muscle precursor cells. Additionally, the expression patterns of Ama and Nrt suggest a potential dual role for Ama, not only in interacting with Nrt but also in a Nrt-independent manner. Summarizing, the study shows the importance of specific bi-directional communication between different cell populations in the formation of functional organs in suggests that Ama and Nrt plays key role during musculoskeletal system development.
The manuscript is well-organized, with clear descriptions of methods and results. The use of transcriptomic datasets and gene expression analyses provides insights into the molecular mechanisms underlying the interaction between muscle and tendon precursors. The immunostaining and in situ hybridization experiments well illustrate the expression patterns of Ama and Nrt in muscle and tendon cells during leg disc development in Drosophila. The functional analyses, including knockdown experiments, support the conclusion that Ama plays a crucial role in maintaining the pool of leg muscle precursor cells and coordinating tendon and muscle precursor growth. Additionally, the authors explore the potential link between Ama and the FGFR pathway, suggesting that Ama may act downstream of the pathway.
However, some aspects in the manuscript, for example how Ama regulates myoblast number and its interaction with the FGFR pathway, could be further explored or clarified. Moreover, the organization of the abstract could be improved to provide a clearer and more comprehensive overview of the study. The abstract currently lacks a structured presentation of essential components such as methods, results, and conclusions. It would greatly benefit from a more systematic arrangement.
Significance
The manuscript significantly enhances our understanding of cell-cell interactions in the musculoskeletal system of Drosophila. The findings have broader implications for the field of developmental biology. In general, this manuscript provides valuable insights into the molecular processes governing leg muscle and tendon development.
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Referee #3
Evidence, reproducibility and clarity
This manuscript is investigating mechanisms how muscle and tendon development is properly coordinated. By using differential expression analysis of developing Drosophila legs, the authors find that Amalgam (Ama) is expressed in the developing leg myoblasts and Neurotactin (Nrt) is expressed in one class of tendon precursors, the tilt in the dorsal femur. Using a new Ama-GFP knock-in line, Ama protein was found in the proximity to the developing tendons at 5h APF, when myoblasts and tendons likely interact.
Using myoblast specific knock-down of Ama, the authors find that Ama acts autonomously in the leg myoblasts to promote their proliferation and survival. A similar function for Ama had been described for flight muscle myoblasts in larval wing discs (Zappa et al. 2020). In leg myoblasts ama likely acts downstream of FGF signalling in the myoblasts to promote their proliferation.
As Ama and Nrt were proposed as a ligand-receptor pair promoting adhesion, the authors knocked-down Ama later during myoblast development, after they have proliferated, and tested where these myoblasts are positioned. They find that Ama knock-down myoblasts stay at a larger distance from tendons compared to wild type; the same was found in Nrt mutants. Thus, the authors propose that Ama protein secreted from myoblasts acts via the membrane-bound Nrt protein on the tendon precursors to coordinate muscle-tendon adhesion and proper positioning.
- While some Ama mRNA expression in myoblasts was confirmed with in situ hybridisation, it was also shown that Ama mRNA is expressed in other sources including tendon precursors. As the interesting AmaGFP protein overlapping with the developing tendon cells is found at some distance from the myoblasts, the source for this Ama protein population is not entirely clear. To identify if it is secreted from myoblasts I suggest to stain for Ama-GFP in the muscle-specific Ama knock-down discs at 5h APF. This could use the late knock-down condition. Generally, it might be useful to move the part of Figure 2 that shows the Ama-GFP Nrt co-staining to the later part in the text that addresses the interaction of both cell types and keep the autonomous Ama role in muscle for the start of paper only.
- To quantify the interaction of the myoblast cell membranes and the tendon cells better it would be useful to combine sr>CAAXmCherry with a myoblast membrane maker (possibly Him-CD8-GFP or use R15B03-Gal4 with R79D08-lexA). This could also improve the "mean distance" measurements. As currently presented, it is not so clear how the mean distance was measured. It could be helpful to indicate some examples in zoom-in vies on Figure 5. Does a distance of 4 µm in wild type mean that the myoblast is not touching the tendon precursors, or is only the myoblast nucleus that is Twi positive at this distance?
- Is the tendon elongation phenotype seen after Ama RNAi in muscle and in Nrt mutants due to the fact that myoblasts are further away from tendons or is it an Ama/Nrt role that is autonomous to tendons? This could be tested by assaying tendon elongation after tendon-specific Ama knock-down as shown in Figure S2.
Minor:
- Is Figure 2Q a zoom-in from Figure 2P? If yes, it would be helpful to indicate the rough position of it in the lower magnification image.
- page 6 - w[1118] is with small "w".
Referees cross-commenting
All reviewer agree that this is an interesting study that is well done and well documented. I agree with reviewer 1 that the study would further benefit from better imaging of the cellular extensions of tendons and myoblasts to see how both cell types interact.
Significance
Myoblast and tendon precursors stem from different developmental origins. Hence, they need to find each other to build a functional muscle-skeleton. How they do so is an exciting biological problem, not only for this reviewer who is working on Drosophila muscle development, too.
As we currently understand little about how myoblasts communicate with tendons during development, I find this manuscript a generally interesting contribution unravelling a new mechanism of cell-cell communication between these two cell types. It proposes a role for 2 interesting proteins that are little studied. Furthermore, Drosophila leg muscle-tendon development is complex and results in an intricate final architecture. Thus, a better understanding of its molecular mechanisms of development is exciting to this reviewer and to the muscle and tendon fields.
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Referee #2
Evidence, reproducibility and clarity
In this well-written, comprehensive, and interesting manuscript, the authors study the molecular circuitry that supports the coordinated activity of tendon cells and myoblasts during development. As the authors themselves point out in the introduction, the assembly of tissues within the musculoskeletal system provides a particularly attractive system in which to study how different cell types coordinate their behaviours to form higher-order structures. Using single-cell transcriptomics, the authors first identify the cell adhesion molecule Ama and transmembrane protein Nrt as enriched in Drosophila myoblasts and tendon cells. Their transcriptomic data suggest that Nrt is specifically expressed in the tendon cells while Ama is expressed in both. They support these data with a variety of in situ, antibody, and endogenous stainings. Using a series of genetic manipulations, they then convincingly show that Ama controls the total number of myoblasts during the larval stages: Ama knockdown is associated with both decreased proliferation and increased apoptosis of myoblasts. Ama's role in regulating myoblast number is shown to be independent of Nrt and likely under the control of the FGF/RTK pathway. Finally, the authors show that the loss of either Nrt or Ama activity is associated with a loss of adhesion between myoblasts and tendon cells and with the stunted growth of the long tendon. Thus, their data point to Ama playing dual roles in muscle development by regulating both myoblast number and cell adhesion.
I very much enjoyed reading the paper, which I think makes an important contribution to our understanding of both the developing musculature and inter-cell-type coordination during development more broadly. I have only a handful of grammatical errors to point out.
Grammar: 'This prompted us to use Drosophila model to search' should read 'This prompted us to use the Drosophila model to search'
Grammar: '...identify Neurotactin (Nrt) and its binding partner, Amalgam (Ama) as candidates...' should read '...identify Neurotactin (Nrt) and its binding partner, Amalgam (Ama), as candidates...'
Grammar: 'As tendon precursors in leg disc' should read 'As tendon precursors in the leg disc'.
Grammar : '...we performed a series of in situ hybridization...' should read '...we performed a series of in situ hybridizations...'
Grammar: 'Because these two antibodies were raised in rabbit, as the Twist antibody' should read 'Because these two antibodies were raised in rabbit, as was the Twist antibody'
Grammar: 'Statistical analysis reveals an increase myoblast total number when overexpressing an activated ERK' should read 'Statistical analysis reveals an increase in the total number of myoblasts when overexpressing an activated ERK'
Grammar: The following section header needs rephrasing: 'Ama, potential downstream effector of FGF pathway in the regulation of myoblast number'. Maybe 'Ama is a potential downstream effector of the FGF pathway in the regulation of myoblast number' or Ama: a potential downstream effector of the FGF pathway in the regulation of myoblast number.
Grammar: 'whereas its reduction (UAS-StyRNAi) lead to more myoblasts' should read 'whereas its reduction (UAS-StyRNAi) leads to more myoblasts'
For clarity 'The expression of the constitutively active form of ERK could rescue the phenotype of Ama depletion in glial cells (Ariss et al. 2020)' might read better as 'Previous work has shown that the expression of the constitutively active form of ERK can rescue the phenotype of Ama depletion in glial cells (Ariss et al. 2020). Therefore, we tried...'
Grammar: 'showed a significant higher number of myoblasts compared' should read 'showed a significantly higher number of myoblasts compared'
Grammar: 'Another, not exclusive, possibility' should read Another, non-mutually exclusive, possibility'.
Grammar: 'We measured the length of the tilt relatively to the length' should read '. We measured the length of the tilt relative to the length'
Significance
I very much enjoyed reading the paper, which I think makes an important contribution to our understanding of both the developing musculature and inter-cell-type coordination during development more broadly
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Referee #1
Evidence, reproducibility and clarity
Moucaud et al carried out single cell sequencing on myoblasts from the developing drosophila leg muscles, focusing on gene expressions overlapping with tendon and muscle cells. This study proposes that neuronal cell adhesion molecules Ama and Nrt interact in myoblast and tendon adhesion to support tendon and in proliferation of muscle progenitors. This study traces Ama and Nrt expression with various drosophila mutant strains and provides evidence to support its claims using single cell sequencing, immuno-fluoresence and in situ hybridisation.
Minor comments:
Page 2 "cell types..." might be worth including other cell types such as vascular/endothelial if listing all cell types in the limb, as the sentence is suggesting.
The authors discuss the interactions between the myoblasts and tendon cells but do not show any cellular resolution of the interaction between the cells and the secreted adhesion proteins. It would enhance the manuscript if the authors could show high resolution images of these cellular interactions with the secreted protein in vivo.
Minor typographical
Lots of examples of definite article (the) missing throughout the text
Second line fo Abstract does not flow Ama encodes secreted proteins to "Ama encodes a secreted protein"
2nd para Intro- this para is essentially discussing vertebrate limb muscle/tendon precursors, although includes a non-vertebrate citation. It could be helpful to (briefly) compare/contrast the non-vertebrate vs vertebrate literature on this topic.
in limb of chick embryo add "the limb"
p6 because these two antibodies were raised in rabbit, as the Twist antibody, needs some additional explanatory text
p8 or FGF receptor add "the"
P9 discussion creeping into results section-with some speculation on Ama forming homophilic adhesions which has not been experimentally tested.
Significance
Thge authors report novel markers to study the interactions between muscle and tendon progenitors in the Drosophila leg provide convincing evidence of theri functions in muscle and muscle and tendon formation.
Ama depletion affects both viability and the proliferation rate of leg disc myoblasts (in a Nrt-independent way)
Does it have similar role in tendon precursors?
Could the authors provide any evidence of apoptosis given proposed role of Ama in glial cells?
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Reply to the reviewers
We thank the referees for their insightful comments and constructive feedback, which have undoubtedly strengthened our manuscript.
Reviewer #1 Comment:
Absence of evidence demonstrating the presence of prions or prion-seeding activity in extraneural tissues.
Response: Our study focuses on all pathophysiological alterations brought about by prion infections in extraneural organs, be they caused directly by prion infections of said organs or through indirect mechanisms. Nevertheless, we share the reviewer's curiosity about a possible correlation between these changes and the presence of prion-seeding activity. We propose to include additional data demonstrating prion presence and seeding activity in skeletal muscle at various timepoints. This will be achieved by employing proteinase K digestion followed by Western blot (PK-WB) and real-time quaking-induced conversion (RT-QuIC) assays to provide a robust correlation with our transcriptomic analyses and Glul upregulation.
Revision: We have conducted preliminary experiments using PK-WB and RT-QuIC assays. These experiments were performed on terminal-stage prion-infected samples and related controls. If prion presence is detected at this stage, we plan to extend the analysis to earlier stages, specifically at 16 weeks post-inoculation (wpi) and 8 wpi, to track the progression of prion-seeding activity over time.
Western blot analysis was performed on skeletal muscle homogenates at the terminal stage of prion disease in mice infected with three prion strains (RML6, ME7, and 22L) and related NBH control. Control samples include CNS brain homogenates, showing prion presence (PrPSc) in RML6 but not in NBH. Neither skeletal muscles from NBH nor from prion-infected samples show detectable prions, indicating that PK-WB may lack the sensitivity to detect prions in skeletal muscle or that prion levels are below detection thresholds (Revision Figure 1A) for this specific technique.
Next, we performed RT-QuIC assays on muscle homogenates using different protocols, including sodium phosphotungstic acid (NaPTA) enrichment (Revision Figure 1B). NaPTA binds and precipitates PrPSc in the presence of MgCl2, removing contaminants and concentrating PrPSc. For this protocol, 100 µg of muscle homogenates were treated with benzonase to degrade DNA contaminants. Then, 4% NaPTA and 170 mM MgCl2 were added, resulting in a final NaPTA concentration of 0.3%. The samples were incubated at 37{degree sign}C while shaking at 1500 rpm for 2 hours, followed by centrifugation at 15,000 g for 30 minutes to precipitate PrPSc. The resulting pellets were used to seed RT-QuIC reactions. Each biological replicate was run in quadruplicate, and replicates were considered positive only if at least 3 out of 4 technical replicates showed detection. Results indicated no amplification for NBH samples. For RML6 skeletal muscles, 2 out of 3 biological replicates were positive. For ME7, 0 out of 4 biological replicates were positive. For 22L, 1 out of 3 biological replicates was positive.
Using the same protocol without NaPTA (Revision Figure 1C), two positive samples were observed in the NBH condition, suggesting that NaPTA is useful for specific prion enrichment.
Similarly, we combined NaPTA and sarkosyl in our further trial. 10% weight/volume muscle homogenates in 1x PBS containing 2% sarkosyl were centrifuged at 80 g for 1 minute, and the supernatant (500 µl) was collected. If still dirty, further centrifugation at 2700 g for 5 minutes was performed. Then, 500 µl PBS containing 2% sarkosyl was added and incubated for 10 minutes at 37{degree sign}C. Benzonase and MgCl2 (final concentrations 50 U/ml and 1 mM, respectively) were added and incubated for 30 minutes at 37{degree sign}C with shaking at 1500 rpm. NaPTA was added to a final concentration of 0.3% and incubated for 30 minutes at 37{degree sign}C with shaking. Samples were centrifuged at 15,000 g for 30 minutes and resuspended in 20 µl of 0.1% sarkosyl-containing PBS condition (Revision Figure 1D). Using this protocol, we also detected signals in the NBH control.
These results indicate ongoing challenges in optimizing prion extraction from skeletal muscle. Unlike brain tissue, where prion levels are significantly higher, skeletal muscle presents difficulties due to lower prion concentrations. In brain samples, dilutions still result in positive signals only from prion-enriched conditions. However, for skeletal muscle, prion extraction is not as straightforward, highlighting the need for further refinement of the protocol to achieve reliable detection and differentiation between prion-infected and control samples.
Inclusion of prion strain with limited extraneural replication. Reliance on three prion strains limits the relevance. Inclusion of a strain with limited extraneural replication is suggested.
Response: To address this limitation, we propose a comprehensive discussion on the systemic nature of prion diseases, emphasizing the need for future research to explore potential strains with restricted replication patterns.
Revision: There is a significant interest in prion deposits in skeletal muscles as potential sources of prion spreading. The consumption of beef products from cattle infected with bovine spongiform encephalopathy (BSE) prions caused new variant Creutzfeldt-Jakob disease, raising early concerns about the transmission of prions from cervids to humans (1-3). This remains a hot topic in the field (4-7), underscoring the importance of our longitudinal transcriptomic analysis in different extraneural organs. However, prion strains with restricted extraneural replication which we could use as control have not been described in mice used as prion animal models. According to our knowledge and the existing literature, there is no documentation of any mouse-adapted prion strains that are unable to propagate prions outside the central nervous system (CNS). Although this does not apply to our study, it is important to note that hamster-derived prion strains such as HY and DY exhibit different replication patterns. Hamsters infected with HY TME prions show detectable infectivity and/or PrPSc in the CNS, lymphoreticular system, skeletal muscle, nasal secretions, and blood (8-11). Conversely, prion infectivity and/or PrPSc in DY TME-infected hamsters is restricted to the CNS (12-14).
In mice, the situation is quite different, and there are no prion strains with restricted extraneural replication. Instead, studies have focused on models where prion protein (PrPC) is absent in all tissues except skeletal muscle, which is essentially the opposite of the condition requested by the reviewer. For instance, research has demonstrated that prion levels in skeletal muscles are 5-10% of those observed in the brain (1). Here, transgenic mice (Tg(α-actin-MoPrP)6906/Prnp0/0) expressing PrPC only in skeletal muscles (and barely detectable in the CNS) were created. After intramuscular prion injection, these mice showed that skeletal muscles could propagate prions (1). Additionally, another study found that prions were not detectable in skeletal muscle at early stages (32 and 60 days post intracerebral prion inoculation) unless experimental autoimmune myositis was induced, which increased prion spread to skeletal muscle (15).
This comprehensive discussion underscores the absence of a mouse model prion strain with limited extraneural replication, highlighting a gap in current research that our study aims to address indirectly through our systemic approach.
Clarification on statistical methods. Lack of details on statistical tests used for comparing GLUL levels in Figures 3 and 4.
Response: We clarified the statistical tests used, specifying whether they are parametric or non-parametric, and provide a rationale for the chosen methods. It is important to note that GLUL upregulation is significant, as evident from Figure 4. At 8 wpi, the fold change for RML6 is above 3, for ME7 is above 1.5, and for 22L is above 2. The fold change in later timepoints is increasingly larger.
Revision: For Figure 3E, normalized raw counts for the GLUL gene in control and sCJD patients were analyzed using the DESeq2 package, with related false discovery rate (FDR) calculations. DESeq2 is appropriate for RNA-seq data as it models count data using a negative binomial distribution, suitable for overdispersed count data commonly found in RNA-seq experiments. The normalization and FDR calculation ensure that the comparisons between control and sCJD patients are statistically robust.
In Figure 3G, Western blot densitometry data were analyzed using the Mann-Whitney U test, resulting in a p-value of 0.01072. The Mann-Whitney U test is a non-parametric test that does not assume normal distribution, making it suitable for small sample sizes and non-normally distributed data, which is often the case in Western blot densitometry. For statistical analyses in Figure 4A, Mann-Whitney U test was used. In Figure 4C, we applied the t-test (as the standard deviations were consistent) with Bonferroni correction to account for multiple comparisons. The t-test is a parametric test suitable for normally distributed data, and the Bonferroni correction adjusts for the increased risk of Type I errors when multiple comparisons are made, ensuring the results are not due to chance. Additionally, we used one-way ANOVA corrected with Kruskal-Wallis, a non-parametric method, to confirm our findings (Revision Figure 2). The results from both statistical tests were in strong agreement, validating our analysis.
In Supplementary Figure 6, the Mann-Whitney U test was used due to its non-parametric nature, which is suitable for data that do not assume a normal distribution. This test was chosen to provide a robust analysis of the data, which did not fit the assumptions required for parametric tests.
These methods were selected based on the data distribution and the need for accurate statistical analysis to validate the significance of our findings. The choice of DESeq2 for RNA-seq data, Mann-Whitney U for non-normally distributed data, and t-tests with Bonferroni correction for normally distributed data ensures that our analyses are appropriately tailored to the characteristics of the data, providing reliable and valid results.
Details on CJD cases analyzed. Information regarding the types of CJD analyzed is missing.
Response: We ensured that details on the types of CJD cases analyzed, including genotype, strain type, and age, are clearly presented in the main text and supplementary materials.
Revision: A comprehensive description of the sCJD cases, including genotype, strain type, and age, was accessible in Supplementary Table 5 (and here, in Revision Figure 3). This table also provides the reasons why some biosamples were excluded from the final bulk RNA sequencing and downstream analysis.
Recent publications on prion seeding activity. Mention recent publications showing prion seeding activity in extraneural tissues.
Response: We will update the Introduction section to include references to recent studies demonstrating prion seeding activity in extraneural tissues of sCJD and vCJD patients using RT-QuIC or PMCA assays.
Revision: We will discuss and cite additional papers on this topic, highlighting the growing body of evidence for prion seeding activity in extraneural tissues. These references will provide a comprehensive background on the detection and significance of prion seeding in peripheral tissues, thereby strengthening the context and relevance of our study.
Reviewer #2 Comment:
The RNA sequencing of human skeletal muscle samples identified only one common gene between human and mouse conditions. There is concern that this gene may be a bystander result of terminal disease stage pathophysiology in both animals and human.
Response: We strengthened the evidence supporting GLUL as early and altered gene by including additional timepoint analyses and showing its presence at earlier disease stages.
Revision: The upregulation of GLUL cannot be attributed to a bystander effect result of terminal disease stage pathophysiology, as it is consistently upregulated across all analyzed timepoints. We performed weighted gene co-expression network analysis (WGCNA) grouped by disease stages and GLUL belongs to the orange module (upregulated genes throughout all timestages in both main (Figure 2A - original manuscript) and validation (Figure 3A - original manuscript) cohort). We also included a comparison of GLUL expression between RML6 and NBH. As shown in the figure (Revision Figure 4), GLUL is upregulated at all individual timepoints. This finding is corroborated at both the transcriptional and protein levels, including other prion strains (Figure 4 - original manuscript) from a further animal cohort for RML6 condition. This consistent upregulation across various stages supports GLUL as a robust altered genes and possible biomarker for prion disease progression.
Cautious interpretation of GLUL dysregulation specificity. The claim that GLUL dysregulation is specific to prion diseases should be mentioned more cautiously due to the small sample number of other neurodegenerative diseases (NDs). The finding would be stronger if a meta-analysis of possible available data from human ND cohorts could be examined.
Response: We will rephrase our conclusion to acknowledge the sample size limitation and suggest further studies for confirmation. However, due to the poor sample availability of skeletal muscles biopsis from other NDs, related metadata are complicated to be found.
Revision: Modify the discussion section to reflect a more cautious interpretation, emphasizing the need for larger cohort studies to confirm GLUL specificity.
Post-transcriptional modifications of GLUL. Explore the possibility of GLUL being modified through RNA editing affecting its expression.
Response: We investigated the potential post-transcriptional modifications of GLUL, such as RNA editing, and their impact on its expression and function.
Revision: We will add a paragraph named "Lack of post-transcriptional changes in extra-neural organs of prion-inoculated mice".
Results: We calculated the genome-wide adenosine-to-inosine editing index (AEI) to measure global RNA editing levels (16), the preferential site of RNA editing in mammals. Blood global editing levels rose steadily during aging but were independent of prion inoculation (Revision Figure 5A). No AEI differences were seen in muscle or spleen (Revision Figure 5B and C). To determine recoding of individual transcripts, we aligned our sequencing results to previously published high-confidence AEI recoding sites (17). We found Flnb and Copa in the spleen and Cog3 in blood to be significantly recoded (Revision Figure 5D). However, Glul did not show significant recoding.
Alternative splicing can give rise to disease-associated differentially used transcripts (18). In contrast to our previous results in the brain (19), the present alternative splicing analyses in extraneural organs showed only minor alterations (Revision Figure 5E). Necap2, Myl6 and Srsf5 transcripts were alternatively spliced across multiple organs and prion incubation times (Revision Table 1). Only in two out of a total of 21 splice variants differential transcript usage was accompanied by differential gene expression: upregulation of Myl6 in blood at 4 wpi and downregulation of Ms4a6c in blood at 14 wpi.
Discussion: Except for Flnb, Copa and Cog3, we were unable to find evidence for broad dysregulation of posttranscriptional RNA editing, in contrast a recent report (20) but in line with our previous findings (19). Furthermore, splicing analysis suggests that alternative splicing was largely unlinked from gene expression changes.
Method: Adenosine-to-inosine editing index (AEI) was calculated as previously published (16). Herein, raw fastq reads were uniquely aligned to a murine mm10 reference genome using STAR v2.7.3 with the filter outFilterMultimapNmax=1. RNAEditingIndexer (https://github.com/a2iEditing/RNAEditingIndexer) was used to calculate per-sample AEI.
We identified gene-specific RNA editing based on a recently published list of high-confidence targets of Adar (17)as follows. RediToolsKnown.py from REDItools (21) was applied on uniquely aligned samples as mentioned above. This yielded per-site lists of A-to-I editing on which we applied the following thresholds: (a) a minimum of 3 alternative reads per site per sample (b) a minimal editing frequency of 1 % per site (c) criteria a) and b) are fulfilled in at least floor(2/3 * n) biological replicates, n is total number of biological replicates per group (d) transcripts of site present in at least 2 biological control replicates. Multiple testing of sites passing above-mentioned thresholds was performed using REDIT (https://github.com/gxiaolab/REDITs) and adjusted for false discovery rate (FDR) according to Benjamini-Hochberg, we considered sites with an FDR For alternative splicing, SGSeq R package (22) was employed to find splicing events characterized by two or more splice variants. Exons and splice junction predictions were obtained from BAM filesPrediction of exons and splice junctions was first made for each sample individually. Then the predictions for all samples were merged and we obtained a common set of transcript features. Overlapping exons were disjoint into non-overlapping exon bins and a genome-wide splice graph was compiled based on splice junctions and exon bins. A single value for each variant was produced by adding up the 5' and 3' counts, or, if these represented the same transcript features, by considering the unique value. These counts were then fed to DEXSeq (23). We analyzed differential usage of variants across a single event, in-stead of quantifying differential usage of exons across a single gene. We retained only variants with at least five counts in at least three samples (of any condition). After filtering, the events associated with a single variant were discarded. Differential analysis was then performed implementing a sam-ple+exon+condition:exon model in DEXSeq. Differentially expressed isoforms were defined as isoforms changing with FDR < 0.05. In the case of differentially used splice variants in muscle on 12 wpi, this dataset was considered as an outlier and hence excluded due to excessively reported splice variants (1,788 events compared to 5 or less on all other time-points and extraneural organs).
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Referee #2
Evidence, reproducibility and clarity
Summary: The authors present a comprehensive transcriptomic analysis of different organ tissue samples (blood, spleen and muscle) from established prion models RML6, or 22L, or ME7 strain mouse-adapted scrapie, at a significant number of timepoints recapitulating early, asymptomatic preclinical, clinical and terminal stages of disease. The transcriptome profile of each tissue at each progression stage is a very useful information, on disease relevant transcriptional changes of variable significance. Eigengene networks were constructed to study the relationships between 39 modules only 2 were significant (orange and dark green) in skeletal muscle from all 3 tissues examined. The authors support they reveal a higher order organization of the late-stage disease transcriptome and provide insights into the modular architecture of gene expression during disease progression (early, presymptomatic and symptomatic) followed by 20 gene hub identification. In the context of network analysis, a high preservation with a Z-summary statistic > 1.96 suggests that the module's connectivity pattern is significantly preserved across different networks of the module's structure as RML6 disease progresses and it is not random compared to control.
The RNA sequencing of human skeletal muscle samples further validated and only 1 common gene between human and mouse condition confirmed by immunoblotting. The GLUL gene was further investigated in terms of gene and protein expression in various mouse disease models along with human skeletal muscle CJD autopsy material. Their findings did not correlate between human and all mice models completely. The increase in GLUL expression is accompanied by changes in glutamate/glutamine metabolism and reduced glutamate levels in CJD skeletal muscle. These alterations were only specific to prion diseases, as they were not confirmed in other neurodegenerative conditions such as amyotrophic lateral sclerosis, Alzheimer's disease, or dementia with Lewy bodies. The authors propose GLUL dysregulation as a potential novel biomarker for prion disease progression, during the preclinical stages with potentially useful efficacy for monitoring of therapies.
Comments
- The RNA sequencing of human skeletal muscle samples identified only 1 common gene between human and mouse condition confirmed by immunoblotting at the terminal stage of the disease. How can they conclude that this specific gene is not a bystander result of the known pathophysiology at the terminal disease stage? The mouse data are not solidly consistent with the biomarker expression.
- The claim that GLUL dysregulation with a result in glutamate/glutamine metabolism is only specific to prion progression only eventhough interesting should be mentioned with a more cautious way as the sample number of other NDs is small. The finding would be significantly stronger if metanalysis of possible available data of human ND cohorts could be examined.
- Post-transcriptional modifications have been described as potential contributors to prion pathogenesis therefore, the authors should also explore the possibility of GLUL being modified through RNA editing affecting its expression.
- The experiments are well designed and executed and the analysis methods are explained in detail. The figures are elegantly presented with adequate information.
Significance
The authors present a very well designed and comprehensive transcriptomic analysis of several tissue organs related to prion disease progression and validation data from multiple mouse models as well as human CJD skleletal muscle tissue.
They provide a strong and logic experimental strategy with adequate validation comparing multiple strains with disease onset differences and human tissue. The result was to identify GLUL as a potential biomarker of prion specific disease progression without any overlap with other NDs sharing pathophysiology.
The finding is interesting to the field but most importantly the established transcriptome profiles available will by of great use for future use from relevant basic research and also translational studies.
The presented study is an important addition to the field without any other comparable datasets regarding skeletal muscle analysis in CJD.
Any novel biomarker for progression of prion disease is extremely important and its potential link with pathogenesis would be of paramount importance.
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Caredio et al is a follow-up of their previous work (Sorce et al,PloS Pathogens 2020), wherein they conducted genome-wide transcriptomic analyses on the brains of prion-infected mice throughout the course of the experimental disease. In the present study, the authors extended their analysis to extraneural tissues of prion-infected mice, including hindlimb skeletal muscles, spleen and blood. Their key findings indicate upregulation of the glutamate-ammonia ligase (GLUL) gene in the skeletal muscle, a pattern also observed across different mouse prion strains and notably in human cases of sporadic CJD, albeit in a relatively small cohort.
A major limitation of the manuscript is the absence of evidence demonstrating the presence of prions or prion-seeding activity, and the lack of correlation with transcriptomic analyses, in any of the extraneural tissues and different timepoints. This omission is surprising given its inclusion in their initial publication in PloS Pathogens. Particularly concerning are the mouse experiments, where intracerebral inoculations were performed, suggesting potential presence of prions in terminal nerve endings and then muscles only at late stage of the disease, due to anterograde axonal transport. The reliance on three prion strains (22L, ME7, and RML) that replicate extraneurally limits the relevance of the study. Including at least one strain with limited or no capacity for extraneural replication could help distinguish whether observed transcriptomic alterations are directly linked to prion replication or are indirect consequences, particularly within the brain. This would also have prevented a significant misinterpretation in the discussion section. The authors delve into the alterations observed in the spleens of affected mice at the terminal disease stage. They attribute these alterations to the consequence of intracerebral inoculation, suggesting a delayed accumulation of prions in lymphatic tissues compared to oral or intraperitoneal inoculation routes. However, considering the high volume and dose inoculated (30 µL 10%), there is likely spillover from the brain, resulting in an intravenous-like inoculation concurrent with the intracerebral infection. Consequently, prion replication occurs rapidly in the lymphoid tissue. Given this, the tardy transcriptomic alterations observed in the spleens become even more surprising.
Information regarding the statistical tests used to compare GLUL levels in Figures 3 and 4, and whether these tests are parametric or not, is missing. Given the relatively low differences observed and the substantial SEM, clarification on statistical methods is imperative for interpreting the results accurately.
Providing details on the types of CJD analyzed, such as genotype, strain type, and age, would enhance the manuscript's comprehensiveness. While this information may be available in supplementary Table 8, we had no access to it.
Minor point: in the introduction, it may be worth mentioning recent publications showing presence of prion seeding activity in many extraneural tissues from humans infected with sporadic CJD and or vCJD, using RT-QuIC or PMCA assays.
Significance
Given the aforementioned concerns, the correlates between prion replication and the GLUL/glutamate-glutamine metabolism alterations are thus highly uncertain. This limits the general significance of the study.
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Referee #3
Evidence, reproducibility and clarity
Brunet and colleagues utilized expansion microscopy to identify the distinct localizations of Alms1a and Alms1b at the centrosome in transgenic flies expressing Alms1a-Tomato or Alms1b-GFP-6Myc. Their imaging results are of exceptional quality and include insightful observations and discussion. However, these experiments were exclusively conducted in the transgenic flies, and no evidence has been provided for the expression of endogenous Alms1b proteins. Moreover, the defect in centriole duplication was observed only when Alms1 was depleted via RNA interference, not through gene knockout. Although some discussion is provided to address this discrepancy, the evidence remains unconvincing. More robust evidence is necessary to support their findings.
Major comments
- One of the primary observations is the defect in centriole duplication in the absence of Alms1. This finding was only observed with RNAi and not with gene knockout. The authors performed the RNAi experiments in Alms1-deleted cells (Alms1del3) and did not observe the defects in centriole duplication. Therefore, authors concluded that acute loss of Alms1 is responsible for centriole duplication in germline stem cells. However, it is still difficult for me to reach that conclusion with only this evidence.
A. Could the authors provide the western blot and staining results of Alms1a and Alms1b proteins after RNAi and gene knockout?
B. The most reliable and widely accepted method to determine the specificity of RNAi (whether it is an off-target effect or not) is through genetic rescue experiments. Could the authors provide rescue data? It would be great if the authors could show the rescue of centriolar signals of Plk4, Sas-6, and Ana2, in addition to centriole duplication.
C. The authors failed to discriminate which gene is crucial for centriole duplication in GSGs due to the sequence similarity. Linked to the above point, could the authors show the rescue results with either or both Alms1a and/or Alms1b genes? If only one gene can rescue the centriole duplication defects, it would clarify the specific functions of Alms1a and Alms1b in this process.
D. (Optional) Considering the results in the manuscript, the authors appear to have good techniques in genetic manipulation in flies. If so, generating transgenic flies of Alms1a and Alms1b tagged with a degron (e.g., dTAG or destabilization domain [DD]) to observe centriole duplication defects upon rapid protein degradation might be feasible to support their observations. 2. There were no results with endogenous Alms1a and Alms1b in the manuscript. Could the authors provide immunostaining results for endogenous Alms1a and Alms1b? If expansion microscopy is not available due to antibody specificity issues, standard confocal imaging would be sufficient. 3. In figure 3C and D, the authors noted measurements from 7 to 8 testes per group. It would be helpful to also know how many centrosomes were measured, as done in Figure 4C.
Minor comments
- In figure S1, the orientation of the gene is not consistent between endogenous and transgenic Alms1. Could the authors make it consistent for readers to intuitively recognize it? Additionally, the promoter used for transgenic gene expression is not specified. Please provide this information as well.
- On page 5, the statement '~, whereas Alms1b is associated with post-duplication maturation of the centriole." needs revision. The authors observed strong Alms1b-GFP signals in the centrioles at the end of the SC stage or during meiotic division but did no observe any defects in centriole maturation at the current stages. Furthermore, Alms1b was not detected from rapidly duplicating centrioles in syncytial embryos and dividing neuroblasts (Page 6), making it making it hard to understand that that Alms1b has a role in post-duplication centriole maturation.
- In figure S2, the centrosome drawings are unclear (initially thought to be 'v' marks). Could a centrosome drawing be added to the legend for clarity?
Significance
ALMS1 is a well-known protein which is important for cilia formation in human cells. Recent research by Chen and Yamashita (eLife, 2020) highlighted its significance in centrosome duplication in Drosophila's germ line stem cells (GSGs). Brunet and colleagues' findings align with these previous studies but go further by elucidating the localization of Alms1a as a centriolar and pericentriolar material protein, and Alms1b solely as a centriolar protein, using expansion microscopy. Moreover, their research suggests that only acute, not chronic, loss of Alms1 leads to defects in centriole duplication, proposing that cells may develop compensatory mechanisms for Alms1 loss in GSGs.
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Referee #2
Evidence, reproducibility and clarity
The study takes a look at the role of Alms1a and b in centriole formation in Drosophila tissues using genetics and microscopy approaches. Alsm1a RNAi was previously shown to lead to loss of centrioles on the male germ line, but the molecular mechanism of how Alsm1a provides this function is not known. This is what this manuscript addresses.
The general proposal of this manuscript is that Alsm1a is a key regulator of centriole formation in Drosophila tissues (embryos, male germ line and neuroblasts analysed). Alms1a is an inner PCM protein and functions downstream of PLK4 to drive procentriole formation.
This is an interesting advance in understanding of centriole formation in flies. The authors managed to pull of beautiful expansion microscopy and produced images of centrioles both by immunofluorescence and electron microscopy. The data quality is very high, the quantifications lack a little behind in places. The manuscript would benefit from thinking about structure and presentation. The proposal that chronic loss of Alms1a (mutants) is well tolerated while acute (RNAi) is not is a bit puzzling. It is not impossible, but RNAi is not that acute either. It would be important to clarify this. To make sure there is no glitch in the tools, the newly generated alms1 deletions should be better characterised to clarify whether a compensatory mechanism exists, that upon chronic depletion rescues all phenotypes described by RNAi. There are also ways to test whether the Alsm1a fluorescent transgene introduce confounding effects, which if tested would be an improvement (see below). The genetic and immunofluorescence analysis got the authors quite far, the manuscript lacks biochemical analysis to strengthen the proposed molecular mechanism and clarify whether key and easy to predict interactions of Alms1 actually do occur. This would be a big plus but is not limiting. There are also inconsistencies that need clarification, for instance the title of figure 6 is that Alms1 proteins act downstream of Plk4, yet the model in the discussion proposes that Alsm1 stabilises Plk4 which does not fit and hinges on quantification of Plk4 levels that is perhaps currently not robust enough. The statement that Alms1 is required for PLK4 activity is too strong based on the data provided or provide data on PLK4 activity. The attempt to check what is going on in other systems is appreciated, in RPE cells Alms1 comes in only after 120nm elongation. Some of the quantifications could be done more robustly, using ratiometric analsysis rather than directly comparing intensity levels. Referencing in the manuscript and discussion should be improved.
Specific points
- Provide a thorough characterisation of the alsm1 deletions generated using qPCR to measure RNA levels in the flies. Provide whether the alleles are viable and fertile and clarify all genotypes in the manuscript. Can the mutants suppress the Plk4-ND overexpression effect?
- BamGal4 alms1a RNAi, SCs loose centrioles, those that keep centrioles, have Alms1a, but fail to initiate procentriole formation. To strengthen this view please provide nos-Gal4 alms1a RNAi, Alms1a-Tomatoe data showing that now Alms1a-Tomato is not present accept on the mother centrosomes in GCS, ruling out anything unpredicted happens by introducing the Alsm1a-Tomatoe.
- Quantifications, measure fluorescence ratios (e.g. Figure 3 C,D quantify the ratio of Asl/PlP to Bld10, similarly for PLK4-ND in the relevant figure).
- A model for Alms1a in the style of Figur5A would be great.
- What happens to Alsm1a upon Plk4 inhibition? Experiments like this could strengthen the validity of the hierarchy of events proposed.
Other comments
The organisation and presentation should be improved. The figures reorganised to group what belongs together, I would suggest moving human RPE cell analysis to supplementary data and bring the beautiful EM of BamGal4-Alms1 RNAi, Alsm1a-Tomatoe into the main manuscript.
In the deletions asterless levels are reduced in SC but not in the testis tip, why is that?
Conclusion: "while Alms1b is only detected on mature centrioles as it is absent from rapidly duplicating centrioles of syncytial embryos or from duplicating centrioles of dividing neuroblasts." Perhaps add, Alms1b when expressed.
BamGal4 alms1a RNAi, SCs loose centrioles, those that keep centrioles, have Alms1a, but fail to initiate procentriole formation. To strengthen this view please provide nos-Gal4 alms1a RNAi, Alms1a-Tomatoe data showing that now Alms1a-Tomato is not present accept on the mother centrosomes in GCS.
The idea to compare nos-gal4 and bam-gal4 driving Alms1a RNAi is good. Providing a scheme of when these are expressed in the male germ line will help illustrate the experimental strategy. BamGal4 Alsm1a RNAi leads to loss of centrioles in SCs
Alms1b (CG12184) is according to published RNAseq data not expressed in neuroblasts, so it makes sense they do not observe (Knoblich lab data), which could be cited here: "in agreement with RNA-seq data showing low expression of alms1b in neurons and glial cells (Li et al., 2022)."
Fig S5 what cells were analysed by TEM?
Fig S6 monochrome images confirm what is what.
Figure 4 A for clarity it would be helpful to provide landmarks, marker for stem cells (Vasa) or the Hub to be able to understand what we are looking at. Same for Fig 4E, Mira or Dpn? The DAPI staining does not allow in the images provided to identify NBs.
Figure 5 For the fluorescence profile plots it would be nice to see the average plus the standard deviation of the signal in the quantifications.
Figure 6 D is not convincing, how were the dots visible chosen for the quantification?
Figure 1C. Quantification of protein levels is not provided (is this because the expanded ultrastructural approach is not linear precluding quantification?)
Figure 2 B radial dimensions, it would be great to show the sample average and standard deviation.
I don't find figure S2 helpful
Significance
Understanding the process of centriole duplication is an important topic that should be relevant to a broader cell biological community especially since the proteins of interest have disease relevance.. This study provides new insights into how Plk4 dependent centriole duplication takes place in Drosophila.
The manuscript is strong on the microscopic images both immune fluorescence and TEM. The function of Alms proteins is dissected genetically no biochemical analysis is provided, which is a limitation. Another limitation currently is the uncertainty about the discrepancy between the RNAi and the mutant results. If the mutants are confirmed and technical issues can be ruled out the finding that a compensatory mechanism exists that suppresses chronic loss of Alms1 proteins could bar very interesting.
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Referee #1
Evidence, reproducibility and clarity
Much is known about the centriole duplication machinery and the centriole duplication cycle thanks to key screens performed in C.elegans in the past 20-25 years and subsequent work in Drosophila or human cells. It has been proposed that the core duplication machinery consists of 5 proteins implicating Cep192-PLK4-SAS6-SAS5 and SAS4, with specific variations according to the model system or cell line studied. Recently, a role for ALM proteins in the duplication of centrioles in the Drosophila male germ line has been reported, but in this study the mechanism involved has not been identified or described.
In the Brunet study, the authors show a role for Alm proteins in centriole duplication in different tissues in flies- showing that these proteins have more a ubiquitous role in centriole duplication rather than a restricted role in the male germ line as initially described. This is already accounting for the novelty of this paper. However, this study goes well beyond previous studies as it proposes two very novel concepts. The first one is that Alm proteins can stabilize PLK4, the sole kinase implicated in centriole duplication, and so be responsible for maintaining kinase activity during a time window where daughter centrioles are generated. This is extremely interesting and makes a lot of sense and the data is very convincing. The second novel concept is that loss of Alm can have different consequences according to the- I do not even know how to call it- loss condition- leading to Alm deficiency. While I think this is quite novel, interesting and maybe even real, I think the authors conclude very strongly on the differences between a gene knock out or gene knock down conditions. I think that they may want to tune down their conclusions related to this part, as many more data would be required to conclude in this way.
In other words, I think the paper is of high interest to a broad field of cell biology with interests in centrosome, cilia and the regulation of centriole duplication.
Major points:
- I did not find a rescue experiment of the Alm deletions with the Alm transgenes.
- If I understood the authors correctly, Alm def compensate for centrosome duplication by a yet unknown process, while Gal4-induced depletions do not. First, calling Gal4- induce RNAi- acute depletion is not correct. This is certainly not acute and so another designation has to be found. Acute is something like a degron such as Auxin or dTag where the protein is degraded in a very acute manner. RNAi targets the mRNA, so if the protein is already made, it will not suffer from the RNAi treatment. Second, are the authors sure that either their crispr strategy did not generate any other knock out- hence the essentiality of rescuing these mutations. Or alternatively, are they sure about their RNAi conditions? So can they add the RNAi conditions on the background of their deficiency and see that nothing changes? Third if depletion through RNAi indeed leads to a more evident role of Alm proteins, one is expected to see this over time. Can they do a clone-using an FRT site recombined with their Alm mutation, so that the initial cell divisions does not contain Alm, and so it is expected to fail duplication, which may be overcome with time? So a large clone might have progeny with cells with centrosomes (the young ones) and without (the older ones). Can they show that RNAi depletion in cells that will generate sensory cilia- these are not assembled? Because I am assuming that the mutants are not uncoordinated...
- If they think that Almdef flies compensate for Alm loss - can they analyse levels of PLK4, SAS6, Ana2 and SAS4 in the mother centrioles?
Minor points
- Some figures (the majority) lack scale bars.
- I do not think that one can consider centrioles that are not in rosettes to be made de novo. They might just have disengaged. The "novo" centriole should be removed. Actually, PLK4 ND generates extra centrosomes, this is sufficient.
Significance
The article by Brunet and colleagues investigates the role of ALMs proteins in centriole duplication. Centrioles are the core constituents of centrosomes and as such contribute to microtubule nucleation. Centrioles can behave as basal bodies, providing essential function sin cilia assembly and function.
Here the authors have use Drosophila to characterize the role of Alm proteins. They show that 2 isoforms with distinct behaviour are expressed with different localizations. Further, through the assessment of different tissues and loss-of-function conditions, they propose a role for Alm proteins in centriole duplication.
Overall the paper is very well written and easy to follow.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors performed extensive genetic analyses in yeast on the functions of Med15 regions under multiple stress conditions, linking cell growth phenotypes, gene expression and protein-protein interaction. Med15 is an activator-contacting subunit of the Mediator complex and the functions of yeast Med15 have been extensively studied. In particular, the authors attempted to understand the roles of a poly-Q region (Q1), its length and composition in stress response phenotypes, Med15-mediated gene expression, and interaction with transcription factor Msn2. Results from this work are consistent with several previous studies and revealed some new insights. The authors concluded that robust Med15 activities required the Q1 tract and the length of Q1 tract modulates activity in a context-dependent manner. While the study is well executed and the conclusions are generally sound, several concerns listed below should be addressed and some clarifications should be made.
Major comments:
- Abstract, "We also observed that distinct glutamine tracts and Med15 phosphorylation affected the activities of the KIX domain". Fig. 1 shows the effects of KIXQ2Q3 deletion and p7 phosphor-dead mutant under Acetic acid and Ketoconazole treatment, but does not demonstrate that these domains or phosphorylation affects KIX domain activities.
- Is it known that Med15 is dephosphorylated under stress conditions other than osmotic challenge? Another explanation for D7P/D30P mutant results (Fig. 1B) might be that Med15 phosphorylation in unstressed cells is important for certain types of stress response (acetic acid and Keto). In contrast, the observation that D7P has no effects on osmotic stress (Fig. 1B) might suggest that phosphor-Med15 is dispensable for function. Some explanations on how to ascertain the roles of Med15 phosphorylation would be needed.
- Fig. 4, what is the rationale of analyzing basal expression rather than activated expression of Gcn4 and Msn2 dependent genes? Gal4 and Hap5 dependent genes could be measured as well, in order to complete the gene expression-phenotype correlation that the authors strive to make in this paper.
- Fig. 4, Error bars should be provided on gene expression analysis. Gcn4 and Msn2 target genes should be highlighted separately to facilitate comparisons.
- Results from Fig. 4 and 5 indicate that Spacer-Q1 and 12PQ-Q1, being the strongest interactors to Msn2, actually reduced HSP12 expression (a known Msn2 target gene). Some explanations would be needed. Page 12 Discussion paragraph 2 "Q1 substitutions that interfered with coiled-coil propensity had no effect on TF activity" would need to be revised and to include some discussions on this result.
- Fig. 5C, additional explanation is needed on how interaction rank is determined and how error bars are obtained.
- The idea that Q1 provides a molecular hinge to facilitate intramolecular interactions is interesting, but sounds like a possible scenario without further evidence. Are there any published structural studies on Med15/Mediator complex that might support this idea?
Minor comments:
- SC-HLUM and SC-HLMU is used interchangeably in the legend and text. Please keep consistent. Explanations for these acronyms are not found in the Methods.
- Fig. 2, 3. AcOH should be Acetic acid, to be consistent with Fig. 1.
- Fig. 3B, error bars should be provided for growth measurements.
Significance
This work provided a detailed analysis on the roles of a specific poly-glutamine region in yeast Med15 functions and regulation. One conceptual advance of this work is that the structural flexibility rather than the sequence itself of Q1 tract proves to be critical for Med15 function. The ability to correlate Med15-Msn2 interaction with gene expression analysis demonstrated some technical novelty, given the power of genetic manipulation in yeast.
Med15 is a key Mediator subunit contacting several sequence-specific transcription activators. Its interaction with a number of transcription activators in yeast such as Gcn4 and Gal4, was previously studied as referenced in this manuscript. This manuscript first provided a quite comprehensive genetic mutational analysis and confirmed several findings in previous studies. The identification of critical Med15 regions for acetic acid response (and Hap5-dependent gene activation) and the analysis of Msn2-Med15 interactions appear to be novel. Researchers interested in eukaryotic transcriptional regulation would benefit from reading this study.
Field of expertise of this reviewer: mechanisms of transcriptional regulation, genetics, nuclear organization and function
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Referee #2
Evidence, reproducibility and clarity
Summary:
The Mediator complex, a multicomponent complex, regulates the interaction between transcription factors and RNA polymerase II using protein interactions. In particular, Intrinsically Disordered Regions (IDRs) with in Med15. Copper and Frassler made extensive mutations to the three IDRs that are characterized with high glutamate content (poly-Q), the KIX domain which interacts with transcription factors and the MAD which interacts with the rest of the Mediator complex. The three poly-Q repeats are adjacent to Activator Binding Domains (ABD). The impact of mutant Med15 was measured with growth assays, co-IPs with a transcription factor, and transcriptional activation of a reporter during different stresses. Med15 is particular important in stress responsive transcription rather than basal transcription because in yeast it is nonessential. It can be repress and activation translation of genes. Using a series of internal deletions and substitutions impact of the mutations was tested on by measuring growth of strains, expression of Med15 regulated genes, and interaction with Msn2, a transcription factor that regulates to various stresses. This work adds to the body of research confirming that multiple weak/ transient interaction domains regulate Med15 function.
Major comments:
- Without knowing the protein levels of the different mutants, it is difficult to contribute deletions of different regions with phenotypes measured. Various internal deletions decrease Med15 protein levels (Jedidi et al. 2010) while other affect the integrity of the Mediator complex. This study did not measure mRNA or protein levels of their mutants. However, (Jedidi et al. 2010) used Myc-tagged Med15 which affects regulation of Med15 via SNF1 (Gallagher et al. 2020). In another study, Med15 was N-terminally tagged and protein levels of some deletions increased (Herbig et al. 2010). It's unknown if other tags such as HA, TAP or FLAG affect Med15 regulation via SNF1. This study used untagged Med15 expressed from the native promoter which avoids these complications. It's also unknown if the differences in Med15 deletions are from reduced transcription, translation, or protein stability. There are commercial antibody that may work (https://www.genetex.com/Product/Detail/Gal-11-S-cerevisiae-antibody/GTX64110). There are several commercial antibodies to human Med15 but the cross reactivity has not been tested.
- Quantification with ImageJ on spot assays is difficult because once growth has maxed out on the dense spots there is no resolution. Using more dilute spots is challenging because colony size is affected by the nearby colonies. The error bars are the mutants are large. Can quantitative growth curves be carried out in flasks or an automatic plate reader for better quantification?
Minor comments:
- Why were the stress conditions chosen in figure 1B? These are only a subset of conditions that the med15 deletion is sensitive to. Aside from acetic acid the phenotypic profile of each deletion is similar. The bigger the deletion, the more severe the growth defect. The keto plate appears under loaded by comparing the number of colonies in the third dilution on the keto plate and the fourth spot on the YPD plate in the BY4742 (MED15 wild-type strain). Does keto lyse cells? Or was there that much variation between mutants? Perhaps the dose of keto needs to be calibrated. In figure 2B it looks like wildtype growth in keto was 70% of untreated growth.
- Figures 1C and 1D are not discussed in the results. The authors should remind what the hGR assay measures when discussing the results. How is it different from the GAL4 transcriptional reporter?
- In the D to A mutants some appear to be required for acetic acid tolerance. What was the pH of the media?
- The labels between Figure 1 and 2 are inconsistent. 90 mM acetic versus 80mM AcOH, YPGal versus YPGalactose, SD+LKHU versus SD+KLUH. The mutants on 0.97M NaCl at 37oC from figure 2A grew more than 0.9M NaCl at 38oC. Also, in the text it says 37 oC.
- Is the MED15 strain BY4742? In Figure 1 was it also transformed the pRS315? How was the plasmid maintained on the plates, specifically YPD?
- Genes such as GAL and URA3 should in italics.
- In the split Ub assays, was wildtype Msn2 and Med15 also present?
- There is inconsistently naming of media in Media and Phenotype Testing section. The media is called synthetic complete media is labeled SC-URA or SC-LEU and at times in Results its called SD+K or SC-HULM. Is SC with all amino acids and SD without any amino acids?
- The plasmid names in the supplemental table don't match the ones labeled in the figures.
- Why was ALG9 used for normalization of qRT PCR?
- The background of the strains is confusing. There appears to be two different med15 knockouts OY320 and JF1368. Which ones were used in which experiments? Some of the trains have a trp1 auxotrophic which affects stress response on it's own (González et al. 2008; Schroeder and Ikui 2019).
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? Extensive mutational analysis of Med15.
- 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 work confirms numerous other studies on the contribution of various Med15 domains on function.
- 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? Incorporation of human domain substitutions could influence how people outside the field would interpret how Med15 interacts with transcription factors.
- 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. Expert in using yeast genetics and natural genetic variation to address underlying mechanisms of stress response to environmental toxins with a particular focus on transcription factors and TORC1.
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Referee #1
Evidence, reproducibility and clarity
Major Comments
The authors show that the amino acid content and length of Q1 affects transcription activity in a media-dependent way in a construct that includes Q1-ABD1 and a tailing Q/N rich region (Q1R). Briefly, different media conditions used as proxies for specific target TF activities varied in their sensitivity to the Q1 sequence content. However, the reason for this variation between target TF activities is not addressed, so the observations seem more anecdotal than insightful. One test performed suggests some of the Q1 sequence dependence may be due to changes in AD-ABD interactions, but this interesting possibility is not investigated further.
A split-ubiquitin two-hybrid assay, meant to detect interactions between Msn2 TAD and Med15-Q1R, showed clear Q1 sequence/composition-dependence when changed from polyQ tracts. In particular, replacement with leucine-rich tracts (12L and RvHs) significantly reduced interactions (as inferred from growth requirements in Fig 5B). Q1 consisting of just 10 spacer residues, 0 to 24 Q residues, or PQ repeats all had quite similar results suggesting retention of some Msn2 and Med15 interactions. Replacement with a helix-forming sequence from NAB3 gave intermediate results. Again, no explanation was offered for the observation but it seems probable that the NAB3 Q1 system is no longer reporting on Msn2 Med15 interactions.
The manuscript presents extensive assays, but a lack of consistency in conditions and constructs tested makes comparing different assays difficult. In particular, it would be valuable to have NAB3-Q1, FrHs-Q1, and RvHS-Q1 tested under conditions of high salt as that is indicated to be the Msn2 target condition (e.g. an additional result that would be presented in Fig 3B); this would be valuable to compare to the two-hybrid results. The relationship between Q1 polyQ length and Msn2 TAD-Med15 ABD1 binding is not clear from this assay as all had similar growth on the plates. A possible explanation for the inferred reduction in TAD-ABD1 binding in the leucine rich Q1 constructs is that this highly hydrophobic linker itself binds to ABD1 and is therefore self-inhibitory. There is also the unexplored/not discussed possibility that NAB3, 12L, and RvHs have off-target interactions that disrupt the TAD-ABD1 interactions.
The framing of the study and the title of the manuscript strongly suggest that there might be a relationship between coiled-coil formation and transcription activity. This is the basis for selection of many of the Q1 sequences tested, with the premise of either increasing or disrupting coiled-coil structure. These 'propensities' are quantified in Supp Fig 1; however, a significant limitation of this interpretation is that these propensities are bulk properties that presume formation of homo-dimers or homo-trimers, a situation that is not shown to be relevant for Med15 at a promoter. This means that Q1 is potentially only one of the multiple partners required for coiled-coil formation. So even if a tested sequence has high coiled-coil propensity, that may not be the case in the actual biological systems at play here. Another consideration to be entertained is how different solvent conditions (different media) may affect coiled-coil propensity. An unanswered question is whether Q1 may form coiled-coil structure either with other regions of Med15 and/or with other Mediator subunits or even other co-factors entirely. This is a question implied by the title of this paper, but the data presented address neither intra- nor inter-molecular interactions of the polyQ regions (the two-hybrid study is designed to probe the ABD-AD interactions).
A final proposed hypothesis was that Q1 acts as a hinge in a way analogous to what was reported for the huntingtin protein (ref 7). This is an attractive model but remains untested in this work. In particular, the Med15-Q1R construct used does not have multiple ABDs that would potentially be brought in close contact, so the results here cannot be interpreted as analogous to the huntingtin hinge model. Minor Comments:
Please explain the choice of the 10-residue spacer instead of a 12-residue spacer.
Page 14: "We observed that Q1 substitutions with increased coiled-coil propensity (Supplementary Figure 1) diminished TF activity while Q1 substitutions that interfered with coiled-coil propensity had no effect on TF activity (Fig. 3, 4), suggesting that the flexibility of the sequence is an important feature." There was no demonstration that those sequences in this context form CCs. There's no evidence of what is actually being modulated whether it's length, flexibility, or ability to interact with other regions of Med15 or even with other co-factors.
Page 15: "We confirmed that Msn2-dependent activities of Med15 are encoded by the region containing the Q1 tract and ABD1 (aa 116-277) and found that the KIX domain alone could also mediate an interaction with Msn2 (Fig. 5). This contrasts with the Gcn4- or Gal4-dependent growth or stress responses which are the result of additive interactions with Med15 that are characterized by weak, highly dispersed, multivalent interfaces. While it is not yet entirely clear if the interaction with Msn2 is similarly multivalent, we have shown that either the KIX domain alone or the Q1R region alone of Med15 was sufficient with no evidence of additivity." These statements are unsupported. While Gcn4 and Gal4 transcription activity has been shown to depend on multiple AD-ABD interactions, none of the data reported here shows that Msn2 does not (as is stated here, which undermines the "contrasts" argument. Further, based on the assays presented in Figure 1B, Msn2, Gal4, and Gcn4 behave similarly for the various Med15 constructs.
Page 16: "In all instances TF activity was reduced in the absence of the Med15 Q1 tract." This seems false based on the data presented. Met10 activity appears to have increased in Figure 4A.
Page 16: Reference to Figure 2C and Figure 2B are mislabeled. Should be Figure 2D and 2C, respectively.
Page 17: "The fact that residues at Q1 were not functionally constrained to be glutamine residues suggests the Q1 tract is not an interaction motif participating directly in protein-protein interactions." This is completely unsupported. There are no data presented that address interactions between Q1 and anything else.
Figure 2: Not clear which assays were at 30{degree sign}C vs 22{degree sign}C as they are not labeled in the figure. In Figure 2A, the label med15 should be med15Δ.
Figure 4: Interpretation of these results seems limited by only reporting YPD media conditions. May be helpful to include the conditions reported in Figure 1.
Figure 6: It is not clear what some elements of this figure are meant to represent. Is saw tooth always polyQ? or Is ABD1 always blue and ABD2 is always red. What then are the loops? The general premise of this figure does not seem to be supported by the actual experiments performed.
Supp Figure 3: "K is the Med15 fragment encompassing the KIX domain, aa 1-277." This aa range is KQ in the main text. Either the residue range is wrong, or the label is wrong.
Significance
This manuscript addresses an interesting topic. There appears to be a disconnect between the stated motivation and what was actually done. The large array of assays and conditions are difficult to compare, leaving the reader with a feeling that the authors have catalogued a lot of possibilities but that no generalizable or unifying insights are at hand. The attempt to present a model (Figure 6) is difficult to parse and is not directly supported by the data presented. Addressing the issues raised here could result in a work that is useful to the specific field of Med15 structure and function but of limited use at the moment to a wider audience.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript by Nabeel-Shah et al. the authors identify ZBTB48 as a novel interactor of FTO. They show that ZBTB48 helps recruiting FTO to mRNA as well as the noncoding RNA Terra. Their results further suggest that this recruitment is required for FTO to demethylated m6A and m6Am RNA modification in target RNAs. This affects cellular rates of RNA turnover. Furthermore, the mechanism is involved in repressing colorectal cancer cell growth.
Overall, the authors present a new role of ZBTB48 in m6A and m6Am mediated RNA metabolism. They also suggest a nice model of ZBTB48 action, via the recruitment of the demethylase FTO. These findings will be of interest for the general RNA community and might be also relevant for cancer treatment. However, I have some concerns about the quality and analysis of the obtained data, especially the iCLIP and miCLIP experiments. The concerns are detailed below and should be addressed before publication.
Major concerns:
- Figure 1B) I think that the results shown in the autoradiograph are not very convincing and suggest that the purification of ZBTB48 is not very clean. The radioactive signal covers the lane from 50 kDa to 200 kDa. The ZBTB48 alone is running around 80 kDa. If the purification is specific most of the signal should be above 80 kDa. Maybe it helps to also use higher RNAse concentrations: Having very short RNA pieces will allow to evaluate specificity of the purification, since protein-RNA complexes will just a bit above the size of the protein. This also applies to Figures S1E,F.
This concern could also apply to the generated iCLIP libraries and indicates that at least part of the obtained reads does not originate from ZBTB48 crosslinked RNA. The validation of protein-RNA interactions with RIP shown in Figure S2E supports the quality of the iCLIP data. Here some control RNAs should be analyzed to show that the RIP is not unspecifically enriching any RNA. 2. Regarding the co-occurrence of m6A sites as well as FTO and ZBTB48 binding sites shown in Figures 2B, C, F and G. The CLIP signal is a lot affected by crosslinking bias and read mappability. Therefore, to make these results more convincing it would be important to include additional iCLIP datasets (published other RBPs) for comparison. 3. Regarding Figure 2D and related analyses: I very much like this experiment and the results obtained here! I just wondered where the remaining reads go in the ZBTB48 knockdown. The introns? Maybe this becomes clearer in the meta profile representation used in Figure 1F.
For me the changes in the 5' UTR look most dramatic. Maybe this means that ZBTB48 is most important for recruitment of FTO to m6Am sites. Therefore, I think it would be good to differentiate the analyses in the remainder of the manuscript for m6A and m6Am sites. I first step would be to treat sites in the 5'UTR separate from the CDS and 3'UTR sites in the following analysis. 4. I have some concerns about the analysis of the miCLIP data. In Figure 3A antibody crosslinking in all conditions appears similar. Yet in panel B there seems less signal for FTO and ZBTB48 overexpression in all areas. Have there been more reads generated for the GFP control? Where is the rest of the reads going? I think it would be required here to identify peaks that significantly change between the conditions. Then ask do those peaks coincide with DRACH and were are they located. Also, in the Genome browser pictures the signal is going down at all locations. Why is that? Usually, miCLIP generates a lot of background peaks. These should be unchanged. 5. I am a bit confused by the author's interpretation of the results shown in Figure 5E. For me this plot shows that target transcripts are less downregulated and less upregulated than non-targets? Basically they are less regulated overall. In this context I also think that the representation in of the mean in figure 5E and F is misleading. Data distribution should be visible as violin or boxplot.
Minor points:
Figure 1D) I think the legend in the panel is confusing and does not add information. Especially since its in a different order than the categories on the y-Axis.
Figure S2D) I wondered if the authors controlled for mappability of reads when picking random sites. How do the authors account for that in iCLIP there will be less reads in introns compared to exons?
5E,F boxplots would be more suitable than barplots.xx
Figure 2I: Please do not use "+ve"
Figures with Microscopy pictures of cells have no scale bars or way too small.
The model that is shown in figure 7 is somehow misleading as it shows FTO binding only to ZBTB48 and not to the RNA.
Significance
Overall, the authors present a new role of ZBTB48 in m6A and m6Am mediated RNA metabolism. They also suggest a nice model of ZBTB48 action, via the recruitment of the demethylase FTO. These findings will be of interest for the general RNA community and might be also relevant for cancer treatment. However, I have some concerns about the quality and analysis of the obtained data, especially the iCLIP and miCLIP experiments. The concerns are detailed below and should be addressed before publication.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, Nabeel-Shah et al. identified that the telomeric zinc finger protein ZBTB48 help recruit FTO onto target RNA, including mRNAs and the telomere-associated regulatory RNA TERRA, to achieve cellular RNA m6A/m6Am demethylation, thereby regulating cell biology such as tumour growth studied in this study. The biochemistry and molecular biology experiments were done in HEK293 cells, and the cell models for tumour growth was done in HCT116 cells. However, I have identified both major and minor concerns that, when addressed, could further strengthen the findings and enhance the impact on the field.
Major concerns
- Regarding the ZBTB48/FTO targets and m6A/m6Am level that are regulated by ZBTB48/FTO axis. Wei et al Molecular Cell 2018 (PMID: 30197295) (Figure 1B) quantified the m6A/m6Am levels upon knockdown of FTO in HeLa, HEK293, and 3T3-L1 cells, and found that the m6A/m6Am levels are generally mildly (10-20%) yet significantly upregulated on poly(A)+ RNAs. Here shown in the Figure 4A in this manuscript, the authors show that (1) siFTO and siZBTB48 led to ~2-3 fold upregulation of m6A and m6Am levels using total RNAs (dominant by rRNA in the population), and (2) that the m6A and m6Am levels are similar between siFTO and siZBTB48. Regarding (1), can the authors explain the discrepancy? This point is also relevant to the m6AIP-qRT-PCR results in this manuscript. Regarding (2), does this result suggest that ZBTB48 helps FTO to demethylate nearly all its targets, rather than a subset?
- Considering the significance of how FTO achieves target specificity, can the author anticipate the extent of applicability of the proposed model? Does the interaction between ZBTB48 and FTO also exist in various human and mouse cell lines? If confirmed, this discovery would hold substantial value for the field. This interaction at least needs to be confirmed in HCT116 cells used for tumour growth model in this study.
Minor concerns.
- The authors realised that knockdown of ZBTB48 does not change FTO levels, whereas overexpression of ZBTB48 leads to elevated FTO. It is unclear about the rationale behind overexpression studies?
- Considering the multifunctional nature of ZBTB48, it's important to disentangle transcriptional and post-transcriptional roles of ZBTB48 to draw conclusions. I appreciate the analysis conducted in this manuscript. Is it possible to overexpress an DNA-binding mutant of ZBTB48 or RNA-binding mutant of ZBTB48 proteins?
- In Fig 2B, the innet panel does not match the metagene profile regarding the difference.
- In Fig 5H, there is a lack of experiments for comparison, i.e. siFTO and o/e FTO.
- Page 3, "Genome-wide studies" should be "Transcriptome-wide studies".
Significance
Strengths: The biochemistry and molecular biology experiments are comprehensive and well designed. The analysis is robust, and the conclusions generally align with the presented data.
Limitations: Cell line-specific and/or species-specific interaction?
Advance: filling a gap in our knowledge of how FTO achieves target specificity.
Audience: Basica research in the field of RNA modifications and RNA Biology.
My expertise: Bioinformatics, gene regulation by RNA m6A modification
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Referee #3
Evidence, reproducibility and clarity
The authors expand upon a previous method (PROMIS) that was developed to detect metabolite-protein interactions from cell extracts through co-elution of proteins and metabolites in size exclusion chromatography. Here, the authors use ion exchange chromatography. By comparing PROMIS and IEX datasets, the authors increase the confidence of detected interactions being true hits. The addition of IEX data significantly improves the power of the method to identify previously known/predicted protein-metabolite interactions. The authors also purify two proteins identified by the method and experimentally validates their novel interactions with dipeptide metabolites.
In general the paper is well written and provides a clear assessment of the two methods. The validation of two interactions is welcomed.
There are some issues that could be addressed with better clarification:
- While the two datasets for omniTICC appear to show good overlap, the two PROMIS replicates appear to have very little. Due to this low overlap, one of the metabolite interactions chosen for validation (dipeptide) was chosen by manually lowering the correlation cutoff of PROMIS as well as relying on two previous PROMIS datasets. Authors could comment on possible reasons for this low overlap.
- Experimentally validating the effect of Val-Leu on the enzyme activity of FabF would be a very good addition, but is not crucial to the paper.
Minor:
Line 173: In STITCH the authors find 1012 known or predicted interactions, while their method finds 92 of these interactions. Authors could comment on limitation of the unbiased method which may lead to missing these interactions (for example, sensitivity of MS detection of low abundance metabolites).
Minor: - Authors could comment on what are some drawbacks to using docking to estimate binding sites. Computational screening could be a powerful way to prioritize hits for validation, so could be worth a more detailed discussion here. - In figure 2 the color labelling of the experiments could be presented more clearly. - Authors could add in the text or methods how they calculate the Pearson correlation coefficient they use for determining significance. Overall the methods are well presented and the technical approach is very well described and impressive. In general the authors present the statistics such as likelihood over chance detection, in a way that helps to evaluate the accuracy of the separate and combined CF approaches.
Some comments:
- The PROMIS method and the omniTICC method are performed only in duplicate, where the two PROMIS experiments also differ in proteomics workflow. Could that be why the overlap between PROMIS experiments is so poor? Why not perform the experiments in biological triplicates?
- Authors correct for poor replicate overlap by lowering the requirements of one subset of metabolites. What's the argument for not doing this with all metabolite subsets? Would this interaction have been discovered just by using PROMIS in three different experiments (i.e. more biological replicates)? The benefit of adding the IEX analysis is clouded by the poor overlap of PROMIS data
- Line 353 - It would also be good to elaborate as to why other methods would be likely to miss this interaction. Is it because the authors method does not require the users to choose specific metabolites or proteins to focus on beforehand?
- Authors could comment on whether any of the high confidence interactions were also been observed in different IP experiments with E. coli (such the Lip-SMAP method reported by Piazza et al., 2018).
- Authors could comment on other ways to identify the potential binding site. For example, limited proteolysis of the protein in presence of metabolite has been used previously
- There is massive interconnectivity between NMPs and ribosomal proteins. It would be good to comment on this. Do the authors believe these are true interactions? Could there be another reason for this cluster? Do the STITCH interactions also show that these proteins interact with so many NMPs?
- Line 157 - The authors state that "isopropylmalate co-eluate with tens of proteins". This feels like a bit of an understatement if the actual numbers are: 92, 119, 303, 287
Minor:
- How did the number 1479 proteins come about? Are those detected in all 4 datasets? What about those detected in only some datasets? This information could be more clear
Significance
General assessment
The paper is a welcome comparison of using two co-elution MS methods for identifying protein-metabolite interactions. It is clear that these types of interactions are important for modulating protein activity but are not well studied. The paper provides a clear workflow for both the experimental and data analysis portions of interaction proteomics. The relatively low number of validated hits could limit its significance outside the specialised field.
Advance.
The paper makes a conceptual advance in interaction proteomics. It is perhaps not unexpected that combining two different interaction proteomics methods gives more accurate target identification than a single method alone. The paper also strengthens the evidence that dipeptides play a role in regulation that may be conserved in bacteria and plants.
Audience.
The audience for this paper is likely researchers that are actively involved in the field of interaction proteomics. The link between dipeptides and feedback regulation has been developed by the group over a few papers, and this report provides further evidence. The paper also shows that a weak kinetic effect in vitro can actually lead to a significant effect in vivo, which is a very interesting finding that is a good point of reference for future in vitro validation efforts. Often the effect of metabolites in vitro is weak, which could lead to the (possibly false) conclusion that it is not relevant in vivo (see e.g. weak effects of metabolites on enzyme activity in vitro in a recent LiP-SMap paper (https://doi.org/10.1038/s42003-023-05318-8)
Reviewer background:
Reviewers are active in the field of interaction proteomics and have previously used the Lip-SMAP method as well as thermal proteome shift method.
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Referee #2
Evidence, reproducibility and clarity
In this work, Wagner et al aimed to comprehensively map protein-metabolite interaction in E. coli. They do so by extending a previously developed SEC-based co-elution method with an IEX-based co-elution method. The results is a more extensive and robust protein-metabolite network. As a validation, selected predicted protein-metabolite interactions are confirmed at the level of binding, as well as enzyme kinetics and even phenotype.
The key conclusions of the paper are mostly convincing, even more so because the authors provide a critical evaluation of the method. My only concern is that the results from the PROMIS replicates seem to be quite different (e.g. Fig 1h and Suppl Fig 1a). One wonders whether, given this apparent variation, two replicates are sufficient to define the protein-metabolite interactions with great confidence.
The author took care to make all data (proteomics & metabolomics) publicly available and the methods are clearly described.
The manuscript is well-written and the figures are clear, although Fig 1 would benefit from and explanation of the color coding in the legend. Personally, I feel that less emphasis on lumichrome would help focus the conclusion section.
Significance
This work is of high significance because it describes a method to comprehensively map protein-metabolite interaction that could relatively easily be applied to any organism. The work is of high quality and similar to work by for example Link et al (https://doi.org/10.1038/nbt.2489) or Piazza et al (https://doi.org/10.1016/j.cell.2017.12.006), but technically less challenging which increases its potential impact.
Beyond these technical advances, global studies like these are a great resource for anyone working on the functional characterization of proteins. Often, a (predicted) protein-metabolite interaction can be a crucial lead to find the function of a protein.
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, the authors demonstrate a strategy for uncovering metabolite-protein interactions, rooted in the concept of identifying co-fractionating metabolites and proteins through independent analyses of each fraction using untargeted proteomics and metabolomics. This approach is applied to E. coli protein lysates.
The same research group has previously advocated for utilizing the simultaneous elution of metabolites and proteins as an indication of interactions, as seen in Luzarowski et al. (2019) and Veyel et al. (2017), where metabolite-protein interactions in yeast and plants were investigated, respectively. Here, a variation of the original proposed method (PROMIS) is introduced, involving an additional chromatography separation (ion exchange) alongside the original size exclusion separation proposed in PROMIS. By incorporating an extra dimension to reduce sample complexity, both experimental and data analysis efforts are effectively doubled, entailing the processing of 48 additional fractions for proteomic and metabolomic analyses, in addition to the 40 already included in the original PROMIS protocol.
While previous studies have demonstrated the isolation of small molecule-protein complexes following ion-exchange based separation (Chan et al., 2012), the method described here does not introduce any fundamentally new concepts. However, it remains to be determined whether the addition of an extra separation dimension genuinely aids in accurately classifying new interactions. Although coincidental co-elution effects may be mitigated, there is a risk of disrupting genuine complexes through excessive handling of lysates.
In my view, there are several points that require clarification:
- Line 138: There's an assumption that the metabolite profile will alter in IEX upon binding to proteins. While this might occur, it's not definite. Additionally, it's unclear why peaks associated with protein binding could also increase in intensity, and how this signifies a binding event.
- Lines 147-154: The most crucial dataset in this paper isn't adequately delineated here. We learn that by combining SEC and IEX, 1479 proteins and 58 metabolites are identified, but what about when only one separation method is employed? What advantages does using IEX provide?
- Line 170: Similar to point 2 regarding PMI.
- Line 174: To gauge whether the proposed strategy aids in accurately classifying new interactions, the authors examined if their predicted interactions also appear in the STITCH database. Out of the 994 PMI in the network, 92 were found in STITCH. I'm uncertain if STITCH is the most suitable metric for this assessment, given it likely hasn't been updated since 2016. How does this PROMIS-IEX protocol mirror known interactions in E. coli's central carbon metabolism, for instance, such as those detailed in this publication: doi: 10.15252/msb.20199008?
- Certain details of Figure 1 are challenging to grasp and inadequately explained in the figure legend. What do the colours of the heatmap in 1d represent?
- In Figure 1h, the proteins co-eluting with 2-isopropylmalic acid in the four separations decrease to 5 from tens of proteins, with only one known interactor of 2-isopropylmalic acid (LeuA) among them. Is this outcome favourable or unfavourable? Are the other four proteins false positives?
- Figure 2b: Why is this expected to work particularly well for NMPs? Is there a specific biological rationale behind it?
Furthermore, among the classified interactions, two were corroborated through microscale thermophoresis and protein docking. One involved the enzyme FabF and the dipeptide Val-Leu. The decision to delve deeper into this pair likely stemmed from the prior focus on dipeptide-protein interactions, extensively discussed in Luzarowski et al.'s 2019 manuscript. The second interaction pertained to lumicrome and PyrE.
Significance
To summarise, this paper advocates for heightening the complexity of experimental and computational analyses in studying metabolite-protein interactions through co-fractionation techniques. While it's anticipated that increased separation would enhance results, I remain unconvinced that the data presented conclusively demonstrates this. Overall, I believe the proposed method possesses only a modest level of originality and novelty, as outlined at the beginning of my review. Nonetheless, the substantial experimental effort and data generation warrant publication following additional meticulous quality control evaluation.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors performed extensive genetic analyses in yeast on the functions of Med15 regions under multiple stress conditions, linking cell growth phenotypes, gene expression and protein-protein interaction. Med15 is an activator-contacting subunit of the Mediator complex and the functions of yeast Med15 have been extensively studied. In particular, the authors attempted to understand the roles of a poly-Q region (Q1), its length and composition in stress response phenotypes, Med15-mediated gene expression, and interaction with transcription factor Msn2. Results from this work are consistent with several previous studies and revealed some new insights. The authors concluded that robust Med15 activities required the Q1 tract and the length of Q1 tract modulates activity in a context-dependent manner. While the study is well executed and the conclusions are generally sound, several concerns listed below should be addressed and some clarifications should be made.
Major comments:
- Abstract, "We also observed that distinct glutamine tracts and Med15 phosphorylation affected the activities of the KIX domain". Fig. 1 shows the effects of KIXQ2Q3 deletion and p7 phosphor-dead mutant under Acetic acid and Ketoconazole treatment, but does not demonstrate that these domains or phosphorylation affects KIX domain activities.
- Is it known that Med15 is dephosphorylated under stress conditions other than osmotic challenge? Another explanation for D7P/D30P mutant results (Fig. 1B) might be that Med15 phosphorylation in unstressed cells is important for certain types of stress response (acetic acid and Keto). In contrast, the observation that D7P has no effects on osmotic stress (Fig. 1B) might suggest that phosphor-Med15 is dispensable for function. Some explanations on how to ascertain the roles of Med15 phosphorylation would be needed.
- Fig. 4, what is the rationale of analyzing basal expression rather than activated expression of Gcn4 and Msn2 dependent genes? Gal4 and Hap5 dependent genes could be measured as well, in order to complete the gene expression-phenotype correlation that the authors strive to make in this paper.
- Fig. 4, Error bars should be provided on gene expression analysis. Gcn4 and Msn2 target genes should be highlighted separately to facilitate comparisons.
- Results from Fig. 4 and 5 indicate that Spacer-Q1 and 12PQ-Q1, being the strongest interactors to Msn2, actually reduced HSP12 expression (a known Msn2 target gene). Some explanations would be needed. Page 12 Discussion paragraph 2 "Q1 substitutions that interfered with coiled-coil propensity had no effect on TF activity" would need to be revised and to include some discussions on this result.
- Fig. 5C, additional explanation is needed on how interaction rank is determined and how error bars are obtained.
- The idea that Q1 provides a molecular hinge to facilitate intramolecular interactions is interesting, but sounds like a possible scenario without further evidence. Are there any published structural studies on Med15/Mediator complex that might support this idea?
Minor comments:
- SC-HLUM and SC-HLMU is used interchangeably in the legend and text. Please keep consistent. Explanations for these acronyms are not found in the Methods.
- Fig. 2, 3. AcOH should be Acetic acid, to be consistent with Fig. 1.
- Fig. 3B, error bars should be provided for growth measurements.
Significance
This work provided a detailed analysis on the roles of a specific poly-glutamine region in yeast Med15 functions and regulation. One conceptual advance of this work is that the structural flexibility rather than the sequence itself of Q1 tract proves to be critical for Med15 function. The ability to correlate Med15-Msn2 interaction with gene expression analysis demonstrated some technical novelty, given the power of genetic manipulation in yeast.
Med15 is a key Mediator subunit contacting several sequence-specific transcription activators. Its interaction with a number of transcription activators in yeast such as Gcn4 and Gal4, was previously studied as referenced in this manuscript. This manuscript first provided a quite comprehensive genetic mutational analysis and confirmed several findings in previous studies. The identification of critical Med15 regions for acetic acid response (and Hap5-dependent gene activation) and the analysis of Msn2-Med15 interactions appear to be novel. Researchers interested in eukaryotic transcriptional regulation would benefit from reading this study.
Field of expertise of this reviewer: mechanisms of transcriptional regulation, genetics, nuclear organization and function
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Referee #2
Evidence, reproducibility and clarity
Summary:
The Mediator complex, a multicomponent complex, regulates the interaction between transcription factors and RNA polymerase II using protein interactions. In particular, Intrinsically Disordered Regions (IDRs) with in Med15. Copper and Frassler made extensive mutations to the three IDRs that are characterized with high glutamate content (poly-Q), the KIX domain which interacts with transcription factors and the MAD which interacts with the rest of the Mediator complex. The three poly-Q repeats are adjacent to Activator Binding Domains (ABD). The impact of mutant Med15 was measured with growth assays, co-IPs with a transcription factor, and transcriptional activation of a reporter during different stresses. Med15 is particular important in stress responsive transcription rather than basal transcription because in yeast it is nonessential. It can be repress and activation translation of genes. Using a series of internal deletions and substitutions impact of the mutations was tested on by measuring growth of strains, expression of Med15 regulated genes, and interaction with Msn2, a transcription factor that regulates to various stresses. This work adds to the body of research confirming that multiple weak/ transient interaction domains regulate Med15 function.
Major comments:
- Without knowing the protein levels of the different mutants, it is difficult to contribute deletions of different regions with phenotypes measured. Various internal deletions decrease Med15 protein levels (Jedidi et al. 2010) while other affect the integrity of the Mediator complex. This study did not measure mRNA or protein levels of their mutants. However, (Jedidi et al. 2010) used Myc-tagged Med15 which affects regulation of Med15 via SNF1 (Gallagher et al. 2020). In another study, Med15 was N-terminally tagged and protein levels of some deletions increased (Herbig et al. 2010). It's unknown if other tags such as HA, TAP or FLAG affect Med15 regulation via SNF1. This study used untagged Med15 expressed from the native promoter which avoids these complications. It's also unknown if the differences in Med15 deletions are from reduced transcription, translation, or protein stability. There are commercial antibody that may work (https://www.genetex.com/Product/Detail/Gal-11-S-cerevisiae-antibody/GTX64110). There are several commercial antibodies to human Med15 but the cross reactivity has not been tested.
- Quantification with ImageJ on spot assays is difficult because once growth has maxed out on the dense spots there is no resolution. Using more dilute spots is challenging because colony size is affected by the nearby colonies. The error bars are the mutants are large. Can quantitative growth curves be carried out in flasks or an automatic plate reader for better quantification?
Minor comments:
- Why were the stress conditions chosen in figure 1B? These are only a subset of conditions that the med15 deletion is sensitive to. Aside from acetic acid the phenotypic profile of each deletion is similar. The bigger the deletion, the more severe the growth defect. The keto plate appears under loaded by comparing the number of colonies in the third dilution on the keto plate and the fourth spot on the YPD plate in the BY4742 (MED15 wild-type strain). Does keto lyse cells? Or was there that much variation between mutants? Perhaps the dose of keto needs to be calibrated. In figure 2B it looks like wildtype growth in keto was 70% of untreated growth.
- Figures 1C and 1D are not discussed in the results. The authors should remind what the hGR assay measures when discussing the results. How is it different from the GAL4 transcriptional reporter?
- In the D to A mutants some appear to be required for acetic acid tolerance. What was the pH of the media?
- The labels between Figure 1 and 2 are inconsistent. 90 mM acetic versus 80mM AcOH, YPGal versus YPGalactose, SD+LKHU versus SD+KLUH. The mutants on 0.97M NaCl at 37oC from figure 2A grew more than 0.9M NaCl at 38oC. Also, in the text it says 37 oC.
- Is the MED15 strain BY4742? In Figure 1 was it also transformed the pRS315? How was the plasmid maintained on the plates, specifically YPD?
- Genes such as GAL and URA3 should in italics.
- In the split Ub assays, was wildtype Msn2 and Med15 also present?
- There is inconsistently naming of media in Media and Phenotype Testing section. The media is called synthetic complete media is labeled SC-URA or SC-LEU and at times in Results its called SD+K or SC-HULM. Is SC with all amino acids and SD without any amino acids?
- The plasmid names in the supplemental table don't match the ones labeled in the figures.
- Why was ALG9 used for normalization of qRT PCR?
- The background of the strains is confusing. There appears to be two different med15 knockouts OY320 and JF1368. Which ones were used in which experiments? Some of the trains have a trp1 auxotrophic which affects stress response on it's own (González et al. 2008; Schroeder and Ikui 2019).
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? Extensive mutational analysis of Med15.
- 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 work confirms numerous other studies on the contribution of various Med15 domains on function.
- 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? Incorporation of human domain substitutions could influence how people outside the field would interpret how Med15 interacts with transcription factors.
- 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. Expert in using yeast genetics and natural genetic variation to address underlying mechanisms of stress response to environmental toxins with a particular focus on transcription factors and TORC1.
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Referee #1
Evidence, reproducibility and clarity
Major Comments
The authors show that the amino acid content and length of Q1 affects transcription activity in a media-dependent way in a construct that includes Q1-ABD1 and a tailing Q/N rich region (Q1R). Briefly, different media conditions used as proxies for specific target TF activities varied in their sensitivity to the Q1 sequence content. However, the reason for this variation between target TF activities is not addressed, so the observations seem more anecdotal than insightful. One test performed suggests some of the Q1 sequence dependence may be due to changes in AD-ABD interactions, but this interesting possibility is not investigated further.
A split-ubiquitin two-hybrid assay, meant to detect interactions between Msn2 TAD and Med15-Q1R, showed clear Q1 sequence/composition-dependence when changed from polyQ tracts. In particular, replacement with leucine-rich tracts (12L and RvHs) significantly reduced interactions (as inferred from growth requirements in Fig 5B). Q1 consisting of just 10 spacer residues, 0 to 24 Q residues, or PQ repeats all had quite similar results suggesting retention of some Msn2 and Med15 interactions. Replacement with a helix-forming sequence from NAB3 gave intermediate results. Again, no explanation was offered for the observation but it seems probable that the NAB3 Q1 system is no longer reporting on Msn2 Med15 interactions.
The manuscript presents extensive assays, but a lack of consistency in conditions and constructs tested makes comparing different assays difficult. In particular, it would be valuable to have NAB3-Q1, FrHs-Q1, and RvHS-Q1 tested under conditions of high salt as that is indicated to be the Msn2 target condition (e.g. an additional result that would be presented in Fig 3B); this would be valuable to compare to the two-hybrid results. The relationship between Q1 polyQ length and Msn2 TAD-Med15 ABD1 binding is not clear from this assay as all had similar growth on the plates. A possible explanation for the inferred reduction in TAD-ABD1 binding in the leucine rich Q1 constructs is that this highly hydrophobic linker itself binds to ABD1 and is therefore self-inhibitory. There is also the unexplored/not discussed possibility that NAB3, 12L, and RvHs have off-target interactions that disrupt the TAD-ABD1 interactions.
The framing of the study and the title of the manuscript strongly suggest that there might be a relationship between coiled-coil formation and transcription activity. This is the basis for selection of many of the Q1 sequences tested, with the premise of either increasing or disrupting coiled-coil structure. These 'propensities' are quantified in Supp Fig 1; however, a significant limitation of this interpretation is that these propensities are bulk properties that presume formation of homo-dimers or homo-trimers, a situation that is not shown to be relevant for Med15 at a promoter. This means that Q1 is potentially only one of the multiple partners required for coiled-coil formation. So even if a tested sequence has high coiled-coil propensity, that may not be the case in the actual biological systems at play here. Another consideration to be entertained is how different solvent conditions (different media) may affect coiled-coil propensity. An unanswered question is whether Q1 may form coiled-coil structure either with other regions of Med15 and/or with other Mediator subunits or even other co-factors entirely. This is a question implied by the title of this paper, but the data presented address neither intra- nor inter-molecular interactions of the polyQ regions (the two-hybrid study is designed to probe the ABD-AD interactions).
A final proposed hypothesis was that Q1 acts as a hinge in a way analogous to what was reported for the huntingtin protein (ref 7). This is an attractive model but remains untested in this work. In particular, the Med15-Q1R construct used does not have multiple ABDs that would potentially be brought in close contact, so the results here cannot be interpreted as analogous to the huntingtin hinge model. Minor Comments:
Please explain the choice of the 10-residue spacer instead of a 12-residue spacer.
Page 14: "We observed that Q1 substitutions with increased coiled-coil propensity (Supplementary Figure 1) diminished TF activity while Q1 substitutions that interfered with coiled-coil propensity had no effect on TF activity (Fig. 3, 4), suggesting that the flexibility of the sequence is an important feature." There was no demonstration that those sequences in this context form CCs. There's no evidence of what is actually being modulated whether it's length, flexibility, or ability to interact with other regions of Med15 or even with other co-factors.
Page 15: "We confirmed that Msn2-dependent activities of Med15 are encoded by the region containing the Q1 tract and ABD1 (aa 116-277) and found that the KIX domain alone could also mediate an interaction with Msn2 (Fig. 5). This contrasts with the Gcn4- or Gal4-dependent growth or stress responses which are the result of additive interactions with Med15 that are characterized by weak, highly dispersed, multivalent interfaces. While it is not yet entirely clear if the interaction with Msn2 is similarly multivalent, we have shown that either the KIX domain alone or the Q1R region alone of Med15 was sufficient with no evidence of additivity." These statements are unsupported. While Gcn4 and Gal4 transcription activity has been shown to depend on multiple AD-ABD interactions, none of the data reported here shows that Msn2 does not (as is stated here, which undermines the "contrasts" argument. Further, based on the assays presented in Figure 1B, Msn2, Gal4, and Gcn4 behave similarly for the various Med15 constructs.
Page 16: "In all instances TF activity was reduced in the absence of the Med15 Q1 tract." This seems false based on the data presented. Met10 activity appears to have increased in Figure 4A.
Page 16: Reference to Figure 2C and Figure 2B are mislabeled. Should be Figure 2D and 2C, respectively.
Page 17: "The fact that residues at Q1 were not functionally constrained to be glutamine residues suggests the Q1 tract is not an interaction motif participating directly in protein-protein interactions." This is completely unsupported. There are no data presented that address interactions between Q1 and anything else.
Figure 2: Not clear which assays were at 30{degree sign}C vs 22{degree sign}C as they are not labeled in the figure. In Figure 2A, the label med15 should be med15Δ.
Figure 4: Interpretation of these results seems limited by only reporting YPD media conditions. May be helpful to include the conditions reported in Figure 1.
Figure 6: It is not clear what some elements of this figure are meant to represent. Is saw tooth always polyQ? or Is ABD1 always blue and ABD2 is always red. What then are the loops? The general premise of this figure does not seem to be supported by the actual experiments performed.
Supp Figure 3: "K is the Med15 fragment encompassing the KIX domain, aa 1-277." This aa range is KQ in the main text. Either the residue range is wrong, or the label is wrong.
Significance
This manuscript addresses an interesting topic. There appears to be a disconnect between the stated motivation and what was actually done. The large array of assays and conditions are difficult to compare, leaving the reader with a feeling that the authors have catalogued a lot of possibilities but that no generalizable or unifying insights are at hand. The attempt to present a model (Figure 6) is difficult to parse and is not directly supported by the data presented. Addressing the issues raised here could result in a work that is useful to the specific field of Med15 structure and function but of limited use at the moment to a wider audience.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This manuscript uses C. elegans as a model to interrogate the effects of autism-associated variants of previously unknown function in the RNA-binding protein RBM-26/RBM27.
Despite its potential impact, there are several concerns related to the technical rigor and specificity of the observed effects.
Major concerns: 1. The effects on PLM are interesting, but why was this neuron selected for study? Was this a lucky guess or are other axons also affected? It is important to clarify whether the effects of RBM-26 are specific to this neuron or act pleiotropically across many or all neurons. According to CeNGEN, rbm-26 is strongly expressed in the well-characterized neurons ASE, PVD, and HSN. Are there morphological defects in these neurons, or others? As a note, there are also functional assays for these neurons (salt sensing, touch response, and egg laying, respectively).
We have added new data to the supplemental materials showing that loss of rbm-26 function also causes the beading phenotype in the axons and dendrites of the PVD neuron (Figure S4 and lines 196-199). We have focused on the PLM neuron because our preliminary studies indicated that it had a higher penetrance of axon defects relative to the PVD neuron. Moreover, we observed expression of endogenously tagged RBM-26 in the PLM neuron (Figure 3A-C and lines 210-215).
Similarly, the choice of the MALSU homolog seemed like a shot in the dark. It is ranked 46th (out of 63 genes) for fold-enrichment following RBM-26 pull-down, and 9th for p-value. Were any of the mRNAs with greater fold-enrichment or smaller p-values examined further? It is important to determine whether many or all of these interacting genes are overexpressed in the absence of RBM-26 and whether they are also required for the phenotypic effects of RBM-26 mutants, or if the MALSU homolog is special.
We have clarified our reasoning for selecting the MALS-1 ortholog of MALSU1 for further study (see lines 283-284 and Table S2). Amongst binding partners with human orthologs, MALS-1 was by far the top ranked candidate. The adjusted p-value for MALS-1 was 0.0008. The next smallest adjusted p-value was two orders of magnitude larger (0.028 for dpy-4). Moreover, the log2fold fold enrichment for MALS-1 was 1.98, about the same as the largest (ACADS with 2.13). Nonetheless, we agree that some of the other interactors may also be of interest and have thus included them in the supplemental table S2. Although these other potential binding partners are outside the scope of this study, we expect that future studies by ourselves or others may focus on the roles of these other binding partners.
In addition to the specificity controls mentioned above, positive and negative controls are needed throughout the results. While each of these may be relatively minor by itself, as a group they raise questions about the technical rigor of the study. Briefly these include: Fig 1C. Missing loading controls and negative control (rbm-26 null allele). Additional exposures should be included to show whether RBM-26(P80L) protein or the lower band for RBM-26(L13V) are present at all, relative to the null allele.
We have added no-stain loading controls to figure 1C. We have also switched to using ECL detection, which is much more sensitive and reveals faint bands for RBM-26(P80L) and additional faint bands for RBM-26(L13V). In addition, we have included a longer exposure for the blot (Figure S1). We are unable to test the null, as we can only produce a limited number of small maternally rescued progeny, thereby precluding western blot analysis.
Fig 2. Controls to distinguish overextension of PLM axon from posterior mispositioning of ALM cell body are needed. Quantification of PLM axon lengths in microns (or normalized to body size) with standard deviation, not error of proportion, should be shown. Measurement of “beading phenotype” should be more rigorous, see for example the approach in Rawson et al. Curr. Biol. 2017 https://doi.org/10.1016/j.cub.2014.02.025 . The developmental stage examined, and the reason for choosing that stage, should be described for this and all figures.
We have added new data that shows PLM axon length relative to body length for each of the RBM-26 mutants (Figure S2 and lines 183-185). These results indicate that the PLM axon has a larger axon length to body length ration, suggesting that the PLM/ALM overlap phenotype is a result of PLM axon overextension. For most experiments, we retain penetrance, as this has been standard practice in the field and allows for a much larger sample size (see examples listed below). We have also added examples of how the beading phenotype was measured (Figure S3). Moreover, we have now analyzed this phenotype and others at multiple developmental stages (Figures 2D-H and Table S1). In general, we have conducted experiments at the L3 stage because the rbm-26(null) mutants don’t survive past this stage. However, for many of our experiments we have also included additional stages as well. We have added this explanation to the methods section of phenotype analysis and also at various locations throughout the text. We have also labeled all graphs to clearly indicate the developmental stages and included.
10.1038/s41467-019-12804-3 Article by laboratory of Brock Grill
10.1371/journal.pgen.1002513 Article by laboratory of Ian Chin-Sang
doi.org/10.1073/pnas.1410263111 Article by laboratory of Chun-Liang Pan
10.1016/j.neuron.2007.07.009 Article by laboratory of Yishi Jin
doi.org/10.1523/JNEUROSCI.5536-07.2008 Article by laboratory of William Wadsworth
Fig 3. Controls without auxin and with neuronal TIR1 expression alone should be included. Controls demonstrating successful RBM-26 depletion, in larvae as well as in embryos at the time of PLM extension, should be included (weak embryonic depletion might explain why the overextension phenotype is only 14% instead of 40% as in the null). According to CeNGEN, rbm-26 expression in PLM is barely detected, thus depletion with a PLM-specific TIR1 should also be tested. To confirm the authors' identification of the cell marked "N" as the PLM cell body, co-expression of rbm-26 and a PLM-specific marker should be added. Rescue of the rbm-26 mutants with neuronal (and PLM-only) expression should be included to test sufficiency in PLM, and as a further control for potential artifacts of the AID system.
We have added new data showing that an endogenously tagged RBM-26::Scarlet protein is expressed in the PLM neuron (Figure 3A-C). Moreover, we have added rescue experiments, showing that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype and the PLM/ALM overlap phenotype (Figure 3 F-G). We have also added controls without auxin (Figure S7) __and without the rbm-26::scarlet::aid gene (Figure S8). We have added a new figure showing auxin-mediated depletion of RBM-26::Scarlet::AID in the PLM neuron (Figure S10)__. We examined auxin-mediated depletion at the L3 stage for consistency with our auxin-mediated phenotypic experiments. Moreover, these were done at the L3 stage for consistency with other experiments that included the rbm-26(null) mutants, which don’t survive past this stage.
In general, auxin-mediated knockdown tends to be hypomorphic in neurons. This is likely due to the fact that the neuronal TIR1 driver is expressed at much lower levels relative to the other drivers. In addition, the lower penetrance observed in auxin-mediated PLM/ALM overlap phenotype could reflect the fact that this phenotype resolves by the L4 stage in the hypomorphic mutants. For example, in P80L mutants at the L3 stage we see only about a 20% penetrance of the PLM/ALM overlap phenotype (relative to about 15% in auxin-mediated knockdown).
Fig 4. More rigorous quantification of the distribution of mitochondria along the axon should be included, not only total number, and it should be clarified what region of the axon the images are taken from. Including the AID-depletion strain with and without auxin would further add to the sense of rigor. For the mitoTimer experiments, why is RBM-26(L13V) not included and why do wild-type values differ ~5-fold between experiments (despite error bars being almost non-existent)? A more rigorous approach to standardizing imaging conditions may be needed. Positive controls using compounds that affect oxidation should be included. Measurements of individual mitochondria with standard deviations should be shown, rather than aggregate averages with error of proportion.
We have changed our methodology for measuring mitochondria, so that we now report the density of mitochondria in the axon (number per 100µm), (Figure 4E-F). We agree that this method is much better than counting the total number of mitochondria per axon, as it corrects for differences in body length and axon length). We also now include data for the whole axon (Figure 4E), proximal axon (Figure 4G), and distal axon (Figure 4H). These data suggest that the mitochondrial density defects occur in the proximal axon but not in the distal axon. Using the null allele, we have also examined the timing of mitochondria defects in the axon and report that the defects begin in the L1 stage and continue throughout larval development (Figure 4F). Individual datapoints have been added for all graphs in Figure 4.
For the mitoTimer experiments (Figure 5), we have added data for L13V and have added the individual datapoints to the graph. In the prior version, the values did not differ 5-fold between experiments with the same stage, rather the different graphs were from different stages (as noted in the figure legends/main text) and the L4 stage has much more oxidation than the L2 stage. To clear this up, we have added labels to the graphs to indicate the stages for each experiment. We have also added new data, so that we now show results for the L2, L3, and L4 stages for all three rbm-26 mutants (see Figure 5C-E). We didn’t test the L1 stage because the signal was not sufficient for accurate quantitation.
Fig 5. Additional positive and negative controls should be added, including additional rbm-26 alleles, the AID-tagged strain with and without auxin, and a rescued mutant.
The old Figure 5 has become Figure 6 in the new version. We have added the rbm-26(L13V) allele to each experiment, (Figure 6B-D). We have also added the loading controls for the western blot along with quantification for 3 biological replicates of the western blot analysis (Figure 6D). We agree that these additions significantly strengthen the data because they show that two independent alleles of rbm-26 cause very substantial increase in the expression of mals-1 at both the mRNA and protein levels. We did not do these experiments with the rescuing transgene or with the AID-tagged strain because these experiments are done on whole worm lysates, whereas the AID-tagged and rescuing transgene are neuron-specific.
Fig 6. Controls showing whether the Scarlet-tagged protein is functional are needed, to rule out dominant negative or toxicity-related effects.
This is Figure 7 in the new version. For this experiment, we are showing that overexpression of MALS-1 does cause defects. The idea is that excessive amounts of MALS-1 causes deleterious effects to the mitochondria. In fact, these defects could be considered as dominant negative or toxic. We considered the possibility of crossing the Pmec-7::mals-1::scarlet transgene with rbm-26; mals-1 double mutants. However, this does not seem workable, because the single copy Pmec-7::mals-1::scarlet transgene produces the phenotypes at penetrances that are similar to what we observe in rbm-26; mals-1 double mutants. We concede that the results of the overexpression experiments in Figure 7 are limited when considered in isolation. However, we think that they are meaningful when considered in combination with the results on the mals-1;rbm-26 double mutants in Figure 8.
Fig 8. Controls for other mitochondrial components need to be included. It is important to determine if the decrease in ribosomes is specific or reflects a general decrease in mitochondria. If there are fewer mitochondria as suggested in Fig. 4, then of course mitochondrial ribosomal protein levels are also reduced. Additional rbm-26 alleles should be included here as well. Is this effect dependent on the MALSU homolog?
This is Figure 8D-E in the new version. We have added new data showing that the decrease in MRPL-58 expression that is caused by the rbm-26(P80L) mutation is dependent on MALS-1. We concede that these experiments cannot be used to determine anything about the mitoribosomes per se, but rather serve as an alternative way of testing the effect of rbm-26 on mitochondria. We have revised the text accordingly (lines 355-357). Given these limitations we have elected not to try additional mitochondrial markers and have also not included additional rbm-26 alleles for this experiment.
Finally the authors should address concerns about image manipulation, which amplify the concerns about technical rigor outlined above. The image in Fig. 2A appears to have a black box placed over the lower-right portion of the field to hide some features. Black boxes also appear to have been placed over the tops of images in Fig. 4B and 4D and at the left of Fig. 6A, 6B, and 6C. While these manipulations probably do not affect the conclusions, they further undermine confidence in data integrity and experimental rigor.
We have corrected all of these image processing errors. The box in 2A was for the purpose of squaring off a corner that was clipped during image rotation. The boxes in Figures 4 and 6 (of the prior version) were added to give space for labels (without obscuring image features). We have now used alternative methods to accomplish the same goals. For example, in Figures 4-D we have placed the labels outside of the images.
Minor points. 1. C. elegans nomenclature conventions should be followed: - C. elegans gene names have three or four letters, thus the MALSU homolog cannot be named "malsu-1". Please have new gene names approved by WormBase BEFORE submitting for publication http://tazendra.caltech.edu/~azurebrd/cgi-bin/forms/gene_name.cgi
We have changed malsu-1 to mals-1. In addition, both mals-1 and mrpl-58 have now been approved by wormbase and will be listed on the website upon its next update.
- If two sequential CRISPR edits are made on the same gene then they should be listed as a compound allele, such as rbm-26(cue22cue25)
We have updated our gene names to reflect this convention.
- Genes on the same chromosome should not be separated with a semicolon, for example rbm-26(cue40) K12H4.2(syb6330)
We have updated our gene names to reflect this convention.
Describing the defects as "neurodevelopmental" is misleading in the case of axon beading or degeneration. Similarly, there is no evidence for an "axon targeting" defect as stated in the abstract.
We have revised such that instead of referring to degeneration phenotypes as neurodevelopmental, we now refer to axon degeneration phenotypes that occur during development. For example, in the abstract we now say, “These observations reveal a mechanism that regulates expression of a mitoribosomal assembly factor to protect against axon degeneration during neurodevelopment.
Regarding targeting defects, this was meant to refer to the misplacement of the PLM axon tip (which contains electrical synapses). However, our subsequent analysis has revealed that these defects are transient in P80L and L13V mutants, as they resolve by the L4 stage. The rbm-26 null axon development defects do not resolve, though these mutant die prior to the L4 stage. Given these findings, we have decided not to use the term of targeting defects. Instead, we now refer to this as an axon tiling defect or PLM/ALM overlap phenotype.
In Fig. 5A, the symbol that appears to correspond to F59C6.15 (lowest p-value) is a different size than the others and is colored as ncRNA, whereas WormBase annotates this gene as snoRNA.
This error has been corrected.
In the Introduction, the last sentences of the first two paragraphs should be varied ("However, little is known about the [...] mechanisms that protect [...] during neurodevelopment.")
This has been done.
Why is RBM-26 protein running as a doublet at both sizes?
We have improved our western blotting methodology by using 12% gel, allowing for better resolution. We have also switched from colorimetric detection to ECL detection, allowing for greater sensitivity. In our new blots, we identify 6 different RBM-26 protein bands. We don’t know the reason for these bands, but speculate that they are the result of post-translational processing (148-150).
When showing the RBM-26 expression pattern (Fig. 3) please include a lower-magnification image of the entire animal.
This has been done (Figure S6)
It is confusing to refer to the RNA IP experiments as an "unbiased screen", which in C. elegans typically refers to a genetic screen.
We now refer to this as a “biochemical screen”.
The relationship between axon overextension, beading, and mitochondrial localization is not clear. What causal connection between these is being proposed? The causal connections between these phenotypes, if any, should be clarified experimentally. For example, if the axon extension defects develop before mitochondrial localization defects, then it is unlikely that mitochondrial defects cause axon overextension.
We have added new data showing that the reduction in mitochondrial density within the axon begins during the L1 stage and increases throughout larval development (Figure 4F). We have also added additional data showing that the increase in mitochondrial oxidation is weak in the L2 stage and surges in the L3 stage (Figure 5C-E), coincident with the beginning of the axon degeneration phenotypes. We propose (lines 383-391) that a low level of mitochondrial defects is present in L1 larvae, giving rise to the axon tiling defects. In the L3 stage there is a surge in excessive mitochondrial oxidation, giving rise to the axon degeneration phenotypes. We have added a new section to the discussion that addresses the relationship between defects in axon development and axon degeneration (lines 375-405).
Please explain how to interpret the difference in axon beading in the two deletion alleles of the MALSU homolog (axon beading defects in tm12122 but not in syb6330). Is syb6330 not a null allele? Or are the defects in tm12122 due to other mutations in this strain background?
One likely reason for this difference is that tm12122 is predicted to cause a partial deletion of the mals-1 coding sequence, whereas the syb6330 is a full deletion. Thus, the tm12122 could be acting as a dominant negative. In fact, prior work on the MALSU1 ortholog has indicated that this protein is subject to interference by a dominant negative construct (see Rorbach et al, Nucleic Acids Res 2012). Nonetheless, we cannot rule out the possibility of a linked second mutation in tm12122. However, since we have found similar phenotypes and genetic interactions with both alleles, we can conclude that these phenotypes and interactions are due to loss of MALS-1, rather than a second mutation.
Are mitochondria reduced in number or mislocalized? If they are reduced in number, is this due to altered balance of fission/fusion?
We have adjusted our methods for quantifying mitochondria and have also analyzed the proximal vs distal axon (Figure 4). We find that the density of mitochondria is decreased in the proximal axon, but not in the distal axon. We speculate that this might reflect a higher demand on mitochondria in the proximal axon, due to a higher amount of trafficking activity in the proximal axon (lines 255-257). We propose that the loss of RBM-26 causes dysfunction in mitochondria. Since fission and fusion are mechanisms that can help to repair damaged mitochondria, it is likely that they would be involved in the phenotypes that we observe.
In Fig. 3A-D, please keep the labels in the same position in all panels and do not alter brightness settings between single-color and merged panels.
These images have been moved to the supplemental data section (Figure S5). We have adjusted the labels as suggested. We have not changed the brightness settings, as they were already the same in all panels. However, the blue signal in the merged panel does obscure some of the red signal, giving an appearance of an alteration in color balance.
The claim that rbm-26 acts cell-autonomously requires PLM-specific depletion and rescue experiments.
We have added new data indicating that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype (Figure 3F-G).
**Referees cross-commenting** I appreciate the use of the consultation session to resolve differences between reviewers, but in this case I fully agree with the content and tone of all the comments from the other reviewer -- I think our remarks are very well aligned!
Reviewer #1 (Significance (Required)):
The study engineers autism-associated variants in conserved residues of RBM27 into the C. elegans homolog RBM-26 and identifies neuronal phenotypes potentially relevant to autism and a potential molecular mechanism involving regulation of mitochondrial ribosome assembly.
The key claims of the study are 1} that autism-associated variants in RBM-26 decrease its protein expression; 2} that impaired RBM-26 function leads to a variety of defects in development and maintenance of a single neuron called PLM, including altered axonal localization of mitochondria; 3} that RBM-26 normally binds the mRNA for the C. elegans homolog of MALSU, a mitochondrial ribosomal assembly factor; 4} that loss of RBM-26 leads to overexpression of the MALSU homolog; and 5} that MALSU is required for some of the deleterious effects on the PLM neuron seen in RBM-26 mutants.
This study will be of interest to the autism research community because it bolsters the idea that variants in RBM27 are likely to disrupt gene function and to affect neuronal health. It will also be of interest to the broader cell biology community because it suggests an interesting potential nucleus-to-mitochondria signaling mechanism, in which a nuclear RNA-binding protein might regulate assembly of mitochondrial ribosomes.
My field of expertise is developmental biology in C. elegans.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary In this manuscript, the authors studied an ASD-associated gene, rbm-26 in neuronal morphology using the touch receptor neuron PLM in C. elegans, and found that loss-of-function rbp-27 causes overextension and the formation of bulb-like structures in the axon. Using UV-crosslinking RNA immunoprecipitation and RNA-Seq, they identify malsu-1 as a target of rbm-26. Genetic analyses suggest malsu-1 likely functions downstream of rbm-26 in controlling the PLM morphology. Major comments:
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The authors describe RBM27 is associated with ASD and ID while they only cite SFARI paper that describes a weak association of RBM27 to ASD. The appropriate referenced that show link between RBM27 and ID should be provided. The link with ID was an error. We had meant to say “ASD or other neurodevelopmental disorders.” This has been corrected.
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SFARI database only has three (P79L, R190Q, G348D) mutations listed as ASD-associated. Where are other mutations L13V and R455H, particularly L13V that the authors used to generate the C. elegans mutant come from? Are they associated with intellectual disabilities? The others came from the devovo-DB. We have added a reference for this database and have also added the primary source references for each of the five de novo variants (see line 121).
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The authors should be very careful when describing 'gene X causes Y diseases'. Many (if not all) of the examples described in this manuscript are disease-associated genes without validation to be causal genes. We have revised accordingly. For example on lines 433-435, we now say,” For example, mutations in the EXOSC3, EXOSC8 and EXOSC9 are thought to cause syndromes that include defects in brain development such as hypoplasia of the cerebellum and the corpus callosum”. We have decided to use the phrase “thought to cause” because three of the five referenced articles on these genes use titles that indicate causation.
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The authors refer PLM axon beading and overextension phenotypes to 'axon degeneration and targeting defects'. The authors must provide additional evidence of axon degeneration (see below). Also the term 'targeting defects' is misleading as the authors did not examine if overextension of the PLM axon causes targeting defects. At least they should examine some synaptic markers. To provide more evidence of degeneration we have analyzed several additional phenotypes at multiple developmental stages (Figure 2 and Table S1). Regarding targeting defects, this was meant to refer to the misplacement of the PLM axon tip (which contains electrical synapses). However, our subsequent analysis has revealed that these defects are transient in P80L and L13V mutants, as they resolve by the L4 stage. The rbm-26 null axon development defects do not resolve, though these mutant die prior to the L4 stage. Given these findings, we have decided not to use the term of targeting defects. Instead, we now refer to this as an axon tiling defect or PLM/ALM overlap phenotype.
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Neuronal phenotypes (axon overextension and beading) should be examined at different developmental timepoints (larval, young adult, and aged animals) to test if these phenotypes are indeed degenerative instead of developmental defects. We have included new data to observe all of these phenotypes at multiple developmental time points (Figure 2 and Table S1).
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The authors use the blebbing (beading) phenotype in the axon as the sole evidence of neurodegenerative properties of the PLM neuron. A more thorough analysis of this phenotype as done by others (Pan PNAS 2006) must be provided to support the authors' claim that this phenotype represents neurodegeneration. We have included new data on multiple degenerative phenotypes in axons including: blebbing, beading, waviness and breaks (Table S1).
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The number of beads per axon should be quantified to better represent the severity of rbm-26 mutant. Individual samples should be plotted in the quantification instead of showing the percentage of animals. We have added data on the density of beads in rbm-26(null), rbm-26(P80L), and rbm-26(L13V) mutants (Figure S3). For most experiments we have decided to use penetrance to measure axon degeneration because this is a standard in the field and allows for a larger sample size. For examples please see:
10.1523/JNEUROSCI.1494-11.2012 (Toth et al, 2012)
https://doi.org/10.1016/j.cub.2014.02.025 (Rawson et al, 2014)
10.1073/pnas.1011711108 (Pan et al, 2012)
https://doi.org/10.7554/eLife.80856 (Czech et al, 2023)
https://doi.org/10.1016/j.celrep.2016.01.050 (Nichols et al, 2016)
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Based on the single gel image in Fig. 1C with no loading control, the P80L mutant appears to have no protein expression. How is the P80L viable while the null mutant is lethal? The authors should quantify the protein expression levels from multiple blots with proper loading controls. If P80L mutation is introduced into RBM-26::mScarlet strain can it cause depletion of the signal in vivo? We have added new data showing that the RBM-26::Scarlet signal is diminished by the P80L mutation in vivo (Figure 1E-F). We have also added quantification from 3 biological replicate blots (Figure 1D). Finally, we have improved the sensitivity of our blots by using ECL detection and also show various exposures to highlight the fainter bands (Figures 1C and S1). Therefore, we are now able to detect low level expression of RBM-26(P80L) mutant protein. It is likely that the low level of RBM-26(P80L) and RBM-26(L13V) seen on western blots is sufficient to prevent the lethal phenotype.
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'Moreover, loss of either the SPTBN1 or ADD1 genes causes a neurodevelopmental syndrome that includes autism and ADHD' References are missing, and as described above, be extra careful when indicating causality. Very few genes are known to cause ASD and ADHD. We have added the citations for this work (line 81). We also note that the titles for both of the cited articles indicate causation. To be on the safe side we have revised this line to say, “Moreover, loss of either the SPTBN1 or ADD1 genes are thought to cause a neurodevelopmental syndrome that includes autism and ADHD”
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Fig. 3E F, the authors should use the strains that express TIR1 specifically in the touch receptor neurons to argue cell autonomous function of RBM-26. Alternatively, the authors may conduct PLM neuron-specific rescue experiments to test the sufficiency. We have added new data indicating that a Pmec-7::rbm-26::scarlet transgene can rescue the beading phenotype and the PLM/ALM overlap phenotype (see Figure 3F-G).
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'Loss of RBM-26 causes mitochondria dysfunction in axons.' The authors did not examine mitochondria function in axons. They only examined the number of mitochondria, and ROS production in the soma. The authors should provide additional evidence to support the idea that elevated ROS production in the soma is due to mitochondrial dysfunction in axons. Also, the authors should use both P80L and L13V for this experiment, and indicate individual datapoint as dots. Here, they quantified at the L4 stage, which the authors should justify. We have added the L13V data to this experiment and now show the individual data points. In addition, we have now conducted this analysis at the L2, L3 and L4 stages (Figure 5C-E). We have also revised the text to indicate that loss of rbm-26 function causes mitochondrial dysfunction in the cell body which could potentially cause a reduction of mitochondria in the axon (see lines 100-101 and 268-270). We speculate that mitochondria in the axon are also dysfunctional. However, the mitoTimer signal is not bright enough in axons to allow for quantification.
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Figure 5B and C: the authors should also use L13V to quantify malsu-1 mRNA and protein level, and include quantifications in panel C (from multiple blots). This is Figure 6 in the new version. We have added new data for expression of mals-1 mRNA and protein in rbm-26(L13V) mutants (Figure 6B-D). We have also included quantifications from 3 biological replicates (Figure 6D).
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In the rbm-26 mutant, the number of mitochondria is reduced, while the amount of MALSU-1 protein is increased. If MALSU-1 is specifically localized at mitochondria in wild type, where does the excessive MALSU-1 go in the rbm-26 mutants? Quantification of MALSU-1 signal intensity should be provided. Our Pmec-7::mals-1::scarlet transgene uses the tbb-2 3’UTR and causes an overexpression phenotype. To address the question posed by the reviewer, we would need to express MALS-1 at endogenous levels. Given that endogenous levels of MALS-1 are very low, it is unlikely that we would be able to visualize its expression. Nonetheless, as a way to address this question we have attempted to create a single copy Pmec-7::mals-1::scarlet transgene that utilizes the mals-1 endogenous 3’UTR. We have tried multiple approaches for generating this construct, but all have failed, likely due to sequence complexities within the mals-1 3’UTR. While we cannot say where the extra MALS-1 protein goes, we think that it is likely overloaded into the remaining mitochondria and could also be in the cytosol as well.
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Figure 7C: malsu-1 knockout mutants exhibit PLM overextension phenotype, which is not consistent with their model. The authors should discuss this in detail. We have added a paragraph to the discussion explaining that mitochondria function could be disrupted by either MALS-1 overexpression or by MALS-1 loss of function (lines 471-480).
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'To validate these findings, we also repeated these experiments with an independent allele of malsu-1, malsu-1(tm12122) and found similar results (Fig. 7A-C).' The malsu-1(tm12122) exhibits beading phenotype and more severe overextension phenotype which the authors must describe and discuss more carefully. One likely reason for this difference is that tm12122 is predicted to cause a partial deletion of the mals-1 coding sequence, whereas the syb6330 is a full deletion. Thus, the tm12122 could be acting as a dominant negative. In fact, prior work on the MALSU1 ortholog has indicated that this protein is subject to interference by a dominant negative construct (see Rorbach et al, Nucleic Acids Res 2012). Nonetheless, we cannot rule out the possibility of a linked second mutation in tm12122. However, since we have found similar phenotypes and genetic interactions with both alleles, we can conclude that these phenotypes and interactions are due to loss of MALS-1, rather than a second mutation (albeit at a slightly different penetrance). We have added these considerations to the results section (lines 342-244).
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Figure 8: The authors should include data from L13V, malsu-1 and rbm-26; malsu-1 mutants. Quantification from multiple blots should be provided. This is Figure 8D in the new version. We have added the malsu-1 and rbm-26;malsu-1 double mutants to this experiment. We have also added quantification from multiple biological replicate blots. As pointed out by the other reviewer, we think that this experiment does not give specific information about mitoribosomes, but is an alternative approach to looking at the reduction in mitochondria. Given this limitation and considering that we have added L13V data to the mitochondria experiment in Figure 8B, we have elected not to add additional data on L13V to the western blot experiment in Figure 8D
Minor comments: • 'Consistent with a role for mitochondria in neurodevelopmental disorders, some of these disorders include a neurodegenerative phenotype.' Why is it consistent to have neurodegenerative phenotypes if mitochondria is associated with neurodevelopmental disorders? A better explanation would help.
We have changed this sentence to, “Some neurodevelopmental syndromes feature neurodegenerative phenotypes that occur during neuronal development.”
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L13V is generally more severe in axon overextension phenotype than P80L while protein level is more abundant. The authors should discuss about this. We have also added a time course for the PLM/ALM overlap phenotype mutants (Figure 2D). This new data shows that the PLM/ALM overlap is quite similar overall between the P80L and L13V mutants. Both of these mutations cause an increase in PLM/ALM overlap in early larval development that is resolved by the L4 stage. The P80L phenotype resolves slightly sooner for reasons that are unknown. This could reflect differences in expression within the PLM that are not reflected in the whole worm lysate. This could also be due to a slight difference in the genetic background or other stochastic factors. The key point is that these two independent alleles cause similar phenotype overall, indicating that this phenotype is the result of loss in RBM-26 function.
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Fig. 2E, F: 'Beading refers to focal enlargement or bubble-like lesions which were at least twice the diameter of the axon in size.' How are the diameters of axons measured? A more detailed quantification method, and examples of measurement should be provided. We have added example measurements to the supplemental section (Figure S3). Additional detail on the measurements are in the Methods section (lines 517-518).
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Figure 3: The authors should also include low-magnification images to show where RBM-26 is expressed. The current image does now allow identifying cells. The transgene that labels the nuclei of hypodermis should be indicated in the manuscript. Specifically, the expression of the RBM-26 in the PLM should be shown. We have added a low magnification image (Figure S6) and have also added images of endogenously tagged RBM-26:Scarlet in the PLM (Figure 3A-C). The transgenic label for the hypodermis has been added to the legend of Figure S5.
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Figure 3: 'Tissue specific degradation of RBM-26::SCARLET::AID was achieved due to cell-type specific TIR-1 driver lines (see methods for details).' This information is not provided in the method section. This information has been added to methods section, ”Auxin proteindegredation”
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Fig. 4 E. Values from individual samples should be indicated as dots. Representative images of P80L and L13V should be included. Conduct quantifications at adult stage as the authors use in other quantifications, or justify use of specific developmental stage (L3) they used. Figure 4 has become Figures 4 and 5 in the revised version. We have updated the graphs to include dots for individual data points. We have added quantifications of the mitoTImer experiments for the L2, L3 and L4 stages (Figure 5C-E). We note that our other experiments were done at the L1, L2, L3 and L4 and adult stages. The mitoTimer signal is not sufficient at the L1 stage for quantification. At the adult stage, the red signal becomes saturated. We have added representative images for mitoTimer in P80L and L13V mutants (Figure S9).
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The genes malsu-1 and mrpl-58 are not listed on wormbase. If the authors would like to designate names to these gene, they should clearly indicate that along with the sequence name. We have changed malsu-1 to mals-1. In addition, both mals-1 and mrpl-58 have now been approved by wormbase and will be listed on the website upon its next update.
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The authors found that MRPL-58 amount is reduced in rbm-26 mutants (which require additional verifications). This can be explained by the fact that axonal mitochondria number is reduced in the rbm-26 mutants. How did the authors confirm that the reduction in MRPL-58 level is due to the disruption of mitoribosome assembly? This is Figure 8D-E in the new version. We have added new data showing that the decrease in MRPL-58 expression that is caused by the rbm-26(P80L) mutation is dependent on MALS-1. We concede that these experiments cannot be used to determine anything about the mitoribosomes per se, but rather serve as an alternative way of testing the effect of rbm-26 on mitochondria. We have revised the text accordingly (lines 355-357).
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'MALSU-1 is a mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module [37-39].' I don't think these are known for C. elegans MALSU-1. We have revised to, “MALS-1 is an ortholog of the MALSU1 mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module”
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'Moreover, our results also suggest that disruption of this process can give rise to neurodevelopmental disorders.' I feel this is a quite a bit of stretch.
This has been replaced with, “Therefore, we speculate that human RBM26/27 could function with the RNA exosome complex to protect against neurodevelopmental defects and axon degeneration in infants.” (lines 371-373)
**Referees cross-commenting** Yes, many of our comments overlap, and I fully agree with all comments from the other reviewer too. Reviewer #2 (Significance (Required)):
I found the manuscript interesting particularly the use of innovative techniques in identifying the target of RBM-26, The genetic analyses of rbm-26 and malsu-1 generally support the authors main conclusions that rbm-26 inhibits malsu-1 and be of potential interest to basic neuroscientists and cell biologists. However, the current manuscript looked premature which made my reading experience less pleasant. The phenotypic analyses is superficial compared to works similar to this work, which are insufficient to support the authors' claim of 'axon degeneration and targeting defects'. A number of issues listed above should be addressed before this manuscript is published. The reviewer's expertise: neurodevelopment in model organisms.
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Referee #2
Evidence, reproducibility and clarity
Summary
In this manuscript, the authors studied an ASD-associated gene, rbm-26 in neuronal morphology using the touch receptor neuron PLM in C. elegans, and found that loss-of-function rbp-27 causes overextension and the formation of bulb-like structures in the axon. Using UV-crosslinking RNA immunoprecipitation and RNA-Seq, they identify malsu-1 as a target of rbm-26. Genetic analyses suggest malsu-1 likely functions downstream of rbm-26 in controlling the PLM morphology.
Major comments:
- The authors describe RBM27 is associated with ASD and ID while they only cite SFARI paper that describes a weak association of RBM27 to ASD. The appropriate referenced that show link between RBM27 and ID should be provided.
- SFARI database only has three (P79L, R190Q, G348D) mutations listed as ASD-associated. Where are other mutations L13V and R455H, particularly L13V that the authors used to generate the C. elegans mutant come from? Are they associated with intellectual disabilities?
- The authors should be very careful when describing 'gene X causes Y diseases'. Many (if not all) of the examples described in this manuscript are disease-associated genes without validation to be causal genes.
- The authors refer PLM axon beading and overextension phenotypes to 'axon degeneration and targeting defects'. The authors must provide additional evidence of axon degeneration (see below). Also the term 'targeting defects' is misleading as the authors did not examine if overextension of the PLM axon causes targeting defects. At least they should examine some synaptic markers.
- Neuronal phenotypes (axon overextension and beading) should be examined at different developmental timepoints (larval, young adult, and aged animals) to test if these phenotypes are indeed degenerative instead of developmental defects.
- The authors use the blebbing (beading) phenotype in the axon as the sole evidence of neurodegenerative properties of the PLM neuron. A more thorough analysis of this phenotype as done by others (Pan PNAS 2006) must be provided to support the authors' claim that this phenotype represents neurodegeneration.
- The number of beads per axon should be quantified to better represent the severity of rbm-26 mutant. Individual samples should be plotted in the quantification instead of showing the percentage of animals.
- Based on the single gel image in Fig. 1C with no loading control, the P80L mutant appears to have no protein expression. How is the P80L viable while the null mutant is lethal? The authors should quantify the protein expression levels from multiple blots with proper loading controls. If P80L mutation is introduced into RBM-26::mScarlet strain can it cause depletion of the signal in vivo?
- 'Moreover, loss of either the SPTBN1 or ADD1 genes causes a neurodevelopmental syndrome that includes autism and ADHD' References are missing, and as described above, be extra careful when indicating causality. Very few genes are known to cause ASD and ADHD.
- Fig. 3E F, the authors should use the strains that express TIR1 specifically in the touch receptor neurons to argue cell autonomous function of RBM-26. Alternatively, the authors may conduct PLM neuron-specific rescue experiments to test the sufficiency.
- 'Loss of RBM-26 causes mitochondria dysfunction in axons.' The authors did not examine mitochondria function in axons. They only examined the number of mitochondria, and ROS production in the soma. The authors should provide additional evidence to support the idea that elevated ROS production in the soma is due to mitochondrial dysfunction in axons. Also, the authors should use both P80L and L13V for this experiment, and indicate individual datapoint as dots. Here, they quantified at the L4 stage, which the authors should justify.
- Figure 5B and C: the authors should also use L13V to quantify malsu-1 mRNA and protein level, and include quantifications in panel C (from multiple blots).
- In the rbm-26 mutant, the number of mitochondria is reduced, while the amount of MALSU-1 protein is increased. If MALSU-1 is specifically localized at mitochondria in wild type, where does the excessive MALSU-1 go in the rbm-26 mutants? Quantification of MALSU-1 signal intensity should be provided.
- Figure 7C: malsu-1 knockout mutants exhibit PLM overextension phenotype, which is not consistent with their model. The authors should discuss this in detail.
- 'To validate these findings, we also repeated these experiments with an independent allele of malsu-1, malsu-1(tm12122) and found similar results (Fig. 7A-C).' The malsu-1(tm12122) exhibits beading phenotype and more severe overextension phenotype which the authors must describe and discuss more carefully.
- Figure 8: The authors should include data from L13V, malsu-1 and rbm-26; malsu-1 mutants. Quantification from multiple blots should be provided.
Minor comments:
- 'Consistent with a role for mitochondria in neurodevelopmental disorders, some of these disorders include a neurodegenerative phenotype.' Why is it consistent to have neurodegenerative phenotypes if mitochondria is associated with neurodevelopmental disorders? A better explanation would help.
- L13V is generally more severe in axon overextension phenotype than P80L while protein level is more abundant. The authors should discuss about this.
- Fig. 2E, F: 'Beading refers to focal enlargement or bubble-like lesions which were at least twice the diameter of the axon in size.' How are the diameters of axons measured? A more detailed quantification method, and examples of measurement should be provided.
- Figure 3: The authors should also include low-magnification images to show where RBM-26 is expressed. The current image does now allow identifying cells. The transgene that labels the nuclei of hypodermis should be indicated in the manuscript. Specifically, the expression of the RBM-26 in the PLM should be shown.
- Figure 3: 'Tissue specific degradation of RBM-26::SCARLET::AID was achieved due to cell-type specific TIR-1 driver lines (see methods for details).' This information is not provided in the method section.
- Fig. 4 E. Values from individual samples should be indicated as dots. Representative images of P80L and L13V should be included. Conduct quantifications at adult stage as the authors use in other quantifications, or justify use of specific developmental stage (L3) they used.
- The genes malsu-1 and mrpl-58 are not listed on wormbase. If the authors would like to designate names to these gene, they should clearly indicate that along with the sequence name.
- The authors found that MRPL-58 amount is reduced in rbm-26 mutants (which require additional verifications). This can be explained by the fact that axonal mitochondria number is reduced in the rbm-26 mutants. How did the authors confirm that the reduction in MRPL-58 level is due to the disruption of mitoribosome assembly?
- 'MALSU-1 is a mitoribosomal assembly factor that functions as part of the MALSU1:LOR8F8:mtACP anti-association module [37-39].' I don't think these are known for C. elegans MALSU-1.
- 'Moreover, our results also suggest that disruption of this process can give rise to neurodevelopmental disorders.' I feel this is a quite a bit of stretch.
Referees cross-commenting Yes, many of our comments overlap, and I fully agree with all comments from the other reviewer too.
Significance
I found the manuscript interesting particularly the use of innovative techniques in identifying the target of RBM-26, The genetic analyses of rbm-26 and malsu-1 generally support the authors main conclusions that rbm-26 inhibits malsu-1 and be of potential interest to basic neuroscientists and cell biologists. However, the current manuscript looked premature which made my reading experience less pleasant. The phenotypic analyses is superficial compared to works similar to this work, which are insufficient to support the authors' claim of 'axon degeneration and targeting defects'. A number of issues listed above should be addressed before this manuscript is published.
The reviewer's expertise: neurodevelopment in model organisms.
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Referee #1
Evidence, reproducibility and clarity
This manuscript uses C. elegans as a model to interrogate the effects of autism-associated variants of previously unknown function in the RNA-binding protein RBM-26/RBM27.
Despite its potential impact, there are several concerns related to the technical rigor and specificity of the observed effects.
Major concerns:
- The effects on PLM are interesting, but why was this neuron selected for study? Was this a lucky guess or are other axons also affected? It is important to clarify whether the effects of RBM-26 are specific to this neuron or act pleiotropically across many or all neurons. According to CeNGEN, rbm-26 is strongly expressed in the well-characterized neurons ASE, PVD, and HSN. Are there morphological defects in these neurons, or others? As a note, there are also functional assays for these neurons (salt sensing, touch response, and egg laying, respectively).
- Similarly, the choice of the MALSU homolog seemed like a shot in the dark. It is ranked 46th (out of 63 genes) for fold-enrichment following RBM-26 pull-down, and 9th for p-value. Were any of the mRNAs with greater fold-enrichment or smaller p-values examined further? It is important to determine whether many or all of these interacting genes are overexpressed in the absence of RBM-26 and whether they are also required for the phenotypic effects of RBM-26 mutants, or if the MALSU homolog is special.
- In addition to the specificity controls mentioned above, positive and negative controls are needed throughout the results. While each of these may be relatively minor by itself, as a group they raise questions about the technical rigor of the study. Briefly these include:
Fig 1C. Missing loading controls and negative control (rbm-26 null allele). Additional exposures should be included to show whether RBM-26(P80L) protein or the lower band for RBM-26(L13V) are present at all, relative to the null allele.
Fig 2. Controls to distinguish overextension of PLM axon from posterior mispositioning of ALM cell body are needed. Quantification of PLM axon lengths in microns (or normalized to body size) with standard deviation, not error of proportion, should be shown. Measurement of "beading phenotype" should be more rigorous, see for example the approach in Rawson et al. Curr. Biol. 2017 https://doi.org/10.1016/j.cub.2014.02.025 . The developmental stage examined, and the reason for choosing that stage, should be described for this and all figures.
Fig 3. Controls without auxin and with neuronal TIR1 expression alone should be included. Controls demonstrating successful RBM-26 depletion, in larvae as well as in embryos at the time of PLM extension, should be included (weak embryonic depletion might explain why the overextension phenotype is only 14% instead of 40% as in the null). According to CeNGEN, rbm-26 expression in PLM is barely detected, thus depletion with a PLM-specific TIR1 should also be tested. To confirm the authors' identification of the cell marked "N" as the PLM cell body, co-expression of rbm-26 and a PLM-specific marker should be added. Rescue of the rbm-26 mutants with neuronal (and PLM-only) expression should be included to test sufficiency in PLM, and as a further control for potential artifacts of the AID system.
Fig 4. More rigorous quantification of the distribution of mitochondria along the axon should be included, not only total number, and it should be clarified what region of the axon the images are taken from. Including the AID-depletion strain with and without auxin would further add to the sense of rigor. For the mitoTimer experiments, why is RBM-26(L13V) not included and why do wild-type values differ ~5-fold between experiments (despite error bars being almost non-existent)? A more rigorous approach to standardizing imaging conditions may be needed. Positive controls using compounds that affect oxidation should be included. Measurements of individual mitochondria with standard deviations should be shown, rather than aggregate averages with error of proportion.
Fig 5. Additional positive and negative controls should be added, including additional rbm-26 alleles, the AID-tagged strain with and without auxin, and a rescued mutant.
Fig 6. Controls showing whether the Scarlet-tagged protein is functional are needed, to rule out dominant negative or toxicity-related effects.
Fig 8. Controls for other mitochondrial components need to be included. It is important to determine if the decrease in ribosomes is specific or reflects a general decrease in mitochondria. If there are fewer mitochondria as suggested in Fig. 4, then of course mitochondrial ribosomal protein levels are also reduced. Additional rbm-26 alleles should be included here as well. Is this effect dependent on the MALSU homolog? 4. Finally the authors should address concerns about image manipulation, which amplify the concerns about technical rigor outlined above. The image in Fig. 2A appears to have a black box placed over the lower-right portion of the field to hide some features. Black boxes also appear to have been placed over the tops of images in Fig. 4B and 4D and at the left of Fig. 6A, 6B, and 6C. While these manipulations probably do not affect the conclusions, they further undermine confidence in data integrity and experimental rigor.
Minor points.
- C. elegans nomenclature conventions should be followed:
- C. elegans gene names have three or four letters, thus the MALSU homolog cannot be named "malsu-1". Please have new gene names approved by WormBase BEFORE submitting for publication http://tazendra.caltech.edu/~azurebrd/cgi-bin/forms/gene_name.cgi
- If two sequential CRISPR edits are made on the same gene then they should be listed as a compound allele, such as rbm-26(cue22cue25)
- Genes on the same chromosome should not be separated with a semicolon, for example rbm-26(cue40) K12H4.2(syb6330)
- Describing the defects as "neurodevelopmental" is misleading in the case of axon beading or degeneration. Similarly, there is no evidence for an "axon targeting" defect as stated in the abstract.
- In Fig. 5A, the symbol that appears to correspond to F59C6.15 (lowest p-value) is a different size than the others and is colored as ncRNA, whereas WormBase annotates this gene as snoRNA.
- In the Introduction, the last sentences of the first two paragraphs should be varied ("However, little is known about the [...] mechanisms that protect [...] during neurodevelopment.")
- Why is RBM-26 protein running as a doublet at both sizes?
- When showing the RBM-26 expression pattern (Fig. 3) please include a lower-magnification image of the entire animal.
- It is confusing to refer to the RNA IP experiments as an "unbiased screen", which in C. elegans typically refers to a genetic screen.
- The relationship between axon overextension, beading, and mitochondrial localization is not clear. What causal connection between these is being proposed? The causal connections between these phenotypes, if any, should be clarified experimentally. For example, if the axon extension defects develop before mitochondrial localization defects, then it is unlikely that mitochondrial defects cause axon overextension.
- Please explain how to interpret the difference in axon beading in the two deletion alleles of the MALSU homolog (axon beading defects in tm12122 but not in syb6330). Is syb6330 not a null allele? Or are the defects in tm12122 due to other mutations in this strain background?
- Are mitochondria reduced in number or mislocalized? If they are reduced in number, is this due to altered balance of fission/fusion?
- In Fig. 3A-D, please keep the labels in the same position in all panels and do not alter brightness settings between single-color and merged panels.
- The claim that rbm-26 acts cell-autonomously requires PLM-specific depletion and rescue experiments.
Referees cross-commenting I appreciate the use of the consultation session to resolve differences between reviewers, but in this case I fully agree with the content and tone of all the comments from the other reviewer -- I think our remarks are very well aligned!
Significance
The study engineers autism-associated variants in conserved residues of RBM27 into the C. elegans homolog RBM-26 and identifies neuronal phenotypes potentially relevant to autism and a potential molecular mechanism involving regulation of mitochondrial ribosome assembly.
The key claims of the study are 1} that autism-associated variants in RBM-26 decrease its protein expression; 2} that impaired RBM-26 function leads to a variety of defects in development and maintenance of a single neuron called PLM, including altered axonal localization of mitochondria; 3} that RBM-26 normally binds the mRNA for the C. elegans homolog of MALSU, a mitochondrial ribosomal assembly factor; 4} that loss of RBM-26 leads to overexpression of the MALSU homolog; and 5} that MALSU is required for some of the deleterious effects on the PLM neuron seen in RBM-26 mutants.
This study will be of interest to the autism research community because it bolsters the idea that variants in RBM27 are likely to disrupt gene function and to affect neuronal health. It will also be of interest to the broader cell biology community because it suggests an interesting potential nucleus-to-mitochondria signaling mechanism, in which a nuclear RNA-binding protein might regulate assembly of mitochondrial ribosomes.
My field of expertise is developmental biology in C. elegans.
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Reply to the reviewers
Manuscript number: RC-2024-02413R
Corresponding author(s): Hammond, Gerald
1. General Statements [optional]
We are grateful to the three reviewers for such thorough and thoughtful comments. Data or re-writes that we have on hand that address many of these comments have been incorporated already. We also have a comprehensive experimental plan to address all of the remaining major comments. Reviewer’s comments are in light italics, whilst our responses appear in regular font below. We added reviewer numbering for ease of cross-reference to the original comments, with the format: reviewer X’s comment number N as #X.N
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*Overall, we were thrilled that the reviewers agreed that our work is of significance and broad interest: *
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“The development of a new, superior PA-sensor is a significant advance in the fields of lipid signaling and specific lipid-protein interactions, that will benefit research on lipid-mediated cellular signaling and intracellular lipid trafficking.” – reviewer 1.
“The lipid biology community would be highly interested in using the new PA-binding tool to study lipid localization in live cells.” – reviewer 2.
“Tracking intracellular phosphatidic acid (PA) in live cells is essential for understanding its cellular functions, leading to the development of genetically encoded lipid biosensors. While several PA biosensors have been developed, they often suffer from limited sensitivity or specificity.” – reviewer 3
2. Description of the planned revisions
#1.2: P 4 and Fig. 2 mention a 'novel domain structure', responsible for binding of PA and PIP2 (in vitro). What exactly is this novel domain structure? Why have the two parts of Nir1-LNS, the AAH and the 263 amino acid domain, not been tested in similar liposome assays as in Fig. 2A? Lipid binding in vivo, as tested in the experiment of Fig. 2D, is confounded by endogenous PA binding proteins.PA is expected to bind the SIDGS motif, as this is conserved from the Lipin catalytic motif (p. 5). However, experimental evidence of this appears to be lacking. Nevertheless, it is several times presented as a fact in the text. Pag. 7: 'This suggests that the SIDGS motif alone is not the sole PA binding pocket as the LNS2 domain requires both that motif and the amphipathic helix for sustained binding to membrane embedded PA.' Pag. 13: '... One reason for this difference could be that the Nir1-LNS2 is not a novel bona fide PA binding pocket. Rather, it requires both the SIDGS motif and an N-terminal amphipathic helix for membrane interactions (Figure 2). These conclusions therefore appear to be not accurate and would need te be rephrased. If the authors could show that PA binding by Nir1-LNS can be eliminated by mutating residues in the SIDGS motif, this would not only substantiate the above claims, but also make for a negative control protein, next to Nir1-LNS2. For future applications of Nir1-LNS2 as PA biosensor in other organisms this would be useful.
In addition to the already included data on cellular binding of the R784E mutant, we do plan to test this variant in the liposome binding assays to show loss of PA binding abilities as the reviewer has suggested. We also plan to evaluate proper folding of the R784 mutant through circular dichroism.
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#1.7. Fig. 2A suggests cooperativity in binding of Nir1-LNS2 to PA-containing liposomes. Please mention/comment! Does binding to PIP2-containing liposomes also exhibit cooperativity?
Using a nonlinear fit, we were able to determine Hill coefficients for PA binding. This has now been included in Figure 2B (formerly Figure 2A) and the following text has been added to page 6, left column, third paragraph: “In addition, we found that Nir1-LNS2 bound PA-rich liposomes in a concentration dependent manner (Figure 2B). By fitting the binding curve to this data, we found that the interaction of Nir1-LNS2 with PA provided a Kd value of ~19 mol%. Interestingly, Nir1-LNS2 binds to PA in a highly cooperative manner. The Hill coefficient for the interaction of Nir1-LNS2 with PA was calculated to be approximately 4 (Figure 2B).”
However, due to the liposome binding assay used that utilizes a set total lipid concentration but alters mol% lipids, the Kd that we determined is not a “traditional” Kd. Therefore, we plan to repeat this assay using constant PA concentrations but increasing total concentrations of lipid so that we can make a better fit and get a more accurate Kd value and Hill coefficient. We also plan to do the same assay with PIP2 to determine Kd values and Hill coefficients for that interaction.
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#2.2. The authors mention high affinity of Nir1-LNS2, but it lacks in vitro characterization that should demonstrate the higher affinity of Nir1-LNS2 compared to conventional probes such as Spo20. The authors should perform side-by-side comparison in Fig 2 to compare the PA affinity and specificity of Nir1-LNS2 compared to Spo20.
We plan to take the reviewer’s advice and directly compare Spo20-PABDx2 (and/or the single PABD depending on what we can get to purify correctly) and Nir1-LNS2 in the liposome binding assay.
Additionally, we propose to further characterize these sensors in cells as well. To start, we have added a direct comparison of the Spo20-PABDx2 and Nir1-LNS2 response to PA production at the PM (by PMA stimulation) and at mitochondria (by FKBP-DGKa) in Figure 4. The text has been updated to reflect this on p.10, left paragraph, 3rd paragraph: “Importantly, we also observed that Nir1-LNS2 responds to this ectopic PA production quicker and more robustly than NES-PABDx2-Spo20 does, as can be seen when the responses from Figure 4F are plotted together (Figure 4H). When analyzing the responses to PA production at the PM by PMA stimulation in Figure 1D and Figure 1F, we similarly see that the Nir1-LNS2 translocates to the PM more robustly and in a shorter timeframe (Figure 4G). This suggests that the Nir1-LNS2 can serve as a high affinity PA biosensor at various cellular locations.”
Furthermore, as suggested in the “General Assessment” we propose to use the FKBP-DGKa system to produce PA on other organelles such as the Golgi and ER and then we can directly compare the response of Spo20-PABDx2 and Nir1-LNS2 to the increase in PA at these organelles. This data will be added to Figure 4 for a full comparison of the sensors across cellular locations.
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#2.4. Fig 3 and Fig 4 need the validation of PIP depletion/production using PIP-binding probes.
We propose to repeat the experiments using TIRF in figure 3 as it will give us increases sensitivity, and also compare selectivity with the currently used spo20-based biosensors.
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#2.“General assessment”: The existing PA-binding probe using Spo20 is indeed quite blunt, which takes minutes to see appreciable accumulation of the probe upon PA production. Nir1-LNS2 can be indeed useful if it offers better spatiotemporal precision. However, the advantage of this tool over existing tools is not convincing without head-to-head comparison of either (1) in vitro characterization of PA binding affinity and selectivity between Nir1-LNS2 and Spo20 or (2) response to PA produced on different subcellular localizations other than plasma membrane and mitochondria (e.g., endosomes, golgi, and endoplasmic reticulum).
In order to address the selectivity of Nir1-LNS2 and Spo20, we propose to repeat the experiments in Figure 3 with the PJ enzymes in order to see how the PM PIPs affect Spo20 membrane binding, as described in our response to #2.4. Previously published data, as well as our own unpublished observations suggest that Spo20 interacts with the anionic PIPs to a greater extent than Nir1-LNS2 does (Nakanishi et al., 2004 doi: 10.1091/mbc.e03-11-0798; Horchani et al., 2014 doi: 10.1371/journal.pone.0113484). If we can show that Spo20’s interactions with the PM are significantly influenced by the PIPs, then this will add more evidence to the idea that Nir1-LNS2 is more selective for PA.
As described in response to #2.2, we are also planning a side-by-side comparison of spo20 based protein binding on liposomes alongside Nir1-LNS2.
Also, as discussed above, we agree with the reviewer that looking at the Nir1-LNS2 and Spo20 responses to PA production at other organelles would increase confidence that Nir1-LNS2 has a higher affinity for PA. We propose to add these experiments to Figure 4.
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#3.1. The direct measurement of the binding affinity of Nir1-LNS2 with PA, e.g., Kd, is essential; this information will help the field explore the potential usage of Nir1-LNS2.
Using a nonlinear fit, we were able to determine Hill coefficients for PA binding. This has now been included in Figure 2B (formerly Figure 2A) and the following text has been added to p.6, left column, 3rd paragraph: “In addition, we found that Nir1-LNS2 bound PA-rich liposomes in a concentration dependent manner (Figure 2B). By fitting a nonlinear curve to this data, we found that the interaction of Nir1-LNS2 with PA provided a Kd value of ~19 mol%. Interestingly, Nir1-LNS2 binds to PA in a highly cooperative manner. The Hill coefficient for the interaction of Nir1-LNS2 with PA was calculated to be 4.323 (Figure 2B)…. This suggests that the amphipathic helix and the SIDGS-containing domain may both interact with the membrane leading to the cooperative nature of Nir1-LNS2’s binding of PA-rich liposomes (Figure 2B).”
However, due to the liposome binding assay used that utilizes a set total lipid concentration but alters mol% lipids, the Kd that we determined is not a “traditional” Kd. Therefore, we plan to repeat this assay using higher total lipid concentrations with a fixed PA mol% so that we can make a better fit and get a more accurate Kd value and Hill coefficient. Furthermore, we plan to directly compare Spo20-PABDx2 (and/or the single PABD depending on what we can get to purify correctly) and Nir1-LNS2 in the liposome binding assay to directly compare their affinities for PA in vitro, as described in response to #2.2.
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#3.2. As mentioned by the authors, there is a confusing inconsistency regarding why Nir1-LNS2 binds to PIP2 in vitro but not in cells. Going beyond what has been discussed in the manuscript, there is a possibility that PIP2 could induce Nir1-LNS2 aggregation, leading to pelleting after centrifugation, among many other possibilities. I recommend the authors perform additional in vitro experiments, including but not limited to the liposome floatation assay to directly examine Nir1-LNS2 binding to the liposomes with varied compositions.
This is an excellent suggestion. We plan to check for aggregation by liposome flotation with Nir1-LNS2 in the presence of high mol% of PA and PIP2. In addition, we will also perform circular dichroism to see if PA or PIP2 liposomes are inducing any unfolding of Nir1-LNS2.
#3.3. In Fig. 2D, it would be beneficial to examine the constructs Nir1-613-630 and Nir1-631-894, comparing them with Nir1-LNS2 using liposome sedimentation and floatation assays to evaluate the contribution of the SIDGS motif and the amphipathic helix in binding PA.
Per our response to #1.2, we looked around the SIDGS motif to find the residue that would mediate the binding of membrane embedded PA, which our data suggests is R784 (Figure 2D). We do plan to test the R784 mutant in the liposome binding assays to show loss of PA binding abilities as the reviewer has suggested. We also plan to evaluate proper folding of the R784 mutant through circular dichroism.
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3. Description of the revisions that have already been incorporated in the transferred manuscript
#1.1: CCH treatment of HEK293A cells leads to the PM localization of the DAG sensor C1ab-Prkd1 as well as Nir1-LNS2 (Fig. 5), and the kinetics of these changes - Nir1-LNS2 would lag behind C1ab-Prkd1 fluorescence - is taken as evidence for Nir1-LNS2's specific binding of PA rather than DAG: Pag. 10: 'When we look at the first 2-minutes after CCh addition, we see that C1ab-Prkd1 moves to the PM much faster than Nir1-LNS2 does (Figure 5D). The delay in Nir1-LNS2 translocation makes sense given DAG is produced first and then converted into PA, again indicating that Nir1-LNS2 is specific for PA. 'Fig. 5 legend: 'The Nir1-LNS2 response to PLC depends on PA and not DAG.(...) (D) Nir1-LNS2 translocation to the PM (data replicated from Figure 5C) lags behind C1ab-Prkd1 (data replicated from Figure 5B) in response to CCh addition. 'The validity of the conclusion from this experiment seems questionable. The argument relies on the low values of Nir1-LNS2's normalized fluorescence intensity compared to C1ab-Prkd1's, in the first two minutes of stimulation, when DAG is expected to accumulate (Fig. 5D). However, if Nir1-LNS2 would bind DAG, the resulting fluorescence is expected to be low, i.e. compared to the much higher signal resulting from subsequent PA binding. Moreover, in this system, which co-expresses the two sensor proteins, competition in binding may account for the apparent precedence of one sensor over the other. Thus, even if the increase in C1ab-Prkd1 fluorescence would precede Nir1-LNS2 - following the authors' interpretation - this would not exclude binding of DAG by Nir1-LNS2.Fig. 5D shows the confocal images of cells after 30 sec CCH treatment using the two sensors, next to the respective controls, replicated from Fig. 5B/C. However, in Fig. 5D, different colors are used for Nir1-LNS2 than in Fig. 5C, which makes comparison along the time course difficult. In conclusion, the data presented in Fig. 5 do not exclude DAG binding by Nir1-LNS2, and modification of the conclusions from this experiment throughout the ms (including the cited sentences) is recommended. Consider removal of Figure 5D. Despite these considerations, the authors' final conclusion regarding the specificity of Nir1-LNS2 towards PA appears well supported (e.g. by the data presented in Figure 4).
We agree with the reviewer that the interpretation of the kinetic data is ambiguous and does not fully negate the idea that Nir1-LNS2 may bind to DAG. We have modified the interpretation accordingly. However, we have left the kinetic comparison of the DAG vs Nir1-LNS2 biosensors since these reflect the expected dynamics of the two lipids downstream of PLC. The data are now interpreted as follows on p. 10, right column, second paragraph: “The PM accumulation lagged that of DAG, consistent with conversion of DAG to PA by DGKs (Figure. 5D). Alternatively, in cells treated with CCh and then atropine, Nir1-LNS2 localized to the PM after CCh was added but was then observed returning to the cytoplasm over the 15-minute treatment with atropine as PA levels declined (Figure 5C). Overall, this experiment shows that Nir1-LNS2 binding to the PM follows the expected kinetic profile of DGK-produced PA.” Likewise, the legend for figure 5 is now labelled “Nir1-LNS2 detects PLC stimulated PA production.” to remove explicit conclusions about PA vs DAG binding.
#1.2: P 4 and Fig. 2 mention a 'novel domain structure', responsible for binding of PA and PIP2 (in vitro). What exactly is this novel domain structure? Why have the two parts of Nir1-LNS, the AAH and the 263 amino acid domain, not been tested in similar liposome assays as in Fig. 2A? Lipid binding in vivo, as tested in the experiment of Fig. 2D, is confounded by endogenous PA binding proteins.PA is expected to bind the SIDGS motif, as this is conserved from the Lipin catalytic motif (p. 5). However, experimental evidence of this appears to be lacking. Nevertheless, it is several times presented as a fact in the text. Pag. 7: 'This suggests that the SIDGS motif alone is not the sole PA binding pocket as the LNS2 domain requires both that motif and the amphipathic helix for sustained binding to membrane embedded PA.' Pag. 13: '... One reason for this difference could be that the Nir1-LNS2 is not a novel bona fide PA binding pocket. Rather, it requires both the SIDGS motif and an N-terminal amphipathic helix for membrane interactions (Figure 2). These conclusions therefore appear to be not accurate and would need te be rephrased. If the authors could show that PA binding by Nir1-LNS can be eliminated by mutating residues in the SIDGS motif, this would not only substantiate the above claims, but also make for a negative control protein, next to Nir1-LNS2. For future applications of Nir1-LNS2 as PA biosensor in other organisms this would be useful.
We have clarified that the domain architecture of the Nir1-LNS2 is not a novel domain structure generally, but novel for a PA binding protein which are typically just helices such as that seen in Spo20. Figure 2 is now titled “Nir1-LNS2 shows specificity for PA and PIP2 in vitro, based on a novel PA-binding domain.”
We have also clarified that the SIDGS motif is not the actual location of PA binding, but rather is only the motif conserved with the Lipin/Pah active site. R784 appears to be a PA coordinating residue near the SIDGS, as the positive residue can interact with the negative lipid. Furthermore, we agreed with the reviewer that mutating this residue to perturb PA binding was a much more convincing experiment. We have now included this data in Figure 2 and rewritten the following passages.
From Page 6, left column, last paragraph: “The putative Lipin catalytic motif DxDxT is partially conserved in Nir1-LNS2 as a SIDGS motif spanning residues 742-746. We looked for positively charged residues nearby that could bind to the PA in the membrane and coordinate its entrance into the SIDGS site. The active site of the Lipins has a nearby Arg residue which was predicted to perform this role (Khayyo et al., 2020). AlphaFold analysis of Nir1-LNS2 showed that this residue was also conserved in Nir1-LNS2 as R784, and that the side chain of the Arg sticks out toward the membrane interface where it would be able to contact the negatively charged PA (Figure 2C).
The conservation of these features between the Lipins and Nir1-LNS2 suggests that PA binds this positively charged residue near the SIDGS pocket within Nir1-LNS2 (Kim et al., 2013; Khayyo et al., 2020). However, for efficient catalytic activity, the Lipins also require an N-terminal amphipathic helix for membrane interaction. This helix is made up of residues 1-18 in Tetrahymena thermophila Pah2 (Khayyo et al., 2020), and residues 613-630 in the N-terminus of Nir1-LNS2 are predicted to form a similar amphipathic helix (Figure 2C). We therefore tested whether the N-terminal helix of Nir1-LNS2 was necessary for interaction with PA at the PM. We made two truncations of the Nir1-LNS2 construct: Nir1-613-630 is the isolated amphipathic helix, while Nir1-631-894 is the rest of the domain excluding the helix but including the SIDGS motif. Surprisingly, neither truncated construct responded to PMA by binding the PM, and they even showed reduced basal PM localization (Figure 2D).
Although Figure 2D suggests that the SIDGS motif alone is not sufficient for membrane interactions, we probed into the suspected PA binding residue R784 by mutating it into a negatively charged Glu residue, which should disrupt its interaction with the negatively charged lipid. The R784E mutation completely ablated Nir1-LNS2 interactions at the PM after PMA stimulation and showed reduced association with the PM even before PMA stimulation (Figure 2D).
Altogether, our data suggests that the LNS2 domain requires both the larger SIDGS-containing domain and the amphipathic helix for sustained binding to membrane-embedded PA, but that the PA may directly interact with R784 near the SIDGS motif. Therefore, the Nir1-LNS2 provides a novel PA binding domain with a tertiary structure beyond the simple amphipathic helices found in Spo20.”
We have also rewritten this sentence in the discussion, p. 14, right column, second paragraph: “As far as the use of Nir1-LNS2 as a biosensor, the one caveat is the discrepancy in its specificity: in vitro PA and PIP2 were sufficient to recruit Nir1-LNS2 to PC liposomes (Figure 2), but in vivo only PA was sufficient for mitochondrial recruitment (Figure 4). One reason for this difference could be that the Nir1-LNS2 requires R784 near the SIDGS pocket and an N-terminal amphipathic helix for membrane interactions (Figure 2).”
#1.3. Pag. 7: '...small caveat to consider when using Nir1-LNS2 to study PA, the data also demonstrates that Nir1-LNS2 is not specifically interacting with any of the PM PIPs in cellular membranes.'This seems not accurate, since the data in Fig. 3 suggest that PI4P could be involved in membrane localization of Nir1-LNS2. It remains however unresolved whether this is a specific interaction with this PIP.
We have rewritten the text on Page 8, right column, first paragraph accordingly: “This data suggests that decreasing the anionic charge of the membrane through depletion of PIPs slightly reduces Nir1-LNS2’s ability to interact with the PM, but it doesn’t fully re-localize the sensor. Therefore, this is a caveat to consider when using Nir1-LNS2 to study PA.”
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#1.4. Please note that the presence of PE increases the ionization (and negative charge) of PIP2 (Graber et al., 2012) rather than dilutes the negative charge as stated in the Discussion on p.13. Please revise!
We have updated the text on Page 14, right column, last paragraph: “The presence of other lipids such as PI, the formation of PIP2-rich domains, and even interactions with neighboring proteins can increase hydrogen bonding of PIP2and dilute the negative charge (Graber et al., 2012; Borges-Araújo and Fernandes, 2020). Phosphatidylethanolamine on the other hand, increases PIP2 ionization and its negative charge, though these effects are also thought to be reduced by PIP2 intramolecular hydrogen bonding which competes for the charges on the lipid (Graber et al., 2012).”
#1.5. P 1 The authors may consider adding a 4th criterium for a lipid biosensor: the sensor should not serve as a sink for the lipid by removing/sequestering it from the active pool, thereby interfering with other interactions/conversions.
We agree that biosensors should not sequester a significant fraction of their cognate lipids and affect downstream pathways by competing with endogenous binding partners. We have rewritten the following text regarding Figure 6 to make this distinction more clear:
Page 13, left column, last paragraph: “As Nir1-LNS2 shows high affinity for PA across cell lines, this brings up the concern that use of Nir1-LNS2 will sequester PA and inhibit endogenous signaling pathways that depend on PA…Therefore, we conclude that use of Nir1-LNS2 as a PA biosensor does not sequester significant amounts of PA. It is suggested that cellular homeostasis may compensate for the amount of bound lipid by increasing synthesis of free lipid, as this has been seen with the PIP2 biosensor PH-PLCd1 (Traynor-Kaplan et al., 2017). While PA has a plethora of cellular functions, the fact that Nir1-LNS2 expression does not disrupt MCS formation shows promise that the high affinity of Nir1-LNS2 will not inhibit downstream PA signaling.
#1.6. Nir1 lacks a PITP domain (Fig. 1), yet is referred to as lipid transfer protein: please elaborate/explain.
The following text has been added to Page 2, left column, last paragraph to clarify this point: “This family of proteins, made up of Nir1, Nir2, and Nir3, form ER-PM membrane contact sites (MCS) to exchange PA and phosphatidylinositol (PI) between the compartments (Cockcroft and Raghu, 2016; Kim et al., 2015). While Nir1 lacks a functional PITP domain, it was initially classified as part of the PITP family based on the homology of its other domains with Nir2 and Nir3. Furthermore, Nir1 has a role in lipid transfer by facilitating Nir2 recruitment to the MCS (Quintanilla et al., 2022).”
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#1.8. Indicate the concentrations of PC and protein in the legend to Fig. 2 panels A and B. M&M says 2 mM PC, according to the PA-concentrations above panel 2A, this should be 1 mM. Please clarify.
We have corrected the typo in panel 2A (now panel 2B) and have updated the Figure 2 legend as follows, “(A) A representative SDS-PAGE gel is shown for Nir1-LNS2 binding to various PM lipids in POPC liposomes. (B) A representative SDS-PAGE gel is shown for Nir1-LNS2 binding of increasing PA molar concentrations in POPC liposomes. For both A and B, the lipids indicated were mixed with POPC to produce a 2 mM solution, then 50 uL of the resulting liposome mixture was incubated with 50 uL of Nir1-LNS2 at ~1 mg/mL. Supernatant (S) and pellet (P) lanes were quantified using ImageJ to determine percent protein bound. The protein-only control pellet was used as a baseline (input). Nir1-LNS2 appears on the gel at 37 kDa.”
#1.9. In Fig. 2B, PI is missing. Any specific reason?
We have updated the text on Page 6, left column, second paragraph to discuss the low levels of PI at the PM, which is why we did not include this lipid. “Using this same PC background, we tested the efficacy of the PM lipids DAG, PA, PS, PI4P and PIP2 in recruiting Nir1-LNS2 to membranes. While PI serves as a substrate for PI4P and PIP2 synthesis (collectively referred to as the phosphatidylinositol phosphates (PIPs)) at the PM, levels of PI at the PM are very low compared to the PIPs and therefore PI itself was not tested (Zewe et al., 2020; Pemberton et al., 2020).”
#1.10. Move Fig. 2C to the Introduction and extend it to illustrate the shared conserved features of Nir1-LNS2 and lipin.
We would like to keep the diagram of the Nir1-LNS2 in Figure 2 where the features are discussed in more detail than in the introduction. However, we did add this sentence to the introduction __on p.2, right column, second paragraph __that refers the reader to the cartoon in Figure 2. “These features are conserved in the Nir LNS2 domains, except for the catalytic Asp in the DxDxT motif and another Mg2+-coordinating residue (Figure 2C).”
#1.11. P 13. 'While real-time IMPACT does not directly report on PA levels as it does not use the endogenous PLD substrate PC, ...'It is true that this method doesn't directly report on PA levels, but that is because it uses a click chemistry probe as substrate for PLD's transphosphatidylation reaction. Contrary though to what is stated by the authors, this reaction still uses the endogenous substrate of PLD, PC (Liang et al. 2019; www.pnas.org/cgi/doi/10.1073/pnas.1903949116).
We have rewritten the aforementioned sentence in the discussion (Page 15, right column, second paragraph): “While real-time IMPACT does not directly report on PA levels as it creates a unique fluorescent lipid, it offers several advantages such as being able to interrogate lipid trafficking over time.”
#1.12. Fig. 1K: Control must have been treated with PMA plus vehicle (DMSO); if so, please indicate that vehicle was added.
Figure 1K and its legend have been updated. The legend now reads “Stimulating HEK293A cells with 100 nM PMA and 750 nM of the PLD inhibitor FIPI reduces the Nir1-LNS2 response to PMA and cell media (Veh)”
#1.13. Figure 6C: How is DFt/Fpre defined? Add to legend.
We have updated the Figure 6 legend to read “MCS formation was quantified as the change in fluorescence at a given time (Ft) divided by the fluorescence before CCh stimulation (Fpre).”
#1.14. P 4: 'The boundaries of the Nir2-LNS2 (Uniprot: O00562) and Nir3-LNS2 (Uniprot: Q9BZ72) were also defined using AlphaFold predictions of the structure of these domains.' How was the extent of each of these domains determined - they are much larger than the previously published sequences of LNS2 domains (Kim et al. 2013; Embo Rep. 14:891-899. doi:10.1038/embor.2013.113)?
We have clarified the definition of boundaries by updating the following sentence on Page 4, right column, 5thparagraph: “The boundaries of the Nir2-LNS2 (Uniprot: O00562) and Nir3-LNS2 (Uniprot: Q9BZ72) were also defined using AlphaFold predictions of the structure of these domains. Previous definitions of the Nir2-LNS2 domain have considered the domain smaller than we do here (Kim et al., 2013, 2015) . However, according to AlphaFold, the boundaries set previously exclude a large N-terminal beta barrel that is conserved in the Lipin/Pah PAPs, as well as disrupt the domain fold that is homologous to the Lipin active site. Therefore, we are confident that our constructs include the entire LNS2 fold.”
#1.15. In Fig. 3 legend, specify the starting condition of Nir1-LNS2 binding?? Which fluorescence are we looking at?
This figure has now been revised in response to __point #3.5, __which hopefully also clarified this point.
#1.16. In legend to Fig. 6 please specify the fluorescent tags used. Have they been shown not to affect protein function?
We have updated the figure legend to specify that GFP-Nir2 was used in conjunction with iRFP-Nir1-LNS2, we also changed the text on Page 13, right column, second paragraph that refers to this experiment. It now reads “We co-expressed a GFP-tagged Nir2 and either iRFP-Nir1-LNS2 or iRFP-TubbyC, a PIP2 biosensor that is not expected to affect MCS formation. It should be noted that although we have used the NG-tagged Nir1-LNS2 the most extensively, the iRFP and mCherry-tagged biosensors have behaved the same as the NG-tagged version in the experiments where we utilized them.”
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#2.1. Nir1-LNS2 seems to show variable basal localization across different representative images presented in the manuscript. A part of them were justified by the effect of other anionic species by PIP (such as Fig 3 where they co-expressed PIP-degrading enzymes). For example, cells in Fig 1F and those in Fig 4F show quite different basal localization of Nir1-LNS2. Is it due to difference in expression level, cell conditions, or other factors The significant amount of plasma membrane basal localization seems to indicate that Nir1-LNS2 localization is affected by its binding to PI(4,5)P2.? The significant and potentially variable plasma membrane localization of Nir1-LNS1 can limit the utility of this probe.
We have added Supplemental Figure 1 __to show the range of Nir1-LNS2 basal localization compared to NES-PABDx2-Spo20 and PASS. We believe that this localization is due to variable amounts of basal PA combined with some non-selective anionic interactions at the PM. The following paragraph has been added to __page 7, left column, first paragraph to discuss this point, “Since the R784E mutant showed reduced basal PM localization, we wanted to further characterize the basal localization of the wild-type Nir1-LNS2. The basal localization of wild-type Nir1-LNS2 varies somewhat between cells, but analysis of all of the cells used throughout this study determines that the basal PM/Cyt ratio of the wild-type Nir1-LNS2 is 1.0644 ± 0.0672, which suggests that at resting conditions Nir1-LNS2 is slightly enriched at the PM (Supplemental Figure 1A, 1D). When we did the same analysis for all the cells where we expressed NES-PABDx2-Spo20 or PASS, we obtained a basal PM/Cyt ratio of 1.1318 ± 0.0954 for NES-PABDx2-Spo20 and a ratio of 0.6861 ± 0.0143 for PASS (Supplemental Figure 1B, 1C, 1E). We believe that the basal localization of these sensors reflects variable PA levels in the PM at resting conditions. FRET based imaging of PA has indicated that there are detectable levels of PA under basal conditions, and this approach also showed some variability in basal PA levels as we see with the spread of Nir1-LNS2’s basal localization (Nishioka et al., 2010). Overall, our data suggests that the high affinity of Nir1-LNS2 for PA is reflected in both its basal localization and its response to stimulations such as PMA.”
To address the idea that PIP2 is responsible for the basal localization of Nir1-LNS, we have added the following to the discussion on p.15, left column, second paragraph: “Aside from concerns about specificity, the ability of Nir1-LNS2 to interact with PIP2 in liposomes could suggest that the basal PM localization of Nir1-LNS2 is due to it binding PIP2. However, selective depletion of PI(4,5)P2 did not affect basal Nir1-LNS2 localization to the PM (Figure 3C) and was not able to recruit the probe to mitochondria (Figure 4A-C). We did see FKBP-PJ reduce the association of Nir1-LNS2 with the PM under resting conditions (Figure 3E, 3F), suggesting a possible non-specific ionic interaction with polyanionic inositol lipids. Another mechanism to explain these data would be phosphoinositide-dependence of PA production. Phosphoinositides are well-known to regulate the recruitment of PLD isoforms and type II DGKs to the PM as well as their catalytic activity there (Sciorra et al., 2002; Du et al., 2003; Hodgkin et al., 2000; Liscovitch et al., 1994; Kume et al., 2016). Therefore, we suggest that the effects of FKBP-PJ could be reducing basal PLD and DGK activity and hence lowering resting PA levels. That could explain the loss of both basal Nir1-LNS2 PM association when FKBP-PJ is expressed, and Nir1-LNS2’s PM interactions as FKBP-PJ is recruited to the membrane to further deplete phosphoinositides. While this study cannot fully substantiate this hypothesis, the role of PIP2 in PLD activity and PA production is an interesting hypothesis that warrants further investigation.
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#2.3. Fig 1 shows Nir1-LNS2 translocates to plasma membrane upon PMA stimulation in a PLD activity-dependent manner. However, the image in Fig1K is not super convincing since there is already a decent amount of plasma membrane localization of the sensor at t = 0 min, which looks considerably different from the t = 0 min image shown in 1F.
We have updated the images both in Figure 1F and Figure 1K to best represent the mean basal localization as determined in Supplemental Figure 1.
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#2.4. Fig 3 and Fig 4 need the validation of PIP depletion/production using PIP-binding probes.
These controls are shown in Figure 4B. We have only included PH-PLCd1 to show PIP2 levels as the large PIP2 production by a PIP5K also indicates the large elevation of the substrate PI4P.
This control data has now been included in Figure 3, and is referenced by the Figure 3 legend and the following text from p.8, left column, 4th paragraph: “As a negative control, we expressed a doubly catalytically dead mutant of PJ. When PJ-Dead was recruited to the PM, we confirmed that PIP2 and PI4P levels remained unaltered by seeing stable association of the PIP2 biosensor Tubby(c) with the PM (Figure 3A). We observed no loss of the PM localization of Nir1-LNS2 with PJ-Dead recruitment (Figure 3A, 3E). When the active PJ was expressed in HEK293A cells, there was a slight loss of Nir1-LNS2 at the PM even before PJ recruitment (Figure 3B, 3E), although this was not significant as compared to pre-stimulated cells expressing PJ-Dead (Figure 3F). However, Nir1-LNS2 did move off the PM into the cytosol after PJ recruitment, to a similar extent that the PIP2 biosensor Tubby(c) moved off the PM (Figure 3B, 3E). AUC analysis of the Nir1-LNS2 response showed there was a significant reduction of Nir1-LNS2 PM localization (Figure 3G).
Since PJ depletes both PIP2 and PI4P, we examined which of these lipids specifically contribute to Nir1-LNS2 membrane binding. We utilized an FKBP-INPP5E construct that depletes PIP2 but does not deplete PI4P at the PM, as seen by the significant loss of PM-localized Tubby(c) (Figure 3C). Then FKBP-Sac1, an FKBP-PJ construct that has a catalytically dead INPP5E domain, but an active Sac1 domain was used to deplete PI4P without altering PIP2 levels, as seen by removal of the PI4P biosensor P4Mx1 from the PM (Figure 3D). Recruitment of FKBP-INPP5E did not significantly affect Nir1-LNS2 localization (Figure 3C, 3E, 3G). However, recruitment of FKBP-Sac1 slightly, but not significantly affected Nir1-LNS2 localization (Figure 3D, 3E, 3G). This data suggests that decreasing the anionic charge of the membrane through depletion of PIPs slightly reduces Nir1-LNS2’s ability to interact with the PM, but it doesn’t fully re-localize the sensor. Therefore, this is a small caveat to consider when using Nir1-LNS2 to study PA.”
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#2.“Advance”: The key significance of the manuscript, which is the superiority of Nir1-LNS2 over existing PA-binding probes, is not clear from the data provided. Other than that part, the study does not seem to include significant finding, since the binding of Nir1-LNS2 to PA itself is already known (EMBO Rep. 2013 Oct; 14(10): 891-899, Mol Biol Cell. 2022 Mar 1;33(3):br2).
While the Kim et al., paper referenced by the reviewer does show that the LNS2 binds to PA, this same group later published data showing that the LNS2 binds to both PA and DAG. (Kim et al., 2015 doi: 10.1016/j.devcel.2015.04.028). Therefore, we believe our data which unequivocally shows that the LNS2 does not bind DAG, is a significant advancement in the field. Aside from the creation of the new biosensor, it progresses our understanding of the mechanism of the Nir family lipid transfer proteins, which are vital to PM lipid homeostasis.
To highlight this point, we have added the following paragraph to the discussion on p.14, right column, 1st paragraph: “The lack of Nir1-LNS2 binding to DAG-rich liposomes (Figure 2), DAG produced at the mitochondria (Figure 4), and DAG analogs (Figure 5) shows that the LNS2 domains only binds to PA rather than to PA and DAG as has been reported previously (Kim et al., 2015). In this study, we redefined the boundaries of the LNS2 domain based on the structure of the Lipin/Pah family domains and the AlphaFold prediction for the Nir1-LNS2. The new boundaries included the entire fold that is conserved between the Lipins/Pahs and the Nirs. Therefore, we suspect that the expansion of the LNS2 domain in our work explains the differences in our data and the published literature regarding DAG binding. Importantly, the data obtained with our amended Nir1-LNS2 suggests that within the context of the lipid transfer cycle and MCS formation, the Nir family of PITPs translocate to the PM solely based on PA. This information will be important as the field continues to determine the exact mechanism of the Nir PITPs in lipid homeostasis.”
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#3.4. Due to PA's versatile biological roles, the evidence provided by the MCS experiment is far from enough to conclude that Nir1-LNS2 does not interfere with PA function. Further examination of various endogenous pathways is warranted before making the statement "Therefore, Nir1-LNS2 can be used ...... without concern of affecting downstream events".
We have rewritten the quoted sentence for a more nuanced interpretation on p.13, right column, second paragraph:“While PA has a plethora of cellular functions, the fact that Nir1-LNS2 expression does not disrupt MCS formation shows promise that the high affinity of Nir1-LNS2 will not inhibit downstream PA signaling.”
#3.5. In Fig. 3A-D (Left), it is unclear to what extent PIPs are reduced after treatment with FKBP-tagged PIP phosphatases. The treatment depicted in the illustration should be accompanied by data, e.g., % of PIPs being degraded after treatment.
This comment is addressed in our response to #2.4, where we show the addition of control biosensors for PIP2 and PI4P, and also propose new experiments in TIRFM for more sensitive and precise measurements.
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4. Description of analyses that authors prefer not to carry out
__#1.1a: __Have the authors considered using the DGK inhibitor R59022 to selectively block the conversion of DAG to PA by DGK? Such an experiment could provide additional evidence for the requirement of DGK activity and consequent PA formation for Nir1-LNS2 membrane localization.
We did indeed attempt experiments with R59022, and have made several unexpected findings with the compound that go way beyond the scope of the current manuscript. In short, although R59022 reduces DGK catalytic activity, it also potently drives over-expressed or endogenous DGKalpha to the plasma membrane, and induces large accumulations of PM PA. This complicated interpretation of data obtained with this compound. We are currently preparing a manuscript detailing the novel and unexpected effects.
#3.6. In Fig. 4C, the plasma membrane (PM) localization of Nir1-LNS2 and NES-PABDx2-Spo20, as determined by the "intensity PM/Cyto," should be analyzed following the ectopic production of PI4P and PIP2. Although mitochondria do not apparently recruit Nir1-LNS2 or NES-PABDx2-Spo20 after induced PI4P and PIP2 production, it remains possible that the subsequent trafficking of PI4P and PIP2 from mitochondria might sequester the biosensors away from the PM into the cytoplasm, thereby reducing the "intensity PM/Cyto" of Nir1-LNS2.
We cannot easily determine the PM/cyt ratio in this experiment as we included a mitochondrial marker rather than a PM marker when imaging. However, based on the images, there is no change in the PM intensity of the Nir1-LNS2 and NES-PABDx2-Spo20 biosensors. The images included in Figure 4 are representative of this localization.
#3.7. It would be valuable to determine the half-life (stability) of Nir1-LNS2.
In all of our transient transfections, the Nir1-LNS2 shows good stability where we don’t expect degradation to be a major concern. Furthermore, stability has not usually been factor considered in the creation any of the current widely used lipid biosensors.
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Referee #3
Evidence, reproducibility and clarity
Weckerly et al. introduced a fluorescently tagged Nir1-LNS2 construct capable of binding to both PA and PIP2 in vitro, yet selectively targeting PA-enriched membranes in cells. Their findings demonstrate that Nir1-LNS2 exhibits heightened responsiveness to PA, allowing the authors to uncover a modest contribution of PLD to PA production downstream of muscarinic receptors, a phenomenon not visualized with previous Spo20-based biosensors. Thus, Nir1-LNS2 is a sensitive biosensor, potentially providing researchers with a powerful new tool for real-time investigation of PA dynamics in live cells. The manuscript is well-written, with major conclusions supported by experimental evidence. The tool developed in this study holds significant importance for the field of lipid biology. However, missing controls and weaknesses from the in vitro analysis reduce the overall impact of this work. The authors are encouraged to address the following comments to further strengthen their conclusions:
Major Points:
- The direct measurement of the binding affinity of Nir1-LNS2 with PA, e.g., Kd, is essential; this information will help the field explore the potential usage of Nir1-LNS2.
- As mentioned by the authors, there is a confusing inconsistency regarding why Nir1-LNS2 binds to PIP2 in vitro but not in cells. Going beyond what has been discussed in the manuscript, there is a possibility that PIP2 could induce Nir1-LNS2 aggregation, leading to pelleting after centrifugation, among many other possibilities. I recommend the authors perform additional in vitro experiments, including but not limited to the liposome floatation assay to directly examine Nir1-LNS2 binding to the liposomes with varied compositions.
- In Fig. 2D, it would be beneficial to examine the constructs Nir1-613-630 and Nir1-631-894, comparing them with Nir1-LNS2 using liposome sedimentation and floatation assays to evaluate the contribution of the SIDGS motif and the amphipathic helix in binding PA.
- Due to PA's versatile biological roles, the evidence provided by the MCS experiment is far from enough to conclude that Nir1-LNS2 does not interfere with PA function. Further examination of various endogenous pathways is warranted before making the statement "Therefore, Nir1-LNS2 can be used ...... without concern of affecting downstream events".
Minor Points:
- In Fig. 3A-D (Left), it is unclear to what extent PIPs are reduced after treatment with FKBP-tagged PIP phosphatases. The treatment depicted in the illustration should be accompanied by data, e.g., % of PIPs being degraded after treatment.
- In Fig. 4C, the plasma membrane (PM) localization of Nir1-LNS2 and NES-PABDx2-Spo20, as determined by the "intensity PM/Cyto," should be analyzed following the ectopic production of PI4P and PIP2. Although mitochondria do not apparently recruit Nir1-LNS2 or NES-PABDx2-Spo20 after induced PI4P and PIP2 production, it remains possible that the subsequent trafficking of PI4P and PIP2 from mitochondria might sequester the biosensors away from the PM into the cytoplasm, thereby reducing the "intensity PM/Cyto" of Nir1-LNS2.
- It would be valuable to determine the half-life (stability) of Nir1-LNS2.
Significance
Tracking intracellular phosphatidic acid (PA) in live cells is essential for understanding its cellular functions, leading to the development of genetically encoded lipid biosensors. While several PA biosensors have been developed, they often suffer from limited sensitivity or specificity.
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Referee #2
Evidence, reproducibility and clarity
Summary:
The authors reported a new PA-binding probe Nir1-LNS2, which potentially offers advantages over conventional tools with its higher sensitivity for PA. The authors performed extensive characterization in different cell lines to test the ability of Nir1-LNS2 to selectively bind to PA without disrupting endogenous PA signaling. While the tool is potentially useful as a new PA-binding probe with higher spatiotemporal precision, the data provided in the manuscript are not enough to support their claims and conclusions. Especially, the data do not fully support that the Nir1-LNS2 offers more sensitive and selective binding to PA than conventional PA-binding probes using Spo20.
Major comments:
- Nir1-LNS2 seems to show variable basal localization across different representative images presented in the manuscript. A part of them were justified by the effect of other anionic species by PIP (such as Fig 3 where they co-expressed PIP-degrading enzymes). For example, cells in Fig 1F and those in Fig 4F show quite different basal localization of Nir1-LNS2. Is it due to difference in expression level, cell conditions, or other factors? The significant amount of plasma membrane basal localization seems to indicate that Nir1-LNS2 localization is affected by its binding to PI(4,5)P2. The significant and potentially variable plasma membrane localization of Nir1-LNS1 can limit the utility of this probe.
- The authors mention high affinity of Nir1-LNS2, but it lacks in vitro characterization that should demonstrate the higher affinity of Nir1-LNS2 compared to conventional probes such as Spo20. The authors should perform side-by-side comparison in Fig 2 to compare the PA affinity and specificity of Nir1-LNS2 compared to Spo20.
Minor comments:
- Fig 1 shows Nir1-LNS2 translocates to plasma membrane upon PMA stimulation in a PLD activity-dependent manner. However, the image in Fig1K is not super convincing since there is already a decent amount of plasma membrane localization of the sensor at t = 0 min, which looks considereably different from the t = 0 min image shown in 1F.
- Fig 3 and Fig 4 need the validation of PIP depletion/production using PIP-binding probes.
- In Discussion: "while in vivo it solely binds to PA (Fig 4)" - this claim does not seem to be true according to Fig 4, where the overexpression of PIP-degrading enzymes did affect the Nir1-LNS2 basal localization.
Significance
General assessment:
The existing PA-binding probe using Spo20 is indeed quite blunt, which takes minutes to see appreciable accumulation of the probe upon PA production. Nir1-LNS2 can be indeed useful if it offers better spatiotemporal precision. However, the advantage of this tool over existing tools is not convincing without head-to-head comparison of either (1) in vitro characterization of PA binding affinity and selectivity between Nir1-LNS2 and Spo20 or (2) response to PA produced on different subcellular localizations other than plasma membrane and mitochondria (e.g., endosomes, golgi, and endoplasmic reticulum).
Advance:
The key significance of the manuscript, which is the superiority of Nir1-LNS2 over existing PA-binding probes, is not clear from the data provided. Other than that part, the study does not seem to include significant finding, since the binding of Nir1-LNS2 to PA itself is already known (EMBO Rep. 2013 Oct; 14(10): 891-899, Mol Biol Cell. 2022 Mar 1;33(3):br2).
Audience:
The lipid biology community would be highly interested in using the new PA-binding tool to study lipid localization in live cells.
My expertise is PA signaling and deveopment of engineered phospholipase Ds, which can produce PA on demand at various subcellular locations.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The authors designed, created, and validated a fluorescently tagged sensor protein that binds with high affinity to the signaling phospholipid PA in cells. The LNS2 PA-binding domain used originates from the lipid transfer protein Nir1, and shares conserved features with lipins. The novel sensor outperforms the commonly used Spo20-based PA probes, although it also suffers from binding to PIP2 in vitro (liposomes) and from PIPs affecting its membrane binding in vivo. Importantly, the authors demonstrate that PA but not DAG or PIP2 is sufficient for membrane binding of Nir1-LNS2 in cells, validating Nir1-LNS2 as a PA-sensor in fluorescence microscopy studies.
Major comments:
- CCH treatment of HEK293A cells leads to the PM localization of the DAG sensor C1ab-Prkd1 as well as Nir1-LNS2 (Fig. 5), and the kinetics of these changes - Nir1-LNS2 would lag behind C1ab-Prkd1 fluorescence - is taken as evidence for Nir1-LNS2's specific binding of PA rather than DAG: Pag. 10: 'When we look at the first 2-minutes after CCh addition, we see that C1ab-Prkd1 moves to the PM much faster than Nir1-LNS2 does (Figure 5D). The delay in Nir1-LNS2 translocation makes sense given DAG is produced first and then converted into PA, again indicating that Nir1-LNS2 is specific for PA.' Fig. 5 legend: 'The Nir1-LNS2 response to PLC depends on PA and not DAG.(...) (D) Nir1-LNS2 translocation to the PM (data replicated from Figure 5C) lags behind C1ab-Prkd1 (data replicated from Figure 5B) in response to CCh addition.' The validity of the conclusion from this experiment seems questionable. The argument relies on the low values of Nir1-LNS2's normalized fluorescence intensity compared to C1ab-Prkd1's, in the first two minutes of stimulation, when DAG is expected to accumulate (Fig. 5D). However, if Nir1-LNS2 would bind DAG, the resulting fluorescence is expected to be low, i.e. compared to the much higher signal resulting from subsequent PA binding. Moreover, in this system, which co-expresses the two sensor proteins, competition in binding may account for the apparent precedence of one sensor over the other. Thus, even if the increase in C1ab-Prkd1 fluorescence would precede Nir1-LNS2 - following the authors' interpretation - this would not exclude binding of DAG by Nir1-LNS2. Fig. 5D shows the confocal images of cells after 30 sec CCH treatment using the two sensors, next to the respective controls, replicated from Fig. 5B/C. However, in Fig. 5D, different colors are used for Nir1-LNS2 than in Fig. 5C, which makes comparison along the time course difficult. In conclusion, the data presented in Fig. 5 do not exclude DAG binding by Nir1-LNS2, and modification of the conclusions from this experiment throughout the ms (including the cited sentences) is recommended. Consider removal of Figure 5D. Despite these considerations, the authors' final conclusion regarding the specificity of Nir1-LNS2 towards PA appears well supported (e.g. by the data presented in Figure 4).
Have the authors considered using the DGK inhibitor R59022 to selectively block the conversion of DAG to PA by DGK? Such an experiment could provide additional evidence for the requirement of DGK activity and consequent PA formation for Nir1-LNS2 membrane localization. 2. P 4 and Fig. 2 mention a 'novel domain structure', responsible for binding of PA and PIP2 (in vitro). What exactly is this novel domain structure? Why have the two parts of Nir1-LNS, the AAH and the 263 amino acid domain, not been tested in similar liposome assays as in Fig. 2A? Lipid binding in vivo, as tested in the experiment of Fig. 2D, is confounded by endogenous PA binding proteins. PA is expected to bind the SIDGS motif, as this is conserved from the Lipin catalytic motif (p. 5). However, experimental evidence of this appears to be lacking. Nevertheless, it is several times presented as a fact in the text. Pag. 7: 'This suggests that the SIDGS motif alone is not the sole PA binding pocket as the LNS2 domain requires both that motif and the amphipathic helix for sustained binding to membrane embedded PA.' Pag. 13: '... One reason for this difference could be that the Nir1-LNS2 is not a novel bona fide PA binding pocket. Rather, it requires both the SIDGS motif and an N-terminal amphipathic helix for membrane interactions (Figure 2). These conclusions therefore appear to be not accurate and would need te be rephrased.
If the authors could show that PA binding by Nir1-LNS can be eliminated by mutating residues in the SIDGS motif, this would not only substantiate the above claims, but also make for a negative control protein, next to Nir1-LNS2. For future applications of Nir1-LNS2 as PA biosensor in other organisms this would be useful. 3. Pag. 7: '...small caveat to consider when using Nir1-LNS2 to study PA, the data also demonstrates that Nir1-LNS2 is not specifically interacting with any of the PM PIPs in cellular membranes.' This seems not accurate, since the data in Fig. 3 suggest that PI4P could be involved in membrane localization of Nir1-LNS2. It remains however unresolved whether this is a specific interaction with this PIP. 4. Please note that the presence of PE increases the ionization (and negative charge) of PIP2 (Graber et al., 2012) rather than dilutes the negative charge as stated in the Discussion on p.13. Please revise!
Minor comments:
- P 1 The authors may consider adding a 4th criterium for a lipid biosensor: the sensor should not serve as a sink for the lipid by removing/sequestering it from the active pool, thereby interfering with other interactions/conversions.
- Nir1 lacks a PITP domain (Fig. 1), yet is referred to as lipid transfer protein: please elaborate/explain.
- Fig. 2A suggests cooperativity in binding of Nir1-LNS2 to PA-containing liposomes. Please mention/comment! Does binding to PIP2-containing liposomes also exhibit cooperativity?
- Indicate the concentrations of PC and protein in the legend to Fig. 2 panels A and B. M&M says 2 mM PC, according to the PA-concentrations above panel 2A, this should be 1 mM. Please clarify.
- In Fig. 2B, PI is missing. Any specific reason?
- Move Fig. 2C to the Introduction and extend it to illustrate the shared conserved features of Nir1-LNS2 and lipin.
- P 13. 'While real-time IMPACT does not directly report on PA levels as it does not use the endogenous PLD substrate PC, ...' It is true that this method doesn't directly report on PA levels, but that is because it uses a click chemistry probe as substrate for PLD's transphosphatidylation reaction. Contrary though to what is stated by the authors, this reaction still uses the endogenous substrate of PLD, PC (Liang et al. 2019; www.pnas.org/cgi/doi/10.1073/pnas.1903949116).
- Fig. 1K: Control must have been treated with PMA plus vehicle (DMSO); if so, please indicate that vehicle was added.
- Figure 6C: How is Ft/Fpre defined? Add to legend.
- P 4: 'The boundaries of the Nir2-LNS2 (Uniprot: O00562) and Nir3-LNS2 (Uniprot: Q9BZ72) were also defined using AlphaFold predictions of the structure of these domains.' How was the extent of each of these domains determined - they are much larger than the previously published sequences of LNS2 domains (Kim et al. 2013; Embo Rep. 14:891-899. doi:10.1038/embor.2013.113)?
- In Fig. 3 legend, specify the starting condition of Nir1-LNS2 binding?? Which fluorescence are we looking at?
- In legend to Fig. 6 please specify the fluorescent tags used. Have they been shown not to affect protein function?
Significance
The development of a new, superior PA-sensor is a significant advance in the fields of lipid signaling and specific lipid-protein interactions, that will benefit research on lipid-mediated cellular signaling and intracellular lipid trafficking.
This reviewer's expertise encompasses lipid metabolism and lipid-protein interactions, not so much fluorescence microscopy.
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Reply to the reviewers
Manuscript number: RC-2023-02235
Corresponding author: Adriano, Aguzzi
1. General Statements
We thank the reviewers for providing valuable comments. We are pleased that our study is considered important to advance the knowledge on IL-1-independent inflammatory functions of inflammasomes. We have clarified and revised the manuscript (track changed) as detailed below in the point-by-point response in this letter.
2. Point-by-point description of the revisions
Referee 1
General: In this manuscript, et al., investigates the role of the inflammasome adapter ASC (in AA amyloidosis). This condition involves the aggregation of serum amyloid A (SAA) and is linked to chronic inflammation. Firstly, I can directly say that I do recommend this study for publication. This is a well conducted and well-written study which advances the knowledge on IL-1-independent inflammatory functions of inflammasomes. Furthermore, I find it particularly impressive that despite the inflammasome research community is well aware that amyloidosis is a hallmark of inflammatory diseases, it took a neuroscientist specialized in prion diseases to raise the question whether ASC would be involved in seeding serum AA aggregation. Key findings include: • ASC forms extracellular aggregates that enhance SAA aggregation, as observed through superresolution microscopy. • In a mouse model, the absence of ASC significantly reduced amyloid load, not due to increased phagocytosis but likely due to diminished aggregation. • Treatment with anti-ASC antibodies reduced amyloid load and mitigated weight loss in mice with AA amyloidosis. These findings suggest that ASC plays a crucial role in AA amyloidosis and that targeting ASC could be a potential therapeutic strategy. The study expands our understanding of the involvement of ASC in proteinopathies beyond neural diseases, pointing to its role in systemic conditions like AA amyloidosis.
__Significance: __In conclusion, this manuscript offers valuable insights into the role of ASC in AA amyloidosis, presenting compelling findings that support its potential as a therapeutic target. Addressing the mentioned concerns and making the suggested revisions will further enhance the manuscript's scientific rigor and impact. Overall, this study is a valuable contribution to the field of inflammasome research and its relevance in systemic conditions like AA amyloidosis.
Comment 1: Overall, the experiments are well-conducted and mostly all controls I would expect were included. With few exceptions, the data is convincing. With that said, I have issues with some of the staining employed in Fig 1. In Fig. 1, the authors assess ASC staining in cardiac tissues from a patient with vasculitis and systemic inflammation-related AA amyloidosis, and a control patient who died of a heart attack but had no signs of amyloidosis. However, most of the data shown is related to the AL177 anti-ASC. More importantly, no isotype stainings are included. We have previously demonstrated that the AL177 anti-ASC, used here, reacts quite strongly with ASC−/− cells, and it is one of the less specific anti-ASC commercially available (PMID: 27221487). As this is data from one patient (understandably), I wonder if the authors could counterstain ASC in the same samples using a specific human anti-ASC with a different color (ex: Biolegend HASC), and confirm that the signal overlays with the AL-177.
Response: We conducted additional experiments to address the anti-ASC antibody specificity, as now described in Results, Method, and Fig. S1. We tested a set of anti-ASC antibodies (AL177, MY6745, 1C3D7) for their ASC specificity. We confirmed that both the AL177 and the MY6745 antibodies have high ASC-specificity (Fig. S1A). Moreover, for illustration purposes (and to warn other scientists), we included a third anti-ASC antibody (1C3D7) found to be unspecific as it yielded a strong signal in PYCARD-/- (ASC-/-) THP-1 cells (Fig. S1B). In addition, isotype controls were included in these experiments (Fig. S1A, right panels), as suggested by the reviewer, showing no target protein detection in both, PYCARD+/+ (ASC+/+) and PYCARD-/- cells underscoring the anti-ASC specificity of AL177 and MY6745 antibodies.
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Comment 2: Finally, in Figure 1H it seens from the description that another anti-ASC was used: "referred in the legend as ASC (MAB ASC, Yellow)". Is this a monoclonal anti-ASC? Also, the images show large and bright antibody aggregates (middle of the image, top left corner behind the "H", and a massive fluorescence in the bottom right of the image), indicating the presence of staining artifacts. Again, no counterstaining with isotype controls are shown.
Response: We apologize for the confusing jargon in Figure 1H. “MAB ASC” refers to the anti-ASCPYD antibody (MAB/MY6745). We have corrected the antibody terminology in the legend. MAB/MY6745 is a monoclonal antibody generated by Mabylon that is highly reactive to both human and murine ASC. This antibody was generated to 1) perform an immunotherapy in vivo study and to 2) be used as alternative specific antibody in addition to AL177 to show co-localization of SAA and ASC in a human AA patient using STED superresolution microscopy. MAB/MY6745 is a rabbit monoclonal anti-ASC antibody targeting the pyrin domain (PYD) from which the rabbit Fcγ domain was replaced with that of a mouse IgG2a domain to avoid xenogeneic anti-drug responses in recipients and to improve its effector functions in vivo. To examine possible staining artefacts which can occur with Formalin-Fixed Paraffin-Embedded (FFPE) human tissues, we assessed the specificity of a variety of anti-ASC antibodies (Fig. S1). Our data presented in Fig. S1 show that the monoclonal anti-ASC antibody binds specifically. It is conceivable that AL177 and MAB/MY6745 target different epitopes of ASC, resulting in different staining patterns. An isotype control, included in __Fig. S1, __was used to test the specificity of the secondary antibodies, and did not show any nonspecific staining. We have adapted and added this to the text body and figure legend accordingly.
Comment 3: Overall, although I don't dispute the possibility that ASC would co-localize with SAA deposits, I don't think the data presented can safely sustain that claim. I would, therefore, suggest that alternative methods to be employed to substantiate these conclusions: Supposedly, would it be possible to immuno-precipitate (IP) amyloid SAA and assess ASC via western blotting? As well as IP ASC and detect SAA? Or use DSS-crosslinking to find ASC oligomers in tissue areas rich in SAA?
Response: In addition to assessing co-localization by means of STED superresolution microscopy (Fig. 1), we also employed LiP-MS with various forms of ASC (monomeric and ASC specks) and identified a previously unrecognized biophysical interaction of SAA and the ASC PYD domain (Fig. 2C-F). As an orthogonal line of evidence, we provided kinetic data showing that SAA aggregation is enhanced in the presence of ASC specks (Fig. 2A-B). We feel that these results are reasonably convincing, but we agree that co-localization is almost invariably an aspirational finding, and even superresolution microscopy cannot fully exclude the presence artifacts (nor can, in fairness, co-immunoprecipitation, which must often rely on overexpression). A sentence acknowledging this limitation was added to the Discussion.
Comment 4: For example, it would be reasonable to quantify the results in Figure 3G and providing clarification regarding the controls in the figure legend. Though there is significantly less SAA in spleen homogenates from Asc−/−, there also seems to be the case for b-actin in Fig 3G. Moreover, in the figure legend the authors state: "...Spleen homogenate from untreated (-ctrl) and AA+ (+ctrl) C57BL/6 wt mice from an independent experiment served as negative and positive control, respectively." I don't know what the authors mean with that. Is this a montage, or samples from different experiments were run together in one blot? And if so, for what reason? This is confusing and should be clarified.
Response: We reworded the figure legend to provide clarity about the technical assay controls and adjusted the labels in Fig. 3E __accordingly: To ascertain SAA antibody functionality, mouse spleen homogenate from independently obtained and Congo red-confirmed AA+ tissue served as positive, whereas non-induced (AA-) spleen tissue served as negative technical controls. (__Fig 3E). We decided to show the two (positive/AA+ and negative/AA-) technical controls in Fig. 3E.
Comment 5: Furthermore, in the Abstract, a slight rephrasing is suggested to accurately describe ASC specks as molecular aggregates formed inside cells, which are subsequently released into the extracellular space.
Response: We thank the referee for bringing this to our attention. We rephrased the abstract accordingly.
Comment 6: Lastly, enhancing the text size in figures, particularly in Fig 3, is advised to improve legibility and overall clarity.
Response: The legibility and style of main Fig. 3 text sizes has been changed and additional figure formatting has been performed.
Referee 2
General: The manuscript by Losa et al., investigates whether ASC is involved in serum AA amyloidosis. The authors report that ASC colocalizes with SAA in human AA amyloidosis and that purified ASC specks accelerate SAA fibril formation in vitro. In addition, splenic AA amyloid was decreased in Pycard-/- mice compared to Pycard+/+ mice and that treatment with anti-ASC antibodies decreased amyloid loads in Pycard+/+ mice. Lastly, they analyzed serum of 19,334 patients to show that the prevalence of anti-ASC antibodies did not correlate with any specific disease. The authors conclude that ASC to play a role in extraneural proteinopathies of humans and experimental animals and suggest that anti-ASC immunotherapy may contribute to resolving such diseases. The findings in the study are novel and demonstrate a new role for ASC in aggregation proteinopathies. However, there are number of issues that need to be addressed before acceptance for publication.
Significance: __The findings in the study are novel and demonstrate a new role for ASC in aggregation proteinopathies. This study reports a crucial role for ASC in SAA interaction and recruitment, SAA serum level modulation, SAA fibril formation acceleration, and controlling the extent of inflammation associated amyloidosis with respect to AA amyloid deposition __
Comment 1: Figure 3 E depicts Western blots of monomeric SAA in spleen of Pycard+/+ and Pycard-/- mice. The authors should include immunoblots depicting the levels of ASC in these tissues and to demonstrate that the Pycard-/- mice lack ASC.
Response: We did not perform ASC immunoblots for Pycard-/- and Pycard+/+ mice since the absence of the ASC protein in this well-established mouse line has been demonstrated in several key publications, including under inflammation conditions (right side of the figure below, from Mariathasan et al., Nature, 2014). However, we show ASC IHC of Pycard+/+ and Pycard-/- AA+ mice on spleen, confirming the absence of an ASC signal in Pycard-/- mice and its presence in the Pycard+/+ (Fig. 3F). Moreover, our genotyping data confirmed the presence and absence of the Pycard gene in Pycard+/+ and Pycard-/- AA+ mice.
Comment 2: Fig. 3B shows that at 96 hours after injection there was no difference in SAA serum concentration. How do the authors explain this drop in SAA serum concentration? No explanation is provided.
Response: Acute-phase response peaks at 24 hours after injury (i.e., Kushner I, 1982; Gabay et Kushner, 1999; Gitlin et Colten, 1987, Calif.: Academic Press, 1987:123-53). Beyond 24 hours, acute phase proteins decay over time mirroring the process of tissue integrity restoration and the clearance of the insulting stimuli. This is in line with our data, where the inflammatory injury was induced by subcutaneous AgNO3 injection, resulting in a non-statistical serum SAA difference between the Pycard+/+ and Pycard-/- experimental mice at 96 hours post AgNO3 injection. In addition, the majority of SAA in Pycard+/+ mice was incorporated into amyloid deposit. As suggested by the reviewer we have included this explanation/references into the revised manuscript.
Comment 3: Figure 4 shows anti-ASC administration reduces amyloid load. The immunoblot in Figure 4C does not represent the quantification of the blot. In fact, there are only 3 samples per treatment group whereas the quantification shows 5-6 animals per group.
Response: We have performed two independent immunoblots at the same time to perform technical replicates (duplicates). As pointed out by the reviewer, this resulted in 6 samples and data points that were visualized and analyzed in main Fig. 4C. To avoid duplicating data, overloading the main figures with technical replicates, we opted to show only one representative immunoblot in the main Fig. 4C. The other blots are shown in the supplementary figures Fig. S13A and Fig. S13B for full transparency.
Comment 4: Additionally, the authors have not shown that the drug penetrates the target tissue and how much drug is present in spleen to provide a therapeutic effect. What is the half-life of the drug? These parameters are critical to assess the MOA of the anti-ASC used in these studies.
Response: To assess the pharmacokinetics of the anti-ASC antibody, we determined its titers in serum by ELISA at various time points up to 96 hpi after the first injection. The anti-ASC antibody serum levels peaked at 24 hpi and declined to about half maximal serum concentration levels at 96 hpi. This serum half-life, for the injected concentration, is in the range of reported kinetic parameters of engineered monoclonal antibodies (e.g., Unverdorben et al., MAbs, 2016; Foss et al., Nat Comm, 2024) (Fig. 4B). Because of the high permeability of splenic red pulp vasculatures, and because of the absence of any selectively permeable barrier, efficacious imbibement of the splenic extracellular space can be plausibly expected. Theoretically, one could perfuse mice intracardially with PBS and then measure antibody in tissue. Such measurements can work relatively well in the brain, which possesses a highly impermeable barrier. However, here we would find it difficult to convince ourselves that such measurements would not be contaminated by residual blood in splenic capillaries that may be difficult to clean up through perfusion. Therefore, we did not measure the antibody levels in the spleen.
Comment 5: The authors should expand the discussion section to include the work of other groups that have successfully employed anti-ASC antibodies. For example, PMID: 35793783, PMID: 32366256
Response: We thank the referee for pointing out that literature. We extended the discussion section accordingly and added these important references into the discussion.
Comment 6: Methods: The authors provide the number of animals employed in the Supplemental Tables 5 and 7. These numbers should be provided in the methods section or in the Figure legends. Additionally, how many replicates were performed for the data in Figure 2?
Response: __As suggested by the reviewer we now provide the number of animals in the figure legends of main __Fig. 2 and Fig. 3 __in addition to those in Table 5 and Supp Table 7__ to enhance clarity.
Referee 3____
General: The manuscript by Losa et al. explores the co-aggregation of ASC with serum amyloid A (SAA) in vivo and in mouse models, It posits that, similar to Amyloid beta, SAA is cross-seeded by ASC foci both in vitro and in vivo. This review only addresses the co-localization and in vitro cross seeding data (Figs. 1 and 2A, B), not the mouse experiments or mass spectrometry data. The manuscript first shows co-deposition of ASC with SAA amyloid. SAA was stained both with Congo red and ThS, both standard dyes for amyloid staining. Figure S2 shows CR birefringence, the hallmark of amyloid deposits. The authors then move to demonstrate co-localization of SAA and ASC in confocal and STED immuno-fluorescence microscopy.
Significance: The discovery of the role of ASC in Alzheimer's disease generated an exciting new hypothesis to the etiology of sporadic AD, for which the cause is unknown. The current manuscript finds that ASC may also play a role in AA amyloidosis, which is a significant finding.
Comment 1: Confocal images C-E show overlapping staining of markers for both SAA and ASC. Similarly, STED images show co-aggregation of ASC and SAA in amyloidosis patients. However, since confocal images F and G seem to show overlapping staining of the yellow and magenta channels as well, a careful quantitative analysis of the data I needed. Quantify co-localization (Pearson coefficient) in confocal and STED images. STED images from control patients are missing and need to be included.
Response: AA amyloidosis is a relatively rare disease, and tissue samples thereof are even rarer. We only had access to the samples of one patient in both control and SAA groups. This limitation prevented us from conducting quantitative analyses. Rather than looking at the Pearson – or, possibly better, Spearman – correlation coefficient, we opted for an unbiased method of correlation in which we reconstructed the picture using 3D surface rendering with the Imaris software (see Fig. 1). From this reconstruction, we exported the barycenter of each surface on a 3D plot for both SAA and ASC markers (see Fig. S2B-C). Each point represents the center of a surface, while the box plots on the sides represent the distribution of the markers in space, demonstrating the overlap of the markers for ASC and SAA. We also understand the suggestion to conduct STED imaging on control samples to show the absence of co-aggregation. However, we could not be sure of which region to capture and how to decide on the focus, as we did not detect strong signal from confocal images of the control sample. Imaging blindly would almost necessarily lead to irrelevant imaging and aberrant comparison. We do not claim any quantitative data out of these images; however, we report an observation. Quantitative and mechanistic co-aggregation data are presented in Fig. 2 using LiP-MS.
Comment 2: The authors then move on to demonstrate that ASC foci can cross-seed SAA amyloid formation in vitro, by recording SAA aggregation kinetics in the presence and absence of ASC foci. Curves recorded in the presence of ASC foci have accelerated kinetics as shown by a decrease in the time to reach half-maximal fluorescence (t1/2). However, these data (Fig 2A, B) are not very clean. Only three data points out of five curves shown in panel A. are presented in the fitting of the control (yellow) aggregation kinetics in panel B. Why was this done? Panel B shows a significant difference between the control and the kinetics seeded with ASC specks. It looks doubtful that the results are still statistically significant if these data are included, so their exclusion impacts the overall conclusion of the paper. The significance of the cross-seeding results needs to be substantiated experimentally.
__Response: __The in vitro SAA aggregation assay was performed under established conditions (Claus S et al., EMBO Rep 2017) and the resulting data was processed using the AmyloFit software from the Knowles lab in Cambridge, UK (Meisl G et al., Nat Protoc 2016). The AmyloFit technology uses global fitting resulting in high-accuracy kinetics. Given the software algorithm, only curves that show a sigmoidal ThT fluorescence signal over time can be fitted. Therefore, replicates that do not show aggregation (characteristic ThT signal) over time cannot be fitted. As a result, only three out of six curves could be fitted resulting in three t1/2. Conversely, in the presence of ASC specks, all six replicates aggregated in a dose-dependent manner, and could be fitted perfectly, yielding six t1/2 values. Thus, all available data points are plotted and used for statistical analysis. Moreover, the fact that in presence of ASC specks all SAA replicates aggregated/converted successfully in a dose-dependent manner (whereas in the SAA-only condition some replicates do not aggregate) further underscores the pivotal role of ASC specks in SAA seeding, conversion, and aggregation enhancement.
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Referee #3
Evidence, reproducibility and clarity
The manuscript by Losa et al. explores the co-aggregation of ASC with serum amyloid A (SAA) in vivo and in mouse models, It posits that, similar to Amyloid beta, SAA is cross-seeded by ASC foci both in vitro and in vivo. This review only addresses the co-localization and in vitro cross seeding data (Figs. 1 and 2A, B), not the mouse experiments or mass spectrometry data.
The manuscript first shows co-deposition of ASC with SAA amyloid. SAA was stained both with Congo red and ThS, both standard dyes for amyloid staining. Figure S2 shows CR birefringence, the hallmark of amyloid deposits. The authors then move to demonstrate co-localization of SAA and ASC in confocal and STED immuno-fluorescence microscopy.
Confocal images C-E show overlapping staining of markers for both SAA and ASC. Similarly, STED images show co-aggregation of ASC and SAA in amyloidosis patients. However, since confocal images F and G seem to show overlapping staining of the yellow and magenta channels as well, a careful quantitative analysis of the data I needed. Quantify co-localization (Pearson coefficient) in confocal and STED images. STED images from control patients are missing and need to be included. The authors then move on to demonstrate that ASC foci can cross-seed SAA amyloid formation in vitro, by recording SAA aggregation kinetics in the presence and absence of ASC foci. Curves recorded in the presence of ASC foci have accelerated kinetics as shown by a decrease in the time to reach half-maximal fluorescence (t1/2). However, these data (Fig 2A, B) are not very clean. Only three data points out of five curves shown in panel A. are presented in the fitting of the control (yellow) aggregation kinetics in panel B. Why was this done? Panel B shows a significant difference between the control and the kinetics seeded with ASC specks. It looks doubtful that the results are still statistically significant if these data are included, so their exclusion impacts the overall conclusion of the paper. The significance of the cross-seeding results needs to be substantiated experimentally.
Significance
The discovery of the role of ASC in Alzheimer's disease generated an exciting new hypothesis to the etiology of sporadic AD, for which the cause is unknown. The current manuscript finds that ASC may also play a role in AA amyloidosis, which is a significant finding.
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Referee #2
Evidence, reproducibility and clarity
The manuscript by Losa et al., investigates whether ASC is involved in serum AA amyloidosis. The authors report that ASC colocalizes with SAA in human AA amyloidosis and that purified ASC specks accelerate SAA fibril formation in vitro. In addition, splenic AA amyloid was decreased in Pycard-/- mice compared to Pycard+/+ mice and that treatment with anti-ASC antibodies decreased amyloid loads in Pycard+/+ mice. Lastly, they analyzed serum of 19,334 patients to show that the prevalence of anti-ASC antibodies did not correlate with any specific disease. The authors conclude that ASC to play a role in extraneural proteinopathies of humans and experimental animals and suggest that anti-ASC immunotherapy may contribute to resolving such diseases. The findings in the study are novel and demonstrate a new role for ASC in aggregation proteinopathies. However, there are number of issues that need to be addressed before acceptance for publication.
Major Points:
Figure 3 E depicts Western blots of monomeric SAA in spleen of Pycard+/+ and Pycard-/- mice. The authors should include immunoblots depicting the levels of ASC in these tissues and to demonstrate that the Pycard-/- mice lack ASC. Fig. 3B shows that at 96 hours after injection there was no difference in SAA serum concentration. How do the authors explain this drop in SAA serum concentration? No explanation is provided.
Figure 4 shows anti-ASC administration reduces amyloid load. The immunoblot in Figure 4C does not represent the quantification of the blot. In fact, there are only 3 samples per treatment group whereas the quantification shows 5-6 animals per group. Additionally, the authors have not shown that the drug penetrates the target tissue and how much drug is present in spleen to provide a therapeutic effect. What is the half-life of the drug? These parameters are critical to assess the MOA of the anti-ASC used in these studies.
The authors should expand the discussion section to include the work of other groups that have successfully employed anti-ASC antibodies. For example, PMID: 35793783, PMID: 32366256
Methods: The authors provide the number of animals employed in the Supplemental Tables 5 and 7. These numbers should be provided in the methods section or in the Figure legends. Additionally, how many replicates were performed for the data in Figure 2?
Significance
The findings in the study are novel and demonstrate a new role for ASC in aggregation proteinopathies. This study reports a crucial role for ASC in SAA interaction and recruitment, SAA serum level modulation, SAA fibril formation acceleration, and controlling the extent of inflammation associated amyloidosis with respect to AA amyloid deposition
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Referee #1
Evidence, reproducibility and clarity
In this manuscript, et al., investigates the role of the inflammasome adapter ASC (in AA amyloidosis). This condition involves the aggregation of serum amyloid A (SAA) and is linked to chronic inflammation.
Firstly, I can directly say that I do recommend this study for publication. This is a well conducted and well-written study which advances the knowledge on IL-1-independent inflammatory functions of inflammasomes. Furthermore, I find it particularly impressive that despite the inflammasome research community is well aware that amyloidosis is a hallmark of inflammatory diseases, it took a neuroscientist specialized in prion diseases to raise the question whether ASC would be involved in seeding serum AA aggregation.
Key findings include:
- ASC forms extracellular aggregates that enhance SAA aggregation, as observed through superresolution microscopy.
- In a mouse model, the absence of ASC significantly reduced amyloid load, not due to increased phagocytosis but likely due to diminished aggregation.
- Treatment with anti-ASC antibodies reduced amyloid load and mitigated weight loss in mice with AA amyloidosis.
These findings suggest that ASC plays a crucial role in AA amyloidosis and that targeting ASC could be a potential therapeutic strategy. The study expands our understanding of the involvement of ASC in proteinopathies beyond neural diseases, pointing to its role in systemic conditions like AA amyloidosis. Main Comments: Overall, the experiments are well-conducted and mostly all controls I would expect were included. With few exceptions, the data is convincing. With that said, I have issues with some of the staining employed in Fig 1.
In Fig. 1, the authors assess ASC staining in cardiac tissues from a patient with vasculitis and systemic inflammation-related AA amyloidosis, and a control patient who died of a heart attack but had no signs of amyloidosis. However, most of the data shown is related to the AL177 anti-ASC. More importantly, no isotype stainings are included. We have previously demonstrated that the AL177 anti-ASC, used here, reacts quite strongly with ASC−/− cells, and it is one of the less specific anti-ASC commercially available (PMID: 27221487). As this is data from one patient (understandably), I wonder if the authors could counterstain ASC in the same samples using a specific human anti-ASC with a different color (ex: Biolegend HASC), and confirm that the signal overlays with the AL-177.
Finally, in Figure 1H it seens from the description that another anti-ASC was used: "referred in the legend as ASC (MAB ASC, Yellow)". Is this a monoclonal anti-ASC? Also, the images show large and bright antibody aggregates (middle of the image, top left corner behind the "H", and a massive fluorescence in the bottom right of the image), indicating the presence of staining artifacts. Again, no counterstaining with isotype controls are shown.
Overall, although I don't dispute the possibility that ASC would co-localize with SAA deposits, I don't think the data presented can safely sustain that claim. I would, therefore, suggest that alternative methods to be employed to substantiate these conclusions: Supposedly, would it be possible to immuno-precipitate (IP) amyloid SAA and assess ASC via western blotting? As well as IP ASC and detect SAA? Or use DSS-crosslinking to find ASC oligomers in tissue areas rich in SAA?
Minor comments:
In addition to these main comments, some minor adjustments are recommended:
For example, it would be reasonable to quantify the results in Figure 3G and providing clarification regarding the controls in the figure legend. Though there is significantly less SAA in spleen homogenates from Asc−/−, there also seems to be the case for b-actin in Fig 3G. Moreover, in the figure legend the authors state: "...Spleen homogenate from untreated (-ctrl) and AA+ (+ctrl) C57BL/6 wt mice from an independent experiment served as negative and positive control, respectively." I don't know what the authors mean with that. Is this a montage, or samples from different experiments were run together in one blot? And if so, for what reason? This is confusing and should be clarified.
Furthermore, in the Abstract, a slight rephrasing is suggested to accurately describe ASC specks as molecular aggregates formed inside cells, which are subsequently released into the extracellular space.
Lastly, enhancing the text size in figures, particularly in Fig 3, is advised to improve legibility and overall clarity.
Significance
In conclusion, this manuscript offers valuable insights into the role of ASC in AA amyloidosis, presenting compelling findings that support its potential as a therapeutic target. Addressing the mentioned concerns and making the suggested revisions will further enhance the manuscript's scientific rigor and impact. Overall, this study is a valuable contribution to the field of inflammasome research and its relevance in systemic conditions like AA amyloidosis.
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Reply to the reviewers
We would like to thank the reviewers for their attentive reading of our manuscript. We appreciate all the comments and suggestions. We have addressed all the concerns and have included point-by-point responses.
Reviewer #1
Evidence, reproducibility and clarity
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Summary:
* Cacioppo et al perform a meta-analysis of public omics data examining AURKA protein and mRNA expression (including mRNA isoforms with alternative cleavage and polyadenylation), and hsa-let-7a miRNA (shown to target AURKA mRNA) in multiple cancer types from The Cancer Genome Atlas. They conclude AURKA mRNA and protein expression may be discordant in cancer in part due to the interplay between alternative polyadenylation and hsa-let-7a miRNA.
Major comments:*
* 1) Unfortunately, there is a major flaw in the TCGA AURKA protein quantification data that underpins much of this study. Following the protein data trail (via https://docs.gdc.cancer.gov/Data/Introduction and its dependents), it appears to rely on the CST anti-AURKA #14475 which is raised to an antigen around Pro70.*
Response: We believe the reviewer refers to work from Bertolin et al. 2018 paper (https://doi.org/10.7554/eLife.38111.001) that describes the appearance of truncated versions of AURKA in mitochondrial fractions of cell extracts and shows they depend upon the presence of PMPCB mitochondrial matrix peptidase. We are not familiar with any other literature describing this phenomenon. In our own hands we find AURKA present in the mitochondrial fraction, but the protein is mostly full-length (Grant et al. 2018, https://doi.org/10.1098/rsob.170272). In both papers the mitochondrial pool is small relative to the total cellular pool of AURKA. In fact, this mitochondrial pool is so difficult to detect in intact cells that it has not been reported by other labs and is not universally acknowledged. Given the small size of the mitochondrial pool, any increased amounts of mitochondrial AURKA in cancers, it would be unlikely to significantly impact the measured total protein levels.
2) Following the flaws identified in the protein foundation data, the study would then benefit from some post-validation of findings with actual biological data derived from their own independent assessment of the cancers being examined.
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Response: The literature thoroughly reports empirical evidence on AURKA protein expression levels in the cancers analysed in this study, therefore we don't believe our own post-validation of findings would add any novelty in this sense.
Minor comments:
* 1) All of the Correlation analysis have been tested for statistical significance and these results are available in the supplementary data. However, I think it would be useful if these statistics were also included in the main figures themselves. (Figures 1B, 2B and 2C) A low correlation that is statistically significant is a more powerful statement.*
Response: We agree, and plan to add the results of the statistical analyses in the Figures 1B, 2B and 2C.
2) In the materials and methods, Correlation is separated into distinct degrees: none to very strong, but apart from some lines on the graphs, these degrees of correlation strength are never revisited, so they should be included. Perhaps there is a biological difference between AURKA post transcriptional regulation and protein levels with different R score strength?
Response: We believe that reiterating a discussion on the degrees of correlation strength in the main text would appear repetitive. We do however plan to add a sentence to appropriate points in the main text to redirect the reader to the materials and methods section for information on the distinct degrees of correlation.
3) In Figure 2D a clustering analysis was performed to show the possible relationships between hsa-let-7a and protein levels. The current visualization is hard to understand. A 3D graph with Protein, mRNA and has-let-7a axis's would be easier to follow. I believe it would also be beneficial to do something similar including the APA data as this is the area that the paper lacks depth.
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Response: We agree that 3D graphs could aid visualization and plan to provide a link to an interactive 3D view of our analysis.
* 4) Figure 3B and 3C, can you apply a statistical test on the SLR ratios given the magnitude difference between CCND1 and AURKA SLRs?*
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Response: Since the values of AURKA and CCND1 SLRs are not always coming from the same dataset and are therefore not matched for patients, we believe it would not be appropriate to make comparisons applying statistical tests.
* 5) Even though the paper does not claim to provide a unifying hypothesis for APA/has-let-7a regulation of AURKA, I think a more in depth look at the data would be useful. The discussion starts off well when describing what was found with the analysis, but as is, is mostly a re-statement of the results without added insight.*
Response: We agree that more in depth analysis of more data would be useful in strengthening conclusions. However, given the variability in interplay between APA and hsa-let-7a we describe, it is well beyond the scope of this study (or the extent of TCGA database) to come up with a unifying hypothesis.
Significance
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The study is novel in attempting to show additional layers of AURKA regulation that hadn't been previously investigated. Furthermore, factors controlling AURKA expression are of broad interest. Overall, I would like to say this is an interesting investigation into AURKA mRNA expression in cancers. In our opinion the choice of bioinformatic tools is appropriate and well controlled.*
General Assessment: As noted in the major comments, a major weakness is the reliance on a flawed measure of AURKA protein levels from the foundation dataset. Thus, the study needs to be repeated using an alternative MS derived dataset to accurately quantify total AURKA protein levels. This would greatly improve the study and subsequent claims.
Advance: The study has potential to extend knowledge in the field in a conceptual way, predicting the complex interplay of factors that regulate AURKA mRNA processing and translation.
Audience: Currently the paper is only fully accessible a specialized bioinformatician audience but the topic (factors controlling AURKA expression) has a broad interest in many fields not limited to just cancer but also development and other non-cancer diseases.*
* This review was jointly completed by a mouse model of human disease AURKA biologist with 24 years' experience, and a bioinformatician.*
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Reviewer #2
Evidence, reproducibility and clarity
In the manuscript "Post-transcriptional control drives Aurora kinase A expression in human cancers", authors Cacioppo, Lindon and colleagues analyze publicly available data on transcript and protein levels for many cancer types to determine correlations between transcript and protein levels for Aurora A and the microRNA hsa-let-7a. This study builds on a recent publication from their lab where they show that different polyadenylation isoforms of the Aurora A transcript in triple negative breast cancer correlate with patient survival and affect protein abundance. In this study, they aim to extend this analysis to 18 different cancer types to determine if posttranscriptional regulation potentially plays a role in Aurora A protein abundance. The authors find that for certain cancer types, Aurora A protein abundance does not correlate with mRNA abundance, suggesting that posttranscriptional regulation may be responsible for differences in protein expression in these cancer types. Furthermore, they find negative correlations between expression of hsa-let-7a and mRNA and protein abundance in certain cancer types, implicating this microRNA as a potential regulator of Aurora A mRNA stability.*
Major comments:
- The biggest issue that I have with this analysis relates to the assumption that Aurora A levels will be meaningfully different between individual tumors in all cancer types. For some cancers, the lack of a correlation between mRNA and protein levels for Aurora A could simply be because Aurora A overexpression is not a feature of that cancer type. Looking at the data, the cancer types where they see little-to-no correlation are the cancer types where none of the tumors have high levels of Aurora A mRNA or protein. Therefore, the lack of correlation is likely because differences in protein levels result from noise in the measurements rather than posttranscriptional regulation. Since the lack of correlation between protein and mRNA in these cancer types is the main evidence for the primary conclusion in the paper that "AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation", I don't think that this conclusion is supported by the data. If anything, the data seems to show that substantial changes in Aurora A protein levels are almost always accompanied by a corresponding change in mRNA levels.
To address this issue, the authors could look at the variability in Aurora A protein levels for each cancer type, and then focus their correlation analyses on cancer types where overexpression of Aurora A is a feature.*
Response: We thank the reviewer for this thoughtful comment. We decided not to consider data on AURKA protein levels between healthy and tumour samples because of the lack of proteomic datasets of matching normal tissues for all cancers (except BRCA) in the TCGA database. For this reason, it cannot be excluded that the tumours where we see little-to-no protein-mRNA correlation have in fact high levels of AURKA protein. Indeed, the literature reports wide empirical evidence that AURKA protein is overexpressed in the cancer tissues where we see little-to-no protein-mRNA correlation (Thyroid cancer: Zhao et al, Cell Biosci, 2022; Jingtai et al, Cell Death Dis, 2023. Prostate cancer: Das et al, Pathol, 2010; Chun Yu Lee et al, Cancer Res, 2006. Kidney cancers: Wen et al, Heliyon, 2024; Li et al, Cell Death Dis, 2022. No evidence available for PCPG). Therefore, we believe that is reasonable to propose that in these cancers, which according to our analysis of TCGA data only show minor or no increase in AURKA mRNA expression compared to the normal tissue, lack of correlation is because of post-transcriptional regulation.
2. The statistical significance of the analyses is often unclear. For the correlations between Aurora A protein levels and hsa-let-7a, authors mention that two cancers have a correlation with "statistical significance", but I cannot find any indication of how that was determined, and it is not shown in the corresponding figure (2C). The only time significance is indicated for a correlation is in Figure 4A. Is this the only correlation in the whole manuscript with a p-value less than .05?
Response: The results of the statistical analyses are included in the corresponding supplementary data (Sup. Fig 1, Sup. Fig. 2A-B). We plan to add them to the Figures 1B, 2B and 2C as requested by another reviewer.
3. The SLR for the Aurora A transcripts is only shown in terms of a ratio between cancer and normal tissue. Without the numbers in the absence of normalization, it is difficult to determine how meaningful this is. Is a two-fold change going from .3 to .6 or .001 to .002?
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Response: We plan to add a supplementary table containing the SLR values for matched normal and cancer samples in the absence of normalization.
4. Figure 5B is nearly impossible to interpret due to the extreme differences in overall transcript levels between the cancer types. The differences in scaling of the y-axis between the plots makes this even more challenging. The authors state that "It is evident that each isoform has an individual profile of expression across cancers", but this could only be determined from relative expression levels between the different isoforms instead of absolute levels.
Response: We retrieved this plot from the GEPIA2 platform without possibility of editing the y-axis. We plan to edit the text to "It is likely that each isoform has an individual profile of expression across cancers, however a measure of the relative expression levels between the different isoforms would be required".
Minor comments:*
- In supplementary figure 3, SLR is plotted on a log scale in A and a linear scale in B.*
Response: We plan to convert the SLR scale in Sup. Fig. 3B to a log scale.
2. Figure 4D is a correlation of correlations. I don't see how to interpret this in a meaningful way.
Response: Figure 4D is not intended for quantitative analysis of correlation of correlations (no quantitative coefficients were in fact calculated), rather to visualize how the link of AURKA SLR with AURKA protein levels and that with hsa-let-7a levels can be differently associated in different cancers.
Significance
Aurora A is overexpressed in a wide variety of cancer types. This overexpression is commonly believed to result primarily from increased mRNA abundance. The identification of additional mechanisms regulating Aurora A protein levels would therefore be of interest to the field, as these regulatory mechanisms could be contributing to Aurora A's role in cancer progression.*
To some degree, the significance of the findings presented here depend on whether they convincingly demonstrate substantial post-transcriptional regulation. My interpretation of the data presented in this manuscript is that it largely supports Aurora A protein levels being extremely well correlated with mRNA levels, which is in line with previous findings.*
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Reviewer #3
Evidence, reproducibility and clarity
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*Aurora A misregulation at both mRNA and protein levels has been known since the 1990s to be casually associated in vivo, and strongly associated in vitro, with tumourigenesis. The study builds the case that dysregulation of Aurora A mRNA and protein levels (most previously established) are more prevalent in cancer cells than 'normal' cells, using data from TCGA, and extends this to a mechanistic explanation. It evaluates miRNA and the ratio of the two short/long ratio (SLR) isoforms of mRNA across cancer types compared to healthy controls. The work concludes that an interplay between APA (alternative polyadenylation) and hsa-let-7a miRNA (which has known tumor suppressor properties) regulation of AURKA mRNA contributes to alternative splicing, revealing a new factor explaining changes in AURKA expression in many (if not all) cancers. *
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*Minor points: *
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*1) To strengthen the study, some analysis of AURKB mRNA would be useful in the same datasets, because this is also an M-phase kinase. *
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Response: We carried out a specific study of AURKA (and to some extent also of the cell cycle regulator CCND1) using time-limited access to private TCGA datasets. Although we agree that investigation of AURKB would potentially enable us to strengthen some conclusions, this would be a new project that we do not currently have resources for.
*2) What happens to TPX2 or CEP192 mRNA (splicing or levels) in the same samples? For TPX2 in particular, this is described in the literature to help form the oncogenic holoenzyme, as well as dictating AURKA protein stability. *
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Response: Again, we like this suggestion but are not in a position to carry out analyses of TPX2 and CEP192 within the scope of this study.
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*3) Does an alternative AURKA splicing change G1/S to G2/M-phase roles of AURKA? I understand that mRNA is repressed by hsa- let-7a in G1 and S phases but not in G2, so how does non M-phase AURKA protein get made? This may be beyond the scope of the study at this point. *
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Response: Whether alternative AURKA transcripts change non-mitotic roles of AURKA is an open and intriguing question. In acknowledgement of this point raised by the reviewer, we plan to add a discussion on this in the main text: "Although there is no evidence to date that different AURKA transcripts might influence AURKA activity, instances of isoform-dependent protein localization and function are increasingly reported (Mitschka and Mayr, Nat Rev Mol Cell Biol, 2022). In a previous study, we have detected higher nuclear localization of a reporter protein under the regulation of AURKA short 3'UTR (Cacioppo et al., eLife, 2023). Therefore, there is a possibility that AURKA mRNA isoforms are targeted to different subcellular localizations to support localized translation - or that AURKA protein is co-translationally targeted to different compartments - and AURKA may be preferentially localized in the nucleus when coded by the short 3'UTR mRNA".
AURKA protein levels are maintained very low in G1 to S phase compared to G2 and M phases. At the level of translation, this is likely ensured by the absence of factors/mechanisms that activate AURKA translation (e.g., hnRNP Q1) and the presence of factors/mechanisms that repress its translation (e.g., hsa-let-7a), the combination of which results in basal translation of AURKA in G1/S until full translational activation in G2 (where a switch likely occurs whereby activating factors operate while repressing factors are disabled). However, the combination and synergy of these factors/mechanisms are likely cell type- and context-dependent.
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Significance
*I think the study is strong overall, and the authors are humble enough to describe the work as an exploratory analysis, which though not directly in my area of expertise (since it relies on data assembly and statistical analysis), has the right team to ask the questions and interrogate the data. It builds on a huge amount of literature and a recent study from this team showing that alternative translation is relevant to activation of AURKA, and which linked let-7a to this process. Overall, the study provides a very useful resource for other researchers, assembling a large amount of data around AURKA mRNA variants, Let-7a miRNA and coming to the conclusions that *
*1) hsa-let-7a potentially negatively controls the rate of degradation or translation of AURKA mRNA in cancer cells. *
*2) Splicing-related architecture of the 5'UTR of AURKA mRNA likely plays a role in determining the context-dependent cancer expression profile of expression. *
Overall, with some extra information around the key regulators of AURKA (TPX2 mRNA?) the work is likely to be cited and spur on future studies.
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Referee #3
Evidence, reproducibility and clarity
Aurora A misregulation at both mRNA and protein levels has been known since the 1990s to be casually associated in vivo, and strongly associated in vitro, with tumourigenesis. The study builds the case that dysregulation of Aurora A mRNA and protein levels (most previously established) are more prevalent in cancer cells than 'normal' cells, using data from TCGA, and extends this to a mechanistic explanation. It evaluates miRNA and the ratio of the two short/long ratio (SLR) isoforms of mRNA across cancer types compared to healthy controls. The work concludes that an interplay between APA (alternative polyadenylation) and hsa-let-7a miRNA (which has known tumor suppressor properties) regulation of AURKA mRNA contributes to alternative splicing, revealing a new factor explaining changes in AURKA expression in many (if not all) cancers.
Minor points:
- To strengthen the study, some analysis of AURKB mRNA would be useful in the same datasets, because this is also an M-phase kinase.
- What happens to TPX2 or CEP192 mRNA (splicing or levels) in the same samples? For TPX2 in particular, this is described in the literature to help form the oncogenic holoenzyme, as well as dictating AURKA protein stability
- Does an alternative AURKA splicing change G1/S to G2/M-phase roles of AURKA? I undersgtand that mRNA is repressed by hsa- let-7a in G1 and S phases but not in G2, so how does non M-phase AURKA protein get made? This may be beyond the scope of the study at this point.
Significance
I think the study is strong overall, and the authors are humble enough to describe the work as an exploratory analysis, which though not directly in my area of expertise (since it relies on data assembly and statistical analysis), has the right team to ask the questions and interrogate the data. It builds on a huge amount of literature and a recent study from this team showing that alternative translation is relevant to activation of AURKA, and which linked let-7a to this process. Overall, the study provides a very useful resource for other researchers, assembling a large amount of data around AURKA mRNA variants, Let-7a miRNA and coming to the conclusions that
1) hsa-let-7a potentially negatively controls the rate of degradation or translation of AURKA mRNA in cancer cells.
2)Splicing-related architecture of the 5'UTR of AURKA mRNA likely plays a role in determining the context-dependent cancer expression profile of expression.
Overall, with some extra information around the key regulators of AURKA (TPX2 mRNA?) the work is likely to be cited and spur on future studies.
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Referee #2
Evidence, reproducibility and clarity
In the manuscript "Post-transcriptional control drives Aurora kinase A expression in human cancers", authors Cacioppo, Lindon and colleagues analyze publicly available data on transcript and protein levels for many cancer types to determine correlations between transcript and protein levels for Aurora A and the microRNA hsa-let-7a. This study builds on a recent publication from their lab where they show that different polyadenylation isoforms of the Aurora A transcript in triple negative breast cancer correlate with patient survival and affect protein abundance. In this study, they aim to extend this analysis to 18 different cancer types to determine if posttranscriptional regulation potentially plays a role in Aurora A protein abundance. The authors find that for certain cancer types, Aurora A protein abundance does not correlate with mRNA abundance, suggesting that posttranscriptional regulation may be responsible for differences in protein expression in these cancer types. Furthermore, they find negative correlations between expression of hsa-let-7a and mRNA and protein abundance in certain cancer types, implicating this microRNA as a potential regulator of Aurora A mRNA stability.
Major comments:
- The biggest issue that I have with this analysis relates to the assumption that Aurora A levels will be meaningfully different between individual tumors in all cancer types. For some cancers, the lack of a correlation between mRNA and protein levels for Aurora A could simply be because Aurora A overexpression is not a feature of that cancer type. Looking at the data, the cancer types where they see little-to-no correlation are the cancer types where none of the tumors have high levels of Aurora A mRNA or protein. Therefore, the lack of correlation is likely because differences in protein levels result from noise in the measurements rather than posttranscriptional regulation. Since the lack of correlation between protein and mRNA in these cancer types is the main evidence for the primary conclusion in the paper that "AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation", I don't think that this conclusion is supported by the data. If anything, the data seems to show that substantial changes in Aurora A protein levels are almost always accompanied by a corresponding change in mRNA levels.
To address this issue, the authors could look at the variability in Aurora A protein levels for each cancer type, and then focus their correlation analyses on cancer types where overexpression of Aurora A is a feature.<br /> 2. The statistical significance of the analyses is often unclear. For the correlations between Aurora A protein levels and hsa-let-7a, authors mention that two cancers have a correlation with "statistical significance", but I cannot find any indication of how that was determined, and it is not shown in the corresponding figure (2C). The only time significance is indicated for a correlation is in Figure 4A. Is this the only correlation in the whole manuscript with a p-value less than .05? 3. The SLR for the Aurora A transcripts is only shown in terms of a ratio between cancer and normal tissue. Without the numbers in the absence of normalization, it is difficult to determine how meaningful this is. Is a two-fold change going from .3 to .6 or .001 to .002? 4. Figure 5B is nearly impossible to interpret due to the extreme differences in overall transcript levels between the cancer types. The differences in scaling of the y-axis between the plots makes this even more challenging. The authors state that "It is evident that each isoform has an individual profile of expression across cancers", but this could only be determined from relative expression levels between the different isoforms instead of absolute levels.
Minor comments:
- In supplementary figure 3, SLR is plotted on a log scale in A and a linear scale in B.
- Figure 4D is a correlation of correlations. I don't see how to interpret this in a meaningful way.
Significance
Aurora A is overexpressed in a wide variety of cancer types. This overexpression is commonly believed to result primarily from increased mRNA abundance. The identification of additional mechanisms regulating Aurora A protein levels would therefore be of interest to the field, as these regulatory mechanisms could be contributing to Aurora A's role in cancer progression.
To some degree, the significance of the findings presented here depend on whether they convincingly demonstrate substantial post-transcriptional regulation. My interpretation of the data presented in this manuscript is that it largely supports Aurora A protein levels being extremely well correlated with mRNA levels, which is in line with previous findings.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Cacioppo et al perform a meta-analysis of public omics data examining AURKA protein and mRNA expression (including mRNA isoforms with alternative cleavage and polyadenylation), and hsa-let-7a miRNA (shown to target AURKA mRNA) in multiple cancer types from The Cancer Genome Atlas. They conclude AURKA mRNA and protein expression may be discordant in cancer in part due to the interplay between alternative polyadenylation and hsa-let-7a miRNA.
Major comments:
- Unfortunately, there is a major flaw in the TCGA AURKA protein quantification data that underpins much of this study. Following the protein data trail (via https://docs.gdc.cancer.gov/Data/Introduction and its dependents), it appears to rely on the CST anti-AURKA #14475 which is raised to an antigen around Pro70.
It has been documented that short isoforms of AURKA exist where up to ~100 amino acids are progressively removed from the N-terminus as part of trafficking AURKA to the mitochondria. The antibody strategy then used here to quantify AURKA levels, would not recognize these short isoforms as the antigen around Pro70 is removed. This means the quantitated AURKA protein levels in the datasets analyzed do NOT reflect total protein levels of AURKA. This key point then casts doubt on all the claimed protein-correlated findings. (The RPPA source data itself also flags the antibody validation with caution due to low correlation).
In light of this the authors should seek to re-validate their protein expression data with datasets generated from alternative protein quantification methods such as Mass Spectrometry (blind to isoform and not antibody biased). 2. Following the flaws identified in the protein foundation data, the study would then benefit from some post-validation of findings with actual biological data derived from their own independent assessment of the cancers being examined.
Minor comments:
- All of the Correlation analysis have been tested for statistical significance and these results are available in the supplementary data. However, I think it would be useful if these statistics were also included in the main figures themselves. (Figures 1B, 2B and 2C) A low correlation that is statistically significant is a more powerful statement.
- In the materials and methods, Correlation is separated into distinct degrees: none to very strong, but apart from some lines on the graphs, these degrees of correlation strength are never revisited, so they should be included. Perhaps there is a biological difference between AURKA post transcriptional regulation and protein levels with different R score strength?
- In Figure 2D a clustering analysis was performed to show the possible relationships between hsa-let-7a and protein levels. The current visualization is hard to understand. A 3D graph with Protein, mRNA and has-let-7a axis's would be easier to follow.
I believe it would also be beneficial to do something similar including the APA data as this is the area that the paper lacks depth. 4. Figure 3B and 3C, can you apply a statistical test on the SLR ratios given the magnitude difference between CCND1 and AURKA SLRs? 5. Even though the paper does not claim to provide a unifying hypothesis for APA/has-let-7a regulation of AURKA, I think a more in depth look at the data would be useful. The discussion starts off well when describing what was found with the analysis, but as is, is mostly a re-statement of the results without added insight.
Significance
Significance:
The study is novel in attempting to show additional layers of AURKA regulation that hadn't been previously investigated. Furthermore, factors controlling AURKA expression are of broad interest. Overall, I would like to say this is an interesting investigation into AURKA mRNA expression in cancers. In our opinion the choice of bioinformatic tools is appropriate and well controlled.
General Assessment: As noted in the major comments, a major weakness is the reliance on a flawed measure of AURKA protein levels from the foundation dataset. Thus, the study needs to be repeated using an alternative MS derived dataset to accurately quantify total AURKA protein levels. This would greatly improve the study and subsequent claims.
Advance: The study has potential to extend knowledge in the field in a conceptual way, predicting the complex interplay of factors that regulate AURKA mRNA processing and translation.
Audience: Currently the paper is only fully accessible a specialized bioinformatician audience but the topic (factors controlling AURKA expression) has a broad interest in many fields not limited to just cancer but also development and other non-cancer diseases.
This review was jointly completed by a mouse model of human disease AURKA biologist with 24 years' experience, and a bioinformatician.
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www.biorxiv.org www.biorxiv.org
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Reply to the reviewers
A. General Statements
We thank the reviewers for their constructive feedback. We have made significant revisions to the mathematical modelling section of the manuscript to address your concerns. Therefore, some of the specific issues and concerns raised in previous reviews no longer apply. Where that is the case, please see the relevant context in the revision as indicated in the point-by-point description section below. We summarize the key points in the revised manuscript as follows.
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The key finding of our study, involving experimental measurements and mathematical modelling, is plasticity in the MinD concentration gradient, which results from spatial differences in molecular interactions and is an intrinsic property of the Min system during cell growth. This study reveals not only the role of the MinD concentration gradient in modulating bacterial cell division site placement but also showcasing an example of cellular components in the form of a concentration gradient in fundamental cellular processes, a concept crucial in cell biology. This work provides conceptual advancement in a quantitative understanding of MinD oscillations in the cellular environment and provides implications for bacterial cell division regulation for further studies in the field.
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The reviewer requested clarification on the differences between our study and previous studies involving experimental measurements and mathematical modelling of Min oscillations in cells. We would like to emphasize that although the goal of the previous works was to measure the spatiotemporal distribution of oscillating MinD concentration gradients as a function of cell length, these works conceived the problem differently and therefore used different experimental designs and execution methods, which differentiates our key conclusions from theirs. This is also true for mathematical modelling. Although similar observations can be found in some respects, they are not directly comparable due to the different mathematics and assumptions used in the simulations. For example, our model was built to adequately investigate the biological question of the MinD concentration gradient during cell elongation but not to evaluate the impact of cell shape and confinement or the nucleation effect of MinD. Thus, our model cannot be generalized to other shapes, such as those observed in the study by Wu et al., 2015 (Wu et al, 2015). Therefore, we would like to draw attention to the experimental rigor and to the specific points and views that contribute to our understanding of Min systems. We now provide a comprehensive comparison between them in the Supplemental Information.
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We have re-run the simulation to refine and improve the modelling procedures and results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 265-279, 614-653) and Fig. S6. In brief, we fixed the diffusion coefficients D_D and D_E from Meacci et al. (2006) (Meacci et al, 2006); the dissociation rate constant k_de from a previous simulation (Wu et al., 2015); and the experimentally measured MinD and MinE concentrations in this study. Meanwhile, the diffusion coefficients D_d and D_de were assumed values based on bacterial membrane protein diffusion (Schavemaker et al, 2018). This operation allowed us to probe for the general behaviours of the system. As a result, we were able to obtain a few parameter sets, including #2728, that generate features of the oscillation period, λ_N and I_Ratio, that highly mimic MinD oscillation in the cellular context (Figs. 4C, S7-9). We further tested the impact of different kinetic constants, k_de, k_dD, k_dE, k_D, and k_(ADP→ATP), which represent different molecular interactions influencing the oscillation period, λ_N and I_Ratio (Fig 4D-H). Our findings have provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions. Furthermore, the modelling results help us understand the possible mechanisms associated with oscillation cycle maintenance and length-dependent variable concentration gradients.
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Regarding the inclusion or removal of results from more culture conditions, we decided to keep only one condition as in the previous version for the following reasons. In order to draw convincing conclusions, we consider it more important to characterize all aspects under the same growth condition and avoid manipulation. Therefore, the main conclusions are drawn from our experiments characterizing several aspects of MinD oscillations in cells growing with 0.4% glucose. In support of these observations, we decided to maintain only one other condition, 0.1% glucose. Further analysis of cells growing under other conditions will not change the main conclusions but will increase the difficulty of determining how the MinD concentration changes with cell growth.
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Studying the variable concentration gradient underlying the dynamic oscillations of the Min system may be of broad interest to cell biologists since the concentration gradient plays a fundamental role in various cellular processes, and the concept of concentration gradients is crucial in cell biology. Examples of related processes include passive and active transport, osmosis, cell signalling, and maintenance of cellular homeostasis. These processes allow cells to respond to their environment, regulate their internal conditions, and perform important functions required for survival and normal function. In addition, variable concentration gradients, characterized by the numerical descriptor λ_N and was reproduced in a simple mathematical model, demonstrate a nonlinear dynamics behaviour in physical biology. Therefore, the audience of this work can include the broader general audience of cell biology and physical biology rather than just the immediate specialized audience interested in the Min system. We will also reiterate the importance of specialized research, which often provides the basis for broader application and understanding.
B. Point-by-point description of the revisions
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary: Parada et al. studied both experimentally and theoretically the MinD concentration distribution of Min waves during cell growth. The main finding was that (i) the gradient of MinD is steeper for longer cells and accordingly the MinD concentration at the middle of cell is lower, (ii) period of the oscillation is independent to the cell length, and (iii) those features are shared even under glucose starvation except the MinD gradient is steeper. (iv) Those results are supplemented by the analyses of the reaction-diffusion equations in which parameters that can reproduce the MinD concentration distribution are identified. I think the results are interesting; basically, as the cell grows, the contrast of the wave becomes clearer, such the MinD concentration at the cell centre decreases. The results may clarify the mechanism of FtsZ accumulation at the cell centre more quantitatively. The experiments were performed by measuring the fluorescent intensity of MinD during cell growth and analysing the intensity distribution along the long axis of the cell. The theoretical results were based on the analyses of the reaction-diffusion model. Both approaches are already well established and the results sound. Nevertheless, I do not think the novelty of this work is not well highlighted in the current manuscript; I think most of the results, except (iii) and (iv), have already been shown explicitly or implicitly in the previous studies. Min oscillations in a growing cell have been analysed both theoretically and experimentally in (Meacci 2005) and [1] (Fischer-Friedrich et al, 2010). The concentration distribution and period of the oscillation were measured. The complete results were presented in [2] (Meacci et al., 2006), and I am not aware of those results in scientific journals (the thesis is available online). Nevertheless, I think it is fair to cite those studies and compare the current results with them. In fact, in [2], it was shown that the concentration of MinD near the cell centre decreases as the cell grows, the total MinD concentration is approximately constant during the growth (therefore, the number of the molecules increases), and that the variance of the period becomes smaller as the cell grows. I do not think those previous studies spoil this work, and this work deserves publication somewhere. Still, the authors should highlight the novelty of this study more clearly.
ANS: We thank the reviewer for recognizing the soundness of our experimental and theoretical approaches and results. The key finding of our study, involving experimental measurements and mathematical modelling, is plasticity in the MinD concentration gradient, which results from spatial differences in molecular interactions and is an intrinsic property of the Min system during cell growth. This study reveals not only the role of the MinD concentration gradient in modulating bacterial cell division site placement but also showcasing an example of cellular components in the form of a concentration gradient in fundamental cellular processes, a concept crucial in cell biology. We believe that the established techniques and methods are integral to a broad range of works and provide confidence in improving them and using them to test hypotheses and obtain results. We also appreciate the reviewer for pointing out that Meacci's PhD thesis entitled "Physical aspects of Min oscillations in Escherichia coli" (Meacci & Kruse, 2005) is available online for public access. This thesis, along with two publications (Meacci & Kruse, 2005) (Meacci et al., 2006), explored Min oscillations in growing cells and used mathematical models. These two published works are cited in the previous version of the manuscript because we agree that these earlier works provide valuable context. As recommended, we went through these works again and the work by Fischer-Friedrich et al. (2010) (Fischer-Friedrich et al., 2010) to compare their wet experiments and mathematical models with ours, which are detailed in the Supplemental Information (Lines 26-147). Here, we emphasize that although the published works and our work set the goal of measuring the spatiotemporal distribution of oscillating MinD concentration gradients as a function of cell length, we conceived the problem differently and therefore used different experimental designs and analysis approaches, which have led to the key conclusions that differentiate our work from theirs.
Major comments: (i) In (Meacci 2005) and [1,2], it was claimed that the standard deviation of the period is comparable with the mean period, particularly for the shorter cell. Therefore, they did not claim the period is independent to the cell length. As far as I understood, the variance arises from the variance of the total protein concentration in the assemble of cells. I am wondering how the authors are able to conclude the constant period in different cell length. I also point out that in the theoretical part of (Meacci 2005), the period is, in fact, increasing as the cell grows and suddenly decreases at the length in which cell division occurs.
ANS: In our experiments, we found that the oscillation periods ranged from 36.8 to 65.6 sec, as measured from a population of cells (length of 1.9-4.5 µm; main text, Fig. 1E). Moreover, the standard deviations of the period ranged from 5.4% to 34.8% of the period, with larger standard deviations more common in shorter cells (Fig. 1D), indicating that regular interpolar oscillations are more likely to occur in longer cells. This observation echoes the study by Fischer-Friedrich et al. (2010) (Fischer-Friedrich et al., 2010), who reported stochastic switching MinD oscillation between two cell poles in cells below 2.5 μm. MinD starts to oscillate regularly from pole-to-pole between 2.5-3 μm with an oscillation period of 80 sec. Above 3.5 μm, MinD invariably undergoes regular oscillation with an initial period of 87 sec and then decreases to 70 sec at the end. In their study, they focused on the length-dependent switching from stochastic to regular oscillation states and speculated that the amount of MinE bound to the membrane critically influenced the shift from stochastic to regular interpolar oscillations. In addition, their observation of a longer period at the initial phase and a shorter period after the cells grew beyond 3.5 μm somewhat coincided with our simulation results, as shown in Fig. 4C-H, left. In Meacci's work (Thesis: Figure 2.14; Meacci and Kruse (2005) (Meacci & Kruse, 2005): Figure 5(b)), the temporal oscillation periods were measured from 40 to 120 sec when focusing on cells with lengths similar to those in our measurements (black dots in Meacci's chart). Our measurements of oscillation periods clearly show much smaller fluctuations than those in Meacci's study and are more comparable to Fischer-Friedrich's measurements. Differences can arise across different bacterial strains and culture conditions that may significantly affect the amount and quality of protein expressed in individual studies. In short, all three works differ in terms of experimental design and execution. Although similar observations can be found in some aspects, they are not directly comparable. Therefore, we would like to draw attention to the experimental rigor and specific points and views that contribute to our understanding of the Min system. We have changed the wording from 'constant period' to 'fairly stable period' throughout the manuscript. This description is based on our experimental measurements (Fig. 1D, E) and is also supported by our mathematical modelling (Fig. 4C-H, left). In response to the statement from the theoretical model of (Meacci & Kruse, 2005): "the period is increasing as the cell grows and suddenly decreases at the length in which cell division occurs." First, our simulation results revealed a mild increase in the oscillation period during cell elongation (Fig. 4C). The increase is adjustable by varying the reaction rate constants in the simulation (Fig. 4D-H). Second, although we did not simulate dividing cells, our experimental measurements clearly showed that this period increased in newborn cells (Fig. S4). As mentioned above, although similar observations can be found in different studies, they are not directly comparable because the experiments were performed differently for different purposes. We have added comparison of different models in the Supplemental Information (Lines 26-147).
(ii) I do not think the explanations of the reaction-diffusion model were well described. The authors mentioned that they studied a one-dimensional model and used the delta function to describe the membrane reaction. Did the authors study 1D cytosol and 0D membrane? Then, why the surface diffusion term exists in (4) and (5)? I believe the authors simply assumed that both the membrane and the cytosol are 1D (with larger diffusion constants for cytosolic Min concentrations). Then, the delta functions in (1)-(5) are not necessary. In (Wu 2015), the delta function was used in order to treat a 2D membrane embedded in 3D space.
Besides that, there is no description of the initial conditions for the concentration fields to solve the reaction-diffusion equations. I think the description of the no-flux boundary condition is better put in the Methods rather than supplementary materials.
ANS: Thank you for your suggestions to improve the description of the numerical model. As summarized below, we have rewritten this section of 'Simulating the dynamic MinD concentration gradient in growing cells' in the manuscript (Lines 237-279). We have specified the dimensionality of the rate and diffusion constants of each molecule, where applicable, in our 1D model from Lines 237-264. Their dimensionality can also be conceived from their units, as listed in Tables 2 and S4. We have specified the initial 'no-flux' boundary conditions in Lines 267, 630, and 647. We agree that the delta function is not necessary and have removed it from the equations.
(iii) As in the previous comment, the current model did not take into account the geometry of the system; namely, cytosol is in 3D and membrane is on 2D. Recent theoretical studies can handle the effect, and also the effect of confinement. I would appreciate it if the authors would make a comment on whether those issues are relevant or not for the conclusion of this work.
ANS: Thank you for pointing out this interesting aspect of cell geometry as investigated in Wu et al., 2015 (Wu et al., 2015). Our model is built to adequately describe changes in the MinD concentration gradient during cell elongation under the assumption that a 1D description is sufficient. Thus, our model cannot be generalized to other shapes, such as those observed in Wu et al., 2015 (Wu et al., 2015). This point is now commented upon in Supplemental Information, lines 120-123.
(iv) I would appreciate it if the authors would describe the screening process more clearly. I did understand the first screening is a finite imaginary part and a positive real part at the first mode of spatial inhomogeneity in the eigenvalues. However, I did not understand the other processes clearly. The second screening is based on \lambda_N and I_Ratio, but its criteria is not clear. I think both quantities fluctuated in experimental results and I am not sure what to define numerical results match them. The third process is based on a fitting error using the fitting function of linear increase plus a constant. I am not sure why we need to exclude, for example, the bottom right example in Fig.S6 because it shows no oscillation until the cell length of 3um but then the gradient linearly increases. Please clarify how to justify the criteria. The same argument applies to the fourth screening process. It is not clear why the slope should be smaller than 2.
ANS: Thank you for your suggestions to improve the description of the screening process. We have re-run the simulation to refine and improve the screening process, and the corresponding text and illustration are provided in the Results section of the main text (Lines 237-279, 614-653) and Fig. S6.
(v) The authors claimed that the steeper gradient of MinD under glucose starvation results in cell division for shorter cells. I do not think the claim is convincing. It is necessary to measure the correlation between the length at the cell division and the gradient. It would also be nicer to show the correlation under other parameters. I think those studies truly support the authors' claim and the novelty of this work.
ANS: Thank you for the comments. We would like to draw your attention to the right side of the graph shown in Fig. 3B, E, where measurements were obtained from cells prior to division. Our claim that "the steeper gradient of MinD under glucose starvation results in cell division for shorter cells" is also supported by the wave slope (λ_N range): 0.4% glucose of 1.49-2.66 (cell length range: 1.7-4.5 µm) and glucose starvation of 1.34-3.54 (cell length range: 2.1-3.8 µm). Therefore, under glucose starvation, λ_N increases more significantly with increasing length, allowing us to speculate on the contribution of steeper concentration gradient in stressed shorter cell to division. In the revised manuscript, the statement is kept in the Results section (Lines 217-218), but removed from the abstract. About the correlation between the concentration gradient and cell length at division under different conditions, we consider it more important to characterize all aspects under the same growth condition and avoid manipulation. In this study, the main conclusions are drawn from our experiments characterizing several aspects of MinD oscillations in cells growing with 0.4% glucose. In support of these observations, we decided to maintain only one other condition, 0.1% glucose. Further analysis of cells growing under other conditions will not change the main conclusions but will increase the difficulty of determining how the MinD concentration changes with cell growth.
(vi) The conclusion at Line 346 "This plasticity arises from spatial differences in molecular interactions between MinD and MinE, as demonstrated..." looks unclear to me. My understanding is that (i) by screening the randomly sampled parameters in the reaction-diffusion model, the authors found the parameters that "match" experimental results, and (ii) the parameters after screening show the correlation between them (k_dD-k_dE and k_D-k_ATP->ADP). The logic heavily relies on the reaction-diffusion model is quantitatively correct. First, I think it is better to explain the logic more explicitly, that is, the claim of the molecular interaction is not based on the experimental facts. Second, I personally think the reaction-diffusion model used in this work does not reproduce quantitatively the experimental results, as discussed in (iii) and also (iv). Please make some discussions on how to justify the comparison between the model and experiments.
ANS: Thank you for your constructive comments. To address these questions, we have re-run the simulation to refine and improve the results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 237-279, 614-653) and Fig. S6. The kinetic parameters used in this study are described in the main text, lines 258-264: 'To randomly search for combinations of the parameter sets k_dD, k_dE, k_D, and k_(ADP→ATP), the following parameters were fixed in the simulation: the diffusion coefficients D_d and D_de were assumed values based on bacterial membrane proteins (Schavemaker et al., 2018), the diffusion coefficients D_D and D_E were from Meacci et al. (2006) (Meacci et al., 2006), and the dissociation rate constant k_de were from a previous simulation (Wu et al., 2015). This operation allowed us to probe for the general behaviours of the system.' Lines 277-279: 'This screening process reduced the parameter sets to 23, including set #2827, which, judging by the correlation plots for length vs. period, λ_N, and I_Ratio (Figs. S7-S9), showed features similar to those of the experimental data (Figs. 1E, 3B, C).' Based on the parameters of set #2827, we rigorously tested the impact of different kinetic constants that represent different molecular interactions on the oscillation period, λ_N and I_Ratio (Fig 4D-H). The results are described in the section of 'Effect of the kinetic rate constant on the MinD concentration gradient' of the main text, lines 323-349. This effort has provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions. In addition, a comparison between our modelling and experimental results is described in the main text, section 'In silico oscillation resembles oscillation in a cellular context', lines 300-321.
(vii) I did not capture the point why the authors can claim "... further distinguishing in vivo and in vitro observations. " at Line 350. I did not find the results comparing with vitro studies. I would appreciate a demonstration of vitro results and/or references.
ANS: To avoid confusion, this sentence has been removed.
Minor comments: (1) Line 214: It should be "Fange and Elf".
ANS: Line 238 in the revised manuscript: This has been corrected.
(2) I think it is better to show sampled points in Fig. 4C and 4D to show how dense the authors sampled in the parameter space.
ANS: Since we have rewritten this part, the suggested revision is no longer applicable.
REFERENCES: [1] Fischer-Friedrich, Elisabeth / Meacci, Giovanni / Lutkenhaus, Joe / Chaté, Hugues / Kruse, Karsten, "Intra- and intercellular fluctuations in Min-protein dynamics decrease with cell length", Proceedings of the National Academy of Sciences, 107, 6134-6139 (2010). [2] Meacci, Giovanni, "Physical Aspects of Min Oscillations in Escherichia Coli", PhD thesis (2006) available at
Reviewer #1 (Significance (Required)):
General assessment: I think the strength of this study is that it potentially shows the quantitative correlation between the MinD concentration gradient during the oscillation and the cell length when it divides. However, the current data of glucose starvation is not convincing enough. The model parts are interesting but their connection to the experiments is not clear in the current manuscript.
ANS: Thank you for your comment. The key finding of our study, involving experimental measurements and mathematical modelling, is plasticity in the MinD concentration gradient, which results from spatial differences in molecular interactions and is an intrinsic property of the Min system during cell growth. We hypothesized that if the plasticity of the MinD concentration gradient is an intrinsic property of the system, then this property would be robust and show consistent behaviour under different growth conditions. Therefore, we tested this hypothesis by studying MinD oscillations under a low-glucose condition, and the results strengthened the main conclusion derived from experiments under the regular growth condition containing 0.4 % glucose. We believe that further analysis of cells growing under other conditions will not change the main conclusions but may increase the difficulty of determining how the MinD concentration changes with cell growth. Therefore, we decide to make this section concise, containing only one additional condition, even though we have more data than presented here. As mentioned earlier in this response letter, we have re-run the simulation to refine and improve the results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 237-279, 614-653) and Fig. S6. This operation allowed us to probe for the general behaviours of the system. As a result, we were able to obtain a few parameter sets, including #2728, that generate features of the oscillation period, λ_N and I_Ratio, that strongly mimic MinD oscillation in the cellular context (Figs. 4C, S7-9). We further tested the impact of different kinetic constants, k_de, k_dD, k_dE, k_D, and k_(ADP→ATP), which represent different molecular interactions influencing the oscillation period, λ_N and I_Ratio (Figs. 4D-H). This effort has provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions.
Advance: The advance of this study is to measure the MinD concentration gradient under glucose starvation, and to compare the experimental results with the (simplified) model under a wide range of parameters. I do not think the advance in the current manuscript looks conceptual level because the conceptual conclusions are not really convincing from the results. In this respect, the advance of this work may be technical.
ANS: Thank you for this constructive comment and have responded as follows. In combination with both experimental and theoretical efforts in the revised manuscript, this work provides conceptual advancement in a quantitative understanding of MinD oscillations in the cellular environment and provides implications for bacterial cell division regulation for further studies in the field. Specifically, we would like to emphasize that this work revealed the inherent plasticity and adaptability of the MinD concentration gradient that contributes to division site selection. The mathematical modelling provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions.
Audience: As a theoretician working on biophysics, including the model of the Min system, I think a specialised audience would be interested in this study. People who are studying the mechanism of the Min oscillation and resulting cell division, particularly those who are interested in both experiments and models, would be interested in this work. For the broad audience, I do not think the novelty of this study is well described.
ANS: Thank you for your comment. We would like to point out that studying the variable concentration gradient underlying the dynamic oscillations of the Min system may be of broad interest to cell biologists since the concentration gradient plays a fundamental role in various cellular processes, and the concept of concentration gradients is crucial in cell biology. Examples include passive and active transport, osmosis, cell signalling, and maintenance of cellular homeostasis. These processes allow cells to respond to their environment, regulate their internal conditions, and perform important functions required for survival and normal function. In addition, the variable concentration gradient, characterized by the numerical descriptor λ_N and reproduced in a simple mathematical model, demonstrates a nonlinear dynamics behaviour in physical biology. Therefore, the audience of this work may include the broader general audience of cell biology and physical biology rather than just the immediate specialized audience interested in the Min system. We will also reiterate the importance of specialized research, which often provides the basis for broader application and understanding.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Summary: This work by Parada et al showed that in the oscillatory Min System, MinD gradient was steeper in longer e.coli cells, while period was stable. This behavior was recapitulated in a mathematical model and it also revealed coordinated reaction rates in a wide range of parameter space.
ANS: We thank the reviewer for the concise summary of our work.
Major comments: 1. There were some inconsistencies between experimental and modeling data. Wave slope (𝜆𝑁) plateaued at ~3um in the model but not shown in the experiment (Fig.3B). The period was much less in the model (Fig. S8) than in the experiment (Fig. 1B).
ANS: Thank you for pointing out this problem. We have re-run the simulation to refine and improve the results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 237-279, 614-653) and Fig. S6. This operation allowed us to probe for the general behaviours of the system. As a result, we were able to obtain a few parameter sets, including #2728, that generate features of the oscillation period, λ_N and I_Ratio, that highly mimic MinD oscillation in the cellular context (Figs. 4C, S7-9). Regarding oscillation period, the simulation result was shorter than the experimental measurements. Even though, based on the parameters of set #2827, we rigorously tested the impact of different kinetic constants that represent different molecular interactions on the oscillation period, λ_N and I_Ratio (Main text, lines 323-349; Fig 4D-H). This effort has provided us with a theoretical view of how oscillation features may be controlled by different molecular interactions. We found that the rate constants k_de, representing detachment of the MinDE complex from the membrane, and k_(ADP→ATP), representing recharging of MinD-ADP with ATP, more significantly affected the oscillation period. The results suggested that the oscillation cycle time is tunable. In response to the question of the wave slope (λ_N) plateaued at ~3um in the modelling (Fig. 3B) but not shown in the experiment (Fig. 1D), we think this is due to experimental examination of a heterogenous population of cells versus simulating a growing bacterial cell. We came up with conclusions and hypotheses through wet experiments, which were further strengthened using mathematical modelling, providing insights into kinetic properties of the Min system.
- Generally, I found that the data of starved condition added little to the major message. Unless the model can recapitulate the even steeper gradient in such condition by tuning starvation-related parameters, it may be removed.
ANS: We thank the reviewer for this suggestion. The key finding of our study, involving experimental measurements and mathematical modelling, is plasticity in the MinD concentration gradient, which results from spatial differences in molecular interactions and is an intrinsic property of the Min system during cell growth. We hypothesized that if the plasticity of the MinD concentration gradient is an intrinsic property of the system, then this property would be robust and show consistent behaviour under different growth conditions. Therefore, we tested this hypothesis by studying MinD oscillations under a low-glucose condition, and the results strengthened the main conclusion derived from experiments under the regular growth condition containing 0.4 % glucose. We agree that further analysis of cells growing under other conditions will not change the main conclusions but may increase the difficulty of determining how the MinD concentration changes with cell growth. Therefore, we decide to make this section concise, containing only one additional condition, even though we have more data than presented here.
- The authors need to compare what was different/novel between the model in this study and previous models such as Wu, et al 2015 and highlight the uniqueness of this work.
ANS: Thank you for this suggestion. We now provide a comprehensive comparison between them in the Supplemental Information (Lines 26-147). We would like to emphasize that although the goal of the previous works was to measure the spatiotemporal distribution of oscillating MinD concentration gradients as a function of cell length, these works conceived the problem differently and therefore used different experimental designs and execution methods, which differentiates our key conclusions from theirs. This is also true for mathematical modelling. Although similar observations can be found in some respects, they are not directly comparable due to the different mathematics and assumptions used in the simulations. Therefore, we would like to draw attention to the experimental rigor and to the specific points and views that contribute to our understanding of Min systems.
- The model explored parameter space of reaction rates and found 60 sets. The KdE, KD, KdD, KADP-ATP ranged 6 orders of magnitude. It is interesting data in itself, but cells were not likely to vary that much for reaction rates. The relevance should be discussed.
ANS: Thank you for pointing out this problem. For this revision, we re-ran the simulation to refine and improve the results, allowing us to identify parameter sets that generate features resembling the experimental measurements. Using set #2728 as an example, the variations in the five rate constants k_de, k_dD, k_dE, k_D, and k_(ADP→ATP) fall within a small range (Table 2, S4), eliminating the concern that arose from the previous version of the manuscript. We found that this parameter set allows for maximum utilization of MinD and MinE molecules, which are fixed in number according to experimental measurements, to drive membrane-associated oscillations in the simulation.
Minor comments: 1. Fig.1B colors were conflicting. The legend was different than diagram. Fig.1C no scale for x axis.
ANS: We have resolved the colour conflict in Fig. 1B, and a time range has been added to Fig. 1C.
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Fig.S6A How the 638 oscillatory parameter sets were matched with experimental data and screened to 174 sets was not clear. Data of fitting errorANS: Thank you for your suggestions to improve the description of the screening process. In this revision, we have re-run the simulation to refine and improve the results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 237-279, 614-653) and Fig. S6. This operation allowed us to probe for the general behaviours of the system. The mentioned filter no longer applies.
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Significant digits were not used properly. For example, the period (table 1) was showed as 46.00 sec, but the imaging interval was 12 sec, the 2 decimal digits were thus meaningless. The same argument goes for length measurement at 2.84 um, while the optical resolution of the microscope used should be no good than 200nm.
ANS: We have corrected this significant digit throughout the manuscript.
- For scatter plot like Fig.1D-G, generally smaller dots would show trend more obvious.
ANS: We have modified the plots and used smaller dots in Figs. 1D-G, 3B, C, E, F, S3D, and S5B, C.
- The molecular mechanism of why MinD gradient increases with length was not the scope of the current study, but better to be discussed.
ANS: Let me address this comment in another way. The key finding of our study, involving experimental measurements and mathematical modelling, is plasticity in the MinD concentration gradient, which results from spatial differences in molecular interactions and is an intrinsic property of the Min system during cell growth. In the revised manuscript, we have re-run the simulation to refine and improve the modelling procedures and results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 265-279, 614-653) and Fig. S6. In brief, we fixed the diffusion coefficients D_D and D_Efrom Meacci et al. (2006) (Meacci et al., 2006); the dissociation rate constant k_de from a previous simulation (Wu et al., 2015); and the experimentally measured MinD and MinE concentrations in this study. Meanwhile, the diffusion coefficients D_d and D_de were assumed values based on bacterial membrane protein diffusion (Schavemaker et al., 2018). This operation allowed us to probe for the general behaviours of the system. As a result, we were able to obtain a few parameter sets, including #2728, that generate features of the oscillation period, λ_N and I_Ratio, that highly mimic MinD oscillation in the cellular context (Figs. 4C, S7-9). We further tested the impact of different kinetic constants, k_de, k_dD, k_dE, k_D, and k_(ADP→ATP), which represent different molecular interactions influencing the oscillation period, λ_N and I_Ratio (Fig 4D-H). Our findings have provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions. Furthermore, the modelling results help us understand the possible mechanisms associated with oscillation cycle maintenance and length-dependent variable concentration gradients.
- Fig. S8, why sudden jump in period in many of the sets of both groups?
ANS: This supplemental figure is now Fig. S7. A slower oscillation at the initiation of oscillation appears to be a common property in our simulation.
Reviewer #2 (Significance (Required)):
Min system was well-studied oscillation mechanism to restrict FtsZ at cell center. Previous work has shown how the system work molecularly, simulated the behavior and reconstituted many different patterns in vitro. The major new information from this work was: 1. the rigorously measured endogenous level of MinD and MinE; 2. gradient increased with length; 3. a model recapitulated this relationship and explored parameter space of reaction rates. The paper was well presented, experiments and analysis were rigorous, and the conclusions were not overstated. It should interest specialized cell biologists studying cell size, oscillation pattern.
ANS: Many thanks to Reviewer 2 for recognizing the contributions of our work to the understanding of the Min system and its role in cell division. We also thank you for identifying professional cell biologists studying cell size and oscillation patterns as readers of our paper. We would like to emphasize that cellular concentration gradients play a fundamental role in various cellular processes and that the concept of concentration gradients is crucial in cell biology. These concentration gradient-mediated processes allow cells to respond to their environment, regulate their internal conditions and perform important functions required for survival. In addition, the variable concentration gradient, characterized by the numerical descriptor λ_N and reproduced in a simple mathematical model, demonstrates a nonlinear dynamics behaviour in physical biology. Therefore, the audience of this work may include a broader audience in the field of cell biology and physical biology rather than just an immediate specialist audience. We will also reiterate the importance of specialized research, which often provides the basis for broader application and understanding.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
The manuscript shows that the concentration of MinD does not change during the division cycle of E. coli. Due to the oscillation pattern the concentration of MinD decreases at the mid-cell which makes it favorable for the division. The mid-cell decrease in concentration of MinD is majorly length dependent. The oscillation pattern is not due to the change in concentration of MinD, but due to the plasticity arises from the spatial differences in molecular interactions between MinD and MinE. The manuscript is well written, the experiments are performed carefully and the results will be of interest to readers from variety of field. However, there are several concerns need explanation.
ANS: We greatly appreciate the positive feedback from the reviewer, and we address the specific concerns below.
Major concerns: One of my major concern is these interactions are not shown experimentally but explained using either the previously published literature or mathematical models. Further, the previous literatures are shown on in vitro models which does not mimic the in vivo system fully.
ANS: We thank the reviewer for the important point that reaction rates in previous studies and in our model of Min oscillations have not been experimentally tested. We are aware of the lack of experimental measurements, but these reaction rates cannot be measured in batch reactions using classical biochemical methods. To accurately measure these reaction rates, the experiments require advanced techniques and methods to handle spatial and temporal resolution, which is beyond the scope of our current study. However, in the revised manuscript, we have re-run the simulation to refine and improve the results, and the corresponding text and illustration are provided in the Results section of the main text (Lines 237-279, 614-653) and Fig. S6. In our simulation, we fixed the diffusion coefficients D_D and D_E from Meacci et al. (2006) (Meacci et al., 2006); the dissociation rate constant k_de from a previous simulation (Wu et al., 2015); and the experimentally measured MinD and MinE concentrations in this study. Meanwhile, the diffusion coefficients D_d and D_de were assumed values based on bacterial membrane protein diffusion (Schavemaker et al., 2018). This operation allowed us to probe for the general behaviours of the system. As a result, we were able to obtain a few parameter sets, including #2728, that generate features of the oscillation period, λ_N and I_Ratio, that highly mimic MinD oscillation in the cellular context (Figs. 4C, S7-9). Interestingly, we found that this parameter set allows for maximum utilization of MinD and MinE molecules, which are fixed numbers from experimental measurements, to drive membrane-associated oscillations in the simulation. We further tested the impact of different kinetic constants, k_de, k_dD, k_dE, k_D, and k_(ADP→ATP), which represent different molecular interactions influencing the oscillation period, λ_N and I_Ratio (Figs. 4D-H). Our findings have provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions, and help us understand the possible mechanisms associated with oscillation cycle maintenance and length-dependent variable concentration gradients.
The concentration of MinD does not change with the increasing length of the cell. Is the MinD concentration (or copy numbers) is different in the case of cells growing in low glucose and when compared to the cells growing at high glucose?
ANS: Thank you for the comments. As shown in Figs. 2B, C, the concentration of MinD changed with cell length, but the number of MinD molecules per unit area did not change significantly with cell length. Although how the number of MinD molecules changes when cells are grown under low-glucose conditions is unclear, this number does not appear to be essential for the following reasons. We focused on studying Min oscillations during the normal growth cycle, minimizing experimental manipulations to analyse oscillation dynamics. Measurements of oscillations in cells grown under low-glucose conditions support the primary measurements. We think that further analysis of MinD concentration changes in growing cells under low-glucose conditions will not change the main conclusion of this manuscript: 'plasticity in the MinD concentration gradient is an intrinsic property of the Min system during cell growth',
As per the current study a particular I-ratio at the mid-cell is required to initiate the cell division. In the case of cells growing at low glucose, how this required I-ratio is achieved at the mid-cell?
ANS: Thank you for the excellent question. As described in the main text, lines 199-201, I_Ratio is defined as the ratio of the minimum intensity to the maximum intensity measured from the experimental data, which gradually decreases as the cell length increases (Fig. 3C). Since the minimum and maximum intensities were measured from the concentration gradient, which is characterized by the slope of the concentration gradient (λ_N), there exists a correlation between I_Ratio and λ_N. That is, a larger λ_N will result in a smaller I_Ratio, and vice versa. When comparing measurements made from cells grown with 0.4% and 0.1% glucose (Fig. 3B, C, E, F), the changes in λ_N are more drastic within a shorter length under low-glucose condition, which is accompanied by more drastic changes in I_Ratio. Furthermore, when the I_Ratio value was approximately 0.5, the corresponding cell length was significantly shorter under low-glucose condition. Therefore, we speculate that there may be an effective I_Ratio that is low enough for stable FtsZ ring formation. This effective I_Ratio can occur at any cell length, allowing us to see that bacteria divide at shorter cell lengths under low-glucose conditions. This property necessitates a faster reduction in the concentration gradient to reach the effective I_Ratio for cells dividing at shorter lengths. As a result, by adjusting λ_N as a function of length, the steepness of the I_Ratio reduction can be altered. Please see the main text, lines 389-406.
There is decrease in the MinD oscillation time observed in low glucose condition. As explained by the authors the MinD oscillation is mainly guided by the FtsE induced removal of MinD from the membrane, how the authors can explain this decrease?
ANS: Thank you for raising the question of how the MinE-induced detachment of membrane-bound MinD contributes to the oscillation time of MinD under low-glucose conditions. Although this is an interesting question, determining what regulates MinE-induced detachment of membrane-bound MinD under low-glucose conditions is beyond the scope of the current study. This unknown regulatory mechanism that regulates MinD-MinE interactions in growing cells under low glucose conditions is worthy of further investigation. However, our modelling results have provided a theoretical view of how oscillation features may be controlled by different molecular interactions between MinD and MinE and may guide future experiments investigating the underlying mechanism involved. Please refer to the Results section: 'Spatiotemporal distribution of the concentration gradient' in the main text, lines 351-373.
Further, it is explained that the concentration of cellular ATP is in much higher concentration compared to the required amount for this oscillation. As the Iratio is majorly dependent on the cell length, what could be the reason for the differential N in the case of low and high glucose condition?
ANS: Please refer to the previous answer to the question: 'As per the current study a particular I-ratio at the mid-cell is required to initiate the cell division. In the case of cells growing at low glucose, how this required I-ratio is achieved at the mid-cell?'. (this letter, Lines 764-779) In addition, our modelling in search of parameter sets that generate characteristics of MinD oscillation resembling oscillation in vivo allowed us to evaluate the impact of different molecular interactions, as represented by different rate constants (Fig. 4), which has provided important information for future mechanistic investigations, although not in the present study. Please see the Results section: 'Effect of the kinetic rate constant on the MinD concentration gradient' in the main text, lines 323-349.
MinD is a highly insoluble protein. It also has an amphipathic helix and thus most of the time it binds to the membrane. The method used by the author to determine the cellular MinD concentration (mentioned in Fig S1) will only give the concentration of the soluble MinD and not of the total MinD. How the authors justify this as the total concentration. This is also the same in the case of MinE copy number calculation. Authors may need to perform the transcriptome analysis and compare both the data.
ANS: We thank the reviewer for the comments. Since the attachment of MinD and MinE to the membrane is transient and MinD-membrane interactions require ATP, we expected that most of the protein would be released from the membrane into the cytoplasm after cell disruption, sufficiently representing the total MinD concentration. Furthermore, our measurements of molecule numbers are within the range of previous measurements (Di Ventura & Sourjik, 2011; Juarez & Margolin, 2010; Meacci & Kruse, 2005; Tostevin & Howard, 2006; Touhami et al, 2006). Thus, we believe that our current measurements are reliable and sufficient for subsequent interpretation.
One of the main question asked by the authors in the abstract is. "How the intracellular Min protein concentration gradients are coordinated with cell growth to achieve spatiotemporal accuracy of cell division is unknown". Although the authors have shown that there is a change in concentration gradient during cell growth, the mechanism for the same is not very well explained. Authors have not provided any specific explanation for the increase in the velocity of the MinD oscillation and the gradient formation. How the velocity of MinD is increasing although there is no increase in the MinD concentration.
ANS: We have changed 'the mechanism' to 'the exact way' in the abstract (Abstract, line 28). Moreover, in the revised manuscript, we have improved the mathematical model and performed a thorough investigation of the variations in the kinetic constants. This effort has provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions. The results may guide future experiments investigating the underlying mechanism involved. Please refer the answers to previous questions above.
Figure 2B: shows the overall concentration of MinD in a single cell varies between 1180 - 1160 molecules/um2. In Fig 2C it is mentioned that mid-cell has a MinD concentration of 120-20 molecuels/ um2. Further, Fig3C and 3F shows I-ratio values varies between 0.6-0.4. Considering the values given the I-ratio (I min/ I max) should be between 0.1- 0.01. Authors need to explain the same. Figure 2C: The data in both the Y-axes are not matching and needs more clarification in the legend. Whether the number of molecules were counted only in the marked 200 nm area? If so, why the Y-axis 1 (molecules/um2) is decreasing 7 times, whereas, Y-axis 2 (molecules) is only by 2 times.
ANS: In this work, we measured sfGFP-MinD intensity through fluorescence microscopy. The fluorescence intensity was converted into molecular numbers based on estimates from Western blot analyses (Fig. S1). This number of molecules for MinD and MinE was assumed to be the mean number, which was fit into the midpoint of the doubling time (Fig. 2B, black dashed line; main text, lines 166-167). Fig. 2C was obtained by further processing the same dataset to restrict the region of analysis to the midcell zone. Please refer to the main text, lines 158-178. However, the λ_N and I_Ratio values were calculated from the processed intensity data (Fig. S2; main text, lines 190-209, 533-559). Because of the conversion from intensity to molecule number in Figs. S2B, C and the image processing procedure applied to the calculation of λ_N and I_Ratio, it is not possible to directly compare the fold change and the upper and lower limits between molecule numbers and the λ_N and I_Ratio values.
Other comments: Line 84: Requires reference for this statement.
ANS: A recent review article has been added in the main text, line 84: '(Cameron & Margolin, 2024)'.
Line 96: Can authors provide other evidence or validation for the determination of the copy numbers such as transcriptome analysis.
ANS: We thank the reviewer for this suggestion. However, we believe that direct measurement of cellular protein abundance is reliable and sufficient for our purposes. Furthermore, transcriptome-measured RNA abundance does not translate directly to protein abundance in living cells because posttranscriptional processing, translation, posttranslational processing, and protein stability issues complicate the interpretation. Therefore, protein abundance measurement from cell extracts is straightforward for our purpose.
Fig 1C: what is the units of time in Fig 1C? Is it equal for all the cell lengths?
ANS: As described in the main text, lines 511-512, 'Time-lapse images of sfGFP-MinD were acquired at 12-sec intervals for 10 min or before the fluorescence diminished'. This condition is applied to all the acquired images in this work.
Page 6, line 136-138: what could be the possible mechanism for change in velocity at different cell cycle time?
ANS: To avoid confusion, we have modified the text and tone down the velocity when mentioned. This is because the mentioned velocity is inferred from the measured oscillation period and cell length but not from direct measurements; our emphasis is on understanding how the oscillation period remains fairly stable during cell growth rather than how the velocity changes. In the revised manuscript, we used modelling results to elucidate the possible mechanism related to period maintenance. The corresponding text and illustration are provided in the Results section (Lines 300-373) and the Discussion section of the main text (Lines 407-446) and Figs. 4, 5. In brief, this simulation allowed us to probe for general behaviours of the system, allowing us to obtain a few parameter sets that generate features of the oscillation period, λ_N and I_Ratio highly mimicking MinD oscillation in the cellular context (Fig 4C, S7-9). We further tested the impact of different kinetic constants, k_de, k_dD, k_dE, k_D, and k_(ADP→ATP), which represent different molecular interactions influencing the oscillation period, λ_N and I_Ratio (Fig 4D-H). This effort has provided us with a solid theoretical view of how oscillation features may be controlled by different molecular interactions. Please see the Results section: 'Effect of the kinetic rate constant on the MinD concentration gradient' in the main text, lines 323-349.
Page 7, line 155: Any evidence for claiming the same?
ANS: The sentence has been modified as follows: 'Thus, the fairly stable oscillation period and variable velocity did not change the precision of the septum placement.' (Main text, lines 155-156)
Page 7, line 156: Is there any proof authors can show that burst MinD synthesis occurs during the division? If not in the case of MinD, is it shown in any other protein?
ANS: The text is now in line 168-171: 'Interestingly, the value after division was not doubled, which could indicate a balanced outcome between de novo synthesis and degradation or a burst of MinD synthesis at cell division followed by constant synthesis.' In previous studies by Männik et al. (2018) (Mannik et al, 2018) and Vischer et al. (2015) (Vischer et al, 2015), the division protein FtsZ increased the cellular concentration throughout the cell cycle under slow growth conditions and degraded rapidly at the end of the cell cycle, a process controlled by the ClpXP protease. Because we do not know the relevance of these observations to our study, which focused on the plasticity of the MinD concentration gradient, we decided not to discuss them in the manuscript.
Page 9, line 217: The Fig 4A is not explained clearly and all the terms mentioned needs to be explained. This figure is used to explain the differential concentration of MinD at the poles and the mid-cell, thus needs to be explain more clearly.
ANS: Thank you for your comments. Please refer to the above answer to the question: 'One of my major concern is these interactions are not shown experimentally but explained using either the previously published literature or mathematical models. Further, the previous literatures are shown on in vitro models which does not mimic the in vivo system fully.', in this letter, lines 691-715.
Page 12, line 285: What is meaning of default speed of MinD oscillation in new-born cells? Do the authors observed any specific velocity in the new-born cells? What is the explanation for length dependent oscillation velocity for MinD?
ANS: Thank you for the questions. As mentioned earlier, the emphasis of this study is on understanding how the oscillation period remains relatively stable while showing plasticity of the concentration gradient during cell growth. The velocity is inferred from the oscillation period and cell length but is not a direct measurement. To avoid confusion, we have modified the text and placed less emphasis on the velocity when mentioned.
Reviewer #3 (Significance (Required)):
General assessment: Major work of the manuscript is relying on the mathematical models, whereas the audience are majorly from the biology fields and thus simplified explanations are required in many places. Many of the legends in the figures require more explanation for better understanding. If possible more experimental data can be added, specifically to explain the model mentioned in figure 4A.
ANS: We have modified the figure legends to include more explanations. As mentioned above, we have also revised Fig. 4 to include improvements in modelling results to better fit the experimental data and to examine the impacts of the kinetics constants of the reaction steps in the Min system. Please refer to lines 691-715 in this letter.
Advance: The study is adding to the existing knowledge and will be helpful to fill the conceptual gaps in understanding the mid-cell MinD concentration and what may favor the initiation of bacterial division. Audience: Majorly the microbiology community will be interested in the study. This will also be interest to Physicists and mathematical persons working to understand bacterial division.
ANS: We thank the reviewer for this positive comment.
Reviewer #4 (Evidence, reproducibility and clarity (Required)):
The study by Parada et al. illuminates the intricate interplay between Min proteins, exemplified by MinD, and cell growth in E. coli. Their findings demonstrate that the MinD concentration gradient steepens progressively as cells elongate, potentially influencing FtsZ ring formation via MinC. Moreover, their comprehensive reaction-diffusion model not only corroborates experimental observations of length-dependent concentration gradients but also underscores the critical role of kinetic interactions involving Min proteins, the membrane, and ATP. This elucidation significantly advances our understanding of the oscillatory mechanisms within the Min system. Both the experimental and simulation data are robust, and the manuscript is exceptionally well-written. I express my full support for publication pending the satisfactory resolution of the outlined concerns.
ANS: We appreciate the reviewer's positive feedback and have addressed most issues to the best of our ability.
- Remove the dot in front of "Min" in line 57.
ANS: This has now been removed.
- In lines 82-84, the statement "...The distribution of the division inhibitor MinC may be synchronized with spatiotemporal differences in MinD concentrations, leading to a stable placement of the FtsZ ring at the midcell..." suggests a potential synchronization between MinC and MinD oscillations. It is crucial to investigate if sfGFP-MinC exhibits similar concentration gradient oscillatory behavior in vivo as observed with MinD.
ANS: Thank you for bringing up this question. The key finding of our study, involving experimental measurements and mathematical modelling, is plasticity in the MinD concentration gradient, which results from spatial differences in molecular interactions and is an intrinsic property of the Min system during cell growth. With many investigations already covered in this manuscript, we prefer to investigate sfGFP-MinC in future studies, which will have different focuses on how MinC dynamics are coupled with the variable MinD concentration gradient to directly impact FtsZ ring formation.
- Ensure consistent significant digits throughout the text. For instance, 1.95{plus minus}0.16 μM in line 97, 1.4{plus minus}0.13 μM in line 98, and 1.9 {plus minus} 0.2 μM in line 100 have varying precision. Consider using integers for molecules.
ANS: We have corrected the significant digits in the main text and supplemental information.
- Address the discrepancy in expression levels of MinD and MinE between strain FW1541 and its parental strain W3110. Given the labeling effect, it is possible that MinD expression levels differ. However, MinC's expression level should be approximately the same. Conduct whole-genome sequencing of both strains to identify any additional mutations.
ANS: Thank you for the comments. As described in the main text (Lines 67-70), the most important aspect is the concentration ratio between MinD and MinE. Although the numbers are not the same, they are comparable to those in previous studies (Hale et al, 2001; Li et al, 2014; Schmidt et al, 2016; Shih et al, 2002) (Main text, lines 113-115). Furthermore, we performed whole-genome sequencing of the W3110 and FW1541 strains. We confirmed that sfGFP was correctly inserted. The sequence alignment of the minCDE locus is provided for your reference but not for publication. Although there are some sporatic point mutations, there is no obvious reason to believe that the mutations would impact Min protein expression. We will organize the deposition data as soon as I can.
- Clarify the apparent discrepancy between lines 112 and 127. Line 112 suggests that the periodic regularity of interpolar oscillations increases with cell length, as demonstrated in Fig 1B-C, 1E, Fig S5. However, in the subsequent section (starting from line 127), the authors state that oscillation periods remain relatively stable across cells of different lengths. Provide clarification on this apparent discrepancy.
ANS: Thank you for pointing out this confusion caused by misuse of the term. In Lines 122-123, the statement has been modified as follows: '...the uniformity of the oscillation intervals appears to increase with length...' In line 139, 'The oscillation period' refers to the time required for the oscillation cycle. Since the correction in line 123 should suffice to clarify, we did not modify the statement in line 139.
- Specify if the analysis was limited to non-constricted cells. If so, state this explicitly in the text, as it could impact the interpretation of results, especially in relation to the linear dependence of cell length on time before constriction, as shown in Fig S3C.
ANS: We did not specifically remove those constricted cells, but cells before splitting were considered one cell. We have added a statement to clarify in Lines 144-145.
- Improve clarity in Fig 2A by using distinct colors (e.g., green and red) for differentiation on the Y-axis.
ANS: The Y axes of Fig. 2A have been modified.
- Correct "of" to "from" in line 223 for improved clarity and accuracy.
ANS: Corrected.
- Include the missing "A" in Fig S6A for completeness and accuracy.
ANS: This figure has been updated.
- Ensure consistency in referencing style (full names versus short names) throughout the manuscript.
ANS: This has now been done.
Reviewer #4 (Significance (Required)):
While numerous commendable in vitro studies have explored the oscillatory behavior of the Min system, this work uniquely delves into the oscillation of MinD within live cells. It unveils the remarkable coordination between intracellular Min protein concentration gradients and cell growth, shedding light on the precise spatiotemporal regulation of cell division.
ANS: We thank the reviewer for this positive comment.
References Di Ventura B, Sourjik V (2011) Self-organized partitioning of dynamically localized proteins in bacterial cell division. Molecular systems biology 7: 457 Fischer-Friedrich E, Meacci G, Lutkenhaus J, Chate H, Kruse K (2010) Intra- and intercellular fluctuations in Min-protein dynamics decrease with cell length. Proceedings of the National Academy of Sciences of the United States of America 107: 6134-6139 Hale CA, Meinhardt H, de Boer PA (2001) Dynamic localization cycle of the cell division regulator MinE in Escherichia coli. The EMBO journal 20: 1563-1572 Juarez JR, Margolin W (2010) Changes in the Min oscillation pattern before and after cell birth. Journal of bacteriology 192: 4134-4142 Li GW, Burkhardt D, Gross C, Weissman JS (2014) Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157: 624-635 Mannik J, Walker BE, Mannik J (2018) Cell cycle-dependent regulation of FtsZ in Escherichia coli in slow growth conditions. Molecular microbiology 110: 1030-1044 Meacci G, Kruse K (2005) Min-oscillations in Escherichia coli induced by interactions of membrane-bound proteins. Phys Biol 2: 89-97 Meacci G, Ries J, Fischer-Friedrich E, Kahya N, Schwille P, Kruse K (2006) Mobility of Min-proteins in Escherichia coli measured by fluorescence correlation spectroscopy. Phys Biol 3: 255-263 Schavemaker PE, Boersma AJ, Poolman B (2018) How Important Is Protein Diffusion in Prokaryotes? Front Mol Biosci 5: 93 Schmidt A, Kochanowski K, Vedelaar S, Ahrne E, Volkmer B, Callipo L, Knoops K, Bauer M, Aebersold R, Heinemann M (2016) The quantitative and condition-dependent Escherichia coli proteome. Nature biotechnology 34: 104-110 Shih YL, Fu X, King GF, Le T, Rothfield L (2002) Division site placement in E. coli: mutations that prevent formation of the MinE ring lead to loss of the normal midcell arrest of growth of polar MinD membrane domains. The EMBO journal 21: 3347-3357 Tostevin F, Howard M (2006) A stochastic model of Min oscillations in Escherichia coli and Min protein segregation during cell division. Phys Biol 3: 1-12 Touhami A, Jericho M, Rutenberg AD (2006) Temperature dependence of MinD oscillation in Escherichia coli: running hot and fast. Journal of bacteriology 188: 7661-7667 Vischer NO, Verheul J, Postma M, van den Berg van Saparoea B, Galli E, Natale P, Gerdes K, Luirink J, Vollmer W, Vicente M, den Blaauwen T (2015) Cell age dependent concentration of Escherichia coli divisome proteins analyzed with ImageJ and ObjectJ. Front Microbiol 6: 586 Wu F, van Schie BG, Keymer JE, Dekker C (2015) Symmetry and scale orient Min protein patterns in shaped bacterial sculptures. Nature nanotechnology 10: 719-726
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Referee #4
Evidence, reproducibility and clarity
The study by Parada et al. illuminates the intricate interplay between Min proteins, exemplified by MinD, and cell growth in E. coli. Their findings demonstrate that the MinD concentration gradient steepens progressively as cells elongate, potentially influencing FtsZ ring formation via MinC. Moreover, their comprehensive reaction-diffusion model not only corroborates experimental observations of length-dependent concentration gradients but also underscores the critical role of kinetic interactions involving Min proteins, the membrane, and ATP. This elucidation significantly advances our understanding of the oscillatory mechanisms within the Min system. Both the experimental and simulation data are robust, and the manuscript is exceptionally well-written. I express my full support for publication pending the satisfactory resolution of the outlined concerns.
- Remove the dot in front of "Min" in line 57.
- In lines 82-84, the statement "...The distribution of the division inhibitor MinC may be synchronized with spatiotemporal differences in MinD concentrations, leading to a stable placement of the FtsZ ring at the midcell..." suggests a potential synchronization between MinC and MinD oscillations. It is crucial to investigate if sfGFP-MinC exhibits similar concentration gradient oscillatory behavior in vivo as observed with MinD.
- Ensure consistent significant digits throughout the text. For instance, 1.95{plus minus}0.16 μM in line 97, 1.4{plus minus}0.13 μM in line 98, and 1.9 {plus minus} 0.2 μM in line 100 have varying precision. Consider using integers for molecules.
- Address the discrepancy in expression levels of MinD and MinE between strain FW1541 and its parental strain W3110. Given the labeling effect, it is possible that MinD expression levels differ. However, MinC's expression level should be approximately the same. Conduct whole-genome sequencing of both strains to identify any additional mutations.
- Clarify the apparent discrepancy between lines 112 and 127. Line 112 suggests that the periodic regularity of interpolar oscillations increases with cell length, as demonstrated in Fig 1B-C, 1E, Fig S5. However, in the subsequent section (starting from line 127), the authors state that oscillation periods remain relatively stable across cells of different lengths. Provide clarification on this apparent discrepancy.
- Specify if the analysis was limited to non-constricted cells. If so, state this explicitly in the text, as it could impact the interpretation of results, especially in relation to the linear dependence of cell length on time before constriction, as shown in Fig S3C.
- Improve clarity in Fig 2A by using distinct colors (e.g., green and red) for differentiation on the Y-axis.
- Correct "of" to "from" in line 223 for improved clarity and accuracy.
- Include the missing "A" in Fig S6A for completeness and accuracy.
- Ensure consistency in referencing style (full names versus short names) throughout the manuscript.
Significance
While numerous commendable in vitro studies have explored the oscillatory behavior of the Min system, this work uniquely delves into the oscillation of MinD within live cells. It unveils the remarkable coordination between intracellular Min protein concentration gradients and cell growth, shedding light on the precise spatiotemporal regulation of cell division.
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Referee #3
Evidence, reproducibility and clarity
The manuscript shows that the concentration of MinD does not change during the division cycle of E. coli. Due to the oscillation pattern the concentration of MinD decreases at the mid-cell which makes it favorable for the division. The mid-cell decrease in concentration of MinD is majorly length dependent. The oscillation pattern is not due to the change in concentration of MinD, but due to the plasticity arises from the spatial differences in molecular interactions between MinD and MinE. The manuscript is well written, the experiments are performed carefully and the results will be of interest to readers from variety of field. However, there are several concerns need explanation.
Major concerns:
One of my major concern is these interactions are not shown experimentally but explained using either the previously published literature or mathematical models. Further, the previous literatures are shown on in vitro models which does not mimic the in vivo system fully.
The concentration of MinD does not change with the increasing length of the cell. Is the MinD concentration (or copy numbers) is different in the case of cells growing in low glucose and when compared to the cells growing at high glucose? As per the current study a particular I-ratio at the mid-cell is required to initiate the cell division. In the case of cells growing at low glucose, how this required I-ratio is achieved at the mid-cell? There is decrease in the MinD oscillation time observed in low glucose condition. As explained by the authors the MinD oscillation is mainly guided by the FtsE induced removal of MinD from the membrane, how the authors can explain this decrease? Further, it is explained that the concentration of cellular ATP is in much higher concentration compared to the required amount for this oscillation. As the Iratio is majorly dependent on the cell length, what could be the reason for the differential N in the case of low and high glucose condition? MinD is a highly insoluble protein. It also has an amphipathic helix and thus most of the time it binds to the membrane. The method used by the author to determine the cellular MinD concentration (mentioned in Fig S1) will only give the concentration of the soluble MinD and not of the total MinD. How the authors justify this as the total concentration. This is also the same in the case of MinE copy number calculation. Authors may need to perform the transcriptome analysis and compare both the data.
One of the main question asked by the authors in the abstract is. "How the intracellular Min protein concentration gradients are coordinated with cell growth to achieve spatiotemporal accuracy of cell division is unknown". Although the authors have shown that there is a change in concentration gradient during cell growth, the mechanism for the same is not very well explained. Authors have not provided any specific explanation for the increase in the velocity of the MinD oscillation and the gradient formation. How the velocity of MinD is increasing although there is no increase in the MinD concentration. Figure 2B: shows the overall concentration of MinD in a single cell varies between 1180 - 1160 molecules/um2. In Fig 2C it is mentioned that mid-cell has a MinD concentration of 120-20 molecuels/ um2. Further, Fig3C and 3F shows I-ratio values varies between 0.6-0.4. Considering the values given the I-ratio (I min/ I max) should be between 0.1- 0.01. Authors need to explain the same. Figure 2C: The data in both the Y-axes are not matching and needs more clarification in the legend. Whether the number of molecules were counted only in the marked 200 nm area? If so, why the Y-axis 1 (molecules/um2) is decreasing 7 times, whereas, Y-axis 2 (molecules) is only by 2 times.
Other comments:
Line 84: Requires reference for this statement.
Line 96: Can authors provide other evidence or validation for the determination of the copy numbers such as transcriptome analysis.
Fig 1C: what is the units of time in Fig 1C? Is it equal for all the cell lengths?
Page 6, line 136-138: what could be the possible mechanism for change in velocity at different cell cycle time?
Page 7, line 155: Any evidence for claiming the same?
Page 7, line 156: Is there any proof authors can show that burst MinD synthesis occurs during the division? If not in the case of MinD, is it shown in any other protein?
Page 9, line 217: The Fig 4A is not explained clearly and all the terms mentioned needs to be explained. This figure is used to explain the differential concentration of MinD at the poles and the mid-cell, thus needs to be explain more clearly.
Page 12, line 285: What is meaning of default speed of MinD oscillation in new-born cells? Do the authors observed any specific velocity in the new-born cells? What is the explanation for length dependent oscillation velocity for MinD?
Significance
General assessment: Major work of the manuscript is relying on the mathematical models, whereas the audience are majorly from the biology fields and thus simplified explanations are required in many places. Many of the legends in the figures require more explanation for better understanding. If possible more experimental data can be added, specifically to explain the model mentioned in figure 4A.
Advance: The study is adding to the existing knowledge and will be helpful to fill the conceptual gaps in understanding the mid-cell MinD concentration and what may favor the initiation of bacterial division.
Audience: Majorly the microbiology community will be interested in the study. This will also be interest to Physicists and mathematical persons working to understand bacterial division.
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Referee #2
Evidence, reproducibility and clarity
Summary:
This work by Parada et al showed that in the oscillatory Min System, MinD gradient was steeper in longer e.coli cells, while period was stable. This behavior was recapitulated in a mathematical model and it also revealed coordinated reaction rates in a wide range of parameter space.
Major comments:
- There were some inconsistencies between experimental and modeling data. Wave slope (𝜆𝑁) plateaued at ~3um in the model but not shown in the experiment (Fig.3B). The period was much less in the model (Fig. S8) than in the experiment (Fig. 1B).
- Generally, I found that the data of starved condition added little to the major message. Unless the model can recapitulate the even steeper gradient in such condition by tuning starvation-related parameters, it may be removed.
- The authors need to compare what was different/novel between the model in this study and previous models such as Wu, et al 2015 and highlight the uniqueness of this work.
- The model explored parameter space of reaction rates and found 60 sets. The KdE, KD, KdD, KADP-ATP ranged 6 orders of magnitude. It is interesting data in itself, but cells were not likely to vary that much for reaction rates. The relevance should be discussed.
Minor comments:
- Fig.1B colors were conflicting. The legend was different than diagram. Fig.1C no scale for x axis.
- Fig.S6A How the 638 oscillatory parameter sets were matched with experimental data and screened to 174 sets was not clear. Data of fitting error<0.12 and slope<2 were filtered. Authors should explain the criterion for data filtering.
- Significant digits were not used properly. For example, the period (table 1) was showed as 46.00 sec, but the imaging interval was 12 sec, the 2 decimal digits were thus meaningless. The same argument goes for length measurement at 2.84 um, while the optical resolution of the microscope used should be no good than 200nm.
- For scatter plot like Fig.1D-G, generally smaller dots would show trend more obvious.
- The molecular mechanism of why MinD gradient increases with length was not the scope of the current study, but better to be discussed.
- Fig.S8, why sudden jump in period in many of the sets of both groups?
Significance
Min system was well-studied oscillation mechanism to restrict FtsZ at cell center. Previous work has shown how the system work molecularly, simulated the behavior and reconstituted many different patterns in vitro. The major new information from this work was: 1. the rigorously measured endogenous level of MinD and MinE; 2. gradient increased with length; 3. a model recapitulated this relationship and explored parameter space of reaction rates.
The paper was well presented, experiments and analysis were rigorous, and the conclusions were not overstated. It should interest specialized cell biologists studying cell size, oscillation pattern.
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Referee #1
Evidence, reproducibility and clarity
Summary:
Parada et al. studied both experimentally and theoretically the MinD concentration distribution of Min waves during cell growth. The main finding was that (i) the gradient of MinD is steeper for longer cells and accordingly the MinD concentration at the middle of cell is lower, (ii) period of the oscillation is independent to the cell length, and (iii) those features are shared even under glucose starvation except the MinD gradient is steeper. (iv) Those results are supplemented by the analyses of the reaction-diffusion equations in which parameters that can reproduce the MinD concentration distribution are identified.
I think the results are interesting; basically, as the cell grows, the contrast of the wave becomes clearer, such the MinD concentration at the cell centre decreases. The results may clarify the mechanism of FtsZ accumulation at the cell centre more quantitatively. The experiments were performed by measuring the fluorescent intensity of MinD during cell growth and analysing the intensity distribution along the long axis of the cell. The theoretical results were based on the analyses of the reaction-diffusion model. Both approaches are already well established and the results sound. Nevertheless, I do not think the novelty of this work is not well highlighted in the current manuscript; I think most of the results, except (iii) and (iv), have already been shown explicitly or implicitly in the previous studies. Min oscillations in a growing cell have been analysed both theoretically and experimentally in (Meacci 2005) and [1]. The concentration distribution and period of the oscillation were measured. The complete results were presented in [2], and I am not aware of those results in scientific journals (the thesis is available online). Nevertheless, I think it is fair to cite those studies and compare the current results with them. In fact, in [2], it was shown that the concentration of MinD near the cell centre decreases as the cell grows, the total MinD concentration is approximately constant during the growth (therefore, the number of the molecules increases), and that the variance of the period becomes smaller as the cell grows. I do not think those previous studies spoil this work, and this work deserves publication somewhere. Still, the authors should highlight the novelty of this study more clearly.
Major comments:
(i) In (Meacci 2005) and [1,2], it was claimed that the standard deviation of the period is comparable with the mean period, particularly for the shorter cell. Therefore, they did not claim the period is independent to the cell length. As far as I understood, the variance arises from the variance of the total protein concentration in the assemble of cells. I am wondering how the authors are able to conclude the constant period in different cell length. I also point out that in the theoretical part of (Meacci 2005), the period is, in fact, increasing as the cell grows and suddenly decreases at the length in which cell division occurs.
(ii) I do not think the explanations of the reaction-diffusion model were well described. The authors mentioned that they studied a one-dimensional model and used the delta function to describe the membrane reaction. Did the authors study 1D cytosol and 0D membrane? Then, why the surface diffusion term exists in (4) and (5)? I believe the authors simply assumed that both the membrane and the cytosol are 1D (with larger diffusion constants for cytosolic Min concentrations). Then, the delta functions in (1)-(5) are not necessary. In (Wu 2015), the delta function was used in order to treat a 2D membrane embedded in 3D space.
Besides that, there is no description of the initial conditions for the concentration fields to solve the reaction-diffusion equations. I think the description of the no-flux boundary condition is better put in the Methods rather than supplementary materials.
(iii) As in the previous comment, the current model did not take into account the geometry of the system; namely, cytosol is in 3D and membrane is on 2D. Recent theoretical studies can handle the effect, and also the effect of confinement. I would appreciate it if the authors would make a comment on whether those issues are relevant or not for the conclusion of this work.
(iv) I would appreciate it if the authors would describe the screening process more clearly. I did understand the first screening is a finite imaginary part and a positive real part at the first mode of spatial inhomogeneity in the eigenvalues. However, I did not understand the other processes clearly. The second screening is based on \lambda_N and I_Ratio, but its criteria is not clear. I think both quantities fluctuated in experimental results and I am not sure what to define numerical results match them.
The third process is based on a fitting error using the fitting function of linear increase plus a constant. I am not sure why we need to exclude, for example, the bottom right example in Fig.S6 because it shows no oscillation until the cell length of 3um but then the gradient linearly increases. Please clarify how to justify the criteria. The same argument applies to the fourth screening process. It is not clear why the slope should be smaller than 2.
(v) The authors claimed that the steeper gradient of MinD under glucose starvation results in cell division for shorter cells. I do not think the claim is convincing. It is necessary to measure the correlation between the length at the cell division and the gradient. It would also be nicer to show the correlation under other parameters. I think those studies truly support the authors' claim and the novelty of this work.
(vi) The conclusion at Line 346 "This plasticity arises from spatial differences in molecular interactions between MinD and MinE, as demonstrated..." looks unclear to me. My understanding is that (i) by screening the randomly sampled parameters in the reaction-diffusion model, the authors found the parameters that "match" experimental results, and (ii) the parameters after screening show the correlation between them (k_dD-k_dE and k_D-k_ATP->ADP). The logic heavily relies on the reaction-diffusion model is quantitatively correct. First, I think it is better to explain the logic more explicitly, that is, the claim of the molecular interaction is not based on the experimental facts. Second, I personally think the reaction-diffusion model used in this work does not reproduce quantitatively the experimental results, as discussed in (iii) and also (iv). Please make some discussions on how to justify the comparison between the model and experiments.
(vii) I did not capture the point why the authors can claim "... further distinguishing in vivo and in vitro observations. " at Line 350. I did not find the results comparing with vitro studies. I would appreciate a demonstration of vitro results and/or references.
Minor comments:
- Line 214: It should be "Fange and Elf".
- I think it is better to show sampled points in Fig.4C and 4D to show how dense the authors sampled in the parameter space.
REFERENCES:
[1] Fischer-Friedrich, Elisabeth / Meacci, Giovanni / Lutkenhaus, Joe / Chaté, Hugues / Kruse, Karsten, "Intra- and intercellular fluctuations in Min-protein dynamics decrease with cell length", Proceedings of the National Academy of Sciences, 107, 6134-6139 (2010).
[2] Meacci, Giovanni, "Physical Aspects of Min Oscillations in Escherichia Coli", PhD thesis (2006) available at https://www.pks.mpg.de/fileadmin/user_upload/MPIPKS/group_pages/BiologicalPhysics/dissertations/GiovanniMeacci2006.pdf
Significance
General assessment:
I think the strength of this study is that it potentially shows the quantitative correlation between the MinD concentration gradient during the oscillation and the cell length when it divides. However, the current data of glucose starvation is not convincing enough. The model parts are interesting but their connection to the experiments is not clear in the current manuscript.
Advance:
The advance of this study is to measure the MinD concentration gradient under glucose starvation, and to compare the experimental results with the (simplified) model under a wide range of parameters. I do not think the advance in the current manuscript looks conceptual level because the conceptual conclusions are not really convincing from the results. In this respect, the advance of this work may be technical.
Audience:
As a theoretician working on biophysics, including the model of the Min system, I think a specialised audience would be interested in this study. People who are studying the mechanism of the Min oscillation and resulting cell division, particularly those who are interested in both experiments and models, would be interested in this work. For the broad audience, I do not think the novelty of this study is well described.
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Reply to the reviewers
Manuscript number: RC-2024-02393
Corresponding author(s): Katja Petzold
1. General Statements [optional]
We thank the reviewers for recognising the impact of our manuscript. The reviewers noted the novelty of the miRNA bulge structure, the importance of the three observed binding modes and their potential for use in future structure-based drug design, and the possible importance of the duplex release phenomenon. We are also thankful for the relevant and constructive feedback provided.
Our responses to the comments are written point by point in blue, and any changes in the manuscript are shown in red.
2. Description of the planned revisions
In response to Reviewer 1 - major comment 2
Some of the data is over-interpreted. For example, in Figure 3A, it is concluded that supplementary regions are more important for weaker seeds. Only two 8-mer seeds are present among the twelve target sites and thus it might be difficult to generalize.
We found the relationship between seed type and the effect of supplementary pairing in our data intriguing. To further investigate this effect, we tested whether it exists in published microarray data from HCT116 cells transfected with six different miRNAs (Linsley et al., 2007; Argawal et al., 2015). Here we found that the for the two miRNAs (miR-103 and miR-106b) where we see an impact of supplementary pairing, the difference is primarily driven by 7mer-m8 seeds.
Since the effect appears to be specific to the miRNA, we would like to test whether it can be observed for miR-34a in a larger dataset. Therefore, we plan to transfect HEK293T cells with miR-34a and analyse the mRNA response via RNAseq. We will repeat the analysis shown above, using the predicted number of supplementary pairs to categorise the dataset into groups with or without the effect of supplementary pairing. We will then compare the three seed types within these groups.
In response to Reviewer 2 - minor comment 1, "why was the 34-nt 3'Cy3-labeled miR34a complementary probe shifted up in the presence of AGO?".
We plan to investigate the upper band, which we hypothesise is a result of duplex release, using EMSA to ascertain whether the band height agrees with the size of the duplex.
3. Description of the revisions that have already been incorporated in the transferred manuscript
Reviewer #1
Evidence, reproducibility and clarity
Sweetapple et al. Biophysics of microRNA-34a targeting and its influence on down-regulation
In this study, the authors have investigated binding of miR-34a to a panel of natural target sequences using EMSA, luciferase reporter systems and structural probing. The authors compared binding within a binary and a ternary complex that included Ago2 and find that Ago2 affects affinity and strengthens weak binders and weakens strong binders. The affinity is, however, generally determined by binary RNA-RNA interactions also in the ternary complex. Luciferase reporter assays containing 12 different target sites that belong to one of three seed-match types were tested. Generally, affinity is a strong contributor to repression efficiency. Duplex release, a phenomenon observed for specific miRNA-target complementarities, seems to be more pronounced when high affinity within the binary complex is observed. Furthermore, the authors use RABS for structural probing either in a construct in CIS or binding by the individual miRNA in TRANS or in a complex with Ago2. They find pronounced asymmetric target binding and Ago2 does not generally change the binding pattern. The authors observe one specific structural group that was unexpected, which was mRNA binding with bulged miRNAs, which was expected sterically problematic based on the known structures. MD simulations, however, revealed that such structures could indeed form.
This is an interesting manuscript that contributes to our mechanistic understanding of the miRNA-target pairing rules. The combination of affinity measurements, structural probing and luciferase reporters allow for a broad correlation of target binding and repression strength, which is a well-thought and highly conclusive approach. However, there are a number of shortcomings that are summarized below.
The manuscript is not easy to read and to follow for several reasons. First, many of the sub-Figures are not referenced in the text of the results section (1C, 1D, 2C, 4D), which is somewhat annoying. Figure 4A seems to be mis-labeled. Second, a lot of data is presented in suppl. Figures. It should be considered to move more data into the main text in order to make it easier for readers to evaluate and follow.
Thank you for bringing this to our attention. We have now revised the figure references accordingly.
We have relocated gel images of BCL2, WNT1, MTA2 and the control samples from Figure S3 and S4 to the main results (Figure 2A-B) to improve readability and provide controls and details that aid in clear understanding. Additionally, we have relocated panel C from Figure S6 to Figure 2C to enhance the clarity of our rationale for using polyuridine (pU) in our AGO2 binding assays.
The updated figure is shown below, with changes to the legend marked in red.
Figure 2. Binary and ternary____ complex binding affinities measured by EMSA. (A) Binary (mRNA:miR-34a) binding assays showing examples of BCL2, WNT1 and MTA2. (B) Ternary (mRNA:miR-34a-AGO2) binding assays showing examples of BCL2, WNT1, MTA2, and the three control targets PERFECT, SCRseed, and SCRall. The Cy5 labelled species is indicated with asterisk (*). F indicates the free labelled species (miR34a or mRNA), B indicates binary complex, and T indicates ternary complex. Adjacent titrations points differ two-fold in concentration, with maximum concentrations stated at the top right. Adjacent titration points for MTA2 differed three-fold to assess a wider concentration range. In theternary assay, miRNA duplex release from AGO2 was observed for amongst others BCL2, WNT1, PERFECT, and SCRseed (band indicated with B), while it was not observed for SCRall and MTA2. See Figures S3 and S4 for representative gel images for all targets. See Supplementary files 2 and 3 for all images and replicates. (C) Titrations with increasing miR-34a-AGO2 concentration against Cy5-labelled SCRall (left) or PNUTS (right) comparing the absence and presence of 20 μM polyuridine (pU) during equilibration. pU acted as a blocking agent, reducing nonspecific binding, as seen by the different KD,app values for SCRall and PNUTS after addition of 20 μM pU. Therefore, all final mRNA:miR-34a-AGO2 EMSAs were carried out in the presence of 20 μM pU. Labels are as stated above. (D) Individual binding profiles for each of the 12 mRNA targets assessed by electrophoretic mobility assay (EMSA). Each datapoint represents an individual experiment (n=3). Blue represents results for the binary complex, and green represents results for the ternary complex. Dotted horizontal lines represent the KD,app values, which are also stated in blue and green with standard deviations (units = nM). Note that the x-axis spans from 0.1 to 100,000 in CCND1, MTA2 and NOTCH2, whereas the remaining targets span 0.1 to 10,000.
Some of the data is over-interpreted. For example, in Figure 3A, it is concluded that supplementary regions are more important for weaker seeds. Only two 8-mer seeds are present among the twelve target sites and thus it might be difficult to generalize.
We have revised our wording to recognise that more 8-mer sites would be required to draw a stronger conclusion based on this hypothesis. This hypothesis would be interesting to confirm in a larger dataset but is unfortunately outside of the scope of this paper.
Our hypothesis also aligns with recent data from Kosek et al. (NAR 2023; Figure 2D) where SIRT1 with an 8mer and 7mer-A1 seed was compared. Only the 7mer-A1 was sensitive to mutations in the central region or switching all mismatched to WC pairs.
Page 21 now states:
"This result indicates that the impact of supplementary binding may be greater for targets with weaker seeds, as has been observed earlier in a mutation study of miR-34a binding to SIRT1 (Kosek et al., 2023), although a larger sample size would be needed to confirm this observation."
Furthermore, we found the relationship between seed type and the effect of supplementary pairing in our data intriguing. To further investigate this effect, we tested whether it exists in published microarray data from HCT116 cells transfected with six different miRNAs (Linsley et al., 2007; Argawal et al., 2015). Here we found that the for the two miRNAs (miR-103 and miR-106b) where we see an impact of supplementary pairing, the difference is primarily driven by 7mer-m8 seeds. We therefore plan to test whether the effect can be observed for miR-34a in a larger dataset. We have outlined our preliminary data and planned experiments in Section 2 - description of the planned revisions.
I did not understand why the CIS system shown in 4A is a good test case for miR-34a-target binding. It appears very unnatural and artificial. This needs to be rationalized better. Otherwise it remains questionable, whether these data are meaningful at all.
Thank you for pointing out the need for clearer rationalisation.
The TRANS construct, where the scaffold carries the mRNA targeting sequence, provides reactivity information for the mRNA side only, while the microRNA is bound within RISC, with the backbone protected by AGO2. Therefore, to gain information on the miR-34a side of each complex we used the CIS construct, which provides reactivity information from both the miRNA and mRNA. We used the miRNA and mRNA reactivities to calculate all possible secondary structures for the binary complex, and then compared these structures to the mRNA reactivity in TRANS to find which structure fitted the reactivity patterns observed in the ternary complex.
We have included an additional statement in the manuscript to clarify this point on pages 12-13:
"Two RNA scaffolds were used for each mRNA target; i) a CIS-scaffold: RNA scaffold containing both mRNA target and miRNA sequence separated by a 10 nucleotide non-interacting closing loop, and ii) a TRANS-scaffold: RNA scaffold containing only the mRNA target sequence, to which free miR-34a or the miR-34a-AGO2 complex was bound (Figure 4A). The CIS constructs therefore provided reactivity information on the miRNA side, which is lacking in the TRANS construct, and was used to complement the TRANS data."
It may be worthwhile noting that a non-interacting 10 nucleotide loop was inserted between then miRNA and mRNA of the CIS constructs, allowing the miRNA and mRNA strands to bind and release freely. The reactivity patterns of each mRNA:miRNA duplex were compared between CIS and TRANS, and showed similar base pairing (Figure 4D). Furthermore, we have previously compared the two scaffolds in our RABS methodology paper (Banijamali et al. 2022), where no differences were observed besides reduced end fraying in the CIS construct.
For the TRANS experiments, only one specific scaffold structure is used. This structure might impact binding as well and thus at least one additional and independent scaffold should be selected for a generalized statement.
For each construct, the potential of interaction with the scaffold was tested using the RNAstructure (Reuter & Mathews, 2010)package. Based on the results of this assessment, two different scaffolds were used for our TRANS experiments. The testing and use of scaffolds has now been clarified further on page 13:
"The overall conformation of each scaffold with the inserted RNA was assessed using the RNAstructure (Reuter & Mathews, 2010) package to ensure that the sequence of interest did not interact with the scaffold. If any interaction was observed between the RNA of interest and the scaffold, then the scaffold was modified until no predicted interaction occurred. The different scaffolds and their sequence details are shown in supplementary information (Table S1)."
We have previously examined the scaffold's effect on binding and structure during the development of the RABS method. We tested the same mRNA (SIRT1) in separate, independent scaffolds to verify the consistency of the results. An example of this can be found in the supplementary information (Figure S1a) of Banijamali et al. (2022).
Generally, it would be nice to have some more information about the experiments also in the result section. Recombinant Ago2 is expressed in insect cells and re-loaded with miR-34a, luciferase reporters are transfected into tissue culture cells, I guess.
We have now stated the cell types used for AGO2 expression and luciferase reporter assays in the results.
On page 17 we have included:
"Samples of each of the 12 mRNA targets, as well as miR-34a and AGO2, were synthesised in-house for biophysical and biological characterisation. Target mRNA constructs were produced via solid-phase synthesis while miR-34a was transcribed in vitro and cleaved from a tandem transcript (Feyrer et al., 2020), ensuring a 5' monophosphate group. AGO2 was produced in Sf9 insect cells."
"To measure the affinity of each mRNA target binding to miR-34a, both within the binary complex (mRNA:miR-34a) and theternary complex (mRNA:miR-34a-AGO2), we optimised an RNA:RNA binding EMSA protocol to suit small RNA interactions. The protocol is loosely based on Bak et al. (2014)36, with major differences being use of a sodium phosphate buffering system so as not to disturb weaker interactions (James et al., 1996; Stellwagen et al., 2000), supplemented with Mg2+ as a counterion to reduce electrostatic repulsion between the two negatively charged RNAs (Misra & Draper, 1998), and fluorescently labelled probes."
Page 19:
" We successfully tested various RNA backgrounds, including polyuridine (pU) and total RNA extract (Figure S6B) to block any unspecific binding. Ultimately, we supplemented our binding buffer with pU at a fixed concentration of 20 µM for the ternary assays to achieve the greatest consistency."
Page 20:
"Repression efficacy for the 12 mRNA targets by miR-34a was assessed through a dual luciferase reporter assay6. Target mRNAs were cloned into reporter constructs and transfected into HEK293T cells."
Page 22:
"To infer base pairing patterns and secondary structure for each of the 12 mRNA:miR-34a pairs, we used the RABS technique (Banijamali et al., 2023) with 1M7 as a chemical probe. All individual reactivity traces are shown in Figure S9. Reactivity of each of the 22 miR-34a nucleotides was assessed upon binding to each of the 12 mRNA targets within a CIS construct, containing both miR-34a and the mRNA target site separated by a non-interacting 10-nucleotide loop. The two RNAs can therefore bind and release freely within the CIS construct and reactivity information is collected from both RNA strands."
In the first sentence of the abstract, Argonaute 2 should be replaced by Argonaute only since other members bind to miRNAs as well.
Thank you for recognising this. It has now been corrected.
Significance
This is an interesting manuscript that contributes to our mechanistic understanding of the miRNA-target pairing rules. The combination of affinity measurements, structural probing and luciferase reporters allow for a broad correlation of target binding and repression strength, which is a well-thought and highly conclusive approach. However, there are a number of shortcomings.
We thank the reviewer for recognising the approach and impact of our work. In addition we thank the reviewer for identifying the need for further data to support our conclusions from the luciferase assays, which is something that we plan to address, as described in section 2.
Reviewer #2
Evidence, reproducibility and clarity
Summary: Sweetapple et al. took the approaches of EMSA, SHAPE, and MD simulations to investigate target recognition by miR-34a in the presence and absence of AGO2. Surprisingly, their EMSA showed that guide unloading occurred even with seed-unpaired targets. Although previous studies reported guide unloading, they used perfectly complementary guide and target sets. The authors of this study concluded that the base-pairing pattern of miR-34a with target RNAs, even without AGO2, can be applicable to understanding target recognition by miR-34a-bound AGO2.
Major comments:
(Page 11 and Figure S4) The authors pre-loaded miR-34a into AGO2 and subsequently equilibrated the RISC with a 5' modified Cy5 target mRNA. Since properly loaded miR-34a is never released from AGO2, it is impossible for the miR-34a loaded into AGO2 to form the binary complex (mRNA:miR-34a) in the EMSA (guide unloading has been a long-standing controversy). However, they observed bands of the binary complex in Figure S4. The authors did not use ion-exchange chromatography. AGOs are known to bind RNAs nonspecifically on their positively charged surface. Is it possible that most miR-34a was actually bound to the surface of AGO2 instead of being loaded into the central cleft? This could explain why they observed the bands of the binary complex in EMSA.
Thank you for mentioning this crucial point which has been a focus of our controls. We have addressed this point in four ways:
Salt wash during reverse IMAC purification. Separation of unbound RNA and proteins via SEC. Blocking non-specific interactions using polyuridine. Observing both the presence and absence of duplex release among different targets using the same AGO2 preparation and conditions.
Firstly, although we did not use a specific ion exchange column for purification, we believe the ionic strength used in our IMAC wash step was sufficient to remove non-specific interactions. We used A linear gradient with using buffer A (50 mM Tris-HCl, 300 mM NaCl, 10 mM Imidazole, 1 mM TCEP, 5% glycerol v/v) and buffer B (50 mM Tris-HCl, 500 mM NaCl, 300 mM Imidazole, 1 mM TCEP, 5% glycerol) at pH 8. The protocol followed recommendation by BioRad for their Profinity IMAC resins where it is stated that 300 mM NaCl should be included in buffers to deter nonspecific protein binding due to ionic interactions. The protein itself has a higher affinity for the resin than nucleic acids.
A commonly used protocol for RISC purification follows the method by Flores-Jasso et al. (RNA 2013). Here, the authors use ion exchange chromatography to remove competitor oligonucleotides. After loading, they washed the column with lysis buffer (30 mM HEPES-KOH at pH 7.4, 100 mM potassium acetate, 2 mM magnesium acetate and 2 mM DTT). AGO was eluted with lysis buffer containing 500 mM potassium acetate. Competing oligonucleotides were eluted in the wash.
As ionic strength is independent of ion identity or chemical nature of the ion involved (Jerermy M. Berg, John L. Tymoczko, Gregory J. Garret Jr., Biochemistry 2015), we reasoned that our Tris-HCl/NaCl/ imidazole buffer wash should have at comparable ionic strength to the Flores-Jasso protocol.
Our total ionic contributions were: 500 mM Na+, 550 mM Cl-, 50 mM Tris and 300 mM imidazole. We recognise that Tris and imidazole are both partially ionized according the pH of the buffer (pH 8) and their respective pKa values, but even if only considering the sodium and chloride it should be comparable to the Flores-Jasso protocol.
We have restated the buffer compositions below written the methods section more explicitly to describe this:
"Following dialysis, any precipitate was removed by centrifugation, and the resulting supernatant was loaded onto a IMAC buffer A-equilibrated HisTrap-Ni2+ column to remove TEV protease, other proteins, and non-specifically bound RNA. A linear gradient was employed using IMAC buffers A and B."
Secondly, after reverse HisTrap purification, AGO2 was run through size exclusion chromatography to remove any remaining impurities (shown Figure S2B).
Thirdly, knowing that AGO2 has many positively charged surface patches and can bind nucleic acid nonspecifically (Nakanishi, 2022; O'Geen et al., 2018), we tested various blocking backgrounds to eliminate nonspecific binding effects in our EMSA ternary binding assays. We were able to address this issue by adding either non-homogenous RNA extract or homogenous polyuridine (pU) in our EMSA buffer during equilibration background experiments. This allowed us to eliminate non-specific binding of our target mRNAs, as shown previously in Supplementary Figure S6. We appreciate that the reviewer finds this technical detail important and have moved the panel C of figure S6 into the main results in Figure 2C, to highlight the novel conditions used and important controls needed to be performed. If miR-34a were non-specifically bound to the surface of AGO2 after washing, this blocking step would render any impact of surface-bound miR-34a negligible due to the excess of competing polyuridine (pU).
Our EMSA results show that, using polyU, we can reduce non-specific interaction between AGO2 and RNAs that are present. And still, duplex release occurs despite the blocking step. It is therefore less likely that duplex release is caused by surface-bound miR-34a.
Finally, the observation of distinct duplex release for certain targets, but not for others (e.g. MTA2, which bound tightly to miR-34a-AGO2 but did not exhibit duplex release; see Figure 2), argues against the possibility that the phenomenon was solely due to non-specifically bound RNA releasing from AGO2.
In response to the reviewers statement "Since properly loaded miR-34a is never released from AGO2, it is impossible for the miR-34a loaded into AGO2 to form the binary complex (mRNA:miR-34a)" we would like to refer to the three papers, De et al. (2013) Jo MH et al. (2015), and Park JH et al. (2017), which have previously reported duplex release and collectively provide considerable evidence that miRNA can be unloaded from AGO in order to promote turnover and recycling of AGO. It is known that AGO recycling must occur, therefore there must be some mechanisms to enable release of miRNA from AGO2 to enable this. It is possible that AGO recycling proceeds via miRNA degradation (TDMD) in the cell, but in the absence of enzymes responsible for oligouridylation and degradation, the miRNA duplex may be released. As TDMD-competent mRNA targets have been observed to release the miRNA 3' tail from AGO2 (Sheu-Gruttadauria et al., 2019; Willkomm et al., 2022), there is a possible mechanistic similarity between the two processes, however, we do not have sufficient data to make any statement on this.
(Page 18 and Figure S5) Previous studies (De et al., Jo MH et al., Park JH et al.) reported guide unloading when they incubated a RISC with a fully complementary target. However, neither MTA2, CCND1, CD44, nor NOTCH2 can be perfectly paired with miR-34a (Figure 1A). Therefore, the unloading reported in this study is quite different from the previously reported works and thus cannot be explained by the previously reported logic. The authors need to explain the guide unloading mechanism that they observed. Otherwise, they might misinterpret the results of their EMSA and RABS of the ternary complex.
The three aforementioned studies have reported unloading/duplex release. However, they did not only report fully complementary targets in this process.
De et al. (2013) reported that "highly complementary target RNAs promote release of guide RNAs from human Argonaute2".
Subsequently, Park et al. (2017) reported: "Strikingly, we showed that miRNA destabilization is dramatically enhanced by an interaction with seedless, non-canonical targets."
A figure extracted from Figure 5 of Park et al. is shown below illustrating the occurrence of unloading in the presence of seed mismatches in positions 2 and 3 (mm 2-3). Jo et al. (2015) also reported that binding lifetime was not affected by the number of base pairs in the RNA duplex.
In addition to these three reports, a methodology paper focusing on miRNA duplex release was published recently titled "Detection of MicroRNAs Released from Argonautes" (Min et al., 2020).
Therefore, we do believe that the previously observed microRNA release is similar to our observation. Here we also correlate it to structure and stability of the complex.
(Page 20) The authors reported, "it is notable that the seed region binding does not appear to be necessary for duplex release." The crystal structures of AGO2 visualize that the seed of the guide RNA is recognized, whereas the rest is not, except for the 3' end captured by the PAZ domain. How do the authors explain the discrepancy?
In this manuscript, we intend to present our observations of duplex release. There are many potential relationships between duplex release and AGO2 activity, which we do not have data to speculate upon. Previous studies, such as Park et al. (2017) have also observed non-canonical and seedless targets leading to duplex release, supporting our findings. Additionally, other publications including McGearly et al. (2019) report 3'-only miRNA targets, Lal et al. (2009) have documented seedless binding by miRNA and their downstream biological effects, and Duan et al. (2022) show that a large number of let-7a targets are regulated through 3′ non-seed pairing.
It is also possible that duplex release is not coupled to classical repression outcomes, and does not need to proceed by the seed, but instead regulates AGO2 recycling before AGO2 enters the quality control mode of recognising the formed seed.
(Pages 22) The authors mentioned, "It follows that the structure imparted via direct RNA:RNA interaction remains intact within AGO2, highlighting the role of RNA as the structural determinant." A free guide and a target can start their annealing from any nucleotide position. In contrast, a guide loaded into AGO needs to start annealing with targets through the seed region. Additionally, the Zamore group reported that the loaded guide RNA behaves quite differently from its free state (Wee et al., Cell 2012). How do the authors explain the discrepancy?
The key point we would like to emphasise is that AGO does not seem to alter the underlying RNA:RNA interactions. The bound state in the ternary complex reflects the structure established in the binary complex. We do not aim to claim a specific sequence of events, as this interpretation is not possible from our equilibrium data. Our data indicates that the protein is flexible enough to accommodate the RNA structure that is favoured in the binary complex. This hypothesis is further supported by our MD simulation, which demonstrates the accommodation of a miRNA-bulge structure within AGO2.
Targets lacking seeds have been identified previously (McGeary et al. 2019, Park et al. 2017, Lal et al. 2009) and can bind to miRNA within AGO. Therefore, there must be a mechanism by which these targets can anneal within AGO, such as via sequence-independent interactions (as discussed in question 3).
With respect to Wee et al., (2012), which studied fly and mouse AGO2 and found considerable differences between the thermodynamic and kinetic properties of the two AGO2 species. Furthermore, they found different average affinities between the two species, with the fly AGO binding tighter the mouse. Following this logic, it is not unexpected that human AGO2 would have unique properties compared to those of fly and mouse.
Below is an extract from Wee et al., (2012):
"Our KM data and published Argonaute structures (Wang et al., 2009) suggest that 16-17 base pairs form between the guide and the target RNAs, yet the binding affinity of fly Ago2-RISC (KD = 3.7 {plus minus} 0.9 pM, mean {plus minus} S.D.) and mouse AGO2-RISC (KD = 20 {plus minus} 10 pM, mean {plus minus} S.D.) for a fully complementary target was comparable to that of a 10 bp RNA:RNA helix. Thus, Argonaute functions to weaken the binding of the 21 nt siRNA to its fully complementary target: without the protein, the siRNA, base paired from positions g2 to g17, is predicted to have a KD ∼3.0 × 10−11 pM (ΔG25{degree sign}C = −30.7 kcal mol−1). Argonaute raises the KD of the 16 bp RNA:RNA hybrid by a factor of > 1011."
In the Wee et al. (2012) paper, affinity data on mouse and fly AGO2 was collected via filter binding assays, using a phosphorothioate linkage flanked by 2′-O-methyl ribose at positions 10 and 11 of the target to prevent cleavage. They then compared the experimentally determined mean KD and ΔG values for each species to predicted values of an RNA:RNA helix of 16-17 base-pairs. No comparison was made between individual targets, and no experimental data was collected for the RNA:RNA binding. The calculated energy values were made based on a simple helix without taking into account any possible secondary structure features. Considering the different AGO species, alternative experimental setup, modified nucleotides in the tested RNA, and the computationally predicted RNA values compared to the averaged experimental values, we believe there is considerable reason to observe differences compared to our findings.
We have expanded our discussion on page 27 to the following:
"An earlier examination of mRNA:miRNA binding thermodynamics by Wee and colleagues (2012) found that mouse and fly AGO2 reduce the affinity of a guide RNA for its target61. Our data indicate that the range of miR-34a binary complex affinities is instead constricted by human AGO2 in the ternary complex - strengthening weak binders while weakening strong binders. The 2012 study reported different average affinities between the two AGO2 species, with the fly protein binding tighter the mouse. Following this logic, it is not unexpected that human AGO2 would have unique properties compared to those of fly and mouse."
The authors concluded that the range of binary complex affinities is constricted by human AGO2 in the ternary complex - strengthening weak binders while weakening strong binders. This may hold true for miR-34a, but it cannot be generalized. Other miRNAs need to be tested.
That is true, we have now adjusted the wording to encompass this more clearly, shown below. Testing of further miRNAs is the likely content of future work from us and others.
"Our data indicate that the range of miR-34a binary complex affinities is instead constricted by human AGO2 in the ternary complex - strengthening weak binders while weakening strong binders."
Minor comments:
(Figure S2) Why was the 34-nt 3'Cy3-labeled miR34a complementary probe shifted up in the presence of AGO?
We believe this observation is also indicative of duplex release. At the time that these activity assays were collected, we were not as aware of the presence of duplex release so did not test it further, assuming it may be due to transient interactions. We plan to investigate this via EMSA and have included this in the planned revisions (section 2).
2.(Page 17) Does the Cy3 affect the interaction of the 3' end of miR-34 with AGO2?
miR-34a-3'Cy5 was used for binary experiments only and the reverse experiment was conducted as a control (where Cy5 was located on the mRNA) (Figure S3b), showing no change in affinity/interaction when the probe was switched to the target. For ternary experiments the mRNA target was labelled on the 5' terminus, to make sure there was no interference with loading miR-34a into AGO2.
A Cy3 labelled RNA probe (fully complementary to miR-34a) was used to detect miR-34a in northern blots, but AGO2 interaction is not relevant here under denaturing conditions.
Otherwise, the 34-nt slicing probe had Cy3 on the 5 nt 3' overhang and should therefore not interact with AGO.
- Several groups reported that overproduced AGOs loaded endogenous small RNAs. The authors should mention that their purified AGO2 was not as pure as a RISC with miR-34a. Otherwise, readers might think that the authors used a specific RISC.
We have now improved our explanation of the loading efficiency to make it more clear to the reader that our AGO2 sample was not fully bound by miR-34a, and that all concentrations refer to the miR-34a-loaded portion of AGO2. The following text can be found in the results on page 18:
"The mRNA:miR-34a-AGO2 assay had a limited titration range, reaching a maximum miR-34a-AGO2 concentration of 268 nM due to a 5% loading efficiency (see Figure S2D for loading efficiency quantification). The total AGO2 concentration was thus 20-fold higher than the miR-34a-loaded portion. Further increase in protein concentration was prevented by precipitation. Weaker mRNA targets (CD44, CCND1, and NOTCH2) did not reach a saturated binding plateau within this range, leading to larger errors in their estimated KD,app values. However, reasonable estimation of the KD,app was possible by monitoring the disappearance of the free mRNA probe. Note that we refer to the miR-34a-loaded portion of AGO2 when discussing concentration values for all titration ranges. To ensure AGO2 binding specificity despite low loading efficiency, a scrambled control was used (SCRall; lacking stable base pairing with miR-34a or other human miRNAs according to the miRBase database57). SCRall showed no interaction with miR-34a-AGO2 (Figure 2B)."
(Figure legend of Figure S5) Binding was assessed "by."
Thank you for pointing this out, it is now fixed.
(Page 17) It would be great if the authors could even briefly describe the mechanism by which the sodium phosphate buffer with magnesium does not disturb weaker interactions by citing reference papers.
We have now added a supplementary methods section to our manuscript and included the description below on page 10:
"We found that a more traditional Tris-borate-EDTA (TBE) buffer disrupted weaker RNA:RNA binding interactions (Supplementary Methods Figure M1). Borate anions form stable adducts with carbohydrate hydroxyl groups (James et al., 1996) and can form complexes with nucleic acids, likely through amino groups in nucleic bases or oxygen in phosphate groups (Stellwagen et al., 2000). This makes TBE unsuitable for assessment of RNA binding, particularly involving small RNA molecules, which typically have weaker affinities. We therefore adapted our buffer system to a sodium phosphate buffer supplemented with magnesium. Magnesium acts as a counterion to reduce electrostatic repulsion between the two negatively charged backbones by neutralisation (Misra et al., 1998)."
We have also clarified the buffer adaptions in our results section on page 17:
The protocol is loosely based on Bak et al. (2014)36, with major differences being use of a sodium phosphate buffering system so as not to disturb weaker interactions(James et al., 1996; Stellwagen et al., 2000), supplemented with Mg2+ as a counterion to reduce electrostatic repulsion between the two negatively charged RNAs(Misra & Draper, 1998), and fluorescently labelled probes. Original gel images and quantification are shown in supplementary Figures S3 and S4. All KD,app values are shown in Supplementary Table 1, and represent the mean of three independent replicates.
Figure M1. Comparison of Tris-borate EDTA (TBE) and sodium phosphate with magnesium (NaP-Mg2+) buffer systems for EMSA. Cy5-labelled miR-34a and unlabelled CD44 were equilibrated in the two different buffer systems, using the same titration range. No mobility shifts were observed in the TBE system, while clear binding shifts were observed in the NaP-Mg2+ system.
6.(Page 22) The authors cited Figure 4C in the sentence, "Comparison between CIS and TRANS ..." Is this supposed to be Figure 4D?
The reviewer was correct in their assumption, and this has now been corrected.
7.(Figure 6) Readers would appreciate it if the guide and target were colored in red and blue. The color codes have been used in most papers reporting AGO structures. The current color codes are opposite.
We have now adjusted the colour schemes throughout the manuscript, and Figure 6 has been modified to the following:
__"Figure 6. The miRNA-bulge structure is readily accommodated by AGO2 as shown by molecular dynamics simulation. __Panel (A) displays a snapshot of the all-atom MD simulation of miR-34a (red) and NOTCH1 (blue) in AGO2. The NOTCH1:miR-34a duplex is shown with AGO2 removed for clarity and is rotated 90{degree sign} to show the miRNA bulge and bend in the duplex. This NOTCH1:miR-34a-AGO2 structure is compared with (B), which shows the crystal structure of miR-122 (orange) paired with its target (purple) via the seed and four nucleotides in the supplementary region (PDB-ID 6N4O17), and (C), which shows the crystal structure of miR-122 (orange) and its target (green) with extended 3' pairing, necessary for the TDMD-competent state (PDB-ID 6NIT19). AGO2 is depicted in grey, with the PAZ domain in green, and the N-terminal domain marked with N. The miRNA duplexes in (B) and (C) feature symmetrical 4-nucleotide internal loops, whereas the NOTCH1 structure in (A) has an asymmetrical miRNA bulge with five unpaired nucleotides on the miRNA side and a 3-nucleotide asymmetry."
Significance
This paper will have a significant impact on the field if seed-unpaired targets can indeed unload guide RNAs. The authors may want to validate their results very carefully.
We thank the reviewer for recognising the significance of duplex release (or guide unloading) from AGO2. We agree that the observations should be tested rigorously and have outlined the actions we took to ensure validity in our AGO2 preparation.
__Reviewer #3 __
Evidence, reproducibility and clarity (Required):
In this manuscript, the authors use a combination of biochemical, biophysical, and computational approaches to investigate the structure-function relationship of miRNA binding sites. Interestingly, they find that AGO2 weakens tight RNA:RNA binding interactions, and strengthens weaker interactions.
Given this antagonistic role, I wonder: shouldn't there be an 'average' final binding affinity? Furthermore, if I understand correctly, not many trends were observed to correlate binding affinity with repression, etc.
Overall, there was no 'average' final binding affinity observed, as the binary assays had a much higher maximum (NOTCH2binary affinity was within the micromolar range) skewing the mean average of the binary affinities to 657 nM, versus 111 nM for the ternary affinities. We also compare the variances of the binary and ternary affinity datasets using the F-test and found that F > F(critical one tail) and thus the variation of the two populations is unequal (binary variation is significantly larger than ternary).
F-Test Two-Sample for Variances
- *
binary affinity
ternary affinity
Mean
657.3
110.971667
Variance
2971596.1
24406.4012
Observations
12
12
df
11
11
F
121.754784
P(F
7.559E-10
F(critical one-tail)
2.81793047
We agree that the overall correlation between affinity and repression was not strong, although we found a stronger correlation within the miRNA-bulge group (Figure 5C and S7C). A larger sample size of miRNA bulge-forming duplexes would be needed to test the generalizability of this observation.
Given the context of the study - whereby structure is being investigated as a contributing factor to the interaction between the miRNA and mRNA, I find it interesting that the authors chose to use MC-fold to predict the structures of the mRNA, rather than using an experimental approach to assess / validate the structures. Thirty-seven RNAs were assessed; I think even for a subset (the 12 that were focused on in the study), the secondary structure should be validated experimentally (e.g., by chemical probing experiments, which the research group has demonstrated expertise in over the last several years). The validation should follow the in silico folding approach used to narrow down the region of interest. It is necessary to know whether an energy barrier (associated with the mRNA unfolding) has to occur prior to miRNA binding; this could help explain some of the unexplained results in the study. Indeed, the authors mention that there are many variables that influence miRNA regulation.
Indeed, experimentally validated structures offer valuable insights that cannot be obtained solely through sequence-based predictions. This is why we opted to employ our RABS method to experimentally evaluate the binary and ternary complex binding of our 12 selected targets (as depicted in Figures 4 and S9 and discussed in the text on pages 23-24). While we (in silico) assessed all 37 RNA targets that were experimentally confirmed at the time, selecting 12 to represent both biological and predicted structural diversity, it would have been impractical to experimentally pre-assess all the targets not included in the final selection. Our in-silico assessment was designed to narrow down the regions of interest and evaluate predicted secondary structures present. The pipeline is shown in Figure 1. Details of the code used in the in-silico analysis are provided in Supplementary File 1.
Regarding the energy of unfolding of mRNA, our constructs considered the isolated binding sites thus the effects of surrounding mRNA interactions were removed. We compared our affinities to dG as well as MFE and have now included this analysis in Figure S8A. Additionally, we have included the text on page 27-28 of the discussion:
"Gibbs free energy (G), which is often included in targeting prediction models as a measure of stability of the miRNA:mRNA pair12,62, correlated with the log of our binary KD,app values, using ΔG values predicted by RNAcofold (R2 = 0.61). There was a weaker correlation with the free energy values derived from the minimum free energy (MFE) structures predicted by RNAcofold (R2 = 0.41) (Figure S8A). This result highlights the contribution of unfolding (in ΔG) as being an important in predicting KD. The differences between ΔG and KD,app are likely primarily due to inaccurately predicted structures used for energy calculations."
Additionally, we assessed the free form of all mRNA targets via RABS (Figure S9) and observed that the seed of each free mRNA was available for miRNA binding (seeds of the free mRNA were not stably bound).
Finally, when designing our luciferase plasmids we used RNAstructure (Reuter & Mathews, 2010) to check for self-folding effects which could interfere with target site binding and ensured that all plasmids were void of such effects.
In the methods, T7 is italicized by accident in the T7 in vitro transcription section. Bacmid is sometimes written with a capital B and other times with a lower-cased b. The authors should be consistent. The concentration of TEV protease that was added (as opposed to the volume) should be described for reproducibility.
Thank you for pointing out these overlooked points. They have now been corrected.
In figure S2D, what is the second species in the gel on the right-hand side of the gel in the miR-34a:AGO lanes? The authors should mention this.
We believe that the faint upper band corresponds to other longer RNA species loaded into AGO2. As AGO2 is loaded with a diversity of RNA species, it is likely that some of them may have a weak affinity for the miR-34a-complementary probe, and therefore show up on the northern blot.
Figure S3B and S3A are referenced out of order in the text. In regard to S3A, what are the anticipated or hypothesized alternative conformations for NOTCH1, DLL1, and MTA2? There are really interesting things going on in the gels, also for HNF4a and NOTCH2. Can the authors offer some explanation for why the free RNA bands don't seem to disappear, but rather migrate slowly? Is this a new species?
The order of the figure references have now been updated, thank you for alerting us to this.
Figure S3A: For MTA2, the two alternative conformations are shown in Figure S9 and S10 (and shown below here, miR-34aseed marked in pink). It appears that a single conformation is favoured at high concentration (> 1 µM) while the two conformations are present at {less than or equal to} 1 µM. The RABS data for MTA2 also indicated multiple binding conformations, as the reactivity traces were inconsistent. We expect that the conformation shown on the left was most dominant within AGO2, based on the reactivity of the TRANS + AGO assays. However, we cannot exclude a possible G-quadruplex formation due to the high G content of MTA2 (shown below right).
Regarding NOTCH1 and DLL1, a faint fluorescent shadow was observed beneath the miR-34a bound band. The RABS reactivity traces indicated a single dominant conformation for these targets, so it is possible that the lower shadow observed was due to more subtle differences in conformation, such as the opening/closing of one or a few base pairs at the terminus or bulge, (i.e. end fraying). HNF4α and NOTCH2 appear to never fully saturate the miR-34a, so a small un-bound population remains visible on the gel. For NOTCH2 this free miR-34a band appears to migrate upwards, possibly due to overloading the gel lane with excess NOTCH2 (which are not observed in the Cy5 fluorescence image).
In the EMSA for Perfect, why does the band intensity for the bound complex increase then decrease? How many replicates were run for this? This needs to be reconciled.
As for all EMSAs, three replicates were carried out for each mRNA target and all gels are shown in Supplementary Files 2 and 3, for the binary and ternary assays respectively.
Uneven heat distribution across the gel can lead to bleaching of the Cy5 fluorophore. To address this, we we used a circulating cooler in our electrophoresis tank, as outlined in our methods (page 10). However, the aforementioned gel for one of thePERFECT sample replicates appears to have been evenly cooled. As the binding ratio (rather than total band volume) was used for quantification, the binding curve was unaffected, and this did not influence KD,app.
We have now replaced the exemplary gel for PERFECT in Figure S3 with a more representative and evenly labelled gel from our replicates (Cy5 fluorescence image shown below). The binding curve for PERFECT is also shown here:
The authors list that the RNA concentration was held constant at 10 nM; in EMSAs, the RNA concentration should be less than the binding affinity; what is the lowest concentration of protein used in the assays shown in S3A? Is this a serial dilution? It seems to me like the binding assays for MTA2, Perfect, and SRCseed might have too high of an RNA concentration. (Actually, now I see in the supplement the concentrations of proteins, and the RNA concentration is too high). Also, why is the intensity of bands for bound complex for SRCseed more intense than the free RNA?
Why are the binding affinity error bars so large (e.g., for NOTCH2 with mir-34a) - 6 uM +/- 3 uM?
No protein was used in the binary assays shown in Figure S3A. For the ternary assays in Figure S4, the maximum concentration of miR-34a-loaded AGO2 (miR-34a-AGO2) was 268 nM, with a serial dilution down to a minimum of 0.06 nM.
Optimal EMSA conditions require a constant RNA concentration that is lower than the binding affinity to accurately estimate high-affinity interactions.
For our tightest binders, such as SIRT1, we can confidently state that the KD,app is less than 10 nM, estimated at 0.4 {plus minus} 1.1 nM. Therefore, the accuracy of this estimation is reduced, and the standard deviation is larger than the estimated KD,app. As NOTCH2 bound miR-34a very weakly and did not reach a fully bound plateau, the resulting high error was expected. Consequently, we do not have the same level of certainty for extremely tight or weak binders. In this study, the relative affinities were of primary importance.
We have included on page 18:
As the Cy5-miR-34a concentration was fixed to 10 nM to give sufficient signal during detection, KD,app values below 10 nM have a lower confidence.
Regarding the control samples PERFECT and SCRseed, our focus was not on determining the exact KD,app of these artificial constructs. Instead, we were primarily interested in whether they exhibited binding and under which conditions. For SCRseed, we neither adjusted the titration range nor calculated KD,app. For PERFECT, the concentration was adjusted to a lower range of 30 nM - 0.001 nM to give a relative comparison with the other tight binder SIRT1. However, further reduction in RNA concentration was not pursued, as it already fell well below the 10 nM sensitivity threshold.
Regarding the intensity of the bound SCRseed band, we observed that the bound fluorophore often resulted in stronger intensity than for the free probe. This was observed for a number of the samples (PERFECT, BLC2, SCRseed). A previous publication reported that Cy5 is sequence dependent in DNA, that the effect is more sensitive to double-stranded DNA, and that the fluorophore is sensitive to the surrounding 5 base pairs (Kretschy, Sack and Somoza, 2016). It is likely that the same phenonenon exists in RNA.
For MTA2, the two alternative conformations (shown in Figure S9 and S10) make assessment of KD,app more difficult. As the higher affinity conformation did not reach a fully-bound plateau before the weaker affinity conformation appeared, the binding curve plateau (where all miR-34a was bound) reflected the weaker conformation KD,app. We increased the range of titration tested by using a three-fold serial dilution, but further reduction in RNA concentration would not have been fruitful as it already dropped below well below the 10 nM sensitivity range. Therefore the MTA2 binary complex had a higher error at (944 {plus minus} 274 nM) and lower confidence.
We then decided to run a competition assay to detect the weaker KD,app of MTA2. The assay was set up using the known binding affinity of CD44, which was labelled with Cy5 to track the reaction. MTA2 was titrated against a constant concentration of Cy5-CD44:miR-34a, and disruption of the CD44 and miR-34a binding was monitored. We fitted the data to a quadratic for competitive binding (Cheng and Prusoff., 1973) to calculate the KD,app for competitive binding, or KC,app.
We validated our competition assay by comparing it with our direct binding assays, specifically assessing CD44 in a self-competition assay. The CD44 KC,app (168 {plus minus} 24 nM; mean and SD of three replicates) was found to be consistent with the KD,app obtained from the direct assay (165 {plus minus} 21 nM).
As we wanted all affinity data to be directly comparable (using the same methodology), we compared the KD,app values obtained via direct assay in the manuscript. It appears that the competitive EMSA assay for MTA2 reflects the weaker affinity conformation observed in the direct assay.
It would be very helpful if the authors wrote in the Kds in Figure 2A in green and blue (in the extra space in the plots). This would help the reader to better understand what's going on, and for me, as a reviewer, to better consider the analysis/conclusions presented by the authors.
KD,app values are written in in green and blue in what is now Figure 2D (originally Figure 2A).
The authors state on page 18 that 'Interestingly, however, we did not observe a correlation between binary or ternary complex affinity and seed type.' They should elaborate on why this is interesting.
The prevailing view is that the miRNA seed type significantly influences affinity within AGO2. The largest biochemical studies of miRNA-target interactions to date, conducted by McGeary et al. (2019, 2022), used AGO-RBNS (RNA Bind-n-Seq) to reveal relative binding affinities. These studies demonstrated strong correlations between the canonical seed types and binding affinity. Therefore, we find it interesting that no such correlation was observed in our dataset (despite its small size).
We have now added to the manuscript (page 20):
"The largest biochemical studies of miRNA-target interactions to date (McGeary et al., 2019, 2022) used AGO-RBNS (RNA Bind-n-Seq) to extract relative binding affinities, demonstrating strong correlations between the canonical seed types and binding affinity. Therefore, it is intriguing that our dataset, despite its small size, showed no such correlation."
Figure 2C is not referenced in the text (the authors should go back through the text to make sure everything is referenced and in order). The Kds should be listed alongside the gels in Figure 2C.
Figure 2 has now been rearranged and updated, with KD,app values listed in what is now Figure 2D.
Figure 3B is rather confusing to understand.
We have now adapted Figure 3 to simplify readability. Panel B has now been moved to C, and we have introduced panel A (moved from Figure 2B). In Figure 3C (originally 3B) we have added arrows to indicate the direction of affinity change from binary to ternary complex, and moved the duplex release information to panel A. We thank the reviewer and think that the data is now much clearer.
Figure 3. AGO2 moderates affinity by strengthening weak binders and weakening strong binders. (A) Correlation of relative mRNA:miR-34a with mRNA:miR-34aAGO2 binding affinities. No seed type correlation is observed, seeds coloured, where 8mer is pink, 7mer-m8 is turquoise, and 7-mer-A1 is mauve. The slope of the linear fit is 0.48, and intercept on the (log y)-axis is 7.11. The occurrence of miRNA duplex release from AGO2 is marked with diamonds. (B) miR-34a-mediated repression of dual luciferase reporters fused to the 12 mRNA targeting sites. Luciferase activity from HEK293T cells co-transfected with each reporter construct, miR-34a was measured 24 hours following transfection and normalised to the miR-34a-negative transfection control. Each datapoint represents the R/F ratio for an independent experiment (n=3) with standard deviations indicated. SCRseed is a scrambled seed control, SCRall is a fully scrambled control, and PERFECT is the perfect complement of miR-34a. Dotted horizontal lines represent the repression values for the 22-nucleotide seed-only controls6 for the respective seed types, in the absence of any other WC base pairing. (C) Comparison of relative target repression with relative affinity assessed by EMSA. Blue represents mRNA:miR-34a affinity (binary complex), while green represents mRNA:miR-34a-AGO2 affinity (ternary complex). Arrows indicate the direction of change in affinity upon binding within AGO2 compared to the binary complex. It is seen that AGO2 moderates affinity bi-directionally by strengthening weak binders and weakening strong binders.
Page 20: Perfect should be italicized.
Thank you for bringing this to our attention, this how now been adjusted.
Have the authors considered using NMR to assess the base pair pattern formed between the miRNA:mRNA complexes (with / without AGO)? As a validation for results obtained by RABS? This could be helpful for the Asymmetric target binding section, the Ago increases flexibility section, and the three distinct structural groups section in the results. It is widely accepted that while chemical probing is insightful, results should be validated using alternative approaches. Distinguishing structural changes and protected reactivity in the presence of protein is challenging.
NMR provides high-resolution information on RNA base-pairing patterns, allowing us to compare our RABS results for SIRT1with those obtained via NMR (Banijamali et al., 2022) for the binary complex. For SIRT1, the RNA:RNA structures identified were consistent between both methods. However, using NMR to measure RNA:RNA binding within AGO2 is challenging due to the protein's large size. Currently, there are no published complete NMR structures of RNA within AGO2. The largest solution-state NMR structures published that include AGO consist solely of the PAZ domain. Our group has been working on method development using DNP-enhanced solid-state NMR to obtain structural information within the complete AGO2 protein, but the current resolution does not allow us to fully reconstruct a complete NMR structure. We hope that in the coming years, this will be a method to evaluate RNA within AGO. This limitation highlights the advantage of RABS in providing RNA base-pairing information within the ternary complex in solution.
Reviewer #3 (Significance (Required)):
The work is helpful for understanding how microRNAs recognize and bind their mRNA targets, and the impact Ago has on this interaction. I think for therapeutic studies, this will be helpful for structure-based design. Especially given the three types of structures identified to be a part of the interaction.
We thank the reviewer for their detailed remarks, especially concerning the importance of technical details the binding assays. We further thank the reviewer for recognising the potential impact of our work for rational design.
4. Description of analyses that authors prefer not to carry out
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In response to Reviewer 2 - major comment 1, we prefer to not run an additional ion exchange purification on the AGO2 protein due to the reasoning discussed above, which is repeated here:
We have addressed this point in three ways:
Thank you for mentioning this crucial point which has been a focus of our controls. We have addressed this point in four ways:
Salt wash during reverse IMAC purification. Separation of unbound RNA and proteins via SEC. Blocking non-specific interactions using polyuridine. Observing both the presence and absence of duplex release among different targets using the same AGO2 preparation and conditions.
Firstly, although we did not use a specific ion exchange column for purification, we believe the ionic strength used in our IMAC wash step was sufficient to remove non-specific interactions. We used A linear gradient with using buffer A (50 mM Tris-HCl, 300 mM NaCl, 10 mM Imidazole, 1 mM TCEP, 5% glycerol v/v) and buffer B (50 mM Tris-HCl, 500 mM NaCl, 300 mM Imidazole, 1 mM TCEP, 5% glycerol) at pH 8. The protocol followed recommendation by BioRad for their Profinity IMAC resins where it is stated that 300 mM NaCl should be included in buffers to deter nonspecific protein binding due to ionic interactions. The protein itself has a higher affinity for the resin than nucleic acids.
A commonly used protocol for RISC purification follows the method by Flores-Jasso et al. (RNA 2013). Here, the authors use ion exchange chromatography to remove competitor oligonucleotides. After loading, they washed the column with lysis buffer (30 mM HEPES-KOH at pH 7.4, 100 mM potassium acetate, 2 mM magnesium acetate and 2 mM DTT). AGO was eluted with lysis buffer containing 500 mM potassium acetate. Competing oligonucleotides were eluted in the wash.
As ionic strength is independent of ion identity or chemical nature of the ion involved (Jerermy M. Berg, John L. Tymoczko, Gregory J. Garret Jr., Biochemistry 2015), we reasoned that our Tris-HCl/NaCl/ imidazole buffer wash should have at comparable ionic strength to the Flores-Jasso protocol.
Our total ionic contributions were: 500 mM Na+, 550 mM Cl-, 50 mM Tris and 300 mM imidazole. We recognise that Tris and imidazole are both partially ionized according the pH of the buffer (pH 8) and their respective pKa values, but even if only considering the sodium and chloride it should be comparable to the Flores-Jasso protocol.
Secondly, after reverse HisTrap purification, AGO2 was run through size exclusion chromatography to remove any remaining impurities (shown Figure S2B).
Thirdly, knowing that AGO2 has many positively charged surface patches and can bind nucleic acid nonspecifically (Nakanishi, 2022; O'Geen et al., 2018), we tested various blocking backgrounds to eliminate nonspecific binding effects in our EMSA ternary binding assays. We were able to address this issue by adding either non-homogenous RNA extract or homogenous polyuridine (pU) in our EMSA buffer during equilibration background experiments. This allowed us to eliminate non-specific binding of our target mRNAs, as shown previously in Supplementary Figure S6. We appreciate that the reviewer finds this technical detail important and have moved the panel C of figure S6 into the main results in Figure 2C, to highlight the novel conditions used and important controls needed to be performed. If miR-34a were non-specifically bound to the surface of AGO2 after washing, this blocking step would render any impact of surface-bound miR-34a negligible due to the excess of competing polyuridine (pU).
Our EMSA results show that, using polyU, we can reduce non-specific interaction between AGO2 and RNAs that are present. And still, duplex release occurs despite the blocking step. It is therefore less likely that duplex release is caused by surface-bound miR-34a.
Finally, the observation of distinct duplex release for certain targets, but not for others (e.g. MTA2, which bound tightly to miR-34a-AGO2 but did not exhibit duplex release; see Figure 2), argues against the possibility that the phenomenon was solely due to non-specifically bound RNA releasing from AGO2.
In response to the reviewers statement "Since properly loaded miR-34a is never released from AGO2, it is impossible for the miR-34a loaded into AGO2 to form the binary complex (mRNA:miR-34a)" we would like to refer to the three papers, De et al. (2013) Jo MH et al. (2015), and Park JH et al. (2017), which have previously reported duplex release and collectively provide considerable evidence that miRNA can be unloaded from AGO in order to promote turnover and recycling of AGO. It is known that AGO recycling must occur, therefore there must be some mechanisms to enable release of miRNA from AGO2 to enable this. It is possible that AGO recycling proceeds via miRNA degradation (TDMD) in the cell, but in the absence of enzymes responsible for oligouridylation and degradation, the miRNA duplex may be released. As TDMD-competent mRNA targets have been observed to release the miRNA 3' tail from AGO2 (Sheu-Gruttadauria et al., 2019; Willkomm et al., 2022), there is a possible mechanistic similarity between the two processes, however, we do not have sufficient data to make any statement on this.
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, the authors use a combination of biochemical, biophysical, and computational approaches to investigate the structure-function relationship of miRNA binding sites. Interestingly, they find that AGO2 weakens tight RNA:RNA binding interactions, and strengthens weaker interactions.
Given this antagonistic role, I wonder: shouldn't there be an 'average' final binding affinity? Furthermore, if I understand correctly, not many trends were observed to correlate binding affinity with repression, etc.
Given the context of the study - whereby structure is being investigated as a contributing factor to the interaction between the miRNA and mRNA, I find it interesting that the authors chose to use MC-fold to predict the structures of the mRNA, rather than using an experimental approach to assess / validate the structures. Thirty-seven RNAs were assessed; I think even for a subset (the 12 that were focused on in the study), the secondary structure should be validated experimentally (e.g., by chemical probing experiments, which the research group has demonstrated expertise in over the last several years). The validation should follow the in silico folding approach used to narrow down the region of interest. It is necessary to know whether an energy barrier (associated with the mRNA unfolding) has to occur prior to miRNA binding; this could help explain some of the unexplained results in the study. Indeed, the authors mention that there are many variables that influence miRNA regulation.
In the methods, T7 is italicized by accident in the T7 in vitro transcription section. Bacmid is sometimes written with a capital B and other times with a lower-cased b. The authors should be consistent. The concentration of TEV protease that was added (as opposed to the volume) should be described for reproducibility.
In figure S2D, what is the second species in the gel on the right-hand side of the gel in the miR-34a:AGO lanes? The authors should mention this.
Figure S3B and S3A are referenced out of order in the text. In regard to S3A, what are the anticipated or hypothesized alternative conformations for NOTCH1, DLL1, and MTA2? There are really interesting things going on in the gels, also for HNF4a and NOTCH2. Can the authors offer some explanation for why the free RNA bands don't seem to disappear, but rather migrate slowly? Is this a new species?
In the EMSA for Perfect, why does the band intensity for the bound complex increase then decrease? How many replicates were run for this? This needs to be reconciled.
The authors list that the RNA concentration was held constant at 10 nM; in EMSAs, the RNA concentration should be less than the binding affinity; what is the lowest concentration of protein used in the assays shown in S3A? Is this a serial dilution? It seems to me like the binding assays for MTA2, Perfect, and SRCseed might have too high of an RNA concentration. (Actually, now I see in the supplement the concentrations of proteins, and the RNA concentration is too high). Also, why is the intensity of bands for bound complex for SRCseed more intense than the free RNA?
Why are the binding affinity error bars so large (e.g., for NOTCH2 with mir-34a) - 6 uM +/- 3 uM?
It would be very helpful if the authors wrote in the Kds in Figure 2A in green and blue (in the extra space in the plots). This would help the reader to better understand what's going on, and for me, as a reviewer, to better consider the analysis/conclusions presented by the authors.
The authors state on page 18 that 'Interestingly, however, we did not observe a correlation between binary or ternary complex affinity and seed type.' They should elaborate on why this is interesting.
Figure 2C is not referenced in the text (the authors should go back through the text to make sure everything is referenced and in order). The Kds should be listed alongside the gels in Figure 2C.
Figure 3B is rather confusing to understand.
Page 20: Perfect should be italicized.
Have the authors considered using NMR to assess the base pair pattern formed between the miRNA:mRNA complexes (with / without AGO)? As a validation for results obtained by RABS? This could be helpful for the Asymmetric target binding section, the Ago increases flexibility section, and the three distinct structural groups section in the results. It is widely accepted that while chemical probing is insightful, results should be validated using alternative approaches. Distinguishing structural changes and protected reactivity in the presence of protein is challenging.
Significance
The work is helpful for understanding how microRNAs recognize and bind their mRNA targets, and the impact Ago has on this interaction. I think for therapeutic studies, this will be helpful for structure-based design. Especially given the three types of structures identified to be a part of the interaction.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Sweetapple et al. took the approaches of EMSA, SHAPE, and MD simulations to investigate target recognition by miR-34a in the presence and absence of AGO2. Surprisingly, their EMSA showed that guide unloading occurred even with seed-unpaired targets. Although previous studies reported guide unloading, they used perfectly complementary guide and target sets. The authors of this study concluded that the base-pairing pattern of miR-34a with target RNAs, even without AGO2, can be applicable to understanding target recognition by miR-34a-bound AGO2.
Major comments:
- (Page 11 and Figure S4) The authors pre-loaded miR-34a into AGO2 and subsequently equilibrated the RISC with a 5' modified Cy5 target mRNA. Since properly loaded miR-34a is never released from AGO2, it is impossible for the miR-34a loaded into AGO2 to form the binary complex (mRNA:miR-34a) in the EMSA (guide unloading has been a long-standing controversy). However, they observed bands of the binary complex in Figure S4. The authors did not use ion-exchange chromatography. AGOs are known to bind RNAs nonspecifically on their positively charged surface. Is it possible that most miR-34a was actually bound to the surface of AGO2 instead of being loaded into the central cleft? This could explain why they observed the bands of the binary complex in EMSA.
- (Page 18 and Figure S5) Previous studies (De et al., Jo MH et al., Park JH et al.) reported guide unloading when they incubated a RISC with a fully complementary target. However, neither MTA2, CCND1, CD44, nor NOTCH2 can be perfectly paired with miR-34a (Figure 1A). Therefore, the unloading reported in this study is quite different from the previously reported works and thus cannot be explained by the previously reported logic. The authors need to explain the guide unloading mechanism that they observed. Otherwise, they might misinterpret the results of their EMSA and RABS of the ternary complex.
- (Page 20) The authors reported, "it is notable that the seed region binding does not appear to be necessary for duplex release." The crystal structures of AGO2 visualize that the seed of the guide RNA is recognized, whereas the rest is not, except for the 3' end captured by the PAZ domain. How do the authors explain the discrepancy?
- (Pages 22) The authors mentioned, "It follows that the structure imparted via direct RNA:RNA interaction remains intact within AGO2, highlighting the role of RNA as the structural determinant." A free guide and a target can start their annealing from any nucleotide position. In contrast, a guide loaded into AGO needs to start annealing with targets through the seed region. Additionally, the Zamore group reported that the loaded guide RNA behaves quite differently from its free state (Wee et al., Cell 2012). How do the authors explain the discrepancy?
- The authors concluded that the range of binary complex affinities is constricted by human AGO2 in the ternary complex - strengthening weak binders while weakening strong binders. This may hold true for miR-34a, but it cannot be generalized. Other miRNAs need to be tested.
Minor comments:
- (Figure S2) Why was the 34-nt 3'Cy3-labeled miR34a complementary probe shifted up in the presence of AGO? 2.(Page 17) Does the Cy3 affect the interaction of the 3' end of miR-34 with AGO2?
- Several groups reported that overproduced AGOs loaded endogenous small RNAs. The authors should mention that their purified AGO2 was not as pure as a RISC with miR-34a. Otherwise, readers might think that the authors used a specific RISC.
- (Figure legend of Figure S5) Binding was assessed "by."
- (Page 17) It would be great if the authors could even briefly describe the mechanism by which the sodium phosphate buffer with magnesium does not disturb weaker interactions by citing reference papers. 6.(Page 22) The authors cited Figure 4C in the sentence, "Comparison between CIS and TRNAS ..." Is this supposed to be Figure 4D? 7.(Figure 6) Readers would appreciate it if the guide and target were colored in red and blue. The color codes have been used in most papers reporting AGO structures. The current color codes are opposite.
Significance
This paper will have a significant impact on the field if seed-unpaired targets can indeed unload guide RNAs. The authors may want to validate their results very carefully.
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Referee #1
Evidence, reproducibility and clarity
Sweetapple et al.
Biophysics of microRNA-34a targeting and its influence on down-regulation
In this study, the authors have investigated binding of miR-34a to a panel of natural target sequences using EMSA, luciferase reporter systems and structural probing. The authors compared binding within a binary and a ternary complex that included Ago2 and find that Ago2 affects affinity and strengthens weak binders and weakens strong binders. The affinity is, however, generally determined by binary RNA-RNA interactions also in the ternary complex. Luciferase reporter assays containing 12 different target sites that belong to one of three seed-match types were tested. Generally, affinity is a strong contributor to repression efficiency. Duplex release, a phenomenon observed for specific miRNA-target complementarities, seems to be more pronounced when high affinity within the binary complex is observed. Furthermore, the authors use RABS for structural probing either in a construct in CIS or binding by the individual miRNA in TRANS or in a complex with Ago2. They find pronounced asymmetric target binding and Ago2 does not generally change the binding pattern. The authors observe one specific structural group that was unexpected, which was mRNA binding with bulged miRNAs, which was expected sterically problematic based on the known structures. MD simulations, however, revealed that such structures could indeed form.
This is an interesting manuscript that contributes to our mechanistic understanding of the miRNA-target pairing rules. The combination of affinity measurements, structural probing and luciferase reporters allow for a broad correlation of target binding and repression strength, which is a well-thought and highly conclusive approach. However, there are a number of shortcomings that are summarized below.
- The manuscript is not easy to read and to follow for several reasons. First, many of the sub-Figures are not referenced in the text of the results section (1C, 1D, 2C, 4D). Figure 4A seems to be mis-labeled. Second, a lot of data is presented in suppl. Figures. It should be considered to move more data into the main text in order to make it easier for readers to evaluate and follow.
- Some of the data is over-interpreted. For example, in Figure 3A, it is concluded that supplementary regions are more important for weaker seeds. Only two 8-mer seeds are present among the twelve target sites and thus it might be difficult to generalize.
- I did not understand why the CIS system shown in 4A is a good test case for miR-34a-target binding. It appears very unnatural and artificial. This needs to be rationalized better. Otherwise it remains questionable, whether these data are meaningful at all.
- For the TRANS experiments, only one specific scaffold structure is used. This structure might impact binding as well and thus at least one additional and independent scaffold should be selected for a generalized statement.
- Generally, it would be nice to have some more information about the experiments also in the result section. Recombinant Ago2 is expressed in insect cells and re-loaded with miR-34a, luciferase reporters are transfected into tissue culture cells, I guess.
- In the first sentence of the abstract, Argonaute 2 should be replaced by Argonaute only since other members bind to miRNAs as well.
Significance
This is an interesting manuscript that contributes to our mechanistic understanding of the miRNA-target pairing rules. The combination of affinity measurements, structural probing and luciferase reporters allow for a broad correlation of target binding and repression strength, which is a well-thought and highly conclusive approach. However, there are a number of shortcomings.
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Reply to the reviewers
We would like to thank the reviewers for their thoughtful evaluation of our work. Our point-by-point responses to reviewer critiques follow below. Please note that any referenced changes to the manuscript are highlighted in yellow in the revised manuscript text.
Response to Common Critiques
- Reviewers 1 and 2 state that some elements of this study confirm previously published results (many in murine systems). However, the reviewers also acknowledge that the mouse and human rDNA repeats may be subject to quite distinct regulation because of the much denser CG content of the human rDNA promoter (26 CpGs) vs. the mouse rDNA promoter (only 2 CpGs); these potential differences in regulation motivated this study in human cells. We evaluate the functions of rDNA methylation in human cells, which is directly relevant to understanding the regulation of rDNA function in human aging, and to understanding the functional implications of DNA methylation "aging clocks" more generally. We also apply a recently developed technology (dCas9-mediated epigenome editing) to directly test the function of rDNA methylation. Novel findings reported in this study include:
- Pol I - engaged rDNA repeats are hypomethylated at sites both in the promoter and the gene body; this contrasts with Pol II transcription, which is coincident with gene body methylation.
- rDNA copy number remains stable with age in mammals, in striking contrast to findings in other eukaryotes. rDNA copy number instability has been proposed to be a universal feature of the aging genome, and this finding refutes that possibility.
- Induction of DNA methylation by an average of ~20% along 7-11 of the 26 CpGs in the human rDNA repeat does not measurably inhibit rDNA transcription.
- Human Pol I and UBTF remain bound to rDNA promoters in the presence of elevated CpG methylation, in contrast to the murine Pol I machinery.
Reviewers 1 and 2 questioned our strategy of mapping sequencing data to the consensus ribosomal DNA (rDNA) repeat alone. We followed the approach of Wang & Lemos Genome Research 2019, who initially described the rDNA methylation clock. Wang & Lemos also mapped genomic data to rDNA consensus sequences alone due to the computational efficiency of this approach, and describe a head-to-head comparison of mapping performance outcomes in their Methods section. Importantly, their analysis indicated that the vast majority (>98%) of sequencing reads can be mapped uniquely to the consensus human rDNA repeat (U13369.1). When we launched our study, we also initially compared the performance of mapping to the rDNA repeat consensus sequence alone versus to the whole human genome. We noted very similar performance in both cases, with the possible exception of a modest increase in simple repeat sequences being erroneously mapped to the intergenic spacer (IGS) region of the rDNA when we mapped to the rDNA repeat alone. As the reviewers pointed out, the IGS contains simple repeat sequences that are also found at numerous other non-rDNA sites in the genome. However, the minor mis-mapping of simple repeats to the IGS did not affect our analyses of non-IGS sequences, which were the focus of this study. We therefore proceeded with mapping to the rDNA consensus sequence only.
Reviewers 1 and 2 pointed out that our dCas9-DNMT strategy induced only a 15-20% increase in rDNA methylation and questioned whether we could expect to detect downstream effects in rDNA transcription. While Reviewer 2 suggested that multiple sgRNAs could enhance methylation efficiency, it turns out that this has already been tested for other target genes and shown that multiple sgRNAs cannot increase efficiency of CpG methylation by dCas9-DNMTs (Stepper et al., Nucleic Acids Research 2017). Separately, the goal of this study was to model the effects of age-linked rDNA hypermethylation, which increases by 15-20% over mammalian lifespan (Wang & Lemos 2019; see also our Figure 1). Importantly for interpreting these data, induction of promoter methylation to a similar extent on the mouse rDNA repeat was able to direct detectable repression of rDNA transcription (Santoro et al., 2011). Further, dCas9-DNMT has been previously shown to induce a ~20% increase in CpG methylation of the Pol II target gene EpCAM and cause measurable transcriptional repression that was detectable by qPCR (Stepper et al., 2017). In contrast, we were able to induce rDNA methylation to a similar extent and observed no change in the levels of either pre-rRNA or mature rRNA. Because we see that UBF and Pol I remain bound to rDNA in spite of higher CpG methylation (Fig. 7 and Fig. S4), we interpret these data together to indicate that the human Pol I machinery can continue to engage with rDNA in the presence of intermediate levels of CpG methylation.
Reviewer 1
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inactivation of rDNA transcription per se does not affect chromatin accessibility, to date only depletion or deletion of UBTF has been found to do this and even this does not enhance CpG methylation, these published findings should be referenced.
Our analyses in Figure 2 focus on defining the relationships between chromatin accessibility, transcriptional activity, and CpG methylation throughout the human rDNA repeat. We cannot determine causation from this analysis - meaning whether chromatin accessibility influences CpG methylation or vice versa - and this point is beyond the scope of our study. Our major goal was to test whether induced CpG methylation affects transcription output.
The authors overstate their results by writing "actively transcribed rDNA repeats are hypomethylated at their promoter" despite only one SmaI site but many CpG sites exist in the human promoter, the latter having not been assayed.
We analyzed several pieces of data to come to this conclusion. First, ATAC-Me indicates that ATAC-accessible rDNA repeats are completely devoid of methylation both in their promoter and throughout the gene body; as UBTF binding controls rDNA accessibility (Sanij et al., JCB 2008; Hamdane et al., PLoS Genet 2014), we infer that ATAC-accessible repeats are engaged with the Pol I transcription machinery and hypomethylated. To more directly probe this question, we evaluated the methylation status of Pol I-bound rDNA repeats at five separate sites by ChIP-chop: two sites in the 5' regulatory region (5' ETS and core promoter, pooled together as "promoter" in Figure 2F) and three sites within the gene body (18S, 5.8S, and 28S, pooled together as "gene body" in Figure 2F). These data clearly indicate that Pol I preferentially binds to these regions when they are hypomethylated, as the extent of CpG methylation at these same sites is higher in input DNA and lower in Pol I-ChIPped DNA. While we do not comprehensively profile CpG methylation status of Pol I-bound DNA, these ChIP-chop analyses are consistent with our interpretation that "actively transcribed (that is, Pol I-engaged) rDNA repeats are hypomethylated at their promoter".
Pol I's preference for binding hypomethylated promoters has been previously described in mouse cells (Santoro & Grummt 2001) and human cells (Brown & Szyf Mol Cell Biol 2007). We confirm this and also report the novel finding that rDNA gene bodies bound by Pol I are hypomethylated. This contrasts with known relationships between Pol II and CpG methylation, where genes actively transcribed by Pol II often have dense gene body CpG methylation.
While we think it is reasonable to infer from ATAC-Me data and ChIP-chop data together that accessible and hypomethylated rDNA repeats reflect transcriptionally active repeats, we appreciate the reviewer's point that we analyzed only a select few CpG sites by Pol I ChIP-chop. We have adjusted the text to make our interpretation more parsimonious (see highlights).
The human rDNA promoter contains many CpGs which may not affect transcription when methylated. RRBS and WGBS data can't tell us much if we don't understand which sites, when methylated, affect transcription*. *
We agree, and this ambiguity is what motivated us to induce methylation and evaluate the consequences. In plasmid reporter experiments where the human rDNA promoter was fused to a luciferase reporter, it was shown that in vitro methylation of the plasmid potently inhibited transcription in human cells (Ghoshal et al., J Biol Chem 2004). In this study, methylation of 7/26 CpGs was sufficient to induce >75% inhibition of reporter plasmid transcription, while methylation at single sites could induce ~50% inhibition. We neglected to site this relevant study and have included a reference to it in the revised manuscript. Importantly, this plasmid reporter assay does not assess the effects of CpG methylation on the full rDNA repeat in its endogenous genomic context. We were able to induce significant CpG hypermethylation on 11/26 promoter CpGs with one guide (P+G) and on 7/26 CpGs with a second guide (P+A) (Figure 3D). This level of methylation did not induce detectable silencing of rRNA transcription. Instead, we found that both UBF (Fig. 7) and Pol I (Fig. S4) remained bound to rDNA in the presence of CpG hypermethylation.
The argument that the mouse rDNA Pol I machinery is "exquisitely sensitive" to CpG methylation is a little misleading as there are only two CpGs in the mouse rDNA promoter. Which of the 26 human CpGs are the critical ones?
Immediately following this statement in the Discussion, we state that "the human rDNA promoter is significantly more CG-rich than the mouse rDNA promoter". We have revised this section to emphasize the difference (26 CpGs in human vs. only 2 in the mouse) and discuss this point raised by the reviewer: which are the critical CpGs in the human rDNA? Here again it is relevant to cite the human rDNA promoter reporter assays performed by Ghoshal et al., J Biol Chem 2004. These data indicate that CpG methylation of 7/26 promoter CpGs interferes with transcription from an rDNA reporter plasmid. Notably, it is unclear how generalizable findings from reporter assays are to the genomic context of the endogenous full length rDNA sequence. Our data indicate that partial methylation of 7-11 CpGs in the human rDNA promoter causes no detectable rDNA inhibition, and indeed does not displace UBF or Pol I (Fig. 7; Fig. S4).
Antibody SC13125 used for UBF ChIP sees nearly exclusively the shorter transcriptionally inactive UBF2 variant. These data need to be repeated with an antibody that detects both UBF forms.
We thank the reviewer for raising the important issue of UBTF splice isoforms. Relevant citations demonstrating that the SC13125 antibody recognizes only UBF2 would have been very helpful. The human UBTF gene is alternatively spliced into full-length UBF1 (exon 8 retained) and UBF2 (exon 8 spliced out). The deletion of exon 8 results in a 37 amino acid deletion in UBF2 corresponding to residues 221-268 in HMG box 2 of UBF1 (see Ensembl entry ENSG00000108312.16). The truncation of HMG box 2 makes UBF2 a far less potent transcriptional activator than UBF1. Because of the small molecular weight difference between these two isoforms, preference of an antibody for one vs. another isoform is not readily apparent by Western blotting. However, according to the manufacturer of the UBTF antibody used in this study, the immunogen corresponds to residues 1-220 of UBTF1, which is immediately N-terminal to the residues deleted in UBF2 (AAs 221-268, encoded by exon 8). The antibody's immunogen is thus entirely sequence that is shared between UBF1 and UBF2. Further, a previous study performed immunoprecipitation followed by mass spectrometry using this antibody and reported detection of UBF1-specific peptides (Drakas et al., PNAS 2004). Therefore, absent our knowledge of any evidence to the contrary, we conclude that this antibody recognizes UBF1 and possibly also UBF2.
We thank the reviewer for raising this point and have adjusted the text to avoid the misleading implication that we are unambiguously detecting only the UBF1 isoform; all mentions of "UBF1" in the revised text have been replaced with "UBTF".
Setting aside the question about the UBTF antibody reagent used, we observe consistent results by evaluating both UBTF (Figure 7) and Pol I (Figure S4) binding to rDNA in spite of CpG methylation; therefore, we conclude that the human Pol I machinery is not displaced from the human rDNA promoter by intermediate levels of CpG methylation.
Reviewer 2
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There is very little discussion concerning the methylation status of the IGS...the Kobayashi lab has convincingly demonstrated that rDNA repeats fall into 2 classes. Those in which the supposedly active repeats lack methylation on promoters and coding regions and those in which both promoters and coding regions are heavily methylated. In both cases the IGS is fully methylated.
We cite this study in the Discussion (reference 18 in bibliography) and agree that this work is relevant to ours; we have adjusted the text to emphasize this point. Notably, this previous analysis of CpG methylation patterns by long-read sequencing implied that active repeats may be entirely hypomethylated along their coding sequence; our data more directly demonstrate this both by ATAC-Me and by Pol I ChIP-chop (Fig. 2).
There is no description of how rRNA levels were assessed. I suggest this could be further complemented by in vivo incorporation studies such as EU labeling.
We apologize for this lack of clarity. rRNA levels were assessed by qPCR of the 45S pre-rRNA (Fig. 3A) and of mature 28S rRNA (Fig. 3B), and these data are presented as a fold change in each rDNA-targeting sgRNA compared to a non-targeting control sgRNA. The primersets used are listed in Supplementary Table 1.
While we agree that EU labeling could be useful for detecting nucleolar transcription, qPCR detection of the 45S rRNA also sensitively reports nascent transcription and we think is sufficient to address this question.
Reviewer 3
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The study points to differences between mouse and human rDNA and the effect of DNA methylation on transcriptional output. Did the mouse rDNA dataset also measure transcription output to correlate with DNA methylation age differences?
The original study that defined the rDNA methylation clock (Wang & Lemos Genome Research 2019) did not evaluate rDNA transcription in parallel. More generally, the relationship of age-linked "clock" CpG methylation sites to expression / function of CpG methylated loci is very unclear, and testing the potential relationship between age-linked rDNA methylation and function was the major goal of this study.
Did the spacer promoter also get methylated and did that affect UBF and Pol I binding?
While the existence and function of a spacer promoter has been more clearly defined in the mouse rDNA repeat, recent evidence indicates that the Pol I transcription machinery also binds a second location about 800 bp upstream of the core promoter in the human rDNA repeat (Mars et al G3 2018). The guides that we used to direct CpG methylation recognize single unique sites in the core rDNA promoter and do not recognize sequences in this putative spacer promoter, and we did not analyze methylation at the spacer promoter. Analysis of the spacer promoter is generally beyond the scope of this study, as it is unknown whether there is any relationship between spacer promoter methylation and aging progression.
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Referee #3
Evidence, reproducibility and clarity
The manuscript Modeling the consequences of age-linked rDNA hypermethylation with dCas9-directed DNA methylation in human cells studies the DNA methylation during aging at the rDNA. The study is well performed and provides several new insights into rDNA transcriptional regulation. The main finding is that in human cells, rRNA methylation does not affect transcription output, UBF and RNA pol I binding, even though the bound gene copies are less methylated than the silent ones. The experimental approach is excellent; the data mining and experiments are appropriate and shows essential points. The results are very interesting and provides new aspects to the state of rDNA that will further the understanding of ribosomal transcription.
Minor concerns
The study points to differences between mouse and human rDNA, and the effect of DNA methylation on transcriptional output. Did the in the mouse rDNA data-set also measure transcription output to correlate with DNA methylation age-differences.
Some rRNA genes, including the human gene repeat, has a second promoter 7-800 base pairs upstream of the promoter. This site also contains a CTCF binding site, upstream of which nucleosomal chromatin state is found. Downstream of the spacer promoter, a UBF associated chromatin state assembles, presumable on active copies. Did the spacer promoter also get methylated and did that affect UBF binding and pol I binding?
Significance
This is a very interesting and novel study which just needs to be extended to other feature of the rDNA to provide a full picture. The results presented in the manuscript are novel and contributes to the understanding of ribosomal transcription, in particular the outstanding question about the impact of DNA methylation on the transcriptional output and chromatin states. It provides important insight into how to think about rRNA transcription in different cell lines, states and diseases, such as cancer. The general aspects of the study suggest a broad broad.
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Referee #2
Evidence, reproducibility and clarity
Summary:
Mammalian genomes typically contain between 150 and 250 copies of a ribosomal gene repeat (rDNA) that are transcribed by RNA polymerase I to yield pre-rRNAs that encode rRNAs. It is generally accepted, that in most cells as many as 50% of repeats are transcriptionally silent. It is now appreciated that the regulatory elements and transcribed regions of these "silent" repeats are heavily methylated. Thus rDNA hypermethylation correlates with silence. However, whether this is a driver of silencing or a consequence of silencing is open to debate. This manuscript weighs into this debate. Initial experiments remap existing bisulfite sequencing data from both the mouse and humans. These results confirm previous data that rDNA hypermethylation correlates with aging. Next, to strengthen links between hypermethylation and silencing, they remap methylation-resolved ATAC sequencing data. This confirms that hypomethylated rDNA is in a more open chromatin conformation, presumably the "active repeats". In mammals there have been competing claims regarding changes in rDNA copy number during aging. Notably it has been claimed previously that rDNA copy number drops during human aging. A potential flaw in that study is that it studies of rDNA copy number utilised genetically diverse human populations. Here, using digital PCR, they survey rDNA copy number in various tissues of an inbred mouse strain. Analysing young mice and old mice, they find no evidence for age related rDNA loss. While the above experiments are well performed the results are largely confirmatory in nature. The next set of experiments attempt to address a critical question, namely, is rDNA hyper methylation a 'cause' of a 'consequence' of silencing. They generated an inducible nuclease dead CAS9 fused with de novo methyltransferase function (dCas9-3A3L) and gRNAs targeting either the promoter of the 28S coding region. Experiments performed in transformed and non-transformed human cell lines demonstrated a 15-20% methylated rDNA. Analysis of pre and mature rRNAs as well as cell staining reveal that transcript levels and nucleolar morphology are unaltered. Furthermore, the finding that UBF 'chipped' rDNA is more heavily methylated argues that directed methylation of the human rDNA promoter does not displace UBF. These experiments suggest that rDNA hypermethylation is more of a consequence of silencing than a cause of silencing.
Major comments:
It is not clear from the methods how previous rDNA was mapped onto rDNA repeats. Did they generate a customised reference genome with rDNA added, or simply map reads to rDNA in isolation. This is of critical importance as only reads that uniquely map to rDNA should be considered. Mammalian genomes typically contain many rDNA pseudo genes. Furthermore, the rDNA intergenic spacer (IGS) contain many retro/repeated elements that are distributed throughout the genome.
There is very little discussion concerning the methylation status of the IGS. Using nanopore sequencing the Kobayashi lab has convincingly demonstrated that rDNA repeats fall into 2 classes. Those in which the supposedly active repeats lack methylation on promoters and coding regions and those in which both promoters and coding regions are heavily methylated. In both cases the IGS is fully methylated.
In the targeted methylation experiments the increase in rDNA methylation remains both local and modest 15-20% increase. Would it be possible to increase the number of gRNA so as to achieve a higher level and more distributed change in rDNA methylation.
Minor comments:
The older U13369 rDNA reference has many sequence errors and should be avoided.
There is no description of how rRNA levels are assessed. I suggest this could be further complemented by in vivo incorporation studies such as EU/click-chemistry.
Significance
Around 50% of data presented in this manuscript (Figs 1-3) is confirmatory rather than novel. While the data regarding targeted methylation of "active rDNA repeats is interesting, and I think pointing us in the right direction, it is not comprehensive enough to overturn the pervasive notion that methylation causes silencing.
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Referee #1
Evidence, reproducibility and clarity
Summary:
The manuscript attempts to provide an answer to why methylation of the human rDNA correlates with aging. They conclude that this correlation is not connected with changes in rDNA activity of copy numbers.
Major comments:
The authors reanalyze public data from RRBS and WGBS that suggests a correlation between aging and rDNA methylation. They then use public ATAC-Me sequence data and show a good correlation between chromatin accessibility and lack of CpG methylation. This correlation has been known for some time, but the ATAC-Me approach is a nice confirmation that it extends through the coding region and probably the promoter and enhancer sequences. In referring to the correlation between open chromatin and hypomethylation the authors state that "these data imply that methylation of the rDNA promoter and gene body both occur exclusively on non-transcribed, silent repeats" However, it is known that inactivation of rDNA transcription per se does not affect chromatin accessibility, to date only depletion or deletion of UBTF has been found to do this and even this does not enhance CpG methylation, these published findings should be referenced. The authors do recognize this and use the so-called ChIP-chop (not ChIP-ChOP) method to analyze methylation of PolI ChIPped DNA at a single SmaI site in the 47S promoter and a site within the 28S (Table S1 showing primers was not available to me to define the exact regions, the ref to Santoro for the technique should be 2014 not 2013). The ChIP-chop assay repeats previous work but here is done on HEK293T, the cell line they use for later study. The authors also overstate their results by writing "actively transcribed rDNA repeats are hypomethylated at their promoter" despite only one SmaI site but many CpG sites exist in the human promoter, the latter having not been assayed.
The authors do go on to convincingly show rDNA copy numbers are constant with age by assaying various mouse tissues from young and old mice, hence excluding this as an affector of aging. They then attempt to use targeted de novo methylation to ask if this has any effect on rDNA transcription. Such effects have been extensively claimed as a source of rDNA regulation, though there is little evidence that this occurs in vivo. The authors use dCas9 targeted DNMT to locally enhance methylation using two promoter and one 28S guide RNAs and are able to show mean increases of 15 to 20% by ChIP-chop (but 40 to 50% at other CpGs by WGB-seq (BSAS), not discussed). Measurement of pre-rRNA and 28S abundance (relative to what control is not stated), cell proliferation, PolI nucleolar distribution and UBF (incorrectly referred to UBF1, see comment below) occupancy at the promoter are all suggested to show no effects of this targeted methylation. Hence the authors conclude that "These data suggest that promoter methylation is not sufficient to impair transcription of the human rDNA and imply that the human rDNA transcription machinery may be resilient to age-linked rDNA hypermethylation" But in fact no more than a 20% change due to the targeted methylation should be expected in any of the parameters measured. It is not at all evident that such a small effect would be detected by the authors.
Specific points:
Mapping was to the rDNA repeat unit in the absence of the human genome. This may bias the mapping data since the human genome contains rDNA pseudogenes and intermediate repetitive elements that are also present in the rDNA unit. These will be present in all the RRBS and WGS datasets, may or may not change methylation levels with age and will be mapped onto the single copy of the rDNA used in the data alignment. These factors need to be controlled.
The human rDNA promoters contain many 26 CpGs, most of which may have no effect on transcription when methylated. Thus, very little of significance can be gleaned from RRBS data and this goes for WBS data without understanding which sites when methylated affect transcription.
The argument that the mouse rDNA is "mouse Pol I machinery is exquisitely sensitive to a single CpG methylation event in the UCE, which blocks UBF binding and prevents transcription". Here the reference is to one of only two CpGs in the mouse promoter and, in this reviewer's opinion, the effect of its methylation has never been convincingly shown in vivo on the endogenous genes. However, if true, it also opens the question of which of the 26 CpGs in the human promoter are critical ones.
Antibody SC13125 used for UBF ChIP sees near exclusively the shorter transcriptionally inactive UBF2 variant. These data need to be repeated with an antibody that detects both UBF forms.
Significance
We believe that the authors are correct in their conclusion that rDNA activity is not significantly affected by the level of CpG methylation. This said, the data presented in the manuscript does not provide strong support for this notion and hence, does not significantly advance our understanding of the role of rRNA in aging.
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Reply to the reviewers
We thank both reviewers for their reviews of our work and suggestions for improvement. Changes to the manuscript are captured with the Track Changes feature, and our point-by-point responses are included below in bold/italic text.
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
Summary Bell et al. overexpress Prom1 or Ttyh1 and test its effect on EV formation from cell lines. They find that Ttyh1 expression leads to an increase in small EVs as well as tubulated EVs, while Prom1 expression leads to a milder increase in small EVs. EV induction by Prom1 is dependent on cholesterol and the authors show that Prom1 makes the cholesterol in EVs more resistant to detergent. The authors show no connection between Ttyh1 EV induction and cholesterol, although they claim it is important. They also show that a disease mutation in Prom1 decreases Prom1 trafficking to the plasma membrane and increases cholesterol resistance to detergent in EVs. The authors also find that the disease mutation decreases the size of the Prom1-induced EVs.
Major Comments
Results - line 99-106 - The EV isolation protocol would remove large EVs like the Prom1+ midbody remnants. It is important to explicitly specify that this study focused on small EVs.
We agree with the reviewers and appreciate the suggestion to make this distinction. We have clarified the Results text (lines 104-105) to specify that our method specifically reconstitutes and isolates small EVs.
Statistics - The t tests appear to have been performed without correction for multiple comparisons (Figure 2C-D, Fig. 4D). Given that >10 comparisons were made, this can alter the biological significance of p__We agree with the reviewers that multiple test correction is appropriate for these figures. We have applied Bonferroni correction to the t-tests in Figs 2C, 2D, and 4D by adjusting our significance thresholds (alpha), and included additional text in the figure legend to indicate how and why the correction was performed.__
The DLS data does not appear to give any insight into EV size (unlike the EM data) and could be removed from the whole manuscript (or moved to supplemental). The authors should also remove any conclusions based on the DLS data.
We appreciate the reviewers raising this point and agree that the DLS is less informative than our other measurements of EV size and morphology. We have moved all DLS figure panels where EV size is characterized by another method to the Supplement.
Discussion - line 382-383 "Because Prom1 EVs arise directly from blebbing of the plasma membrane23, this finding suggests that Prom1 and Ttyh1 traffic to similar regions of the plasma membrane." The authors have not examined where Prom1 or Ttyh1 localize in the plasma membrane and can not draw this conclusion. That both proteins promote plasma membrane budding would only suggest that both proteins localize to the plasma membrane, not subregions of the plasma membrane. However, the authors have not demonstrated that Ttyh1 specifically induces plasma membrane budding. The different size of Ttyh1 EVs could be due to different biogenesis mechanisms (i.e. derived from intracellular organelles instead of the plasma membrane), making this statement an over-interpretation on both parts.
This is a fair point. We have removed this sentence from the Discussion (lines 402-403) as the reviewer requests.
Discussion - line 398-400 "Membrane cholesterol is necessary for Prom1-mediated remodeling20,21 and is present at similar levels in purified Prom1 and Ttyh1 EVs (Fig 5E), indicating that it is undoubtedly important for EV formation by both proteins." & line 415-417 "We find that conservative mutations in several of these adjacent aromatic residues impair EV formation by Prom1, but do not mimic the stable cholesterol binding of W795R (Figs 2C, 4D). " The author's data suggests that cholesterol is not important for Ttyh1 to induce EV formation. The authors show that cholesterol depletion does not alter Ttyh1 EV production. Similarly, they find separable effects on cholesterol binding and EV formation with Prom1 mutants, which suggest that there is more to Prom1-mediated EV formation than cholesterol. That cholesterol is present at similar levels can reflect that overexpression of these proteins does not alter the amount of cholesterol in the EV source membrane (i.e. plasma membrane). Also, wouldn't molecular crowding of a membrane protein be predicted to influence how easy it is to extract lipids?
We thank the reviewer for highlighting this imprecisely phrased sentence. We only meant to indicate that cholesterol is present in both sets of EVs and contributes globally to membrane fluidity. We have removed this sentence from the Discussion (lines 419-421) to avoid over-interpretation or confusion.
The reviewer is also correct to point out that molecular crowding could alter how extractable lipids are from EVs. We have included additional explanatory text in the Discussion (lines 421-426) addressing this point.
Discussion - line 431-433 "Our findings suggest that the dynamic interaction of Prom1 with cholesterol may promote efficient maturation and trafficking of Prom1 between the endomembrane system and the plasma membrane. The authors did not investigate whether depleting cholesterol improved Prom1(W795R) trafficking to the plasma membrane, making this inference untested. Soften interpretation or test experimentally.
We appreciate the reviewer raising this point. We have altered the text in this paragraph (lines449-459) to soften our interpretation of these results, as suggested by the reviewer.
Minor Comments Abstract - "the EVs produced are biophysically similar" The authors don't perform any typical biophysical characterization (beyond size and perhaps density), so do they mean physically similar? Given the Prom1 and Ttyh1 EVs can have different shapes and are significantly different sizes, this statement feels misleading.
We thank the reviewer for pointing out the ambiguity around this word. We agree that "physically similar" is a more precise and accurate term, and have revised all instances of this language in the manuscript.
Intro - line 59-60 - "Large Prom1 EVs (500-700 nm in diameter) appear to form from bulk release of membrane from the cell midbody" Midbody remnants are well defined (if variously named, i.e. flemmingsome) large EVs derived from the spindle midbody, intercellular bridge, and cytokinetic ring. I'm not sure what the authors are trying to express by "bulk release of membrane". Midbody remnants are also a site of membrane tubulation.
The reviewer is correct to point out that midbody remnant release is a well defined process. We originally included this statement to avoid indicating that we are studying the only known class of Prominin EVs, but now recognize that including this creates more confusion that it alleviates. To improve clarity concurrently with the changes referenced above emphasizing that we are specifically studying small EVs, we have removed this reference to the larger class of EVs from the introduction (lines 61-63).
The effect on total numbers of EVs is buried in the y-axes of the EM graphs, making it difficult to distinguish where a higher n of images was examined vs. where there is an increase in EVs. This is especially hard to interpret given the high difference in n values.
The reviewers raise a valid critique of these figure panels. To improve clarity, we have adjusted the y-axes to represent the fraction of EVs rather than the absolute value of EVs, and listed the n values in figure legends.
Fig. 2C - Missing WT error bars
We appreciate the reviewer's concern for the WT error bars in these figures. The measurements underlying these plots are derived from quantification of Western blots. Because the blots have a limited number of lanes, the WT sample was run as a normalization control on each of several sets of blots. By employing this approach, we could make quantitative comparisons within each blot without needing to make direct comparisons between blots, eliminating confounding variables such as blotting times, positions of blots on rotary shakers, developer incubation time, exposure times, etc. Because WT lanes were used for normalization, each "WT" blot condition has its own set of error bars that was used for t-test comparison with the samples that share a blot. For this purely technical reason, we can represent the data either normalized against WT values or with three separate WT measurements for each plot. In the interest of clarity and transparency, we elected to report the values normalized to WT and to include all raw blot images in Supplementary Fig. S4. We understand that we could have made this more transparent, so to clarify this decision for readers, we now explicitly reference the raw blot images in both the Results text (lines 185) and in the Figure 2 legend.
Fig. 3H, 5C - Why not show raw numbers on the y-axes of the inset graphs like the main graph? Also, if it is only showing a subset of roundness ranges, then the x-axis should not go to 1 (i.e. axis range 0-0.8 would be clearer). I had a hard time figuring out what these insets were trying to show me, so please think about presenting this data more clearly (and larger).
For clarity, we have moved the inset graphs to separate panels alongside the main panel and implemented the requested changes to the axes (see Figs. 3G, 5B).
Discussion - line 377 - "Though we do not claim that Ttyh1 endogenously induces EV formation" This statement could be misinterpreted to say that you do not think endogenous Ttyh1 regulates EV formation. Rephrase as "although we have not examined whether..."
We thank the reviewer for pointing out this unclear sentence and have applied the requested change (line 397).
Discussion - line 400-402 "Our results do not indicate that Ttyh1 does not bind cholesterol, merely that it does not form an interaction that is sufficiently kinetically stable to be co-immunoprecipitated." The phrasing here is confusing with multiple "not". It is better to leave things open than to say what you have not shown. Rephrase suggestion: "Although Ttyh1 was not able to form a kinetically stable interaction for co-immunoprecipitation, it remains to be determined whether Ttyh1 is able to bind cholesterol."
We thank the reviewer for their suggestion and have modified the sentence to avoid double-negative phrasing (lines 422-426).
Movies - I'm not sure what the two videos add. It's difficult to convince myself that I see plasma membrane labeling in either movie, especially in comparison to the over-exposed WGA staining. Also, why are there ~5 sec of empty movie at the end of each?
We appreciate the reviewer's feedback and have removed the movies from the manuscript.
Reviewer #1 (Significance (Required)):
The data is interesting and well presented, but over interpreted in the discussion. The data on Ttyh1 expression inducing EVs is novel, but limited to overexpression studies. This study will be of interest to the EV, membrane curvature, and Prmn1/Tthy1 fields My expertise is in basic research on membrane trafficking (including EV formation) and lipids
We thank the reviewer for their favorable review and helpful suggestions.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this study, authors investigated the role of Prom1 and Ttyh1 proteins on EV formation. They showed that both proteins can induce EV formation, while the mechanisms by which they do it might differ slightly. Ttyh1 binding to cholesterol is not as pronounced as Prom1. Surprisingly, cholesterol binding efficiency inversely correlates with EV formation. Also, EVs induced by Tthy1 and Prom1 are structurally different.
My suggestions to improve the manuscript are below.
- Figure 2E is not very convincing. As the authors mentioned, the signal is too low to have a concrete conclusion. The line scans somehow show that WT is more membrane-localized than mutant, but colocalization of Prom1 and WGA seems very similar in both cases. Is it certain that the addition of fluorophore did not change the trafficking? Does endogenous Prom-1 staining look like this? Also, why is WGA staining brighter in mutant sample, just a usual variation or biologically important?
We understand the reviewer's concern about low signal, but respectfully disagree that the signal is too low to draw a meaningful conclusion. The only point we conclusively make in Fig. 2E is that WT Prom1 is more efficiently trafficked to the plasma membrane than W795R Prom1. We feel that this effect is sufficiently well evidenced by the line scan analysis in Supp. Fig. S5, where Prom1 peaks are cleanly visible for WT but not for W795R protein.
We observe somewhat variable WGA staining in our experiments, and the differences we show in this figure panel are representative of typical staining variation. We do not draw any biological conclusions from the level of WGA present, only from its localization. Because both the plasma membrane and late endosomes are WGA+, we suspect that the W795R Prom1 is failing to traffic from endosomes to the plasma membrane. However, given the limitations of our fluorescence assay, we have removed any claim beyond the change plasma membrane trafficking efficiency from discussion of this experiment.
We cannot conclude whether the mStayGold fluorophore alters trafficking of Prom1 to the plasma membrane. In response to the reviewer's comment, we attempted to use immunofluorescence to measure membrane localization of untagged Prom1 with the AC133-1 antibody. Unfortunately, we were unable to optimize this protocol to achieve sufficient membrane staining for quantification. We have softened our interpretation of Fig. 2E in the Results and Discussion (lines 203-204, 450) to acknowledge that the effects we observe are only measured with fluorophore-tagged Prom1.
- I also recommend showing the localization of Ttyh1 on cells.
We appreciate the reviewer's suggestion here, and it is an experiment we considered. One of the challenges we faced in this assay was quantitatively measuring fluorescent signal along cell-boundary plasma membranes without saturating signal from the very bright WGA+ endosomes. Because Ttyh1 globally expresses at higher levels than Prom1 (see Figs. 3C, 3I), direct comparison of membrane-localized Prom1 and Ttyh1 is technically challenging in these cells. However, Ttyh membrane localization has been widely reported in other papers (Matthews et al., J. Neurochem, 2007; Jung et al., J. Neurosci., 2017; Sukalskaia et al., Nat. Commun., 2021; Melvin et al., Comm. Biol., 2022) that we now explicitly mention and cite for reader clarity in both the Introduction and Results (lines 69-71, 224-225).
- A graph directly showing cholesterol binding vs EV formation efficiency would be very useful.
We agree with the reviewer that this would be an interesting and useful addition to the paper. We now include this panel in the revised manuscript as Fig. 4F.
- "Prominin and Tweety homology proteins are homologous and functionally analogous" involves speculation and authors should clearly mention this. Revealing that they are both contributing to EV formation does not make them definitely functionally analogous.
We agree with the reviewer that this sentence is indeed ambiguous and somewhat speculative. We have revised the section heading to "Prominin and Tweety homology proteins are homologous proteins that both promote EV formation" (lines 461-462) to indicate the specific analogous function we observe.
Reviewer #2 (Significance (Required)):
Overall, it is a useful addition to the field of cell biology, particularly EV field. EV formation and efficiency are both important topics, and this manuscript might give insights.
We thank the reviewer for their favorable review and helpful suggestions.
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Referee #2
Evidence, reproducibility and clarity
In this study, authors investigated the role of Prom1 and Ttyh1 proteins on EV formation. They showed that both proteins can induce EV formation, while the mechanisms by which they do it might differ slightly. Ttyh1 binding to cholesterol is not as pronounced as Prom1. Surprisingly, cholesterol binding efficiency inversely correlates with EV formation. Also, EVs induced by Tthy1 and Prom1 are structurally different.
My suggestions to improve the manuscript are below.
- Figure 2E is not very convincing. As the authors mentioned, the signal is too low to have a concrete conclusion. The line scans somehow show that WT is more membrane-localized than mutant, but colocalization of Prom1 and WGA seems very similar in both cases. Is it certain that the addition of fluorophore did not change the trafficking? Does endogenous Prom-1 staining look like this? Also, why is WGA staining brighter in mutant sample, just a usual variation or biologically important?
- I also recommend showing the localization of Ttyh1 on cells.
- A graph directly showing cholesterol binding vs EV formation efficiency would be very useful.
- "Prominin and Tweety homology proteins are homologous and functionally analogous" involves speculation and authors should clearly mention this. Revealing that they are both contributing to EV formation does not make them definitely functionally analogous.
Significance
Overall, it is a useful addition to the field of cell biology, particularly EV field. EV formation and efficiency are both important topics, and this manuscript might give insights.
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Referee #1
Evidence, reproducibility and clarity
Summary
Bell et al. overexpress Prom1 or Ttyh1 and test its effect on EV formation from cell lines. They find that Ttyh1 expression leads to an increase in small EVs as well as tubulated EVs, while Prom1 expression leads to a milder increase in small EVs. EV induction by Prom1 is dependent on cholesterol and the authors show that Prom1 makes the cholesterol in EVs more resistant to detergent. The authors show no connection between Ttyh1 EV induction and cholesterol, although they claim it is important. They also show that a disease mutation in Prom1 decreases Prom1 trafficking to the plasma membrane and increases cholesterol resistance to detergent in EVs. The authors also find that the disease mutation decreases the size of the Prom1-induced EVs.
Major Comments
Results - line 99-106 - The EV isolation protocol would remove large EVs like the Prom1+ midbody remnants. It is important to explicitly specify that this study focused on small EVs.
Statistics - The t tests appear to have been performed without correction for multiple comparisons (Figure 2C-D, Fig. 4D). Given that >10 comparisons were made, this can alter the biological significance of p<0.05 (1 incorrect in 20 comparisons). Please reanalyze with a more appropriate statistical test for multiple comparisons (i.e. ANOVA) or apply a correction to the t test values (i.e. Bonferroni).
The DLS data does not appear to give any insight into EV size (unlike the EM data) and could be removed from the whole manuscript (or moved to supplemental). The authors should also remove any conclusions based on the DLS data.
Discussion - line 382-383 "Because Prom1 EVs arise directly from blebbing of the plasma membrane23, this finding suggests that Prom1 and Ttyh1 traffic to similar regions of the plasma membrane." The authors have not examined where Prom1 or Ttyh1 localize in the plasma membrane and can not draw this conclusion. That both proteins promote plasma membrane budding would only suggest that both proteins localize to the plasma membrane, not subregions of the plasma membrane. However, the authors have not demonstrated that Ttyh1 specifically induces plasma membrane budding. The different size of Ttyh1 EVs could be due to different biogenesis mechanisms (i.e. derived from intracellular organelles instead of the plasma membrane), making this statement an over-interpretation on both parts.
Discussion - line 398-400 "Membrane cholesterol is necessary for Prom1-mediated remodeling20,21 and is present at similar levels in purified Prom1 and Ttyh1 EVs (Fig 5E), indicating that it is undoubtedly important for EV formation by both proteins." & line 415-417 "We find that conservative mutations in several of these adjacent aromatic residues impair EV formation by Prom1, but do not mimic the stable cholesterol binding of W795R (Figs 2C, 4D). " The author's data suggests that cholesterol is not important for Ttyh1 to induce EV formation. The authors show that cholesterol depletion does not alter Ttyh1 EV production. Similarly, they find separable effects on cholesterol binding and EV formation with Prom1 mutants, which suggest that there is more to Prom1-mediated EV formation than cholesterol. That cholesterol is present at similar levels can reflect that overexpression of these proteins does not alter the amount of cholesterol in the EV source membrane (i.e. plasma membrane). Also, wouldn't molecular crowding of a membrane protein be predicted to influence how easy it is to extract lipids?
Discussion - line 431-433 "Our findings suggest that the dynamic interaction of Prom1 with cholesterol may promote efficient maturation and trafficking of Prom1 between the endomembrane system and the plasma membrane. The authors did not investigate whether depleting cholesterol improved Prom1(W795R) trafficking to the plasma membrane, making this inference untested. Soften interpretation or test experimentally.
Minor Comments
Abstract - "the EVs produced are biophysically similar" The authors don't perform any typical biophysical characterization (beyond size and perhaps density), so do they mean physically similar? Given the Prom1 and Ttyh1 EVs can have different shapes and are significantly different sizes, this statement feels misleading.
Intro - line 59-60 - "Large Prom1 EVs (500-700 nm in diameter) appear to form from bulk release of membrane from the cell midbody" Midbody remnants are well defined (if variously named, i.e. flemmingsome) large EVs derived from the spindle midbody, intercellular bridge, and cytokinetic ring. I'm not sure what the authors are trying to express by "bulk release of membrane". Midbody remnants are also a site of membrane tubulation.
The effect on total numbers of EVs is buried in the y-axes of the EM graphs, making it difficult to distinguish where a higher n of images was examined vs. where there is an increase in EVs. This is especially hard to interpret given the high difference in n values.
Fig. 2C - Missing WT error bars
Fig. 3H, 5C - Why not show raw numbers on the y-axes of the inset graphs like the main graph? Also, if it is only showing a subset of roundness ranges, then the x-axis should not go to 1 (i.e. axis range 0-0.8 would be clearer). I had a hard time figuring out what these insets were trying to show me, so please think about presenting this data more clearly (and larger).
Discussion - line 377 - "Though we do not claim that Ttyh1 endogenously induces EV formation" This statement could be misinterpreted to say that you do not think endogenous Ttyh1 regulates EV formation. Rephrase as "although we have not examined whether..."
Discussion - line 400-402 "Our results do not indicate that Ttyh1 does not bind cholesterol, merely that it does not form an interaction that is sufficiently kinetically stable to be co-immunoprecipitated." The phrasing here is confusing with multiple "not". It is better to leave things open than to say what you have not shown. Rephrase suggestion: "Although Ttyh1 was not able to form a kinetically stable interaction for co-immunoprecipitation, it remains to be determined whether Ttyh1 is able to bind cholesterol."
Movies - I'm not sure what the two videos add. It's difficult to convince myself that I see plasma membrane labeling in either movie, especially in comparison to the over-exposed WGA staining. Also, why are there ~5 sec of empty movie at the end of each?
Significance
The data is interesting and well presented, but over interpreted in the discussion. The data on Ttyh1 expression inducing EVs is novel, but limited to overexpression studies. This study will be of interest to the EV, membrane curvature, and Prmn1/Tthy1 fields My expertise is in basic research on membrane trafficking (including EV formation) and lipids
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
The authors report a mass spectrometry (MS)-based interactomics technique, time-resolved interactome profiling (TRIP), which allows for tracking temporal changes in the interactome of protein of interest. To show that TRIP can successfully deconvolute interactomes over time, they pulsed thyroid cells with homopropargylglycine (Hpg), immunoprecipitated the Hpg incorporated thyroglobulin (Tg) and its interacting proteins at different time points, and subjected the samples to tandem mass tag (TMT)-based quantitative MS analysis. The MS results show that WT and variant Tg proteins indeed associate with different proteostasis network factors in a differential manner over the course of time. In addition, they utilized an siRNA-based luciferase fusion assay to evaluate whether silencing each proteostasis network component changes the levels of Tg in both lysate and media. From the combination of the TRIP and siRNA-based assays, they found many hits, including hits implicated in protein degradation, VCP and TEX264, which they validated with multiple experiments.
I am overall quite positive and think this is an important study. But there are some meaningful points to consider.
Our Response: We thank Reviewer #1 for their positive outlook on our manuscript and their constructive feedback. We have addressed the comments below.
Significant comments:
Reviewer #1, Comment #1: Oonly two replicates of the main data (the TRIP-MS experiments) for this paper is problematic. Especially since the manuscript is supposed to be demonstrating and validating the new technique. Consistent with this concern, the relative enrichment profiles for some of the results were surprising. For instance, interaction with CCDC47 was tapering off but then at 3 h it suddenly reaches the maximum level of engagement. Is this a real finding or the variability in the method? Impossible to tell with two replicates. Presenting heat maps based on biological duplicates is also very problematic. It masks the error, which is large as can be seen in some of the panels showing individual proteins. In my view, triplicates and a clear understanding of the error in the technique should be required.
Our Response: The TRIP datasets for WT Tg contains 5 biological replicates, while the A2234D and C1264R Tg contains 6 biological replicates. Two replicates are typically included in a TMTpro 16plex mass spectrometry run, and each analysis consists of 3 MS runs. We apologize that the number of replicates and layout of the MS runs was not clearly explained. Data for individual replicates is found in Dataset EV1, Dataset EV3, and a newly added Table EV3 delineates the sample layout across the TMT channels and MS runs. We clarified the text as follows:
"Subsequently, two sets of TRIP time course samples (0, 0.5, 1, 1.5, 2, and 3 hr) could be pooled using the 16plex TMTpro and analyzed by LC-MS/MS (Fig 2A). In total, 5 biological replicates were analyzed for WT and 6 biological replicates were analyzed for A2234D and C1264R, respectively (Table EV3)."
Reviewer #1, Comment #2: The same concern arises for the high-throughput siRNA screen, which was performed only in duplicate for WT and A2234D.
Our Response: While the initial screen was performed in duplicate for WT and A2234D, which is common for larger screens due to resource constraints, we would like to direct the reviewer to the fact that we followed up on observed hits using thyroid cell lines with many more replicates. Furthermore, most hits came from the C1264R Tg variant, which had three replicates in the initial screen. Hits were also extensively followed-up.
Reviewer #1, Comment #3: *There are issues with some of the immunoprecipitation experiments: In Figure 1C, a negative control for FLAG IP is missing. *
*-In Figure 2B, I am curious why the band (Hpg -, chase time 0 h) is so faint for the first WB (IB for FLAG) - is Hpg treatment indeed leading to much more Tg present at 0 h? If so, that is a concern. *
-Also, a negative control must be included (either plain cells or cells expressing fluorescent protein or a different epitope-tagged WT Tg).
-In this same figure, I am puzzled why the bands for 1.5-3 timepoints in Biotin PD elution, probed for Rhodamine, are very faint especially considering that in Figure 1D, the corresponding bands, which are 4 h after the pulse, look fine. It seems like the IP failed here?
Our Response: In Fig 2B, we have updated this figure with higher-quality images that are more representative of the results found when performing this experiment. Furthermore, to address the missing negative controls in Fig. 1C, we have added a separate figure (Fig EV2) where (-) FLAG-tagged Tg is included in this panel. We updated the text as follows:
"Furthermore, the C-terminal FLAG-tag and Hpg labeling are necessary for this two-stage enrichment strategy, and DSP crosslinking is necessary to capture these interactions after stringent wash steps (Fig 1D, Fig EV2)."
Regarding the Biotin PD rhodamine/TAMRA signal in Fig 2B: The blots in this figure panel represent the time-resolved Tg fractions from cell lysate, corresponding only to intracellular thyroglobulin. The decrease in band intensity for 1.5-3 hr time points is expected due to continued secretion and/or degradation dynamics taking place that decrease the intracellular population of labeled thyroglobulin that is able to be captured. For comparison, please note the C1264R panel (Fig 2C), where the rhodamine/TAMRA signal in the Biotin PD elutions is more stable compared to WT, indicating the cellular retention of C1264R while WT Tg is efficiently secreted and the signal is lost more rapidly. Fig 1D contains samples derived from a 4 hr Hpg pulse (without chase), explaining why the overall fluorescent Tg signal is more intense.
Suggestion to consider:
Reviewer #1, Comment #4: This manuscript, supported by the title and abstract, mainly focuses on the presentation of the development and application of TRIP, which is highly significant. The story becomes less coherent and harder to follow as significant amounts of text/figures are dedicated to siRNA-based high throughput screening and follow-up. In addition, although the discovery of TEX264 as one of the hits is very interesting and exciting, TEX264 apparently was not a hit in the TRIP experiment and is pretty distracting from the main point of the paper highlighted in the abstract and title, therefore. The siRNA-based assay and follow-up studies could be a separate scientific story of their own. Especially considering my concerns on the number of replicates for both the TRIP and siRNA-based assay, it could be beneficial to actually split the manuscript into two and conduct more replicates of the -omic work, which should corroborate the exciting discoveries the authors have made.
Our Response: We have edited the manuscript to hopefully provide a more cohesive presentation of all data, findings, and conclusions within the paper. Given the generally positive outlook on the manuscript from other reviewers and our responses to significant comments from Reviewer #1 we opted to keep the manuscript as a single piece and address all reviewer comments.
Minor comments:
Reviewer #1, Comment #5: Throughout the manuscript, the authors have not defined what FT is; presumably it means FLAG tag.
Our Response: Reviewer #1 is correct in FT corresponding to FLAG tag. We have now edited the manuscript text to clarify this as follows:
"Thyroglobulin was chosen as model secretory client protein, and we generated isogenic Fischer rat thyroid cells (FRT) cells that stably expressed FLAG-tagged Tg (Tg-FT), including WT or mutant variants (A2234D and C1264R)."
Reviewer #1, Comment #6: The authors might discuss their rationale for choosing 0-3 hrs for their TRIP studies. That includes any relevant information about the half-life of WT versus variant Tg, whether the Hpg pulse time is short enough to avoid missing key features of the temporal interactome, and discussion of what would happen if the TRIP were performed at prolonged time points (e.g. 6-10 h).
Our Response: Apologies that we omitted this important point, which is indeed related to the secretion and degradation half-life. We edited the manuscript text to discuss the rationale for 0-3 hr, length of the Hpg pulse and the impact on capturing interactions, and performing TRIP at prolonged time points as follows:
"Our previous study indicated that ~70% of WT Tg-FT was secreted after 4 hours, while approximately 50% of A2234D and 15% of C1264R was degraded after the same time period (Wright et al, 2021). Therefore, we reasoned that a 3-hr chase period would be a enought time to capture the majority of Tg interactions throughout processing, secretion, cellular retention, and degradation, while still being able to capture an appreciable amount of sample for analysis."
We explain the labeling timeline and limitations further in the discussion:
"To address this, we utilized a labeling time of 1 hr which allows us to generate a large enough labeled population of Tg-FT for TRIP analysis, but some early interactions are likely missed within the TRIP workflow. In the case of mutant Tg, performing the TRIP analysis for much longer chase periods (6-8 hrs) may provide insightful details to the iterative binding process of PN components that is thought to facilitate protein retention within the secretory pathway."
Reviewer #1, Comment #7: Lines 68-69: the two citations should probably come one sentence earlier (at least Coscia et al 2020 is a structure paper).
Our Response: We agree. We have edited the manuscript as follows to correct this:
"In earlier work, we mapped the interactome of the secreted thyroid prohormone thyroglobulin (Tg) comparing the WT protein to secretion-defective mutations implicated in congenital hypothyroidism (CH) (Wright et al, 2021). Tg is a heavily post-translationally modified, 330 kDa prohormone that is necessary to produce triiodothyronine (T3) and thyroxine (T4) thyroid specific hormones (Citterio et al, 2019; Coscia et al, 2020). Tg biogenesis relies extensively on distinct interactions with the PN to facilitate folding and eventual secretion."
Reviewer #1, Comment #8: Line 91: "(Figure 1A)" should follow the sentence "To develop the time-resolved..." to help readers better understand the system.
Our Response: __We agree. We have edited the manuscript to add the Fig 1A reference. Furthermore, we redesigned the schematic in Fig 1A to better explain the experimental system. (see also __Reviewer #2, comment 10)
"To develop the time-resolved interactome profiling method, we envisioned a two-stage enrichment strategy utilizing epitope-tagged immunoprecipitation coupled with pulsed biorthogonal unnatural amino acid labeling and functionalization (Fig 1A). Cells can be pulse labeled with homopropargylglycine (Hpg) to synchronize newly synthesized populations of protein. After pulsed labeling with Hpg, samples can then be collected across time points throughout a chase period (Fig 1A, Box 1) (Kiick et al, 2001; Beatty et al, 2006). The Hpg alkyne incorporated into the newly synthesized population of protein can be conjugated to biotin using copper-catalyzed alkyne-azide cycloaddition (CuAAC) (Fig 1A, Box 2). Subsequently, the first stage of the enrichment strategy can take place where the client protein of interest is globally captured and enriched using epitope-tagged immunoprecipitation, followed by elution (Fig 1A, Box 3)."
Reviewer #1, Comment #9: Line 101: Fisher should be Fischer
Our Response: Thank you. We have edited the manuscript text to correct this.
Reviewer #1, Comment #10: Line 131: Should be 1.5 hrs instead of 2 hrs.
Our Response: We edited this point (see below in comment #11)
Reviewer #1, Comment #11: Lines 135-136: I do not agree with the claim that HSPA5 profile looked similar for MS and WB. I do not see a peak for HSPA5 at 2 hrs in Figure 2D.
Our Response: We replaced the mass spectrometry quantification in Fig 2D, E with the scaled, relative enrichments. This provides a more meaningful comparison, as all interactions are scaled in the same way. Unfortunately, it is still difficult to directly compare the Western blot results in Fig. 2B-C to the mass spectrometry quantifications in Fig 2D-E because the WB intensities are not normalized to the Tg bait protein amounts, which is changing over time. At 2-3hrs time points, little WT Tg is pulled down as most of it is secreted. Therefore, the HSPA5 interactions are no longer detectable by Western blot. On the other hand, MS is much more sensitive to capture the interactions. We modified the text as follows:
"For C1264R, interactions with HSPA5 were highly abundant at the 0 hr time point and remained mostly steady throughout the first 1.5 hours (Fig 2C). A similar temporal profile was also observed for HSP90B1. Additionally, interactions with PDIA4 were detectable for C1264R and were found to gradually increase throughout the first 1.5 hr of the chase period, before rapidly declining (Fig 2C). We noticed similar temporal profiles for PDIA4 and HSPA5 to our western blot analysis, when measured via TMTpro LC-MS/MS as further outlined below (Fig 2D-E). In particular, the HSPA5 WT Tg interaction declined within the first hours, yet for C1264R Tg, the HSPA5 interactions remained mostly steady over the 3-hour chase period. (Fig 2E)."
Reviewer #1, Comment #12: Line 186: The cited paper Shurtleff et al 2018 is missing in the reference list.
Our Response: Thank you. We have corrected this in the citation management system and it is now available in the reference list.
Reviewer #1, Comment #13: Line 188: I disagree with the authors' claim here because, at least for CCDC47, interactions with C1264R seem to come back at the 3 hr time point.
Our Response: We have removed the discussion of EMC and PAT complex components from the text. The implications of these interactions for Tg biogenesis remain unclear and were therefore a distraction from the discussion of other core proteostasis network components pertinent to Tg processing. Nonetheless, the full dataset - including these interactions - remains available to readers in Appendix Fig S1 for further perusal.
Reviewer #1, Comment #14: Line 203: I am not sure if P4HA1 can be included in the examples for showing distinct patterns for mutants compared to the WT according to their data in Figure 3H.
Our Response: We agree. We have edited the text to remove the discussion of prolyl hydroxylation and isomerization family members and elected to discuss the new clustering analysis and the robustness of the TRIP method in more detail. The full TRIP data is nonetheless available to interested readers in Appendix Fig S1.
Reviewer #1, Comment #15: Line 216: The authors should add citations about the functions of STT3A and STT3B proteins.
Our Response: We've edited the manuscript text to include a reference to the primary literature for STT3A and STT3B functions, as follows:
"Previously, we showed that A2234D and C1264R differ in interactions with N glycosylation components, particularly the oligosaccharyltransferase (OST) complex. Efficient A2234D degradation required both STT3A and STT3B isoforms of the OST, which mediate co-translational or post-translational N-glycosylation, respectively (Kelleher et al, 2003; Cherepanova & Gilmore, 2016)."
Reviewer #1, Comment #16: Lines 248-251, "We found that interactions with these components...": this sentence should refer to Figure 3 - Figure Supplement 3 instead of Figure 3L and S4.
Our Response: Thank you. This section of the manuscript was significantly rewritten and the figure references updated.
Reviewer #1, Comment #17: Lines 258-260, "Another striking observation was that the temporal profile of EMC interactions for C1264R correlated with RTN3, PGRMC1, CTSB, and CTSD interactions.": Please provide more evidence to support the potential correlation between different interaction profiles. Or the authors should move this sentence to the discussion section as it sounds speculative. This highlights the issue of only having duplicates, as well.
Our Response: We agree that this point was highly speculative and we removed discussion of the EMC interactions.
To further investigate the correlation of interaction profiles across the dataset, we performed unbiased k-means clustering. This led to the identification of 7 and 6 unique clusters of interactors for WT and C1264R Tg-FT, respectively. These data are represented in Fig 3F and Fig EV5. Unique clusters highlight similar temporal interaction profiles for Tg-FT interactors, and provide a quantitative representation of correlative interactions that take place during Tg-FT processing.
"To assess temporal interaction changes in an unbiased fashion and identify protein groups exhibiting comparative behavior, we carried out k-means clustering of the temporal profiles for WT and C1264R. This analysis revealed a large divergence in the interaction profiles. For WT Tg, only one cluster exhibited steadily decreasing interactions (cluster 4), while others increased with time, or showed peaks at intermediate times (Fig 3F, Fig EV5A). On the other hand, C1264R largely exhibited clusters with decreasing interactions over time (Fig 3F, Fig EV5B). Cluster 2 for WT with biomodal interactions at early and late time points contains many Hsp70/90 chaperoning components. For C1264R Tg, many Hsp70/90 chaperoning components and disulfide/redox-processing components are instead part of cluster 2', which exhibited an initial rise in interactions strength before plateauing (Fig 3F, Fig EV5A,B). This divergent temporal engagement between WT Tg and the destabilized C1264R mutant is aligned with the patterns observed in the manual grouping (Fig 3B,C), highlighting that the unbiased temporal clustering can reveal broader patterns in the reorganization of the proteostasis dynamics."
One of the clusters of the C1264R Tg interactions contained autophagy interactors along with glycosylation components. We therefore postulate that this could point to a coordination of these processes. We discuss this new point in the updated manuscript:
"In the k-means clustered profiles, autophagy interactions largely group together in the same cluster, showing stronger interactions at earlier time points. In the same cluster are glycosylation components (UGGT1 and STT3B, MLEC), further supporting a possible coordination for C1264R Tg between lectin-dependent protein quality control and targeting to autophagy (Fig EV5B,C)."
Reviewer #1, Comment #18: Line 340: As written, should cite more than one paper
Our Response: Thank you. We reworded the manuscript to correct this, as follows:
"The discovery of several protein degradation components as hits for rescuing mutant Tg secretion may suggest that the blockage of degradation pathways can broadly rescue the secretion of A2234D and C1264R mutant Tg, a phenomenon similarly found for destabilized CFTR implicated in the protein folding disease cystic fibrosis (Vij et al, 2006; Pankow et al, 2015; McDonald et al, 2022)."
Reviewer #1, Comment #19: Line 371: Should be Figure 4 - figure supplement 2
Our Response: We edited the manuscript to correct this error.
Reviewer #1, Comment #20: Line 1231: "Zhang et al 2018" needs to be removed
Our Response: We have removed this citation.
Reviewer #1, Comment #21: Line 1286: FRTR should be FRT
Our Response: Thank you. We have corrected this within the text.
Reviewer #1, Comment #22: Figure 3E: Color used to highlight the three proteins (CCDC47, EMC1, EMC4) should match the color used in Figure 3 - Figure Supplement 3
Our Response: __We have edited Figure 3 to remove the section related to membrane protein biogenesis. This data is still available in __Appendix Fig S1 with consistent color coding.
Reviewer #1, Comment #23: Figure 4A: The bottom figure where lysate signal is inversely proportional to time is misleading because the authors are assessing steady-state level of proteins in this assay.
__Our Response: __We agree. We updated the schematic in __Fig 4A __to better explain the workflow and differentiate the steady-state protein level being measured within the lysate.
Reviewer #1, Comment #24: Figure 4 - Figure Supplement 1 caption: in (C), (F) should be (B). (K) should be (G) and I am not sure what the authors mean when they refer to (J) in caption of (G).
Our Response: We have corrected this lettering mistake to match the figure properly. Please note that this figure is now Fig EV6, and it includes some new and reorganized panels.
Reviewer #1, Comment #25: Figure 5 caption for (C and D): Need to specify the time that the samples were collected (8 hrs), as it seems different from A and B according to the main text.
Our Response: We have specified the collection time within the caption for these data in Fig 5C __and __5D.
Reviewer #1, Comment #26: Figure 5 - Figure Supplement 1: Data for HERPUD1 and P3H1 should be included.
Our Response: We have now included data to confirm the knockdown for HERPUD1 and LEPRE1 (P3H1) in Fig EV7F-G.
Reviewer #1, Comment #27: Figure 5 - Figure Supplement 2B: Please mention in the caption how degradation is defined.
Our Response: We have updated the Fig EV7H caption to include how "degradation" is defined within these experiments:
"% Degradation is defined as . Where is the fraction of Tg-FT detected in the lysate at a given timepoint n, and is the fraction of Tg-FT detected in the media at a given timepoint n."
Reviewer #1 (Significance (Required)):
Reviewer #1, Comment #28: This manuscript is highly significant because the authors (1) designed and validated a new methodology for time-resolved interactomics study, (2) presented the dynamic changes in Tg interactome for WT and variants, and (3) discovered how proteins implicated in degradation pathways (e.g. VCP, TEX264, RTN3) can change the secretion profile of WT and mutant Tg proteins. With TRIP, the authors demonstrated that they could obtain valuable data that were previously not captured from steady-state interactomics studies (Wright et al. 2021; Figure 3M and Figure 3 - Figure supplement 4D-4I). Furthermore, the authors treated cells with VCP inhibitors and performed both 35S pulse-chase analyses and TRIP. These experiments provide valuable information to the field by (1) presenting a new method to rescue Tg secretion defect, and (2) demonstrating a broader applicability of TRIP. If the major comments above can be addressed I believe this is a tremendous contribution to the field.
Our Response: We thank Reviewer #1 for their review comments and praise for the work presented within this manuscript.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
Reviewer #2: In the manuscript 'Time-Resolved Interactome Profiling Deconvolutes Secretory Protein Quality Control Dynamics' Wright et al. developed an approach for time-resolved protein protein interaction mapping relying on pulsed unnatural amino acid incorporation, protein cross linking, sequential affinity purification, and quantitative mass spectrometry named time-resolved interactome profiling (TRIP). The authors applied the TRIP method to compare the interactions of the secreted thyroid prohormone thyroglobulin (Tg) comparing the WT protein to secretion-defective mutations implicated in congenital hypothyroidism. They further employed an RNA interference screening platform (1) to investigate if (1) interactors identified via TRIP are functionally relevant for Tg protein quality control and (2) to identify factors that can rescue mutant Tg secretion. The screen was initially performed in HEK293 cells, but selected hits with a phenotype in HEK cells were then followed up in Fisher rat thyroid cells. Further functional validation was performed by pharmacologic inhibition of VCP, a hit from the RNAi screen with an effect on Tg lysate abundance and Tg secretion. While the authors present a comprehensive study including identification of protein-protein interactions using proteomics followed up by an RNA interference screen for functional validation, major comments need to be addressed for both the proteomics as well as the functional genomics aspects of the study (see comments below).
Our response: Thank you to reviewer 2 for their constructive feedback. We addressed all comments in detail below.
Major comments:
Reviewer #2, Comment #1: The authors describe a new method for quantitative, temporal interaction mapping. The protocol involves two enrichment steps as well as several reactions including cross-linking of the samples as well as functionalization of the unnatural amino acids. Given all these steps, the authors should rigorously characterize the quantitative reproducibility of the experiment when performed in independent biological replicates. This is important because in the final quantitative MS experiment, the authors only use two biological replicates, which is too low especially for such an involved sample preparation procedure, which would expect to have a high variability between replicates. Given the low number of replicates and the unknown reproducibility of the quantification for this protocol, it is questionable at this point how reliable the quantification over the time course is.
__Our Response: __We apologize that the number of replicates and robustness of the analysis was not entirely clear in our manuscript. We thank the reviewer for the feedback, as this is important point to clarify. We included several additional analyses to further explain the robustness and quantitative reproducibility of our results:
- We clarified the number of replicates For quantitative MS experiments five biological replicates were analyzed for WT, while six biological replicates were analyzed for A2234D and C1264R Tg-FT, respectively not two as mistakenly presumed by Reviewer #2. These data are available in Dataset EV1 and Table EV3. There is only one place where two biological replicates are included, C1264R Tg-FT FRT cells treated with ML-240 treatment for TRIP analysis. We have further clarified the number of biological replicates within the manuscript text as follows (see also reviewer #1, comment 1):
"Subsequently, two sets of TRIP time course samples (0, 0.5, 1, 1.5, 2, and 3 hr) could be pooled using the 16plex TMTpro and analyzed by LC-MS/MS (Fig 2A). In total, 5 biological replicates were analyzed for WT and 6 biological replicates were analyzed for A2234D and C1264R, respectively (Table EV3)."
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We displayed the reproducibility of TRIP time profiles for several individual proteins in Fig EV3 __and in __Fig 3K (VCP). We included shading to indicate the standard error of the mean (SEM) for the individual protein time courses to provide further assessment of the quantitative reproducibility. We updated the text as follows: "To benchmark the TRIP methodology, we chose to monitor a set of well-validated Tg interactors and compare the time-resolved PN interactome changes to our previously published steady-state interactomics dataset (Wright et al, 2021). Previously, we found that CALR, CANX, ERP29 (PDIA9), ERP44, and P4HB interactions with mutants A2234D or C1264R Tg exhibited little to no change when compared to WT under steady state conditions (Fig EV4A). However, in our TRIP dataset we were able to uncover distinct temporal changes in engagement that were previously masked within the steady-state data. Our time-resolved data deconvolutes these aggregate measurements, revealing prolonged CALR, ERP29, and P4HB engagements for both A2234D and C1264R Tg mutants compared to WT (Fig EV4B-F). We found that these measurements for key interactors and PN pathways exhibited robust reproducibility, as exemplified by the standard error of the mean for the TRIP data (Fig EV4B-I, Appendix Figure S1B)."
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For full transparency, we also include the SEM of all TRIP profiles in the heatmap in Appendix Fig S1B.
- Furthermore, we included 25-75% quartile ranges for the pathway aggregated time courses (Fig 3B,C,J,K) and the k-means hierarchical clustering analysis (Fig 3F, Fig EV5). Especially these clustering data allow for the visualization and analysis of temporal protein interactions that are correlated with one another, while the accompanying quartile ranges provide further context for the reproducibility of these measurements and cluster profiles (see __Reviewer #1, Comment 17 __above for further explanation about the k-means clustering).
Reviewer #2, Comment #2: Compared to the previous dataset published last year, the authors discover an overlap in interactors, but also a huge discrepancy, with 96 previously identified interactors not detected in the current study, but 198 additional interactors identified. How do the authors explain the big differences between these datasets?
__Our Response: __We can only speculate here but this difference in overlapping interactors may stem from several different factors, including but not limited to cell line, instrumentation, LC-MS/MS methodology, and sample processing workflows. Our previous dataset was published using transiently transfected HEK293 cell lines expressed FLAG-tagged constructs of Tg. The HEK293 cell line makes for a robust cell line used throughout several biological investigations, but it is not representative of the native cellular environment in which Tg is expressed. Moreover, transiently transfected cells can lead to high protein expression that may not always represent what is found within the native cellular environment and proteome. Here, we used Fischer rat thyroid (FRT) cells engineered to stably express FLAG-tagged constructs of Tg. This cell line model should more accurately represent the native cellular environment Tg is expressed as it is exclusively found within thyroid tissue. Our previous dataset was collected across two different instruments with similar LC-MS/MS methodology. Here, this dataset was collected on a single instrument after performing further method optimization from our methodology used to acquire the first dataset. In line with our LC-MS/MS methodology development, the sample processing workflows here are quite different. Our previous dataset utilized 6plex TMT labeling with globally immunoprecipitated samples from various Tg constructs. Global immunoprecipitation of Tg leads to much larger protein sample amounts than the TRIP methodology presented here, which we coupled with 16plex TMTpro labeling. This is also one of the reasons we chose to deploy a booster/carrier channel within our experimental labeling schemes.
Reviewer #2, Comment #3: For the temporal interaction analysis the authors describe differences in the temporal profiles of selected interactions comparing wt and mutant, however no statistical analysis is performed comparing wt and mutant interaction profiles across the time course. Furthermore the variability between the replicates for the temporal profiles is not shown and some of the temporal profiles appear to be noisy. A more rigorous statistical analysis should be performed including additional biological replicates to evaluate the changes over the time course, especially as the temporal interaction analysis is the novelty of this study.
Our Response: Please also see our response to Reviewer #2, comment 1 above. We previously presented an analysis of the variability of the TRIP measurements (SEM) (now in Appendix Fig S1B). We have since provided further statistical analysis found in the updated Fig 2B,C,J, which include 25-75% quartile ranges for respective proteostasis network pathways. We also included SEM for the time profiles of individual interactors in Fig EV4.
To assess the divergence in time profiles in an unbiased way, we added a k-means hierarchical clustering analysis (Fig 3F, Fig. EV5). These clustering data allow for the visualization and analysis of temporal protein interaction profiles that are similar to one another and how groups of interactors shift between different clusters for WT Tg and the C1264R mutant.
Reviewer #2, Comment #4: To functionally validate interactors derived from the TRIP analysis as well as to identify factors that can rescue mutant Tg secretion the authors developed an RNA interference screen. There are a number of aspects that need to be addressed/clarified for this part of the study.
Our Response: We have added some clarifying changes to the text and the figure panels associated with the siRNA screening and follow-up experiments on the trafficking and degradation factors that rescue Tg secretion. We have addressed other comments from Reviewers #3 and #4 related to these portions of the paper and hope that Reviewer #2 finds them satisfactory.
Reviewer #2, Comment #5: While the authors validate the stable cell lines expressing the nanoluciferase tagged Tg and the linearity of luminescence signal in lysate and media carefully, they do not validate their platform in combination with the RNAi knockdown strategy. The authors should select genes as positive controls that are expected to modulate Tg secretion and demonstrate that the knockout of these positive controls indeed results in changes in Tg secretion in their system.
Our Response: This is an excellent suggestion and certainly something we would have done given any prior knowledge on known control genes that would positively or negatively regulate Tg secretion. The purpose for developing the siRNA screening platform was to investigate and hopefully discover genes that are able to positively or negatively regulate Tg processing. We have done so to the best of our ability, identifying for example NAPA which positively regulates WT Tg secretion, as seen by the decrease in WT Tg secretion when treated with NAPA siRNA. Conversely, we found that VCP may negatively regulate C1264R Tg secretion, as discovered by the increase in secretion with VCP siRNA or ML-240 treatment. We included a standard "TOX" siRNA control, which we knew would likely negatively affect WT Tg secretion and this was indeed the case. As we stated within the manuscript:
"This is the first study to broadly investigate the functional implications of Tg in-teractors and other PQC network components on Tg processing."
Reviewer #2, Comment #6: For the screen the authors select 167 Tg interactors and PN (Proteostasis network) related factors. This statement is very vague and the authors should clarify which genes were knocked down and which criteria were applied to narrow down the list of interactors and to select PN factors. The authors should therefore provide a supplementary table including all genes included in the screen, their source (were this derived from the initial study by Wright et al, from the current study or compiled from prior knowledge about PN), as well as their results from the screen based on luminescence in media and lysate. It is unclear how many of the selected factors are actually coming from the TRIP analysis.
Our Response: The list of genes included within the siRNA screen, as well as the results were previously included, and are now included in Appendix Fig S2. We have further provided the information requested by Reviewer #2 within Dataset EV5 indicating whether a gene was included in the siRNA screen due to its identification within our previous proteomics dataset (Wright et al, 2021.), the proteomics dataset presented here, or based upon primary literature. We added a comment in the text:
"Moreover, we were interested in identifying factors whose modulation may act to rescue mutant Tg secretion. HEK293 cells were engineered to stably express nanoluciferase-tagged Tg constructs (Tg-NLuc) and screened against 167 Tg interactors and related PN components (see Dataset EV5 for the list of genes)."
Reviewer #2, Comment #7: Only a small number of the 167 selected genes shows an effect on Tg abundance/secretion. How do the authors explain this result? Would we not expect that Tg interactors, especially those from the TRIP method which interact with the newly synthesized are more enriched for functionally relevant genes.
Our Response: The proteostasis network contains genes and proteins of high redundancy in structure and function, and many single-gene knockdowns are likely insufficient to have a large impact on Tg abundance or secretion. In fact, these results are in line with what we would have expected when designing these experiments. Our goal here was to identify the key players that control Tg protein quality control.
We explain the proteostasis network redundancy in the manuscript:
"The functional implications of protein-protein interactions can be difficult to deduce, especially in the case of PQC mechanisms containing several layers of redundancy across stress response pathways, paralogs, and multiple unique proteins sharing similar functions (Wright & Plate, 2021; Bludau & Aebersold, 2020; Karagöz et al, 2019; Braakman & Hebert, 2013)."
Reviewer #2, Comment #8: The authors initially performed the screen in HEK293 cells and as a second step wanted to validate the hits from the HEK cells in more relevant Fisher rat thyroid cells. Indeed they could show that knockdown of NAPA increased WT TG in lysate and decreased WT Tg secretion. Furthermore, they further validated genes to modulate mutant Tg lysate and media abundance. The authors should perform a rescue experiment to demonstrate that the observed phenotype can be reversed through re-introduction of NAPA.
Our Response: We have now performed the requested NAPA complementation experiments and provided the data within Fig EV 7I. Overexpression of a human, siRNA-resistant NAPA construct partially reversed the increase in WT Tg lysate retention. These results further support the identification of NAPA as a pro-trafficking factor for WT Tg. We updated the manuscript text to include these data as follows:
"To understand if these results were directly attributable to NAPA function, we performed complementation experiments where FRT cells treated with NAPA siRNAs were co-transfected with a human NAPA plasmid. WT Tg lysate abundance decreased when NAPA expression was complemented, confirming that the observed retention phenotype could be attributed to NAPA silencing (Fig EV7I). These results established that NAPA acts as a pro-secretion factor for WT Tg."
Reviewer #2, Comment #9: One hit from this analysis was the ER-phagy receptor TEX264, while TEX264 was not identified in the TRIP data, is selectively increased the C1264R secretion, but not wt and the other Tg mutant. Following Co-IP data however revealed some interaction between the C1264R and to a lesser extent the A2234D mutant. How do the authors explain that TEX264 was missed in the TRIP dataset?
Our Response: The TRIP samples are of much lower protein abundance compared to globally purified samples used for the Co-IP analysis. While the interaction is seen with the globally purified Co-IP samples, this interaction is likely much more difficult to capture with the low abundance, time-resolved samples that are acquired through the TRIP workflow, especially if this interaction is transient or requires the coordination of other accessory proteins as has been detailed in the literature and discussed within the manuscript presented here:
"While A2234D and C1264R Tg were preferentially enriched with TEX264 compared to WT, it remains unclear what other accessory proteins may be necessary for the recognition of TEX264 clients (Chino et al, 2019; An et al, 2019). Furthermore, TEX264 function in both protein degradation and DNA damage repair further complicates siRNA-based investigations (Fielden et al., 2022). Further investigation is needed to fully elucidate 1) if Tg degradation takes place via ER-phagy and 2) by which mechanisms this targeting is mediated."
Minor comments:
Reviewer #2, Comment #10: The workflow needs to be described clearer. For example, it should be better explained why the authors selected a two-stage enrichment strategy, I assume that the first based on the Flag affinity tag is to purify the protein of interest and the second step based on the incorporation and functionalization of the unnatural amino acids to enrich for the newly synthesized fraction at specific time points after protein synthesis? These are critical steps for the method but the rationals are not well explained, neither in the text nor the figures captures all these steps of the method very clearly, which makes it really difficult for the reader to understand the individual steps of the method. Moreover, the structures in Figure 1 workflow are not clearly labeled, so that it is confusing which part represents which protein/molecule.
Our Response: Thank you for this feedback. We have updated Fig 1 to provide more detail to provide more clarity for the readers. Furthermore, we have edited the text to more clearly describe the workflow:
"To develop the time-resolved interactome profiling method, we envisioned a two-stage enrichment strategy utilizing epitope-tagged immunoprecipitation coupled with pulsed biorthogonal unnatural amino acid labeling and functionalization (Fig 1A). Cells can be pulse labeled with homopropargylglycine (Hpg) to synchronize newly synthesized populations of protein. After pulsed labeling with Hpg, samples can then be collected across time points throughout a chase period (Fig 1A, Box 1) (Kiick et al, 2001; Beatty et al, 2006). The Hpg alkyne incorporated into the newly synthesized population of protein can be conjugated to biotin using copper-catalyzed alkyne-azide cycloaddition (CuAAC) (Fig 1A, Box 2). Subsequently, the first stage of the enrichment strategy can take place where the client protein of interest is globally captured and enriched using epitope-tagged immunoprecipitation, followed by elution (Fig 1A, Box 3). The second enrichment step can then utilize a biotin-streptavidin pulldown to capture the Hpg pulse-labeled, and CuAAC conjugated population, enriching samples into time-resolved fractions (Fig 1A, Box 4) (Li et al, 2020; Thompson et al, 2019)."
Reviewer #2, Comment #11: Except for the general workflow shown in Figure 1, a more detailed workflow showing the experimental steps, such as the sample fractions with the following steps could be added so that the design of the method is clearer. Also the style of the workflows including Figure 1, Figure 2A, and Figure 3A are different. It would be helpful to make them the same style and make the Figure 2A as a zoom in or more detailed illustration on part of Figure 1.
Our Response: Thank you for this feedback. In addition to updating Fig 1, we also expanded Fig 2A to more clearly outline the experimental steps in the TRIP workflow. Assuming the term "style" used here is in reference to color pallets and figure schematics used, these have been updated to ensure they are agreeable aesthetically across manuscript figures.
Reviewer #2, Comment #12: A summary of proteomics results of time course labeling after all enrichment steps, including the total number of identified proteins at different conditions and control would be helpful for having an overview impression on the proteomics results
Our Response: __We have included an updated __Dataset EV1 that provides a summary of proteomics data included which runs given proteins were identified in, % of TMT channels quantified, % of Hpg Pulse channels quantified, and generally number of proteins quantified across runs for each construct.
Reviewer #2, Comment #13: In Figure 2B, the WB for PDIA4 in the Biotin PD elution is missing. Why was the PDIA4 interaction missing for the time course analysis, but the interaction was captured in the initial test for Wt Tg (Figure 1D). Additionally, in this panel the Rhodamine Probe Gel shows inconsistencies at the time points 1.5 - 3h. Does this mean that the labeling did not work well for these conditions? As we would expect a consistent Rhodamine Probe signal at every time point.
Our Response: Please also see our response to Reviewer #1, comments 3 & 11. Fig 1D features continuous Hpg labeling for 4 hours to ensure that most intracellular Tg is labeled for this proof-of-concept experiment for the two-stage enrichment strategy. Fig 2B features a shorter 60 minute pulse of Hpg labeling, prior to the full chase period and two-stage enrichment strategy. PDIA4 interactions were detectable throughout Fig 1D because those measurements captured a larger population of labeled Tg, whereas in Fig 2B Tg bait protein amounts were much smaller after the two-stage enrichment procedure to capture the time-synchronized population.
The Rhodamine/TAMRA Probe Gel in Fig 2B does not have inconsistencies in Tg abundance, but highlights the fact that pulse labeled WT Tg is being secreted or degraded in FRT cells. As you would expect as time continues during the chase period, intracellular WT Tg signal decreases as secretion and degradation take place. Constant Rhodamine/TAMRA probe signal would not be expected here. Consistent with this, the C1264R Tg signal remains more stable for the intial time course. This is expected as the C1264R Tg variant is retained intracellular undergoing increased interactions the proteostasis network. We have removed the PDIA4 panel for WT Tg because there was no signal above the detection limit. This is now explained as follows:
"For WT Tg, interactions with HSPA5 peaked within the first 30 minutes of the chase period and rapidly declined, in line with previous observations, but PDIA4 interactions were not detectable by western blot analysis (Fig 2B) (Menon et al, 2007; Kim & Arvan, 1995)."
Reviewer #2, Comment #14: In Figure 2, why was there no WB results for the A2234D? In Figure 2D and 2E, at which time point are the changes significant compared to WT?
Our Response: We did not perform the WB experiments with A2234D. We used WT and C1264R Tg in our proof of concept experiments via WB and decided to move forward with analyzing A2234D Tg by LC-MS/MS. Please see our response above to Reviewer #2, comment 3 for information on the statistical analysis.
Reviewer #2, Comment #15: All figure legends should indicate how many biological replicates were performed for each experiment represented in the figure.
Our Response: We have updated the figure captions to include this information where applicable.
Reviewer #2, Comment #16: The heatmaps shown in Figure 3, Figure 3 - Figure Supplement 3, and Figure 7 are in the current form incomprehensible. The heatmaps depict the relative enrichment vs the control sample, which was scaled between 1 and -1. The color coding with 5 different colors from 1 to -1 is very confusing and should be changed to just two colors, one for positive and one for negative relative enrichment. I would also suggest changing the visualization of the heatmap showing the wt and mutants side by side, instead of stacked on top of each other for each individual protein.
Our Response: Thank you for this feedback, and we apologize for the confusion. We adjusted our data analysis approach by removing previous negative enrichment values. As these served only as "background" within the dataset, they did not carry much meaning. The TRIP enrichment is now scaled from 0 to 1, where a value of 1 represents the time point at which the enrichment is greatest, while 0 represents the background intensity in the (-) Hpg control sample. The associated figures have been updated accordingly, and we feel they are now more comprehensible and aesthetically pleasing.
We opted to keep the Viridis color scheme in the heatmap to allow for more nuanced differentiation of the enrichment values.
Reviewer #2, Comment #17: The data analysis method for generating relative enrichment shown in the heatmap is not explained. This should be described in the method section for a better understanding of the data analysis.
Our Response: We have edited the methods section as follows to better explain the analysis:
"For time resolved analysis, data were processed in R with custom scripts. Briefly, TMT abundances across chase samples were normalized to Tg TMT abundance as described previously and compared to (-) Hpg samples for enrichment analysis (Wright et al, 2021). For relative enrichment analysis, the means of log2 interaction differences were scaled to values from 0 to 1, where a value of 1 represented the time point at which the enrichment reached the maximum, and 0 represented the background intensity in the (-) Hpg channel. Negative log2 enrichment values were set to 0 as the enrichment fell below the background."
Reviewer #2, Comment #18: There are no legends of flowcharts in Figure 2A and Figure 3A and it is difficult to understand which are the key components in the complex and what are the differences among different periods of labeling.
Our Response: We have now consolidated Fig 2A and Fig 3A into a single panel found in Fig 2A, which is significantly reorganized to better explain the TRIP workflow. The caption has additionally been updated to highlight key steps within the workflow with numbering to allow readers to follow and visualize the steps more easily. The figure caption now reads as follows:
"(A) Workflow for TRIP protocol utilizing western blot or mass spectrometric analysis of time-resolved interactomes. (1) Cells are pulse-labeled with Hpg (200μM final concentration) for 1 hr, chased in regular media for specified time points, and cross-linked with DSP (0.5mM) for 10 minutes to capture transient proteoastasis network interactions; (2) Lysates are functionalized with a TAMRA-Azide-PEG-Desthiobiotin probe using copper CuAAC Click reaction; (3) Lysates undergo the first stage of the enrichment strategy where the Tg-FT is globally captured and enriched using immunoprecipitation; (4) Eluted Tg-FT populations from the global immunoprecipitation undergo biotin-streptavidin pulldown to capture the pulse Hpg-labeled, and CuAAC conjugated population of Tg-FT, enriching samples into time-resolved fractions; (5) Time-resolved fraction may then undergo western blot analysis or (6) quantitative liquid chromatography - tandem mass spectrometry (LC-MS/MS) analysis with tandem mass tag (TMTpro) multiplexing or analysis. The (-) Hpg control channel is used to identify enriched interactors and a (-) Biotin pulldown channel to act as a booster (or carrier)."
Reviewer #2, Comment #19: Why did only one of the VCP inhibitors (ML-240) exhibit a phenotype in Tg abundance and secretion, but not the other VCP inhibitors?
Our Response: Please also see our response to Reviewer #3, comment 2 below. This could be due to a number of reasons, but we added a brief discussion on the mechanisms of action for the inhibitors that may at least partially explain the differences in phenotype seen with the VCP inhibitors. We updated the text as follows:
"ML-240 and CB-5083 are ATP-competitive inhibitors that preferentially target the D2 domain of VCP subunits, whereas NMS-873 is a non-ATP-competitive allosteric inhibitor which binds at the D1-D2 interface of VCP subunits (Chou et al, 2013, 2014; Anderson et al, 2015; le Moigne et al, 2017; Tang et al, 2019). ML-240 and NMS-873 have been shown to decrease both proteasomal degradation and autophagy, in line with VCP playing a role in both processes (Chou et al, 2013, 2014; Her et al, 2016). Conversely, while CB-5083 is known to decrease proteasomal degradation it has been shown to increase autophagy. (Anderson et al, 2015; le Moigne et al, 2017; Tang et al, 2019)."
Reviewer #2 (Significance (Required)):
Reviewer #2, Comment #20: __The authors __describe a novel and elegant method to map time resolved protein interactions of newly synthesized proteins, which allows monitoring of proteins regulating protein quality control.
Authors describe it as a general method, however, they only demonstrate the applicability to one protein and do not systematically evaluate the quantitative nature of their approach by determining quantitative reproducibility, which would be necessary to be able to claim that this is a method with broad applicability.
Given my expertise in quantitative proteomics, I can mainly comment on the technological aspects of the proteomics part of the manuscript, but do not feel qualified to evaluate the significance of this study in terms of novel biology. Nevertheless, it feels that there is a stronger emphasis on the biology in the current form of the manuscript which will raise interest of scientists with a focus on protein quality control and Tg biology.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
In this manuscript, the authors describe their efforts to develop a methodology for determining time-resolved protein-protein interactions using quantitative mass spectrometry. With TRIP (time-resolved interactome profiling), they combine a pulsed bio-orthogonal unnatural amino acid labelling (homopropargylglycine, Hpg), CuAAC conjugation and biotin-streptavidin pulldowns to enrich at different timepoints and time-resolve by combining TMT labelling and LC-MS/MS (Figure 1). This technique is then applied to the maturation of the secreted WT and mutant thyroglobulin (Tg-WT, Tg-C1264R, Tg-A2234D) expressed in HEK293 and rat thyroid cells (FRT) and linked to hyperthyroidism. There, they identify a collection of ER resident proteins involved in protein folding/processing (e.g. chaperones, redox, glycans, hydroxylation) as well as degradation (e.g. autophagy, ERAD/proteasomes) (Fig. 2). Here the authors effectively use pulse-labelled form of TRIPs to highlight the different interactions formed with Tg-WT vs. Tg-mutants during biogenesis and secretion (or retention). The analysis found ~200 new interactions compared to previous studies along with about 40% of those identified previously. Differences in interactions were observed for mutants, which shown extended interaction with chaperones and redox processing pathways. While many interactions appeared as might be expected, the identification of membrane protein processing elements (e.g. EMC, PAT) was puzzling and raised some questions about the specificity within the protocol. Mutants enriched for CANX CALR and UGGT, suggesting prolonged association with glyco-processing factors. Interaction of C1264R with the ER-phagy factors CCPG1 and RTN3 was greater than WT. The authors note that their interaction correlated with that of EMC1 & 4, but it is not clear why that might be.
With interactors in hand, the authors complemented the TRIP protocol with siRNA KD of identified factors, to investigate any changes to secreted vs intracellular Tg upon loss. KD of NAPA (a-SNAP) and LMAN1 increased WT lysate (intracellular) Tg but not mutants. NAPA also reduced Tg-WT secretion. In contrast, KD of NAPA increased A2234D secretion while LEPRE1 increased C1264R (but not A2234D or WT), suggesting mutants have differential processing paths and requirements. KD of VCP increased secretion of both mutants. Some ER-phagy receptors were found among interactors (e.g. RTN3 in Tg-C1264R only) but often their KD had no impact on secretion (CCPG1, SEC62, FAM134B). NAMA observations were recapitulated in thyroid derived cell line (FRT). KD of TEX264 and VCP increased Tg-C1264 secretion while RTN3 KD in FRTs decreased Tg-C1264 secretion. This was in contrast to data from HEK293s for reasons that are not clear. Co-IP with TEX264 enriched for all Tg forms but more so for C1264R and A2234D - motivating the authors to propose selective targeting of Tg to TEX264 and the consideration of ER-phagy as a "major" degradative pathway during Tg processing.
Given the observations with siRNAs to VCP, the authors next use a selection of VCP inhibitors to ask whether secretion can be rescued upon pharmacological impairment of the AAA ATPase. They observed that ML-240, but interestingly not the more conventionally used CB-5083 or NMS-873, increased secretion of Tg-C1264R but not lysate. Inhibitors increased lysate but decreased the secreted fraction for Tg-WT (Fig 7). Finally, the authors used TRIP again in ML-240 treated Tg-C1264R expressing cells to look for changes to interactome with treatment - observed decreases to glycan and chaperone interactions, CANX and UGGT1, decreased interaction with DNAJB11 and C10, like that of WT. There was no apparent change to the UPR, although activation was not directly measured.
Major comments:
Reviewer #3, Comment #1: __Are the key conclusions convincing? __The TRIP methodology appears to be quite robust and should be a powerful strategy for this field and others going forward. The drawback will be the length of pulse required will limit the number/type of proteins to be monitored to ones with longer t1/2's. There were interesting interactions found with Tg and the mutants linked to hyperthyroidism, but cut and dry differences did not appear as obvious, even though strong "trends" appear to be present. The path from identifying interactors in a time-resolved manner to then following them up with targeted KD does provides some clarity, which is important.
Our Response: We thank Reviewer #3 for their time in reviewing our manuscript and providing this positive feedback. We have enhanced our analysis of the TRIP data to more clearly highlight difference in time profiles between WT and mutant variants. Please see our response to Reviewer #2, comment 1 & 3. We also highlight the limitations of the time resolution in the discussion (see also Reviewer #2, comment 6):
"To address this, we utilized a labeling time of 1 hr which allows us to generate a large enough labeled population of Tg-FT for TRIP analysis, but some early interactions are likely missed within the TRIP workflow. In the case of mutant Tg, performing the TRIP analysis for much longer chase periods (6-8 hrs) may provide insightful details to the iterative binding process of PN components that is thought to facilitate protein retention within the secretory pathway."
We have addressed all further comments below.
__Reviewer #3, Comment #2: __Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The data regarding VCP silencing and pharmacological impairment appear clear but leave some questions outstanding in this reviewer's opinion. The lack of effect with the 2 highly selective inhibitors suggests that the underlying mechanism for switching fate of intracellularly retained Tg-C1264R towards secreted forms is not at all clear. ML-240 is an early derivative of DBeQ and reportedly impairs both ERAD and autophagic pathways, similarly to DBeQ. The differences between the VCP inhibitors' mechanism of action were not discussed, but perhaps should be elaborated upon, particularly in the matter of how ERAD and ER-phagy pathways might be being differentially affected. At the risk of asking for too many additional experiments, this reviewer would just prefer to see this fleshed out in a bit more detail.
Our response: We agree with Reviewer #3 that the underlying mechanism for switching fate of the intracellular retained Tg-C1264R towards secreted forms remains unclear. We have added additional text to discuss further the details surrounding the inhibitors used and the general manner in which ERAD and ER-phagy pathways can be affected. This added text reads as follows:
"ML-240 and CB-5083 are ATP-competitive inhibitors that preferentially target the D2 domain of VCP subunits, whereas NMS-873 is a non-ATP-competitive allosteric inhibitor which binds at the D1-D2 interface of VCP subunits (Chou et al, 2013, 2014; Anderson et al, 2015; le Moigne et al, 2017; Tang et al, 2019). ML-240 and NMS-873 have been shown to decrease both proteasomal degradation and autophagy, in line with VCP playing a role in both processes (Chou et al, 2013, 2014; Her et al, 2016). Conversely, while CB-5083 is known to decrease proteasomal degradation it has been shown to increase autophagy. (Anderson et al, 2015; le Moigne et al, 2017; Tang et al, 2019)."
"As we discovered that pharmacological VCP inhibition with ML-240 can rescue C1264R Tg secretion yet is detrimental for WT Tg processing, it is unclear whether VCP may exhibit distinct functions for WT and mutant Tg PQC. Finally, as ML-240 is shown to block both the proteasomal and autophagic functions of VCP it is unclear which of these pathways may be playing a role in the rescue of C1264R, or detrimental WT processing (Chou et al, 2013, 2014)."
__Reviewer #3, Comment #3: __Q1. The degree (if any) of Tg-C1264 aggregation during and/or detergent solubility do not appear to have been considered as a potential source of the increase in released secreted material (Figure 4, 5). Do Tg mutants partition into RIPA-insoluble fractions at all? That is to say.. is the total population of synthesized Tg being considered? A full accounting? Could the authors address this and if biochemical extraction data (via urea or high SDS) is available, include it to answer this concern.
Our response: The transient aggregation of Tg has been investigated in some detail previously (Kim et al, 1992, 1993). The transient aggregates have the ability to partition into RIPA-insoluble fractions. Of note, these aggregates are shown to be made up, at least in part, of mixed disulfide linkages requiring reducing agent to fully resolubilize. With that being said, these aggregates represent a minority of the overall Tg population. In our prior manuscript (Wright, et al. 2021), we quantified the RIPA-insoluble fraction found in the pellet (see Supplemental Info Fig. 5). As the majority of Tg remains soluble during processing it should be able to be captured via our TRIP methodology. That is to say, we are capturing most of the Tg that is available for analysis while understanding that some smaller population of Tg remains in RIPA-insoluble fractions.
__Reviewer #3, Comment #4: __Q2. Along the same lines, what does Tg-WT and mutant expression look like by microscopy? Is Tg-WT uniformly distributed while Tg-mutants appear in puncta... more aggregated - perhaps reflecting the increased engagement of chaperones and redox machinery? Changes in the pattern of Tg-C1264R mutant (e.g. w/ VCP KD or inhibition) would add additional support for the authors interpretation of improved secretion. If this data is at hand, including it might be worth consideration.
Our response: Thank you for this suggestion. The subcellular localization of Tg and any changes from proteostasis modulation is an ongoing area of follow up work in our lab. We have some preliminary results that the localization for WT and C1264R Tg indeed differs. However, given that this manuscript is already dense in information, we opted to reserve this data for a future manuscript where we plan to further elucidate the targeting mechanism of mutant Tg to VCP or TEX264. We direct the reviewer to work published by Zhang et al, 2022,(https://doi.org/10.1016/j.jbc.2022.102066) showing a staunch difference of WT vs mutant Tg in the localization from intracellular to a secreted population in rat tissue. While most all WT Tg is found in the follicular lumen (secreted), mutant Tg heavily co-localizes with the ER resident chaperone BiP. While this paper does not go into detail on the differences in subcellular localization, it further highlights the drastic changes in Tg processing and how these manifest in distinct differences in localization within tissue.
__Reviewer #3, Comment #5: __Q3. Does the level of Tg mutant expression in the FRT clones impact the profiles obtained by TRIP? (Figure 3). This is a question of gauging the relative saturation of QC machinery and how that might impact profiles from TRIP. Were clones expressing at different levels tested? Perhaps a brief discussion of this.
Our response: We do not foresee an impact from level of Tg expression on the profiles obtained by TRIP. We were able to identify distinct profiles because we processed the data and normalized it based on the relative Tg amount. For example, while WT and A2234D Tg are expressed at similar levels intracellularly, we were able to identify distinct differences in the interaction profiles across the two constructs. When developing FRT clones, we selected those that were expressed at similar levels and, therefore, did not have the capability to directly test differences, if any, in observed profiles that may be the result of different expression levels of the same Tg construct. Furthermore, Tg can make up 50% of all protein content within thyroid tissue (Di Jeso & Arvan, 2016). As such, thyroid cells are adept at maintaining the balance of QC machinery to process thyroid. Therefore, we do not anticipate that the amount of Tg expressed in TRIP experiments would have a significant impact on the profiles that we were able to observe.
__Reviewer #3, Comment #6: __Q4. For Figure 3, the hour-long labelling period seems a bit long, compared with 3 hr of chase. Perhaps this reviewer missed this but how long does Tg take to mature and/or mutants to misfold and degrade? Is there any possibility to shorten this so that the profiles of labelled Tg could be more synchronized? If not, perhaps this could just be discussed.
Our response: While the 1-hour labeling period may seem long, we had to balance the labeling time to 1) label a large enough population of Tg for it to remain detectible throughout the chase period, and 2) keep the chase period long enough to capture the large majority of Tg processing. In our hands we found that by 4 hours WT Tg was ~63% secreted, with ~25% retained intracellular (Fig EV7H). Conversely, we found that C1264R remains very stable over this period with most protein being retaining intracellularly and little degradation taking place (Fig EV9A). Hence, we opted for the overall ~4 hour total for sample processing (1 Hr pulse labeling + 3 hour chase period for time point collections). Literature suggest that WT Tg takes ~2 hours to be processed within the ER and reach the medial golgi. This is exemplified by the EndoH resistant population that appears at this ~2 hour time point (Menon et al. JBC. 2007). Please also see our response to Reviewer #1, comment 6. We updated the text as follows:
"We pulse labeled WT Tg FRT cells with Hpg for 1 hr, followed by a 3 hr chase in regular media capturing time points in 30-minute intervals and analyzing via western blot or TMTpro LC-MS/MS (Fig 2A). Our previous study indicated that ~70% of WT Tg-FT was secreted after 4 hours, while approximately 50% of A2234D and 15% of C1264R was degraded after the same time period (Wright et al, 2021). Therefore, we reasoned that a 3-hr chase period would be a enought time to capture the majority of Tg interactions throughout processing, secretion, cellular retention, and degradation, while still being able to capture an appreciable amount of sample for analysis."
We anticipate that this labeling period can be decreased with future iterations of this methodology. This will also be bolstered by the continued improvements that come about within quantitative proteomics in increased instrument sensitivity and improved sample preparation methods that have the ability to decrease sample loss.
We explain the labeling timeline and limitations further in the discussion:
"To address this, we utilized a labeling time of 1 hr which allows us to generate a large enough labeled population of Tg-FT for TRIP analysis, but some early interactions are likely missed within the TRIP workflow. In the case of mutant Tg, performing the TRIP analysis for much longer chase periods (6-8 hrs) may provide insightful details to the iterative binding process of PN components that is thought to facilitate protein retention within the secretory pathway."
__Reviewer #3, Comment #7: __Q5. It is curious that only ML-240 and not other well characterized inhibitors of VCP/p97, has an effect, as both are used far more often than ML-240. The authors do not really address this in detail but does it suggest that the ML-240 effect on VCP/p97 could be affecting different pathways, given the nature of this compound. Is this compound acting on Tg-C1264R maturation at the level of translation or post-translationally? If the latter, through what means?
Our Response: We thank Reviewer #3 for appreciating this surprising finding. We were similarly curious as to how, or why ML-240 was able to elicit this effect compared to other VCP inhibitors. We elaborated in the manuscript text on these compounds and on how the ERAD and ERphagy pathways, utilizing VCP, may be differentially regulated (See response to__ Reviewer #3, Comment 2__). While speculative, we believe that ML-240 acts on C1264R Tg maturation post-translationally. This is given by the fact that ML-240 does not seem to affect the translational velocity of C1264R Tg, as Fig EV9A shows similar levels of 35S-labeled C1264R in DMSO or ML-240 treated cells. It may be the case that acute treatment with ML-240 alters the folding vs degradation balance of the ER proteostasis network in such a way that some population of C1264R that is usually degraded is able to be secreted. Another Tg mutation G2320R was shown to be degraded via the proteasome in PLCCL3 thyrocytes, as MG-132 treatment slowed mutant Tg degradation (Menon et al. JBC. 2007), although G2320R degradation was not be exclusively proteasomal. The L2284P Tg mutation exemplified similar results to G2340R where MG-132 slowed degradation. Furthermore, L2284P Tg was not affected by autophagic/lysosomal inhibitors chloroquine and E64 (Tokunaga et al. JBC. 2000), suggesting ERAD more exclusively degrades L2284P. It is unclear which degradation pathway, ERAD or ER-phagy, may be the predominate pathway for C1264R Tg degradation. Furthermore, we do not exclude the possibility that both may be at play and affected by treatment with ML-240.
We utilized our HEK293 Tg-NLuc cells and screened other proteasomal and lysosomal inhibitors bafilomycin and bortezomib. Neither of these compounds were able to rescue A2234D or C1264R secretion, highlighting that the effect is specific to ML-240 treatment. This new data is now shown in __Fig EV10A,B __and described in the text:
"To understand whether this rescue in secretion was uniquely linked to VCP inhibition or could be more broadly attributed to blocking Tg degradation, we tested the proteasomal inhibitor bortezomib, and lysosomal inhibitor bafilomycin. Bafilomycin increased WT Tg lysate abundance, and bortezomib significantly increased A2234D lysate abundance, consistent with a role of these degradation processes in Tg PQC (Fig EV10A). When monitoring Tg-NLuc media abundance, neither bafilomycin nor bortezomib significantly altered WT, A2234D, or C1264R abundance (Fig. EV10B). confirming that general inhibition of proteasomal or lysosomal degradation does with rescue mutant Tg secretion."
__Reviewer #3, Comment #8: __Q6. Continuing from Q5.. At what point and where is VCP/p97 able to affect mutant Tg processing? In line 317, the authors seem to correlate increased VCP association with mutants to their increased secretion. It is not clear how this would result, as engagement with VCP would be in a compartment different to that which supports trafficking and secretion. Could the authors expand on how this might come about. This is also relevant to the ML-240 data in Figure 7. Moreover, VCP is associated with ERAD (as is HerpUD1) rather than ER-phagy and at least in the siRNA raw data, there are also effects from Derlin3 and FAF2 KDs.. both ERAD factors. Some clarity here would be appreciated.
Our Response: This line of discussion in the text was meant to suggest that, since VCP showed a higher enrichment for mutant Tg, particularly C1264R, it would make sense that inhibiting VCP would have a larger effect on mutant Tg processing as compared to WT Tg. As we saw with the siRNA screening data, suppression of VCP resulted in increased C1264R secretion, while not affecting WT Tg processing. This passage was not intended to suggest that increased VCP association with mutant Tg found within the TRIP dataset was the reason for rescued secretion. These are two different sets of experiments and environments in which these data are captured. We were simply looking for the opportunity to bridge the findings from the two sets of experiments to a single discussion point. Of note, we understand that VCP is associated with ERAD and acts to regulate autophagy. Given that core autophagy machinery is relevant for both bulk autophagy and ER-phagy, we did not want to rule out the fact that VCP inhibition via ML-240 could affect autophagic flux in these experiments (Chou et al. Chemmedchem. 2013; Khaminets et al. Nature. 2015; Hill et al. Nat. Chem. Bio. 2021.)
It is great that the reviewer also noted that DERL3 and FAF2 knockdown increased C1264R Tg secretion. Since these ERAD factors did not reach the defined threshold in the screen, we did not include further discussion, but this data remains available in Appendix Fig S3. We have updated the manuscript text to clarify the previous points we aimed to make. The text now reads as follows:
"VCP silencing exclusively affecting mutant Tg corroborates our TRIP dataset, and suggest a more prominent role for VCP in mutant Tg PQC compared to WT. VCP interactions were sparse for WT Tg while they remained more steady throughout the chase period for the mutants (Fig 3H,K)."
__Reviewer #3, Comment #9: __Q7. There does not appear to be a direct demonstration of Tg-C1264R turnover by ER-phagy (via TEX264). Given the inconsistency with it not being detected by TRIP, while another receptor RTN3 was, but has not impact on Tg-C1264R secretion, perhaps including that data would go some way to demonstrating a fate of ER-phagy (at least partly) for this mutant.
Our response: We performed follow-up experiments to test interactions with Tg and the wider panel of ER-phagy receptors. We transiently expressed FLAG-tagged CCPG1, RTN3L, and TEX264 in HEK293 cells stably expressing Tg-NLuc and performed FLAG IPs followed by western blot analysis. We found that WT and C1264R Tg were enriched, albeit modestly, in the RTN3L Co-IP compared to control samples expressing GFP. Additionally, we found that WT, A2234D, and C1264R Tg were all enriched with CCPG1 compared to control samples expressing GFP. CCPG1 was found to be a C1264R Tg interactor within our mass spectrometry datasets, along with RTN3. We have now integrated these data into the manuscript as Fig EV8, and updated the manuscript text as follows:
"Additionally, we monitored Tg enrichment with ER-phagy receptors CCPG1 and RTN3 via Western blot as both were found to be C1264R Tg interactors within our TRIP dataset. RTN3L is found to be the only RTN3 isoform involved in ER turnover via ER-phagy (Grumati et al, 2017). WT and C1264R Tg-NLuc were modestly enriched with RTN3L compared to control samples expressing GFP. Conversely, we found that all Tg variants exhibited modest interactions with CCPG1 compared to control samples expressing GFP, although less than with TEX264 (Fig EV8).
Together, these data suggest that TEX264, CCPG1, or RTN3L engage with Tg during processing, and CH-associated Tg mutants may be selectively targeted to TEX264. Furthermore, ER-phagy may be considered as a degradative pathway in Tg processing, as other studies have mainly focused on Tg degradation through ERAD (Tokunaga et al, 2000; Menon et al, 2007)."
Whether the TEX246 recruitment of mutant Tg leads to degradation remains to be tested. When we monitored C1264R Tg degradation by pulse-chase assay (Fig. EV9A), only a small fraction (
__Reviewer #3, Comment #10: __Q9. The authors provide data that the UPR was not induced by ML-240 at 3hrs (10µM) (Figure 7, supplemental 1). This is in stark contrast to the results of Chou et al (2013) which the authors reference, reporting that ML-240 induced ATF4 and CHOP by 2 hrs at concentrations lower than used here (albeit a different cell type). While not exclusively UPR, could the authors address the potential activation of the integrated stress response (eIF2a phosphorylation, ATF4 and CHOP) in the FRT cells due to ML-240 treatment? If present, is there some link that could this provide an explanation for increased Tg-C1264R secretion? [Basal PERK/UPR activation with mutants.]
Our Response: Thank you for bringing up this important point. As the reviewer acknowledges, the difference in UPR activation could stem from the different cell lines. Additionally, we measured activation via qPCR, whereas Chou et al. measured via immunoblot. We would like to point out that while we did not observe the upregulation of HSPA5 or ASNS (markers of ATF6 and PERK/ISR activation, respectively) in the presence of short ML-240 treatment (2-3 hr), we did observe the upregulation of DNAJB9 (a marker of IRE1/XBP1s activation).
To address Reviewer #3's point, we performed further experiments monitoring the potential activation of the ISR in FRT cells due to ML-240 treatment. We treated C1264R Tg-FT FRT cells with ML-240 (10μM) for 2 hours, and monitored eIF2a phosphorylation via immunoblot. Indeed, we observed that ML-240 induced eIF2a phosphorylation compared to cells treated with DMSO. Tunicamycin (1mg/mL) was used a positive control, and showed similar results to ML-240. We have integrated these results into the manuscript, available in Fig EV10C.
However, we would like to point out that all of these markers represent signs of early UPR inductions. Importantly, our results that HSPA5 transcript levels are not induced suggest that there is only very modest upregulation of ER chaperone levels occurring. Typically, the ER proteostasis network remodeling requires a longer time than the acute 2-4 hr treatment with ML-240. We have updated the manuscript text as follows:
"Finally, we monitored activation of the unfolded protein response (UPR) in the presence of ML-240 in FRT cells expressing C1264R Tg-FT. Phosphorylation of eIF2a, an activation marker for the PERK arm of the UPR, was induced within 2 hr of ML-240 treatment (Fig EV10C). We further investigated the induction of UPR targets via qRT-PC. HSPA5 and ASNS transcripts, markers of ATF6 and PERK UPR activation respectively, remained unchanged or slightly decreased after 3 hr treatment with ML-240 in C1264R Tg cells (Fig EV10D). Only DNAJB9 transcript expression showed a significant increase in both WT Tg and C2164R Tg FRT cells (Fig EV10D). Moreover, ML-240 did not significantly alter cell viability after 3 hr, as measured by propidium iodide staining (Fig EV10E). Overall, these results highlight that the short ML-240 treatment induces early UPR markers, but the selective rescue of C1264R Tg secretion via ML-240 treatment is unlikely the results of global remodeling of the ER PN due to UPR activation."
__Reviewer #3, Comment #11: __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. Any of the suggested experiments above all use reagents reported in the manuscript and so would presumably incur minimal cost and hopefully time. This reviewer is sympathetic to time and financial constraints and so discussion of the issue could suffice.
Our response: We have addressed follow-up experiments whenever possible or provided further discussion details where applicable. We are appreciative of Reviewer #3's sympathy for the time and financial constraints that go into this work and addressing manuscript revisions. Unfortunately, the 1st and 2nd authors both left the lab immediately after the reviews were received. Hence, many of the experiments had to be addressed by other lab members joining the project, which took considerably longer than anticipated. We apologize for the long delay with our revisions.
__Reviewer #3, Comment #12: __Are the data and the methods presented in such a way that they can be reproduced? Yes. The methodology is explained in detail.
Our Response: Thank you.
__Reviewer #3, Comment #13: __Are the experiments adequately replicated and statistical analysis adequate? Yes. Relevant information is either in the figure legends or is provided in the source data.
Our Response: Thank you.
Minor comments:
__Reviewer #3, Comment #14: __Are prior studies referenced appropriately? The references are generally appropriate, with a few exceptions of more general references used
Our Response: Thank you.
__Reviewer #3, Comment #15: __Are the text and figures clear and accurate? The text is clearly written, and the figures are clear.
Our Response: Thank you.
__Reviewer #3, Comment #16: __Do you have suggestions that would help the authors improve the presentation of their data and conclusions? A summary figure comparing the changing profiles of WT and C1264R and the factors implicated for them could be helpful.
Our Response: We opted not to include a summary figure because the paper and figures area already dense in information.
__Reviewer #3, Comment #17: __Perhaps include common nomenclature for proteins as well (e.g. HSP5A - BiP, HSP90B1 - Grp94, etc..)
Our Response: We updated the manuscript throughout to reference common nomenclature or other protein names where applicable at their first mention.
__Reviewer #3, Comment #18: __Line 317 - our is misspelled
Our Response: Thank you. We have made this correction.
__Reviewer #3, Comment #19: __Figure 4 - Supplemental Figure 1 - Legend has text referring to panels J and K, but Figure only goes up to F.
Our Response: Thank you. This was an error in references to Figure panel lettering and we have since corrected this. Please note that this Figure is now Fig EV6.
Reviewer #3 (Significance (Required)):
__Reviewer #3, Comment #20: __
-
Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
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Place the work in the context of the existing literature (provide references, where appropriate).
Protein-protein interactions are often used to illustrate complexes and functionality, but these provide only snapshots, rather than "movies". There are many datasets out there exploring P-P interactions, but most if not all lack any temporal resolution for the interactions they report. The TRIP method described approaches this from the dynamic perspective - identifying the transient interactions formed by folding nascent chains with proteins that aid in their maturation and trafficking, or degradation. This represents an important technical advance in our ability to dynamically monitor protein interactions. The use of Tg mutants is valuable and perhaps this will lead to new perspectives on how to rescue it or other pathophysiological mutants with loss of function phenotypes.
- State what audience might be interested in and influenced by the reported findings.
This work should appeal to a broad audience within cell biology, particularly as the TRIP technique is attempting to address a fundamental question - what interactions form during the biogenesis/lifetime of a protein. Moreover, the effort to try to understand the different interactions formed with pathologically relevant mutant proteins as a strategy to try to rescue functionality, is a valuable exercise of this approach.
- 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.
ER quality control
Our Response: We thank reviewer #3 for this positive endorsement.
Reviewer #4 (Evidence, reproducibility and clarity (Required)):
Summary
In this manuscript, Wright et al. developed an approach (termed TRIP) that allowed to map the temporal changes in the interaction landscape of a newly synthesized protein of interest. Using their TRIP approach, the authors found that the extensive interactions of thyroglobulin (Tg) with the proteostasis network (PN) during its passage through the secretory pathway were profoundly altered in response to disease-causing mutations (e.g. C1264R). The authors cross-validated their findings with a focus RNAi screen monitoring the cellular and secreted abundance of Tg variants upon deletion of PN components. In subsequent experiments the authors focused on two hits, VCP and TEX264, for which they confirmed their inhibitory effect on the secretion of Tg C1264R. Importantly, the authors found that TEX264 increasingly interacts with the Tg mutant and that pharmacological inhibition of VCP yielded the same phenotype than depletion of VCP. Overall, Wright and colleagues__ established an elegant method to map protein interaction in a time-resolved manner and demonstrated its value by the analysis of disease-related Tg mutants__. Hence, this work has the potential to serve as a rich resource for Tg-related research and as a powerful new tool to examine protein interactions. However, several concerns remain.
Our response: Thank you to reviewer #4 for their valuable feedback and positive assessment. We addressed all comments in detail below.
Major points:
__Reviewer #4, Comment #1: __Overall, the TRIP workflow is quite difficult to understand at a first glance - even for a reader with a background in proteomics, biochemistry and cell biology. The authors may want to improve the description of the TRIP methodology and explain in more detail what the individual components and steps are good for. Along the same line, from the main text and the figure legend it was not clear that Tg was actually Flag-tagged. However, without this information it is difficult to follow the workflow. While Figure 1A is certainly helpful, the bulky graphics are deflecting the reader's attention. A more schematic version might be more informative.
Our Response: Thank you for this feedback, which was also mirrored by Reviewer #2 (comment 10). We have made significant updates to clarify Fig 1 to provide more detail and eliminate some of unnecessary bulky graphics. We also expanded the schematic for the TRIP workflow in Fig 2A and we aligned all symbols used. Furthermore, we have edited the text to describe the workflow more clearly:
"To develop the time-resolved interactome profiling method, we envisioned a two-stage enrichment strategy utilizing epitope-tagged immunoprecipitation coupled with pulsed biorthogonal unnatural amino acid labeling and functionalization (Fig 1A). Cells can be pulse labeled with homopropargylglycine (Hpg) to synchronize newly synthesized populations of protein. After pulsed labeling with Hpg, samples can then be collected across time points throughout a chase period (Fig 1A, Box 1) (Kiick et al, 2001; Beatty et al, 2006). The Hpg alkyne incorporated into the newly synthesized population of protein can be conjugated to biotin using copper-catalyzed alkyne-azide cycloaddition (CuAAC) (Fig 1A, Box 2). Subsequently, the first stage of the enrichment strategy can take place where the client protein of interest is globally captured and enriched using epitope-tagged immunoprecipitation, followed by elution (Fig 1A, Box 3). The second enrichment step can then utilize a biotin-streptavidin pulldown to capture the Hpg pulse-labeled, and CuAAC conjugated population, enriching samples into time-resolved fractions (Fig 1A, Box 4) (Li et al, 2020; Thompson et al, 2019)."
Additionally, we have improved text to very clearly state that for the TRIP experiments Tg is FLAG-tagged and this epitope tag is required for the two-stage enrichment strategy. As one small example:
"Thyroglobulin was chosen as the model secretory client protein. We generated isogenic Fischer rat thyroid cells (FRT) cells that stably expressed FLAG-tagged Tg (Tg-FT), including WT or mutant variants (A2234D and C1264R) (Fig EV1)"
"Furthermore, the C-terminal FLAG-tag and Hpg labeling are necessary for this two-stage enrichment strategy, and DSP crosslinking is necessary to capture these interactions after stringent wash steps (Fig 1D, Fig EV2)."
__Reviewer #4, Comment #2: __To what extend do the difference in protein abundance between Tg WT and Tg C1264R contribute to the increase binding of their interactors (e.g., HSP5 and PDIA4). The authors should perform a TRIP coupled immunoblot analysis where WT and Mutant are loaded side-by-side on the SDS-PAGE.
Our Response: As Reviewer #3 (comment 5) had a similar inquiry, we provide the same response as listed above:
We do not foresee an impact from level of Tg expression on the profiles obtained by TRIP. We were able to identify distinct profiles because we processed the data and normalized it based on the relative Tg amount. For example, while WT and A2234D Tg are expressed at similar levels intracellularly, we ere able to identify distinct differences in the interaction profiles across the two constructs. When developing FRT clones, we selected those that were expressed at similar levels and, therefore, did not have the capability to directly test differences, if any, in observed profiles that may be the result of different expression levels of the same Tg construct. Furthermore, Tg can make up 50% of all protein content within thyroid tissue (Di Jeso & Arvan, 2016). As such, thyroid cells are adept at maintaining the balance of QC machinery to process thyroid. Therefore, we do not anticipate that the amount of Tg expressed in TRIP experiments would have a significant impact on the profiles that we were able to observe.
__Reviewer #4, Comment #3: __While the RNAi screen was done with pooled siRNA, it is not clear what was used for the RNAi validation experiments shown in Figure 5. This should be done by individual siRNA and not the same pooled reagents as used for the screen.
Our Response: Similarly, pooled siRNAs were initially utilized for the data shown in Figure 5. The RNAi screen utilized siRNAs optimized for human cells, where as those found for Figure 5 were for rat cells. For the revisions, we performed control experiments with individual siRNAs, which are now shown in Fig EV7J,K. While we did not find that any one single siRNA recapitulated the full phenotype, we did find that several single siRNAs for VCP and TEX264 at least partially restored the observed phenotype of increased C1264R Tg secretion. This result is expected given that we reasoned the siRNAs are likely providing an additive effect contributing to the observed phenotypes. We provided these single siRNA control experiments in Fig EV7J,K, and updated the manuscript text as follows:
"Several individual VCP and TEX264 siRNAs were able to partially recapitulate these increased secretion phenotype on C1264R Tg-FT, confirming that the effect is mediated by the respective gene silencing (Fig EV7J,K)."
Reviewer #4, Comment #4: __In Figure 5A it is not clear which band was used to quantify the effect of NAPA reduction. Also, this analysis lacks normalization to an unrelated protein or loading control. Moreover, the authors should also examine the effect of the siRNA targets shown in Figure 5C for Tg WT and not only the mutant.__
Our Response: The uppermost band in Fig 5A was used for quantification. We added a red asterisk similar to that found in Fig 5C to denote this lower back in the lysate panel(s) as a non-specific background band found within the Western blot. These data are the result of immunoprecipitations of both cell lysate and medium content, as such there is no applicable loading control that can be used within the western blots. For experiments, cell amounts were normalized by seeding and subsequently culturing the same amount of cells, as denoted within the Materials and Methods - FRT siRNA validation studies section of the manuscript. Furthermore, there are no loading controls that are easily utilized for analyzing cell culture medium. We have further clarified the Fig 5 caption to provide clearer experimental detail:
"(A and B) Western blot analysis (A) and quantification (B) of WT Tg-FT secretion from FRT cells transfected with select siRNAs hits from initial screening data set. Red asterisk denotes a non-specific background band within the western blot. Cells were transfected with 25nM siRNAs for 36 hrs, media exchanged and conditions for 4 hrs, Tg-FT was immunoprecipitated from lysate and media samples, and Tg-FT amounts were analyzed via immunoblotting. N = 6.
(C and D) Western blot analysis (C) and quantification (D) of C1264R Tg-FT secretion from FRT cells transfected with select siRNA hits from the initial screening data set. Red asterisk denotes a non-specific background band within the western blot. Cells were transfected with 25nM siRNAs for 36 hrs, media exchanged and conditions for 8 hrs, Tg-FT was immunoprecipitated from lysate and media samples, and Tg-FT amounts were analyzed via immunoblotting. All statistical testing performed using an unpaired student's t-test with Welch's correction. *pFinally, as the siRNA targets shown in Fig 5C were shown to be hits exclusively for C1264R Tg-FT we did not believe it was necessary to follow-up on these with WT Tg-FT. Similarly, we did not follow-up on hits that were exclusive to WT Tg-FT with C1264R and A2234D Tg-FT.
__Reviewer #4, Comment #5: __The authors should also test for the binding of RTN3 to Tg WT and mutant - in particular in comparison to TEX264. This would be important in the context that only RTN3 but not TEX264 was detected in the TRIP approach. Do the authors also detect VCP and LC3B in their pulldowns?
Our response: Please also see Reviewer #3, comment 9, who made a similar point.
We performed follow-up experiments to test interactions with Tg and the wider panel of ER-phagy receptors. We transiently expressed FLAG-tagged CCPG1, RTN3L, and TEX264 in HEK293 cells stably expressing Tg-NLuc and performed FLAG IPs followed by western blot analysis. We found that WT and C1264R Tg were enriched, albeit modestly, in the RTN3L Co-IP compared to control samples expressing GFP. Additionally, we found that WT, A2234D, and C1264R Tg were all enriched with CCPG1 compared to control samples expressing GFP. CCPG1 was found to be a C1264R Tg interactor within our mass spectrometry datasets, along with RTN3. We have now integrated these data into the manuscript as Fig EV8, and updated the manuscript text as follows:
"Additionally, we monitored Tg enrichment with ER-phagy receptors CCPG1 and RTN3 via Western blot as both were found to be C1264R Tg interactors within our TRIP dataset. RTN3L is found to be the only RTN3 isoform involved in ER turnover via ER-phagy (Grumati et al, 2017). WT and C1264R Tg-NLuc were modestly enriched with RTN3L compared to control samples expressing GFP. Conversely, we found that all Tg variants exhibited modest interactions with CCPG1 compared to control samples expressing GFP, although less than with TEX264 (Fig EV8).
Together, these data suggest that TEX264, CCPG1, or RTN3L engage with Tg during processing, and CH-associated Tg mutants may be selectively targeted to TEX264. Furthermore, ER-phagy may be considered as a degradative pathway in Tg processing, as other studies have mainly focused on Tg degradation through ERAD (Tokunaga et al, 2000; Menon et al, 2007)."
Regarding VCP, we can detect it routinely in our AP-MS experiment as presented previously (Wright et al. 2021), and here in Fig 3, Appendix Fig S1. However, we have not been able to detect interactions via western blot, which may be attributed to the increased sensitivity that LC-MS offers. We have not probed for LC3 interactions via western blot as we did not detect it by LC-MS either, but we identified several lysosomal and other autophagy-related components previously (Wright et al. 2021), and here shown in Appendix Fig S1 and Fig EV5C.
__Reviewer #4, Comment #6: __The effect of TEX264 depletion on Tg secretion should be confirmed by TEX263 KO experiments. Do the authors observe a similar increase in secreted Tg C1264R in BafA1- or SAR405-treated cells? Moreover, the authors should show that Tg C1264R is actually delivered to lysosomes using biochemical assays such as LysoIP or colocalization experiments.
Our response: To address this concern, we generated stable TEX264 knockout FRT cell lines by CRISPR, and probed several clones for their impact on Tg secretion. We found that TEX264 knockout did not recapitulate the increase in C1264R Tg secretion observed with transient siRNA knockout. While disappointing, these results are not necessarily surprising, considering that prolonged TEX264 knockout may lead the cell to adapt compensation mechanisms by modulating other proteostasis factors and/or autophagy machinery.
We performed experiments utilizing the autophagy inhibitor Bafilomycin A1, and have now included these results with the manuscript available in Fig EV10A,B. We found that BafA1 treatment led to the accumulation of WT Tg in the lysate but not for the C1264R Tg. We updated the manuscript text to accompany these data as follows:
"To understand whether this rescue in secretion was uniquely linked to VCP inhibition or could be more broadly attributed to blocking Tg degradation, we tested the proteasomal inhibitor bortezomib, and lysosomal inhibitor bafilomycin. Bafilomycin increased WT Tg lysate abundance, and bortezomib significantly increased A2234D lysate abundance, consistent with a role of these degradation processes in Tg PQC (Fig EV10A). When monitoring Tg-NLuc media abundance, neither bafilomycin nor bortezomib significantly altered WT, A2234D, or C1264R abundance (Fig. EV10B). confirming that general inhibition of proteasomal or lysosomal degradation does with rescue mutant Tg secretion."
These results raise the possibility that the mutant Tg interaction with TEX264 may not lead to active autophagic degradation of mutant Tg. This is also consistent with the slow degradation of C1264R Tg observed in the pulse-chase experiment in Fig EV9A. Whether the TEX246 recruitment of mutant Tg leads to degradation or assumes an alternative function, for example, intracellular sequestration, remains to be tested. Importantly, we have refrained from making claims in the manuscript that C1264R Tg is delivered to the lysosome but have presented data showing that it interacts with ER-phagy-related components and have further speculated on the possibility how autophagy could play a role in Tg processing.
Thank you for the LysoIP suggestion. Ongoing work in the lab is addressing this question and experiments suggested by the reviewer, but this is better reserved for a follow-up manuscript.
__Reviewer #4, Comment #7: __Figure 7A and 7C lack loading controls. The quantification shown in Figure 7B and 7D should be normalized to this control. Since VCP activity is often coupled to the of the proteasome, the authors should check whether blocking the proteasome yields a similar effect than ML-240.
Our Response: Like Fig 5A discussed above (Reviewer #4, comment 4), these data are the result of immunoprecipitations from cell lysate and medium. As a result, there is not applicable loading control that can be used within the western blots. For experiments, cell amounts were normalized by seeding and subsequently culturing the same amount of cells, as denoted within the Materials and Methods - FRT siRNA validation studies section of the manuscript and Material and Methods - VCP pharmacological inhibition studies.
Regarding the effect of proteasome inhibition, we tested whether bortezomib treatment can increase C1264R Tg secretion. We found that bortezomib led to a small but significant increase in A2234D Tg accumulation in the lysate, but did not increase secretion of Tg for WT or any of the mutant variants. This new data is shown in Fig EV10A,B. We updated the text as follow:
"To understand whether this rescue in secretion was uniquely linked to VCP inhibition or could be more broadly attributed to blocking Tg degradation, we tested the proteasomal inhibitor bortezomib, and lysosomal inhibitor bafilomycin. Bafilomycin increased WT Tg lysate abundance, and bortezomib significantly increased A2234D lysate abundance, consistent with a role of these degradation processes in Tg PQC (Fig EV10A). When monitoring Tg-NLuc media abundance, neither bafilomycin nor bortezomib significantly altered WT, A2234D, or C1264R abundance (Fig. EV10B). confirming that general inhibition of proteasomal or lysosomal degradation does with rescue mutant Tg secretion."
__Reviewer #4, Comment #8: __With regard to Figure 7 - Figure supplement 1: The authors should monitor the effect of ML-240 on Tg secretion such that WT and C1264R mutants are directly compared (side-by-side on the same immunoblot). Otherwise, it is difficult to claim that ML-240 rescues the secretion of the mutant.
Our response: The reviewer is referring to the S35 pulse-chase experiments now shown in Fig EV9. We would like to clarify that these images are not immunoblots but autoradiographs. Even though the samples for WT and C1264R Tg were loaded onto separate gels, the gels were imaged at the same time and are therefore directly comparable. Regardless, the more meaningful information that can be gleaned from these experiments are the absolute rates of protein secretion and degradation and how they change in response to ML-240 treatment. The scale in the quantifications (0 - 100%) is the same and corresponds to the total amount of WT or C1264R Tg that is labeled with 35S during the 30 min pulse. Importantly, we found that C1264R Tg-FT secretion is significantly increased in the presence of ML-240, changing from
__Reviewer #4, Comment #9: __How did ML-240 affect the ER-phagy components (in particular RTN3) in the TRIP analysis of Tg C1264R (Figure 7G-L)?
Our response: This is a great discussion point raised by reviewer #4. We have updated the manuscript text to discuss in more detail changes in interactions with degradation components, especially with proteasomal degradation machinery (Fig 7M,N). The manuscript text now reads as follows:
"The most striking interaction changes occurred with proteasomal degradation components, which remained steady until 1.5 hr, but then abruptly declined with ML-240 treatment at later time points (Fig 7M,N). This decline tracks with changes to the glycan processing machinery, highlighting that the coordination between N-glycosylation and diverting Tg away from ERAD may be a key to the rescue mechanism."
Minor points:
__Reviewer #4, Comment #10: __The candidate labeling in Figure 3 - Figure supplement 2 and 3 is too small und unreadable. The authors should provide a higher resolution of these figures or increase the font.
Our response: These figures are now in the Appendix and we have edited this figure to provide higher resolution.
Reviewer #4 (Significance (Required)):
Please see above
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Referee #4
Evidence, reproducibility and clarity
Summary
In this manuscript, Wright et al. developed an approach (termed TRIP) that allowed to map the temporal changes in the interaction landscape of a newly synthesized protein of interest. Using their TRIP approach, the authors found that the extensive interactions of thyroglobulin (Tg) with the proteostasis network (PN) during its passage through the secretory pathway were profoundly altered in response to disease-causing mutations (e.g. C1264R). The authors cross-validated their findings with a focus RNAi screen monitoring the cellular and secreted abundance of Tg variants upon deletion of PN components. In subsequent experiments the authors focused on two hits, VCP and TEX264, for which they confirmed their inhibitory effect on the secretion of Tg C1264R. Importantly, the authors found that TEX264 increasingly interacts with the Tg mutant and that pharmacological inhibition of VCP yielded the same phenotype than depletion of VCP. Overall, Wright and colleagues established an elegant method to map protein interaction in a time-resolved manner and demonstrated its value by the analysis of disease-related Tg mutants. Hence, this work has the potential to serve as a rich resource for Tg-related research and as a powerful new tool to examine protein interactions. However, several concerns remain.
Major points
- Overall, the TRIP workflow is quite difficult to understand at a first glance - even for a reader with a background in proteomics, biochemistry and cell biology. The authors may want to improve the description of the TRIP methodology and explain in more detail what the individual components and steps are good for. Along the same line, from the main text and the figure legend it was not clear that Tg was actually Flag-tagged. However, without this information it is difficult to follow the workflow. While Figure 1A is certainly helpful, the bulky graphics are deflecting the reader's attention. A more schematic version might be more informative.
- To what extend do the difference in protein abundance between Tg WT and Tg C1264R contribute to the increase binding of their interactors (e.g., HSP5 and PDIA4). The authors should perform a TRIP coupled immunoblot analysis where WT and Mutant are loaded side-by-side on the SDS-PAGE.
- While the RNAi screen was done with pooled siRNA, it is not clear what was used for the RNAi validation experiments shown in Figure 5. This should be done by individual siRNA and not the same pooled reagents as used for the screen.
- In Figure 5A it is not clear which band was used to quantify the effect of NAPA reduction. Also, this analysis lacks normalization to an unrelated protein or loading control. Moreover, the authors should also examine the effect of the siRNA targets shown in Figure 5C for Tg WT and not only the mutant.
- The authors should also test for the binding of RTN3 to Tg WT and mutant - in particular in comparison to TEX264. This would be important in the context that only RTN3 but not TEX264 was detected in the TRIP approach. Do the authors also detect VCP and LC3B in their pulldowns?
- The effect of TEX264 depletion on Tg secretion should be confirmed by TEX263 KO experiments. Do the authors observe a similar increase in secreted Tg C1264R in BafA1- or SAR405-treated cells? Moreover, the authors should show that Tg C1264R is actually delivered to lysosomes using biochemical assays such as LysoIP or colocalization experiments.
- Figure 7A and 7C lack loading controls. The quantification shown in Figure 7B and 7D should be normalized to this control. Since VCP activity is often coupled to the of the proteasome, the authors should check whether blocking the proteasome yields a similar effect than ML-240.
- With regard to Figure 7 - Figure supplement 1: The authors should monitor the effect of ML-240 on Tg secretion such that WT and C1264R mutants are directly compared (side-by-side on the same immunoblot). Otherwise, it is difficult to claim that ML-240 rescues the secretion of the mutant.
- How did ML-240 affect the ER-phagy components (in particular RTN3) in the TRIP analysis of Tg C1264R (Figure 7G-L)?
Minor points
- The candidate labeling in Figure 3 - Figure supplement 2 and 3 is too small und unreadable. The authors should provide a higher resolution of these figures or increase the font.
Significance
Please see above
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Referee #3
Evidence, reproducibility and clarity
Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate). Please place your comments about significance in section 2.
In this manuscript, the authors describe their efforts to develop a methodology for determining time-resolved protein-protein interactions using quantitative mass spectrometry. With TRIP (time-resolved interactome profiling), they combine a pulsed bio-orthogonal unnatural amino acid labelling (homopropargylglycine, Hpg), CuAAC conjugation and biotin-streptavidin pulldowns to enrich at different timepoints and time-resolve by combining TMT labelling and LC-MS/MS (Figure 1). This technique is then applied to the maturation of the secreted WT and mutant thyroglobulin (Tg-WT, Tg-C1264R, Tg-A2234D) expressed in HEK293 and rat thyroid cells (FRT) and linked to hyperthyroidism. There, they identify a collection of ER resident proteins involved in protein folding/processing (e.g. chaperones, redox, glycans, hydroxylation) as well as degradation (e.g. autophagy, ERAD/proteasomes) (Fig. 2). Here the authors effectively use pulse-labelled form of TRIPs to highlight the different interactions formed with Tg-WT vs. Tg-mutants during biogenesis and secretion (or retention). The analysis found ~200 new interactions compared to previous studies along with about 40% of those identified previously. Differences in interactions were observed for mutants, which shown extended interaction with chaperones and redox processing pathways. While many interactions appeared as might be expected, the identification of membrane protein processing elements (e.g. EMC, PAT) was puzzling and raised some questions about the specificity within the protocol. Mutants enriched for CANX CALR and UGGT, suggesting prolonged association with glyco-processing factors. Interaction of C1264R with the ER-phagy factors CCPG1 and RTN3 was greater than WT. The authors note that their interaction correlated with that of EMC1 & 4, but it is not clear why that might be.
With interactors in hand, the authors complemented the TRIP protocol with siRNA KD of identified factors, to investigate any changes to secreted vs intracellular Tg upon loss. KD of NAPA (a-SNAP) and LMAN1 increased WT lysate (intracellular) Tg but not mutants. NAPA also reduced Tg-WT secretion. In contrast, KD of NAPA increased A2234D secretion while LEPRE1 increased C1264R (but not A2234D or WT), suggesting mutants have differential processing paths and requirements. KD of VCP increased secretion of both mutants. Some ER-phagy receptors were found among interactors (e.g. RTN3 in Tg-C1264R only) but often their KD had no impact on secretion (CCPG1, SEC62, FAM134B). NAMA observations were recapitulated in thyroid derived cell line (FRT). KD of TEX264 and VCP increased Tg-C1264 secretion while RTN3 KD in FRTs decreased Tg-C1264 secretion. This was in contrast to data from HEK293s for reasons that are not clear. Co-IP with TEX264 enriched for all Tg forms but more so for C1264R and A2234D - motivating the authors to propose selective targeting of Tg to TEX264 and the consideration of ER-phagy as a "major" degradative pathway during Tg processing.
Given the observations with siRNAs to VCP, the authors next use a selection of VCP inhibitors to ask whether secretion can be rescued upon pharmacological impairment of the AAA ATPase. They observed that ML-240, but interestingly not the more conventionally used CB-5083 or NMS-873, increased secretion of Tg-C1264R but not lysate. Inhibitors increased lysate but decreased the secreted fraction for Tg-WT (Fig 7). Finally, the authors used TRIP again in ML-240 treated Tg-C1264R expressing cells to look for changes to interactome with treatment - observed decreases to glycan and chaperone interactions, CANX and UGGT1, decreased interaction with DNAJB11 and C10, like that of WT. There was no apparent change to the UPR, although activation was not directly measured.
Major comments:
- Are the key conclusions convincing?
The TRIP methodology appears to be quite robust and should be a powerful strategy for this field and others going forward. The drawback will be the length of pulse required will limit the number/type of proteins to be monitored to ones with longer t1/2's. There were interesting interactions found with Tg and the mutants linked to hyperthyroidism, but cut and dry differences did not appear as obvious, even though strong "trends" appear to be present. The path from identifying interactors in a time-resolved manner to then following them up with targeted KD does provides some clarity, which is important. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?
The data regarding VCP silencing and pharmacological impairment appear clear but leave some questions outstanding in this reviewer's opinion. The lack of effect with the 2 highly selective inhibitors suggests that the underlying mechanism for switching fate of intracellularly retained Tg-C1264R towards secreted forms is not at all clear. ML-240 is an early derivative of DBeQ and reportedly impairs both ERAD and autophagic pathways, similarly to DBeQ. The differences between the VCP inhibitors' mechanism of action were not discussed, but perhaps should be elaborated upon, particularly in the matter of how ERAD and ER-phagy pathways might be being differentially affected. At the risk of asking for too many additional experiments, this reviewer would just prefer to see this fleshed out in a bit more detail. - 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.
Q1. The degree (if any) of Tg-C1264 aggregation during and/or detergent solubility do not appear to have been considered as a potential source of the increase in released secreted material (Figure 4, 5). Do Tg mutants partition into RIPA-insoluble fractions at all? That is to say.. is the total population of synthesized Tg being considered? A full accounting? Could the authors address this and if biochemical extraction data (via urea or high SDS) is available, include it to answer this concern.
Q2. Along the same lines, what does Tg-WT and mutant expression look like by microscopy? Is Tg-WT uniformly distributed while Tg-mutants appear in puncta... more aggregated - perhaps reflecting the increased engagement of chaperones and redox machinery? Changes in the pattern of Tg-C1264R mutant (e.g. w/ VCP KD or inhibition) would add additional support for the authors interpretation of improved secretion. If this data is at hand, including it might be worth consideration.
Q3. Does the level of Tg mutant expression in the FRT clones impact the profiles obtained by TRIP? (Figure 3). This is a question of gauging the relative saturation of QC machinery and how that might impact profiles from TRIP. Were clones expressing at different levels tested? Perhaps a brief discussion of this.
Q4. For Figure 3, the hour-long labelling period seems a bit long, compared with 3 hr of chase. Perhaps this reviewer missed this but how long does Tg take to mature and/or mutants to misfold and degrade? Is there any possibility to shorten this so that the profiles of labelled Tg could be more synchronized? If not, perhaps this could just be discussed.
Q5. It is curious that only ML-240 and not other well characterized inhibitors of VCP/p97, has an effect, as both are used far more often than ML-240. The authors do not really address this in detail but does it suggest that the ML-240 effect on VCP/p97 could be affecting different pathways, given the nature of this compound. Is this compound acting on Tg-C1264R maturation at the level of translation or post-translationally? If the latter, through what means?
Q6. Continuing from Q5.. At what point and where is VCP/p97 able to affect mutant Tg processing? In line 317, the authors seem to correlate increased VCP association with mutants to their increased secretion. It is not clear how this would result, as engagement with VCP would be in a compartment different to that which supports trafficking and secretion. Could the authors expand on how this might come about. This is also relevant to the ML-240 data in Figure 7. Moreover, VCP is associated with ERAD (as is HerpUD1) rather than ER-phagy and at least in the siRNA raw data, there are also effects from Derlin3 and FAF2 KDs.. both ERAD factors. Some clarity here would be appreciated.
Q7. There does not appear to be a direct demonstration of Tg-C1264R turnover by ER-phagy (via TEX264). Given the inconsistency with it not being detected by TRIP, while another receptor RTN3 was, but has not impact on Tg-C1264R secretion, perhaps including that data would go some way to demonstrating a fate of ER-phagy (at least partly) for this mutant.
Q9. The authors provide data that the UPR was not induced by ML-240 at 3hrs (10µM) (Figure 7, supplemental 1). This is in stark contrast to the results of Chou et al (2013) which the authors reference, reporting that ML-240 induced ATF4 and CHOP by 2 hrs at concentrations lower than used here (albeit a different cell type). While not exclusively UPR, could the authors address the potential activation of the integrated stress response (eIF2a phosphorylation, ATF4 and CHOP) in the FRT cells due to ML-240 treatment? If present, is there some link that could this provide an explanation for increased Tg-C1264R secretion? - 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.
Any of the suggested experiments above all use reagents reported in the manuscript and so would presumably incur minimal cost and hopefully time. This reviewer is sympathetic to time and financial constraints and so discussion of the issue could suffice. - Are the data and the methods presented in such a way that they can be reproduced?
Yes. The methodology is explained in detail. - Are the experiments adequately replicated and statistical analysis adequate?
Yes. Relevant information is either in the figure legends or is provided in the source data.
Minor comments:
- Specific experimental issues that are easily addressable.
- Are prior studies referenced appropriately?
The references are generally appropriate, with a few exceptions of more general references used - Are the text and figures clear and accurate?
The text is clearly written, and the figures are clear. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?
A summary figure comparing the changing profiles of WT and C1264R and the factors implicated for them could be helpful.
Perhaps include common nomenclature for proteins as well (e.g. HSP5A - BiP, HSP90B1 - Grp94, etc..)
Line 317 - our is misspelled
Figure 4 - Supplemental Figure 1 - Legend has text referring to panels J and K, but Figure only goes up to F.
Significance
- Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.
- Place the work in the context of the existing literature (provide references, where appropriate).
Protein-protein interactions are often used to illustrate complexes and functionality, but these provide only snapshots, rather than "movies". There are many datasets out there exploring P-P interactions, but most if not all lack any temporal resolution for the interactions they report. The TRIP method described approaches this from the dynamic perspective - identifying the transient interactions formed by folding nascent chains with proteins that aid in their maturation and trafficking, or degradation. This represents an important technical advance in our ability to dynamically monitor protein interactions. The use of Tg mutants is valuable and perhaps this will lead to new perspectives on how to rescue it or other pathophysiological mutants with loss of function phenotypes.<br /> - State what audience might be interested in and influenced by the reported findings.
This work should appeal to a broad audience within cell biology, particularly as the TRIP technique is attempting to address a fundamental question - what interactions form during the biogenesis/lifetime of a protein. Moreover, the effort to try to understand the different interactions formed with pathologically relevant mutant proteins as a strategy to try to rescue functionality, is a valuable exercise of this approach. - 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.
ER quality control
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Referee #2
Evidence, reproducibility and clarity
In the manuscript 'Time-Resolved Interactome Profiling Deconvolutes Secretory Protein Quality Control Dynamics' Wright et al. developed an approach for time-resolved protein protein interaction mapping relying on pulsed unnatural amino acid incorporation, protein cross linking, sequential affinity purification, and quantitative mass spectrometry named time-resolved interactome profiling (TRIP). The authors applied the TRIP method to compare the interactions of the secreted thyroid prohormone thyroglobulin (Tg) comparing the WT protein to secretion-defective mutations implicated in congenital hypothyroidism. They further employed an RNA interference screening platform (1) to investigate if (1) interactors identified via TRIP are functionally relevant for Tg protein quality control and (2) to identify factors that can rescue mutant Tg secretion. The screen was initially performed in HEK293 cells, but selected hits with a phenotype in HEK cells were then followed up in Fisher rat thyroid cells. Further functional validation was performed by pharmacologic inhibition of VCP, a hit from the RNAi screen with an effect on Tg lysate abundance and Tg secretion. While the authors present a comprehensive study including identification of protein-protein interactions using proteomics followed up by an RNA interference screen for functional validation, major comments need to be addressed for both the proteomics as well as the functional genomics aspects of the study (see comments below).
Major comments:
- The authors describe a new method for quantitative, temporal interaction mapping. The protocol involves two enrichment steps as well as several reactions including cross-linking of the samples as well as functionalization of the unnatural amino acids. Given all these steps, the authors should rigorously characterize the quantitative reproducibility of the experiment when performed in independent biological replicates. This is important because in the final quantitative MS experiment, the authors only use two biological replicates, which is too low especially for such an involved sample preparation procedure, which would expect to have a high variability between replicates. Given the low number of replicates and the unknown reproducibility of the quantification for this protocol, it is questionable at this point how reliable the quantification over the time course is.
- Compared to the previous dataset published last year, the authors discover an overlap in interactors, but also a huge discrepancy, with 96 previously identified interactors not detected in the current study, but 198 additional interactors identified. How do the authors explain the big differences between these datasets?
- For the temporal interaction analysis the authors describe differences in the temporal profiles of selected interactions comparing wt and mutant, however no statistical analysis is performed comparing wt and mutant interaction profiles across the time course. Furthermore the variability between the replicates for the temporal profiles is not shown and some of the temporal profiles appear to be noisy. A more rigorous statistical analysis should be performed including additional biological replicates to evaluate the changes over the time course, especially as the temporal interaction analysis is the novelty of this study.
- To functionally validate interactors derived from the TRIP analysis as well as to identify factors that can rescue mutant Tg secretion the authors developed an RNA interference screen. There are a number of aspects that need to be addressed/clarified for this part of the study.
- While the authors validate the stable cell lines expressing the nanoluciferase tagged Tg and the linearity of luminescence signal in lysate and media carefully, they do not validate their platform in combination with the RNAi knockdown strategy. The authors should select genes as positive controls that are expected to modulate Tg secretion and demonstrate that the knockout of these positive controls indeed results in changes in Tg secretion in their system.
- For the screen the authors select 167 Tg interactors and PN (Proteostasis network) related factors. This statement is very vague and the authors should clarify which genes were knocked down and which criteria were applied to narrow down the list of interactors and to select PN factors. The authors should therefore provide a supplementary table including all genes included in the screen, their source (were this derived from the initial study by Wright et al, from the current study or compiled from prior knowledge about PN), as well as their results from the screen based on luminescence in media and lysate. It is unclear how many of the selected factors are actually coming from the TRIP analysis.
- Only a small number of the 167 selected genes shows an effect on Tg abundance/secretion. How do the authors explain this result? Would we not expect that Tg interactors, especially those from the TRIP method which interact with the newly synthesized are more enriched for functionally relevant genes.
- The authors initially performed the screen in HEK293 cells and as a second step wanted to validate the hits from the HEK cells in more relevant Fisher rat thyroid cells. Indeed they could show that knockdown of NAPA increased WT TG in lysate and decreased WT Tg secretion. Furthermore, they further validated genes to modulate mutant Tg lysate and media abundance. The authors should perform a rescue experiment to demonstrate that the observed phenotype can be reversed through re-introduction of NAPA.
- One hit from this analysis was the ER-phagy receptor TEX264, while TEX264 was not identified in the TRIP data, is selectively increased the C1264R secretion, but not wt and the other Tg mutant. Following Co-IP data however revealed some interaction between the C1264R and to a lesser extent the A2234D mutant. How do the authors explain that TEX264 was missed in the TRIP dataset?
Minor comments:
- The workflow needs to be described clearer. For example, it should be better explained why the authors selected a two stage enrichment strategy, I assume that the first based on the Flag affinity tag is to purify the protein of interest and the second step based on the incorporation and functionalization of the unnatural amino acids to enrich for the newly synthesized fraction at specific time points after protein synthesis? These are critical steps for the method but the rationals are not well explained, neither in the text nor the figures captures all these steps of the method very clearly, which makes it really difficult for the reader to understand the individual steps of the method. Moreover, the structures in Figure 1 workflow are not clearly labeled, so that it is confusing which part represents which protein/molecule.
- Except for the general workflow shown in Figure 1, a more detailed workflow showing the experimental steps, such as the sample fractions with the following steps could be added so that the design of the method is clearer. Also the style of the workflows including Figure 1, Figure 2A, and Figure 3A are different. It would be helpful to make them the same style and make the Figure 2A as a zoom in or more detailed illustration on part of Figure 1.
- A summary of proteomics results of time course labeling after all enrichment steps, including the total number of identified proteins at different conditions and control would be helpful for having an overview impression on the proteomics results
- In Figure 2B, the WB for PDIA4 in the Biotin PD elution is missing. Why was the PDIA4 interaction missing for the time course analysis, but the interaction was captured in the initial test for Wt Tg (Figure 1D). Additionally, in this panel the Rhodamine Probe Gel shows inconsistencies at the time points 1.5 - 3h. Does this mean that the labeling did not work well for these conditions? As we would expect a consistent Rhodamine Probe signal at every time point.
- In Figure 2, why was there no WB results for the A2234D? In Figure 2D and 2E, at which time point are the changes significant compared to WT?
- All figure legends should indicate how many biological replicates were performed for each experiment represented in the figure.
- The heatmaps shown in Figure 3, Figure 3 - Figure Supplement 3, and Figure 7 are in the current form incomprehensible. The heatmaps depict the relative enrichment vs the control sample, which was scaled between 1 and -1. The color coding with 5 different colors from 1 to -1 is very confusing and should be changed to just two colors, one for positive and one for negative relative enrichment. I would also suggest changing the visualization of the heatmap showing the wt and mutants side by side, instead of stacked on top of each other for each individual protein.
- The data analysis method for generating relative enrichment shown in the heatmap is not explained. This should be described in the method section for a better understanding of the data analysis. There are no legends of flowcharts in Figure 2A and Figure 3A and it is difficult to understand which are the key components in the complex and what are the differences among different periods of labeling.
- Why did only one of the VCP inhibitors (ML-240) exhibit a phenotype in Tg abundance and secretion, but not the other VCP inhibitors?
Significance
The authors describe a novel and elegant method to map time resolved protein interactions of newly synthesized proteins, which allows monitoring of proteins regulating protein quality control. Authors describe it as a general method, however, they only demonstrate the applicability to one protein and do not systematically evaluate the quantitative nature of their approach by determining quantitative reproducibility, which would be necessary to be able to claim that this is a method with broad applicability.
Given my expertise in quantitative proteomics, I can mainly comment on the technological aspects of the proteomics part of the manuscript, but do not feel qualified to evaluate the significance of this study in terms of novel biology. Nevertheless, it feels that there is a stronger emphasis on the biology in the current form of the manuscript which will raise interest of scientists with a focus on protein quality control and Tg biology.
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Referee #1
Evidence, reproducibility and clarity
The authors report a mass spectrometry (MS)-based interactomics technique, time-resolved interactome profiling (TRIP), which allows for tracking temporal changes in the interactome of protein of interest. To show that TRIP can successfully deconvolute interactomes over time, they pulsed thyroid cells with homopropargylglycine (Hpg), immunoprecipitated the Hpg incorporated thyroglobulin (Tg) and its interacting proteins at different time points, and subjected the samples to tandem mass tag (TMT)-based quantitative MS analysis. The MS results show that WT and variant Tg proteins indeed associate with different proteostasis network factors in a differential manner over the course of time. In addition, they utilized an siRNA-based luciferase fusion assay to evaluate whether silencing each proteostasis network component changes the levels of Tg in both lysate and media. From the combination of the TRIP and siRNA-based assays, they found many hits, including hits implicated in protein degradation, VCP and TEX264, which they validated with multiple experiments.
I am overall quite positive and think this is an important study. But there are some meaningful points to consider.
Significant comments:
- Only two replicates of the main data (the TRIP-MS experiments) for this paper is problematic. Especially since the manuscript is supposed to be demonstrating and validating the new technique. Consistent with this concern, the relative enrichment profiles for some of the results were surprising. For instance, interaction with CCDC47 was tapering off but then at 3 h it suddenly reaches the maximum level of engagement. Is this a real finding or the variability in the method? Impossible to tell with two replicates. Presenting heat maps based on biological duplicates is also very problematic. It masks the error, which is large as can be seen in some of the panels showing individual proteins. In my view, triplicates and a clear understanding of the error in the technique should be required.
- The same concern arises for the high-throughput siRNA screen, which was performed only in duplicate for WT and A2234D.
- There are issues with some of the immunoprecipitation experiments: In Figure 1C, a negative control for FLAG IP is missing. In Figure 2B, I am curious why the band (Hpg -, chase time 0 h) is so faint for the first WB (IB for FLAG) - is Hpg treatment indeed leading to much more Tg present at 0 h? If so, that is a concern. Also, a negative control must be included (either plain cells or cells expressing fluorescent protein or a different epitope-tagged WT Tg). In this same figure, I am puzzled why the bands for 1.5-3 timepoints in Biotin PD elution, probed for Rhodamine, are very faint especially considering that in Figure 1D, the corresponding bands, which are 4 h after the pulse, look fine. It seems like the IP failed here?
Suggestion to consider:
This manuscript, supported by the title and abstract, mainly focuses on the presentation of the development and application of TRIP, which is highly significant. The story becomes less coherent and harder to follow as significant amounts of text/figures are dedicated to siRNA-based high throughput screening and follow-up. In addition, although the discovery of TEX264 as one of the hits is very interesting and exciting, TEX264 apparently was not a hit in the TRIP experiment and is pretty distracting from the main point of the paper highlighted in the abstract and title, therefore. The siRNA-based assay and follow-up studies could be a separate scientific story of their own. Especially considering my concerns on the number of replicates for both the TRIP and siRNA-based assay, it could be beneficial to actually split the manuscript into two and conduct more replicates of the -omic work, which should corroborate the exciting discoveries the authors have made.
Minor comments:
Throughout the manuscript, the authors have not defined what FT is; presumably it means FLAG tag.
The authors might discuss their rationale for choosing 0-3 hrs for their TRIP studies. That includes any relevant information about the half-life of WT versus variant Tg, whether the Hpg pulse time is short enough to avoid missing key features of the temporal interactome, and discussion of what would happen if the TRIP were performed at prolonged time points (e.g. 6-10 h).
Lines 68-69: the two citations should probably come one sentence earlier (at least Coscia et al 2020 is a structure paper).
Line 91: "(Figure 1A)" should follow the sentence "To develop the time-resolved..." to help readers better understand the system.
Line 101: Fisher should be Fischer
Line 131: Should be 1.5 hrs instead of 2 hrs.
Lines 135-136: I do not agree with the claim that HSPA5 profile looked similar for MS and WB. I do not see a peak for HSPA5 at 2 hrs in Figure 2D.
Line 186: The cited paper Shurtleff et al 2018 is missing in the reference list.
Line 188: I disagree with the authors' claim here because, at least for CCDC47, interactions with C1264R seem to come back at the 3 hr time point.
Line 203: I am not sure if P4HA1 can be included in the examples for showing distinct patterns for mutants compared to the WT according to their data in Figure 3H.
Line 216: The authors should add citations about the functions of STT3A and STT3B proteins.
Lines 248-251, "We found that interactions with these components...": this sentence should refer to Figure 3 - Figure Supplement 3 instead of Figure 3L and S4.
Lines 258-260, "Another striking observation was that the temporal profile of EMC interactions for C1264R correlated with RTN3, PGRMC1, CTSB, and CTSD interactions.": Please provide more evidence to support the potential correlation between different interaction profiles. Or the authors should move this sentence to the discussion section as it sounds speculative. This highlights the issue of only having duplicates, as well.
Line 340: As written, should cite more than one paper
Line 371: Should be Figure 4 - figure supplement 2
Line 1231: "Zhang et al 2018" needs to be removed
Line 1286: FRTR should be FRT
Figure 3E: Color used to highlight the three proteins (CCDC47, EMC1, EMC4) should match the color used in Figure 3 - Figure Supplement 3
Figure 4A: The bottom figure where lysate signal is inversely proportional to time is misleading because the authors are assessing steady-state level of proteins in this assay.
Figure 4 - Figure Supplement 1 caption: in (C), (F) should be (B). (K) should be (G) and I am not sure what the authors mean when they refer to (J) in caption of (G).
Figure 5 caption for (C and D): Need to specify the time that the samples were collected (8 hrs), as it seems different from A and B according to the main text.
Figure 5 - Figure Supplement 1: Data for HERPUD1 and P3H1 should be included.
Figure 5 - Figure Supplement 2B: Please mention in the caption how degradation is defined.
Significance
This manuscript is highly significant because the authors (1) designed and validated a new methodology for time-resolved interactomics study, (2) presented the dynamic changes in Tg interactome for WT and variants, and (3) discovered how proteins implicated in degradation pathways (e.g. VCP, TEX264, RTN3) can change the secretion profile of WT and mutant Tg proteins. With TRIP, the authors demonstrated that they could obtain valuable data that were previously not captured from steady-state interactomics studies (Wright et al. 2021; Figure 3M and Figure 3 - Figure supplement 4D-4I). Furthermore, the authors treated cells with VCP inhibitors and performed both 35S pulse-chase analyses and TRIP. These experiments provide valuable information to the field by (1) presenting a new method to rescue Tg secretion defect, and (2) demonstrating a broader applicability of TRIP. If the major comments above can be addressed I believe this is a tremendous contribution to the field.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
This manuscript investigates the dynamics of GC-content patterns in the 5'end of the transcription start sites (TSS) of protein-coding genes (pc-genes). The manuscript introduces a quite careful and comprehensive analysis of GC content in pc-genes in humans and other vertebrates, specially around the TSS. The result of this investigation states that "GC-content surrounding the TSS is largely influenced by patterns of recombination." (from end of Introduction)
My main concern with this manuscript is one of causal reasoning, whether intended or not. I hope the authors can follow my reasoning bellow on how the logic sometimes seems to fail, and that they introduce changes to clarify their suggested mechanisms of action.
The above quoted sentence form the end of the Intro is in conflict with this other sentence that appears at the end of the Abstract "the dynamics of GC-content in mammals are largely shaped by patterns of recombination". The sentence in the Intro seems to indicate that the effect is specific to TSSs, but the one in the abstract seem to indicate the opposite, that is, that the effect is ubiquitous.
We are sorry about the lack of clarity. We have now rewritten the abstract and intro to emphasize that our results are restricted to the 5' end of genes, and that by "patterns of recombination" we mean "historic patterns of recombination".
The observations as stated in the abstract are: "We observe that in primates and rodents, where recombination is directed away from TSSs by PRDM9, GC-content at protein-coding gene TSSs is currently undergoing mutational decay."
If I understand the measurements described in the manuscript correctly, and the arguments around them, you seem to show that the mutational decay of GC-content in humans is independent of location (TSSS or not), as noted here (also from the abstract) "These patterns extend into the open reading frame affecting protein-coding regions, and we show that changes in GC-content due to recombination affect synonymous codon position choices at the start of the open reading frame."
Again, we have rewritten this section to clarify these points.
There is one more result described in the manuscript, that in my mind is very important, but it is not given the relevance that it appears to me that it has. That is presented in Figure S3G. "we concluded that GC-content at the TSS of protein-coding genes is not at equilibrium, but in decay in primates and rodents. This decay rate is similar to the decay seen in intergenic regions that have the same GC-content (Figure S3G)"
Thus, if the decaying effect happens everywhere, how can it be related to "recombination being directed away from TSSs by PRDM9" as it is stated in the abstract and in the model described in Figure 7?
We make the argument that the GC-peak as likely caused by past recombination events. This is based on:
1) The change in GC-content at the TSS in Dogs and Fox, coupled to the fact that they perform recombination at the TSS
2) That the TSS can act as a default recombination site in mice when PRDM9 is knocked out
3) That some forms of PRDM9 allow for recombination at TSS (see Schield et al., 2020, Hoge et al. 2023, and Joseph et al., 2023) and that this is expected to cause an increase in GC-content
We thus speculate that the GC-peak in humans and rodents was caused by past recombination at TSSs that were permitted by ancient variants of PRDM9. We further point out that PRDM9 is undergoing rapid evolution, and some of the past versions of the protein may have had this property.
We have tried to clarify these points in the latest version of the text.
The fact that the decay rate is similar to any other region with similar GC-content should be an indication that the effect is not related to anything having to do with TSS or recombination being directed away from TSSs by PRDM9.
We are sorry about the lack of clarity. TSSs in humans, chimpanzees, mouse and rats are are experiencing GC-decay at the same rate as in non-functional DNA regions with high GC-content. Thus the GC-peak is not being maintained by selection. This is surprising, given the role that GC-content plays in gene expression. This is a critical point, and we added it to the "conclusion" section of the abstract.
I hope these paragraphs show my confusion about the relationship between the results presented which I think are very comprehensive and their interpretation and suggested model for GC-content dynamics around TSSs in human.
On another note, can you provided a bit more background on recombination and its mechanisms?
We have done our best to clarify these issues.
You seem to have confident sets of genes under high/low/med recombination. How are those determined.
We used the recombination rates per gene provided in Pouyet et al 2017 to identify the sets of genes under low/med/high recombination. Those rates were estimated from the HapMap genetic map (Frazer et al., 2007). This is now all specified in the methods section.
You also seem to concentrate the cause of recombination on PRDM9, please explain. Is PRDM9 the unique indicator of recombination?
PRDM9 has been shown to be the primary determinant of where recombination occurs in the genome (Grey et al., 2011, Brick et al., 2012). This is very well established. We now reword some of the introduction to make this clear.
specific comments
Figure 1, it is very hard to understand the differences between the three rows. Please explain more clearly in the legend, and add more information to the figure itself.
We altered the axis titles to make this clearer. We also label "Upsream", "Exon 1" and "Part of Intron 1" in Figure 1C, F and I, and in Figure 2C. We now spell this out in the Figure Legend.
Figure 7, express somewhere in the figure that the y axis measures GC content.
We now added "GC Content" to the left of the first "graph" in Figure 7.
Figure seems to introduce a 'causal' model of GC-content dismissing (diminishing?) based on recombination being directed away from TSSs. How about the diminishing of GC-content on any other genomic regions as you have shown in Figure S3G?
Our focus in this model, and manuscript, is on TSSs. I think that to add the dynamics of other GC-rich regions is distracting. We do not know what caused these intergenic genomic regions to be high in GC-content prior to decay. After excluding known recombination sites and TSSs, these regions are very rare in the human genome. They may be ancient recombination sites that are decaying in GC-content. However, unlike TSSs, which have some connection to recombination (i.e. data from PRDM9 knockout mice and dogs and fox), we do not have any direct or indirect evidence that these other sites were used for recombination in the past. Alternatively, there could have been some other pressure on these sites in the past to increase GC-content that we are not aware of.
-- The title is too selective, as to the results, and it has the implication that the decay is exclusive to the surrounding of the TSSs.
Decay of GC-content towards equilibrium is the default state for non-functional DNA. That it is occurring at the TSS is surprising, as it indicates that the GC-peak is not maintained by selection. We now state this in the paper and include this in the "conclusion" portion of the abstract.
Reviewer #1 (Significance (Required)):
The statistical analysis is comprehensive and robust.
We thank the reviewer for this.
Their model interpretation as is describe induces confusion and needs to be clarified.
We are sorry about this. Hopefully our revised text will clear up the confusion.
I am an expert computational biologist, I do not have a deep knowledge of sequence implications of recombination, and it would be good if the manuscript could add some more background on that.
We thank the reviewer for their perspective, and we hope that our text changes better explain to the non-expert why our findings are so surprising. We further clarify how recombination affects DNA sequence by gBGC and some of these changes are detailed in our response to the other reviewers.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
In this work, the author present various analyses suggesting that GC-content in TSS of coding genes is affected by recombination. The article findings are interesting and novel and are important to our understanding of how various non-adaptive evolutionary forces shape vertebrate genome evolutionary history.
We thank the reviewer for these kind words.
The Methods section includes most needed details (see comments below for missing information), and the scripts and data provided online help in transparency and usability of these analyses.
I have several comments, mostly regarding clarifications in the text and several suggestions:
- In introduction: CpG islands, have been shown to activate transcription (Fenouil et al., 2012) - what is known about CpG Islands is somewhat inaccurately described. It should be rephrased more accurately, e.g. - CpG Islands found near TSS are associated with robust and high expression level of genes, including genes expressed in many tissues, such as housekeeping genes.
We thank the reviewer for that. We have rewrote this part of the introduction.
- The following claim (in Introduction), regarding retrogenes and their GC content is not in agreement recent analyses: "Indeed, it has been observed that these genes have elevated GC-content at their 5' ends in comparison to their intron-containing counterparts, suggesting that elevation of GC-content can be driven by positive selection to drive their efficient export (Mordstein et al., 2020). Moreover, retrogenes tend to arise from parental genes that have high GC-content at their 5'ends (Kaessmann et al.,2009)." Recent work showed that retrogenes in mouse and human are significantly depleted of CpG islands in their promoters (PMID: 37055747). This follows the notion that young genes, such as these retrogenes, have simple promoters (PMID: 30395322) with few TF binding sites and without CpGs. The two reported trends should be both mentioned with some suggestions regarding why they seem to be contrasting each other and how they can be reconciled.
We thank the reviewer for this information. The previous report (Mordstein et al., 2020) indicated that the increase in GC-content occurs downstream of the TSS in retrogenes. Since sequences upstream of the TSS are not part of the retro-insertion, it is not surprising that GC-content may differ between the retrogene and the parental gene. That retrogenes have lower numbers of CpGs upstream of the TSS, bolsters the idea that GC-content is not required for transcription and that the GC-peak is not being maintained in most genes by purging selection.
- In "Thus GC-content is expected, and is indeed observed to be higher near recombination hotspots due to gBGC (REF)." I think you forgot the reference...
We thank the reviewer for catching this.
- In Results, regarding average GC content (Fig 2X): "Interestingly, this pattern is different in the nonamniotes examined, including anole lizard, coelacanth, shark and lamprey." - in lizard, it seems that the genomic average is lower (and lizards are amniotes)
You are absolutely right. We now fix this.
- In Discussion, the statement: "This model is supported by findings in a recent preprint, which documents the equilibrium state of GC-content in TSS regions from numerous organisms" seems to contrast with the findings of the mentioned preprint. If "most mammals have a high GC-content equilibrium state" but still have a functional PRDM9, in the lack of evidence for functional differences between ortholog PRDM9 proteins (such as signatures for positive selection or functional assays), the authors' findings regarding the relationship between a lack of PRDM9 in canids and the trends observed in their TSS, are weakened.
We are sorry about the confusion. We were not exactly sure what points were being commented on. 1) whether GC-content is at equilibrium for most mammals or 2) that the equilibrium state is high for most mammals despite containing PRDM9. We rewrote this sentence to clarify both issues (especially given that these concepts may not be clear to non-experts, such as the first reviewer). To answer the first potential concern, the paper in question (Joseph et al., 2023), does not show that GC-content at the TSS in mammals is at equilibrium, rather, it calculates what the equilibrium state is given the nucleotide substitution rates. In most organisms, the TSS is not at equilibrium. To answer both 1 and 2, Joseph et al., show that the equilibrium GC-content at the TSS for canids is much higher than for other mammals. They and others infer that the diversity between other mammals (where the equilibrium state is higher than humans and rodents but lower than canids) has to do with the variation between PRDM9 orthologues, however this has yet to be tested. Although the action of PRDM9 has not been evaluated in most mammals, we do point out that in snakes PRDM9 allows for some recombination at the TSS.
- In Methods, the ENSEMBL version (in addition of the per-species genome version) should be mentioned.
This has been fixed.
- In Fig 1, it is worth clarifying in the legend that the differences between the first and second rows of panels is in the length of the plotted region.
We have now indicated this in the figure legend.
Reviewer #2 (Significance (Required)):
The manuscript provides a rigorous analysis of the possible processes that have impacted the TSS GC-content during evolution. It should be of interest to a diverse set of investigators in the genomics community, since it touches on different topics including genome evolution, transcription and gene structures.
Thank you.
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
This study analyzes the distribution of GC-content along genes in humans and vertebrates, and particularly the higher GC-content in the 5'-end than in the 3'-end of genes. The results suggest that this pattern is ancient in vertebrates, currently decaying in mouse and humans, and probably driven by recombination and GC-biased gene conversion. It is proposed that the 5'-3' gradient was generated during evolution when PRDM9 was less active (in which case recombination occurs mostly near transcription start sites), and decays when PRDM9 is very active, as it is currently in humans and mouse. This is a very interesting hypothesis, also corroborated by a recent, similar analysis in mammals (Joseph et al. 2023). These two preprints, which appeared around the same time, are, I think, quite novel and important. The analyses performed here are thorough and convincing. Source code and raw data sets are openly distributed. I only have a couple of minor comments and suggestions, which I hope might help improve the manuscript.
Thank you very much for the kind words.
A1. There has been quite some work on the 5'-3' GC-content gradient in plants (e.g. Clément et al. 2014 GBE, Ressayre et al. 2015 GBE, Brazier & Glemin 2023 biorxiv), which you might like to cite.
Thank you for pointing out these very interesting papers, we have incorporated them into the latest version.
A2. CpG-content and GC-content are related in various ways (e.g. see Galtier & Duret 2000 MBE, Fryxell & Moon 2005 MBE) that you might like to discuss; currently the manuscript discusses the CpG hypermutation rate as a driver of GC-content but the picture might be a bit more complex.
Thank you for this, we have incorporated these citations.
A3. The model introduced by this manuscript (figure 7) is dependent on the evolution of recombination determination in vertebrates and the role of PRDM9. A recent preprint by Raynaud et al (biorxiv) seems relevant to this issue.
Thank you for pointing out this pre-print. We have added a paragraph to the discussion that mentions this work. This also initiated a conversation with the authors, and we include some "personal communications" that illuminate what is going on in teleost fish.
Line-by-line comments
B1. "First, highly spliced mRNAs tend to have high GC-content at their 5' ends despite the fact that it is not required for export and does not affect expression levels (Mordstein et al., 2020)" -> I do not totally understand this sentence, which seems to imply some link between splicing and export/expression, could you please clarify?
We rewrote that sentence to make it clearer.
B2. "mismatches will form in the heteroduplex which are typically corrected in favor of Gs and Cs over As and Ts by about 70%" -> This 70% figure is human-specific, and varies a lot among species; I know in this introduction you're mainly reviewing the human literature but since this part of the text introduces gBGC as a process maybe clarify by adding "in humans" or refrain from giving this figure?
Thank you. This is a good point. We fixed this.
B3. "Thus GC-content is expected, and is indeed observed to be higher near recombination hotspots due to gBGC (REF)." -> reference missing here; actually I'm not sure you will find a good reference for this because PRDM9-dependent hotspots are so short-lived that GC-content would only respond weakly; mayber rather refer to the equilibrium GC-content (and cite, for instance, Pratto et al 2014 Science), or to high-recombining regions instead of hotspots (and you have plenty of papers to cite)?
Thanks for this.
B4. Paragraph starting: "PRDM9 and recombination hotspots also experience accelerated rates of evolution..." -> I would suggest removing the word "also" and moving this paragraph up, just before the sentence I'm commenting above (the one starting "Thus GC-content..."). This will justify my suggestion in comment B3 of mentioning high-recombining regions instead of hotspots, while also avoiding to have the important paragraph on recombination at TSS (the one starting "There are interesting connections...") being sandwiched between two sections on PRDM9.
We did not move this paragraph, although we did adjust the wording slightly.
B5. Paragraph starting "There are interesting connections..." is crucial to your discussion and might be emphasized a bit more in introduction, in my opinion. For instance, what about adding a sentence like "Also not directly relevant to humans, these observations suggest that gBGC might have played a role in shaping the observed 5'-3' GC-content gradient."
We did not alter the structure of this paragraph but we did reword sections of it.
- "Interestingly, this pattern is different in the non-amniotes examined, including anole lizard, coelacanth, shark and lamprey. These organisms had clear differences in GC-content between their first exon and surrounding sequences (upstream and intronic sequences), which came close to the overall genomic GC-content." -> I'm not sure I got the point the authors are intending to make here. Also please note that lizards are amniotes.
We thank the reviewer for catching this error, we have fixed this.
Reviewer #3 (Significance (Required)):
This is one of two preprints having appeared ~at the same time (the other one being the cited Joseph et al 2023), which I think are quite important and convincing regarding the role of PRDM9-dependent and PRDM9-independent recombination on GC-content evolution in vertebrates. I support publication of this preprint in a molecular evolutionary journal.
We thank the reviewer for their kind assessment!
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Referee #3
Evidence, reproducibility and clarity
This study analyzes the distribution of GC-content along genes in humans and vertebrates, and particularly the higher GC-content in the 5'-end than in the 3'-end of genes. The results suggest that this pattern is ancient in vertebrates, currently decaying in mouse and humans, and probably driven by recombination and GC-biased gene conversion. It is proposed that the 5'-3' gradient hass generated during evolution when PRDM9 was less active (in which case recombination occurs mostly near transcription start sites), and decays when PRDM9 is very active, as it is currently in humans and mouse. This is a very interesting hypothesis, also corroborated by a recent, similar analysis in mammals (Joseph et al. 2023). These two preprints, which appeared around the same time, are, I think, quite novel and important. The analyses performed here are thorough and convincing. Source code and raw data sets are openly distributed. I only have a couple of minor comments and suggestions, which I hope might help improve the manuscript.
A1. There has been quite some work on the 5'-3' GC-content gradient in plants (e.g. Clément et al. 2014 GBE, Ressayre et al. 2015 GBE, Brazier & Glemin 2023 biorxiv), which you might like to cite.
A2. CpG-content and GC-content are related in various ways (e.g. see Galtier & Duret 2000 MBE, Fryxell & Moon 2005 MBE) that you might like to discuss; currently the manuscript discusses the CpG hypermutation rate as a driver of GC-content but the picture might be a bit more complex.
A3. The model introduced by this manuscript (figure 7) is dependent on the evolution of recombination determination in vertebrates and the role of PRDM9. A recent preprint by Raynaud et al (biorxiv) seems relevant to this issue.
Line-by-line comments
B1. "First, highly spliced mRNAs tend to have high GC-content at their 5' ends despite the fact that it is not required for export and does not affect expression levels (Mordstein et al., 2020)" -> I do not totally understand this sentence, which seems to imply some link between splicing and export/expression, could you please clarify?
B2. "mismatches will form in the heteroduplex which are typically corrected in favor of Gs and Cs over As and Ts by about 70%" -> This 70% figure is human-specific, and varies a lot among species; I know in this introduction you're mainly reviewing the human literature but since since this part of the text introduces gBGC as a process maybe clarify by adding "in humans" or refrain from giving this figure?
B3. "Thus GC-content is expected, and is indeed observed to be higher near recombination hotspots due to gBGC (REF)." -> reference missing here; actually I'm not sure you will find a good reference for this because PRDM9-dependent hotspots are so short-lived that GC-content would only respond weakly; mayber rather refer to the equilibrium GC-content (and cite, for instance, Pratto et al 2014 Science), or to high-recombining regions instead of hotspots (and you have plenty of papers to cite)?
B4. Paragraph starting: "PRDM9 and recombination hotspots also experience accelerated rates of evolution..." -> I would suggest removing the word "also" and moving this paragraph up, just before the sentence I'm commenting above (the one starting "Thus GC-content..."). This will justify my suggestion in comment B3 of mentioning high-recombining regions instead of hotspots, while also avoiding to have the important paragraph on recombination at TSS (the one starting "There are interesting connections...") being sandwiched between two sections on PRDM9.
B5. Paragraph starting "There are interesting connections..." is crucial to your discussion and might be emphasized a bit more in introduction, in my opinion. For instance, what about adding a sentence like "Also not directly relevant to humans, these observations suggest that gBGC might have played a role in shaping the observed 5'-3' GC-content gradient."
- "Interestingly, this pattern is different in the non-amniotes examined, including anole lizard, coelacanth, shark and lamprey. These organisms had clear differences in GC-content between their first exon and surrounding sequences (upstream and intronic sequences), which came close to the overall genomic GC-content." -> I'm not sure I got the point the authors are intending to make here. Also please note that lizards are amniotes.
Significance
This is one of two preprints having appeared ~at the same time (the other one being the cited Joseph et al 2023), which I think are quite important and convincing regarding the role of PRDM9-dependent and PRDM9-independent recombination on GC-content evolution in vertebrates. I support publication of this preprint in a molecular evolutionary journal.
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Referee #2
Evidence, reproducibility and clarity
In this work, the author present various analyses suggesting that GC-content in TSS of coding genes is affected by recombination. The article findings are interesting and novel and are important to our understanding of how various non-adaptive evolutionary forces shape vertebrate genome evolutionary history.
The Methods section includes most needed details (see comments below for missing information), and the scripts and data provided online help in transparency and usability of these analyses.
I have several comments, mostly regarding clarifications in the text and several suggestions:
- In introduction: CpG islands, have been shown to activate transcription (Fenouil et al., 2012) - what is known about CpG Islands is somewhat inaccurately described. It should be rephrased more accurately, e.g. - CpG Islands found near TSS are associated with robust and high expression level of genes, including genes expressed in many tissues, such as housekeeping genes.
- The following claim (in Introduction), regarding retrogenes and their GC content is not in agreement recent analyses: "Indeed, it has been observed that these genes have elevated GC-content at their 5' ends in comparison to their intron-containing counterparts, suggesting that elevation of GC-content can be driven by positive selection to drive their efficient export (Mordstein et al., 2020). Moreover, retrogenes tend to arise from parental genes that have high GC-content at their 5'ends (Kaessmann et al.,2009)." Recent work showed that retrogenes in mouse and human are significantly depleted of CpG islands in their promoters (PMID: 37055747). This follows the notion that young genes, such as these retrogenes, have simple promoters (PMID: 30395322) with few TF binding sites and without CpGs. <br /> The two reported trends should be both mentioned with some suggestions regarding why they seem to be contrasting each other and how they can be reconciled.
- In "Thus GC-content is expected, and is indeed observed to be higher near recombination hotspots due to gBGC (REF)." I think you forgot the reference...
- In Results, regarding average GC content (Fig 2X): "Interestingly, this pattern is different in the nonamniotes examined, including anole lizard, coelacanth, shark and lamprey." - in lizard, it seems that the genomic average is lower (and lizards are amniotes)
- In Discussion, the statement: "This model is supported by findings in a recent preprint, which documents the equilibrium state of GC-content in TSS regions from numerous organisms" seems to contrast with the findings of the mentioned preprint. If "most mammals have a high GC-content equilibrium state" but still have a functional PRDM9, in the lack of evidence for functional differences between ortholog PRDM9 proteins (such as signatures for positive selection or functional assays), the authors' findings regarding the relationship between a lack of PRDM9 in canids and the trends observed in their TSS, are weakened.
- In Methods, the ENSEMBL version (in addition of the per-species genome version) should be mentioned.
- In Fig 1, it is worth clarifying in the legend that the differences between the first and second rows of panels is in the length of the plotted region.
Significance
The manuscript provides a rigorous analysis of the possible processes that have impacted the TSS GC-content during evolution. It should be of interest to a diverse set of investigators in the genomics community, since it touches on different topics including genome evolution,transcription and gene structures.
-
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 manuscript investigates the dynamics of GC-content patterns in the 5'end of the transcription start sites (TSS) of protein-coding genes (pc-genes). The manuscript introduces a quite careful and comprehensive analysis of GC content in pc-genes in humans and other vertebrates, specially around the TSS. The result of this investigation states that "GC-content surrounding the TSS is largely influenced by patterns of recombination." (from end of Introduction)
My main concern with this manuscript is one of causal reasoning, whether intended or not. I hope the authors can follow my reasoning bellow on how the logic sometimes seems to fail, and that they introduce changes to clarify their suggested mechanisms of action.
The above quoted sentence form the end of the Intro is in conflict with this other sentence that appears at the end of the Abstract "the dynamics of GC-content in mammals are largely shaped by patterns of recombination". The sentence in the Intro seems to indicate that the effect is specific to TSSs, but the one in the abstract seem to indicate the opposite, that is, that the effect is ubiquitous.
The observations as stated in the abstract are: "We observe that in primates and rodents, where recombination is directed away from TSSs by PRDM9, GC-content at protein-coding gene TSSs is currently undergoing mutational decay."
If I understand the measurements described in the manuscript correctly, and the arguments around them, you seem to show that the mutational decay of GC-content in humans is independent of location (TSSS or not), as noted here
(also from the abstract) "These patterns extend into the open reading frame affecting protein-coding regions, and we show that changes in GC-content due to recombination affect synonymous codon position choices at the start of the open reading frame."
There is one more result described in the manuscript, that in my mind is very important, but it is not given the relevance that it appears to me that it has. That is presented in Figure S3G. "we concluded that GC-content at the TSS of protein-coding genes is not at equilibrium, but in decay in primates and rodents. This decay rate is similar to the decay seen in intergenic regions that have the same GC-content (Figure S3G)"
Thus, if the decaying effect happens everywhere, how can it be related to "recombination being directed away from TSSs by PRDM9" as it is stated in the abstract and in the model described in Figure 7?
The fact that the decay rate is similar to any other region with similar GC-content should be an indication that the effect is not related to anything having to do with TSS or recombination being directed away from TSSs by PRDM9.
I hope these paragraphs show my confusion about the relationship between the results presented which I think are very comprehensive and their interpretation and suggested model for GC-content dynamics around TSSs in human.
On another note, can you provided a bit more background on recombination and its mechanisms? You seem to have confident sets of genes under high/low/med recombination. How are those determined. You also seem to concentrate the cause of recombination on PRDM9, please explain. Is PRDM9 the unique indicator of recombination?
Specific comments
Figure 1, it is very hard to understand the differences between the three rows. Please explain more clearly in the legend, and add more information to the figure itself.
Figure 7, express somewhere in the figure that the y axis measures GC content. Figure seems to introduce a 'causal' model of GC-content dismissing based on recombination being directed away from TSSs. How about the diminishing of GC-content on any other genomic regions as you have shown in Figure S3G?
The title is too selective, as to the results, and it has the implication that the decay is exclusive to the surrounding of the TSSs.
Significance
The statistical analysis is comprehensive and robust. Their model interpretation as is describe induces confusion and needs to be clarified.
I am an expert computational biologist, I do not have a deep knowledge of sequence implications of recombination, and it would be good if the manuscript could add some more background on that.
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Reply to the reviewers
Reviewer #1 (Evidence, reproducibility and clarity (Required)):
In "BDNF signaling requires matrix metalloproteinase-9 during structural synaptic plasticity", Legutko et al. used two-photon microscopy and glutamate uncaging to show that rapid release (seconds) of MMP-9 from dendritic spines following synaptic stimulation as well as MMP-9 dependent activation of TrkB. The authors also show and MMP-dependent increase on dendritic spine volume. These data support the possibility that MMP-9 rapidly activates BDNF to promote the spine maturation required for LTP. All is all the manuscript is well written, and the data is convincing and important.
Answer: We thank the reviewer for that comment.
Questions/Concerns:
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The authors show cell free cleavage of BDNF by recombinant MMP-9. It would be more convincing to show that MMP-9 cleaved BDNF using concentrated supernatants following synaptic stimulation in control versus inhibitor treated slices. Answer: In the present study we focus on a single-spine approach; thus, we did not include general stimulation techniques and biochemical analyses. To our knowledge, there is no method to show BDNF cleavage by MMP-9 directly at a single synapse. We agree with the reviewer that the general stimulation is important; however, at the synapse, there is potentially a whole array of proteases such as plasmin, tissue plasminogen activator (tPA) that might not only create catalytic cascade and proteolytically activate MMP-9 but also directly cleave proBDNF. When stimulating neurons and analysing supernatants, it is therefore impossible to determine if MMP-9 directly digests proBDNF to mBDNF or, alternatively, whether it is just a part of a proteolytic cascade leading to BDNF maturation. Therefore, our result where we use recombinant proteins provide an important piece of evidence that MMP-9 can indeed cleave proBDNF directly. Of note, experiments using brain extracts have been published previously, for example in a paper of Mizoguchi et al., J.Neurosci. (2011); DOI:10.1523/JNEUROSCI.3118-11.2011, where the authors showed increased cleavage of BDNF after pentylenetetrazole kindling and the kindling induced proBDNF cleavage was decreased in MMP-9 KO mice.
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The concentration of the MMP-9/13 inhibitor used was quite high and would also inhibit MMP-1, -3 and -7. This concern is, however, abrogated by the use of the MMP-9 KO. But it might be important to mention that the inhibitor is not MMP-9 specific at higher concentrations. Answer: To comply with this remark, we have stressed the notion in the Discussion of the revised ms.:
"There are over twenty MMPs with overlapping substrate specificity (Fields, 2015; Cieplak & Strongin, 2017) and there are no fully specific, commercially available inhibitors for MMP-9. Since Inhibitor I might affect also other MMPs, to further test the involvement of the protease in sLTP, we have used hippocampal slice cultures prepared from MMP-9 KO mice and their WT littermates (Fig. 1E, 1F)."
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In figure 1C vs E, as well as Fig 3C vs E, it appears that the DMSO to inhibitor (1C and 3C) change is larger than the WT vs MMP-9 KO (1E and 3E). Is this possibly because DMSO has a potentiating effect and/or because the inhibitor is getting other MMPs or the MMP-9 KO has compensatory increases in other MMPs? __Answer: __At the concentrations used in the study (not exceeding 0.08%), we do not consider DMSO having any potentiating effect. As we discuss in the manuscript, the difference between DMSO control and MMP-9 WT is most likely due to differences between genetic lines of the mice. This is also a reason why each set of experiments has its own control. Of note, in the paper preceding this study, Harvard et al., Nature (2016); doi:10.1038/nature19766, spine volume change induced by uncaging, vary between 200 and 300% depending on mice strain used in the experiment.
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The idea that MMP-9 and pro-BDNF are in the same vesicular stores is an interesting and very plausible one. Perhaps the authors could discuss what is known about the types of vesicles thought to harbor these two proteins. Answer: To follow on this remark, we added information about the vesicles containing BDNF and MMP-9 in the Discussion:
"Given that the release kinetics of BDNF and MMP-9 are similar, one could speculate that the effect of MMP-9 inhibition on early TrkB activation can be achieved because both, MMP-9 and BDNF are co-localized and co-released from the same release vesicles. BDNF is widely considered to be stored and released from Large Dense-Core Vesicles (Dieni et al, 2012; Kojima et al, 2020), and MMP-9 release although not studied in neurons but in cell lines, also points to the same type of vesicles (Stephens et al, 2019)."
- It might be useful to add to the discussion pathological conditions such as major depression and post-stroke plasticity in which MMP-9 dependent BDNF activation could be important. Answer: We thank the reviewer for that suggestion. We have added the information about MMP-9 and BDNF link in the brain pathologies in the Discussion:
"Additionally, our data may provide a functional link between the involvement of MMP-9 and BDNF in various brain pathologies, in which such a link has previously been implicated, for example in addiction (Cheng et al, 2019), schizophrenia (Pan et al, 2022; Yamamori et al, 2013), ischemic stroke (Li et al, 2022) or even following cochlear implantation (Matusiak et al, 2023)."
Reviewer #1 (Significance (Required)):
The results are significant to understanding synaptic plasticity in health and disease.
__Answer: __We thank the reviewer for that comment stressing the importance of our study.
Reviewer #2 (Evidence, reproducibility and clarity (Required)):
The study addresses the molecular mechanisms of activity-dependent morphological plasticity of dendritic spines, focusing on the role of MMP-9 and BDNF-TrkB in the signalling and biochemical activities that lead to and maintain spine enlargement ('structural LTP', sLTP) induced experimentally.
It is based on a combination of 2-photon imaging of spine morphology, 2-photon imaging of MMP-9-SEP fluorescence, 2-photon FLIM of a biosensor for TrkB activity and 2-photon glutamate uncaging in organotypic hippocampal brain slices. In addition, it includes an assay of protein digestion based on Western immuno blots.
As results, the study reports that diminishing MMP-9 activity (pharmacologically or genetically) in the slices reduces sLTP, that repetitive glutamate uncaging evokes the release of MMP-9 from the spines that undergo sLTP, and that this effect can be blocked by pharmacological blockade of NMDA or exocytosis, that repetitive glutamate uncaging on a spine increases TrkB activation in the spine, and that this effect is diminished in slices from MMP-9 KO animals or treated by an MMP-9 blocker, and that MMP-9 can cleave proto-BDNF into mature BDNF in a cell-free medium.
The experiments are technically challenging but they are well conceived, designed and executed. The conclusions are well supported by the results, which are clearly discussed in light of the substantial and somewhat contradictory literature.
Reviewer #2 (Significance (Required)):
The study provides a finer view of the dynamic role of MMP-9 in activity-dependent spine plasticity, reinforcing and expanding existing knowledge on this timely topic.
The study is well executed and the conclusions are warranted. The study is an experimental tour de force, even if the biological results and insights are rather incremental and don't force us to revise our main assumptions or expectations.
Answer: We thank the reviewer for that comment and the appreciation of our work.
I only have a few questions and suggestions:
- 2: Do TeTx and AP5 treatments also block spine enlargement? The MMP9-SEP and mCherry signals in the spines are going up, what about their ratio F/R? __Answer: __Yes, we do have results showing that TeTx and AP5 block spine enlargement, however we did not present them in the original manuscript. The AP5 application on spine enlargement was previously demonstrated for example by Tanaka and co-workers (2008); DOI: 10.1126/science.1152864, and the effect of TeTx on LTP and insertion of AMPA receptors has also been demonstrated multiple times for example by Penn et al., Nature (2017); doi:10.1038/nature23658. To comply with the reviewer's request we have included the data in the revised version of the manuscript (Figure 2C).
As far as the F/R ratio is concerned we shall stress that the aim of our experiments was to show the release of MMP-9 into extracellular space upon uncaging. We have initially tried to analyse the ratio of F/R, however the green signal that comes from MMP9-SEP does not accumulate at the spine, apparently being rapidly diffused. Therefore, the overall red signal for mCherry increases much faster (mCherry fills the cytoplasm in the spine) than the MMP9-SEP; therefore, the F/R ratio is decreasing over time. Figure 2G shows that increases in MMP9-SEP fluorescence are only transient (around 0.5 s) after uncaging pulses.
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3B shows increased TrkB activation after glutamate uncaging, but is it possible to see the spine enlargement in the FRET-FLIM signal/images? Answer: Yes, it is possible to observe spine enlargement during FRET-FLIM experiments by counting photons from the red channel (RFP) as well as from the green one (GFP), however due to technical difficulties spine volume change was measured in separate experiments.
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Fix: mW and Chameleon in the Methods section - corrected
- Consider streamlining the Discussion a bit - we have reviewed the discussion
- Consider adding a schematic to summarise the new and existing findings Answer: We thank the reviewer for the suggestion, we have added a schematic summarising the paper as a separate figure (Fig.4).
Reviewer #3 (Evidence, reproducibility and clarity (Required)):
In this short report, Legutko et al address the role of MMP9 in BDNF signaling in the context of structural long-term potentiation (sLTP). In particular, they assess whether MMP9 is secreted fast enough to mediate the cleavage of proBDNF in mBDNF during sLTP. The study uses 2-photon imaging of hippocampal organotypic slices, glutamate uncaging and FRET-based sensors of TrkB activity. The authors demonstrate that MMP9 is secreted within seconds upon 2-photon glutamate uncaging and that MMP9 secretion precedes spine enlargement. They also show that MMP9 can cleave proBDNF in vitro. However, the role of MMP9 in sLTP and associated TrkB signaling remains speculative at the end of the manuscript.
Major comments
- The title of the first result section "spine head enlargement during structural plasticity depends on MMP9 activity" is an overstatement. The authors provide evidence that MMP inhibition and MMP9 KO decrease spine enlargement during the early phase of sLTP. However, after the first few minutes, spines still display long-term enlargement, and no difference between WT and MMP9 KO mice can be detected. These data suggest that MMP9 is only involved in the initial phase of sLTP, and that other MMPs are involved in sLTP.
__Answer: __We thank the reviewer for that comment. We have change the wording in the revised manuscript to accommodate the suggestion.
- The authors cannot conclude that "spine head enlargement during sLTP depends on MMP9 activity".
__Answer: __We thank the reviewer for that comment. We have changed the title and wording in the revised manuscript to accommodate the suggestion.
- The authors should apply Inhibitor I on MMP9 KO slices to determine if MMPs other than MMP9 are involved in spine enlargement.
__Answer: __We thank the reviewer for the suggestion, and indeed we agree that other MMPs might be involved in spine enlargement induced by glutamate uncaging. Furthermore, applying Inhibitor I will not resolve the question which MMPs or other proteases are involved in the spine enlargement. Applying Inhibitor I on MMP-9 KO slices would only eliminate one of the proteases. To deal with this difficult issue, we have used slices from MMP-9 KO mice and showed the influence of MMP-9 on the transient phase of spine enlargement induced by glutamate uncaging.
- If Inhibitor I still impacts sLTP in MMP9 KO slice, it would greatly benefit this study to determine which MMPs are involved (for example by analyzing the expression patterns of MMPs in their neurons and selectively inactivating those expressed with shRNAs).
Answer: The proposed experiment is an excellent suggestion for a future project however it is not an easy experiment to perform. MMPs expression pattern could be assessed by single cell RNA sequencing to distinguish it from for example astrocytic expression, however it often fails to detect mRNAs which are expressed at low level. For example mRNA coding MMP-9 belongs to this group as its mRNA is kept at very low level, see, e.e.g, Konopacki et al., Neuroscience (2007); https://doi.org/10.1016/j.neuroscience.2007.08.026, Dziembowska et al. J.Neurosci (2012); https://doi.org/10.1523/JNEUROSCI.6028-11.2012. There is also quite low correlation between mRNA levels and protein levels at a global scale, see e.g., Reimegård et al., Comm. Biol. (2021); https://doi.org/10.1038/s42003-021-02142-w, therefore predictive power of mRNA sequencing for the importance of a particular protein might not be sufficiently informative. Moreover, the situation is even more complex in neurons which are strongly compartmentalized, and where local translation plays a significant role. We have previously studied this particular aspect for MMP-9, Dziembowska et al. J.Neurosci. (2012); DOI:10.1523/JNEUROSCI.6028-11.2012..
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The title of the third/last result section "TrkB signaling depends on MMP9 activity" is also an overstatement. In Figure 3, the authors show that the pharmacological inhibition of MMPs slightly inhibits TrkB signaling in the early phase of sLTP, and almost abolishes TrkB signaling in the second phase (> 3 min after uncaging). However, the data suggesting a specific role for MMP9 in TrkB signaling are not convincing (Figure 3E-F). The activation of trkB during sLTP is weak even in WT, the peak of trkB activation upon glutamate uncaging in not disrupted in MMP9 KO mice, and the data are noisy. It is a major concern that the authors cannot convincingly show that TrkB signaling is altered in MMP9-deficient neurons. Answer: To the best of our knowledge, using FRET-FLIM sensors is the best and state-of-the-art method to track biochemical changes (such as receptor activation) in real time using live preparations. The method is very sensitive and published previously by one of the authors of the current study where TrkB sensor is activated in the same magnitude (Harward et al., Nature, 2016; doi: 10.1038/nature19766). Moreover similar magnitude of sensor activation was reported previously in single dendritic spines for other sensors using FLIM-FRET method: Rho GTPases (Hedrick et al., Nature 2016; doi: 10.1038/nature19784), IGF1R (Tu et al., Sci Advanc. 2023; doi: 10.1126/sciadv.adg0666) or CaMKII (Chang et al., Nat. Commun. 2019; https://doi.org/10.1038/s41467-019-10694-z). The noise is to be expected, as we are imaging small compartments in a short time where collecting enough number of photons is challenging. Similarly to previous studies using FRET-FLIM sensors, we bin experimental points to reduce noise for statistical analysis. Notably, the biological effect we observe, namely sensor activation, is well above the experimental noise that in inevitable in this experimental approach. For statistical analyses we have used repeated measures ANOVA, which is very sensitive to noise and signal fluctuation. The differences we measure are statistically significant.
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The authors discuss that the problem might stem from mouse genetic backgrounds. However, if the MMP9 KO mouse model is not appropriate to answer the question, the authors should use another one (i.e. MMP9 knockdown using sh/siRNAs).
Answer: We believe that the effect of MMP-9 KO in this experiment is evident, as supported by Fig. 3 E,F and statistical analysis. Furthermore, the experiment with the inhibitor further supports our reasoning.
- In addition to the graphs, the authors should mention in the text the percentage of inhibition compared to WT). This would make the results easier to read.
Answer: To comply with this request the appropriate information has been added to the revised manuscript.
- The change in TrkB activation following glutamate uncaging is low (max 5-7 % at the peak, compared to 200% for spine volume). This raises the question of the physiological relevance of TrkB activation in this model. The authors should include experiments with a trkB inhibitor to assess whether it prevents sLTP in WT and MMP9 KO mice. They should also discuss other potential targets of MMP9. This would strengthen the rationale of the experiments. Answer: Previously published results using the same TrkB sensor (Harward et al., Nature, 2016; doi: 10.1038/nature19766), show exactly the same change in binding fraction calculated from a change in GFP fluorescence lifetime. These data are also in agreement with well-established standard in the field, see, e.g., Rho GTPases (Hedrick et al., Nature 2016; doi: 10.1038/nature19784), IGF1R (Tu et al., Sci Advanc. 2023; doi: 10.1126/sciadv.adg0666) or CaMKII (Chang et al., Nat. Commun. 2019; https://doi.org/10.1038/s41467-019-10694-z). In response to the comment we have addressed this issue in the Discussion in the revised ms.
Minor comments
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In the introduction, the authors should provide more context. Could the authors develop the "long standing debate on which enzymes process proBDNF to mBDNF"? Answer: We have removed the sentence as we realized it was too confusing and the paper does not compare between different proteases which may process proBDNF to mBDNF.
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In the result section:
- First paragraph, the last sentence should be moved from the end of the paragraph to before "During sLTP induction...".
Answer: Following the reviewer suggestion, we have moved the sentence.
- Several paragraphs in the result section lack a proper conclusion/interpretation, which makes it difficult to read. Examples: after (Fig. 2E), after (Fig. 2F). The authors should explicit what their results mean.
Answer: We have changed the paragraphs and tried to explain the results better.
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Clarify when and for how long the MMP inhibitor was applied. Answer: The inhibitor was applied 30 min. before stimulation. We have added the information in the Methods section.
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In figure 1, The authors observe a specific alteration of the early, transient, sustained increase in spine head volume in MMP9 KO mice. The later phase of sLTP is not impacted, which means that sLTP is induced and maintained in the KO. Could the authors discuss the role/importance of this transient peak in spine head volume? Answer: In response to this comment, we have discussed this issue in the revised ms. as follows:
" The transient spine expansion might be important for the remodeling of the synapse (Lang et al, 2004) and is associated with NMDAR-dependent formation of "memory gel" created by enlargement pool of actin (Honkura et al, 2008; Kasai et al, 2010; Bonilla-Quintana & Rangamani, 2024). It has also been reported that TrkB activity can influence actin dynamics (Woo et al, 2019; Hedrick et al, 2016), in some instances in concert with integrin 1 (Wang et al, 2016), which is also activated by MMP-9 (Wang et al, 2008; Michaluk et al, 2009, 2011) and further supports our observations."
Reviewer #3 (Significance (Required)):
The manuscript aims to bring conceptual advance in our understanding of structural synaptic plasticity by investigating the role and timing of MMP9 secretion in TrkB signaling. Previous work from the Yasuda lab and others have shown that trkB is activated early on by BDNF during sLTP. However, how, when and where BDNF is cleaved from proBDNF in mBDNF is poorly understood. The authors demonstrate that the pharmacological inhibition of metalloproteases attenuates structural long-term plasticity (sLTP) and that MMP9 is secreted early enough to cleave proBDNF. They also show that MMP9 can cleave proBDNF in BDNF in vitro. Whether MMP9 specifically cleaves BDNF during sLTP and whether this cleavage is physiologically relevant for sLTP remain an open question.
This report will be of interest to neurobiologists interested in the molecular mechanisms of synaptic plasticity.
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Referee #3
Evidence, reproducibility and clarity
In this short report, Legutko et al address the role of MMP9 in BDNF signaling in the context of structural long-term potentiation (sLTP). In particular, they assess whether MMP9 is secreted fast enough to mediate the cleavage of proBDNF in mBDNF during sLTP. The study uses 2-photon imaging of hippocampal organotypic slices, glutamate uncaging and FRET-based sensors of TrkB activity. The authors demonstrate that MMP9 is secreted within seconds upon 2-photon glutamate uncaging and that MMP9 secretion precedes spine enlargement. They also show that MMP9 can cleave proBDNF in vitro. However, the role of MMP9 in sLTP and associated TrkB signaling remains speculative at the end of the manuscript.
Major comments
- The title of the first result section "spine head enlargement during sLTP depends on MMP9 activity" is an overstatement.
The authors provide evidence that MMP inhibition and MMP9 KO decrease spine enlargement during the early phase of sLTP. However, after the first few minutes, spines still display long-term enlargement, and no difference between WT and MMP9 KO mice can be detected. These data suggest that MMP9 is only involved in the initial phase of sLTP, and that other MMPs are involved in sLTP.
- The authors cannot conclude that "spine head enlargement during sLTP depends on MMP9 activity".
- The authors should apply Inhibitor I on MMP9 KO slices to determine if MMPs other than MMP9 are involved in spine enlargement.
- If Inhibitor I still impacts sLTP in MMP9 KO slice, it would greatly benefit this study to determine which MMPs are involved (for example by analyzing the expression patterns of MMPs in their neurons and selectively inactivating those expressed with shRNAs).
- The title of the third/last result section "TrkB signaling depends on MMP9 activity" is also an overstatement. In Figure 3, the authors show that the pharmacological inhibition of MMPs slightly inhibits TrkB signaling in the early phase of sLTP, and almost abolishes TrkB signaling in the second phase (> 3 min after uncaging). However, the data suggesting a specific role for MMP9 in TrkB signaling are not convincing (Figure 3E-F). The activation of trkB during sLTP is weak even in WT, the peak of trkB activation upon glutamate uncaging in not disrupted in MMP9 KO mice, and the data are noisy. It is a major concern that the authors cannot convincingly show that TrkB signaling is altered in MMP9-deficient neurons.
- The authors discuss that the problem might stem from mouse genetic backgrounds. However, if the MMP9 KO mouse model is not appropriate to answer the question, the authors should use another one (i.e. MMP9 knockdown using sh/siRNAs).
- In addition to the graphs, the authors should mention in the text the percentage of inhibition compared to WT). This would make the results easier to read.
- The change in TrkB activation following glutamate uncaging is low (max 5-7 % at the peak, compared to 200% for spine volume). This raises the question of the physiological relevance of TrkB activation in this model. The authors should include experiments with a trkB inhibitor to assess whether it prevents sLTP in WT and MMP9 KO mice. They should also discuss other potential targets of MMP9. This would strengthen the rationale of the experiments.
Minor comments
- In the introduction, the authors should provide more context. Could the authors develop the "long standing debate on which enzymes process proBDNF to mBDNF"?
- In the result section:
- First paragraph, the last sentence should be moved from the end of the paragraph to before "During sLTP induction...".
- Several paragraphs in the result section lack a proper conclusion/interpretation, which makes it difficult to read. Examples: after (Fig. 2E), after (Fig. 2F). The authors should explicit what their results mean.
- Clarify when and for how long the MMP inhibitor was applied.
- In figure 1, The authors observe a specific alteration of the early, transient, sustained increase in spine head volume in MMP9 KO mice. The later phase of sLTP is not impacted, which means that sLTP is induced and maintained in the KO. Could the authors discuss the role/importance of this transient peak in spine head volume?
Significance
The manuscript aims to bring conceptual advance in our understanding of structural synaptic plasticity by investigating the role and timing of MMP9 secretion in TrkB signaling. Previous work from the Yasuda lab and others have shown that trkB is activated early on by BDNF during sLTP. However, how, when and where BDNF is cleaved from proBDNF in mBDNF is poorly understood. The authors demonstrate that the pharmacological inhibition of metalloproteases attenuates structural long-term plasticity (sLTP) and that MMP9 is secreted early enough to cleave proBDNF. They also show that MMP9 can cleave proBDNF in BDNF in vitro. Whether MMP9 specifically cleaves BDNF during sLTP and whether this cleavage is physiologically relevant for sLTP remain an open question.
This report will be of interest to neurobiologists interested in the molecular mechanisms of synaptic plasticity.
- The title of the first result section "spine head enlargement during sLTP depends on MMP9 activity" is an overstatement.
The authors provide evidence that MMP inhibition and MMP9 KO decrease spine enlargement during the early phase of sLTP. However, after the first few minutes, spines still display long-term enlargement, and no difference between WT and MMP9 KO mice can be detected. These data suggest that MMP9 is only involved in the initial phase of sLTP, and that other MMPs are involved in sLTP.
-
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 study addresses the molecular mechanisms of activity-dependent morphological plasticity of dendritic spines, focusing on the role of MMP-9 and BDNF-TrkB in the signalling and biochemical activities that lead to and maintain spine enlargement ('structural LTP', sLTP) induced experimentally.
It is based on a combination of 2-photon imaging of spine morphology, 2-photon imaging of MMP-9-SEP fluorescence, 2-photon FLIM of a biosensor for TrkB activity and 2-photon glutamate uncaging in organotypic hippocampal brain slices. In addition, it includes an assay of protein digestion based on Western immuno blots.
As results, the study reports that diminishing MMP-9 activity (pharmacologically or genetically) in the slices reduces sLTP, that repetitive glutamate uncaging evokes the release of MMP-9 from the spines that undergo sLTP, and that this effect can be blocked by pharmacological blockade of NMDA or exocytosis, that repetitive glutamate uncaging on a spine increases TrkB activation in the spine, and that this effect is diminished in slices from MMP-9 KO animals or treated by an MMP-9 blocker, and that MMP-9 can cleave proto-BDNF into mature BDNF in a cell-free medium.
The experiments are technically challenging but they are well conceived, designed and executed. The conclusions are well supported by the results, which are clearly discussed in light of the substantial and somewhat contradictory literature.
Significance
The study provides a finer view of the dynamic role of MMP-9 in activity-dependent spine plasticity, reinforcing and expanding existing knowledge on this timely topic.
The study is well executed and the conclusions are warranted. The study is an experimental tour de force, even if the biological results and insights are rather incremental and don't force us to revise our main assumptions or expectations.
I only have a few questions and suggestions:
- Fig. 2: Do TeTx and AP5 treatments also block spine enlargement? The MMP9-SEP and mCherry signals in the spines are going up, what about their ratio F/R?
- Fig. 3B shows increased TrkB activation after glutamate uncaging, but is it possible to see the spine enlargement in the FRET-FLIM signal/images?
- Fix: mW and Chameleon in the Methods section
- Consider streamlining the Discussion a bit
- Consider adding a schematic to summarise the new and existing 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
In "BDNF signaling requires matrix metalloproteinase-9 during structural synaptic plasticity", Legutko et al. used two-photon microscopy and glutamate uncaging to show that rapid release (seconds) of MMP-9 from dendritic spines following synaptic stimulation as well as MMP-9 dependent activation of TrkB. The authors also show and MMP-dependent increase on dendritic spine volume. These data support the possibility that MMP-9 rapidly activates BDNF to promote the spine maturation required for LTP. All is all the manuscript is well written, and the data is convincing and important.
Questions/Concerns:
- The authors show cell free cleavage of BDNF by recombinant MMP-9. It would be more convincing to show that MMP-9 cleaved BDNF using concentrated supernatants following synaptic stimulation in control versus inhibitor treated slices.
- The concentration of the MMP-9/13 inhibitor used was quite high and would also inhibit MMP-1, -3 and -7. This concern is, however, abrogated by the use of the MMP-9 KO. But it might be important to mention that the inhibitor is not MMP-9 specific at higher concentrations.
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- In figure 1C vs E, as well as Fig 3C vs E, it appears that the DMSO to inhibitor (1C and 3C) change is larger than the WT vs MMP-9 KO (1E and 3E). Is this possibly because DMSO has a potentiating effect and/or because the inhibitor is getting other MMPs or the MMP-9 KO has compensatory increases in other MMPs?
- The idea that MMP-9 and pro-BDNF are in the same vesicular stores is an an interesting and very plausible one. Perhaps the authors could discuss what is known about the types of vesicles thought to harbor these two proteins.
- It might be useful to add to the discussion pathological conditions such as major depression and post-stroke plasticity in which MMP-9 dependent BDNF activation could be important.
Significance
The results are significant to understanding synaptic plasticity in health and disease.
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Reply to the reviewers
Compared to our initial submission to Review Commons, we have addressed all the reviewers' comments. We have extensively re-written the manuscript to make it clearer to a larger audience. In particular, we have transferred Figure EV1 to Figure 1 with more complete panels and included a scheme (Figure EV3) on the steps of D2R internalization which we measure with live cell imaging. We have added a new paragraph to the start of the Discussion to summarize our main conclusions and reordered the discussion on the possible mechanisms of membrane PUFA enrichment on D2R endocytosis. All the changes in the text are in red for easier comparison with the previous version.
As suggested by reviewer 1, we have performed additional experiments to test the specificity of the effects of PUFA treatments on D2R endocytosis, reinforcing the results shown in Figure 4 using feeding assays. We show with live cell TIRF imaging and the ppH assay that TfR-SEP endocytosis is not affected (Figure EV5) and that SEP-β2AR endocytosis and βarr2-mCherry recruitment to the plasma membrane are not affected (Figure EV6).
Reviewer #1
Evidence, reproducibility and clarity
*The manuscript, using different live and fixed cell trafficking assays, demonstrates that incorporation of poly-unsaturated, but not saturated, free fatty acids in the membrane phospholipids reduce agonist induced internalization of the D2 dopamine receptor but not the adrenergic beta2 receptors or the transferrin receptor. Pulsed pH (ppH) live microscopy further demonstrated that the reduced internalization by incorporation of free fatty acid was accompanied by a blunted recruitment of Beta-arrestin for the D2R.
I believe said claims put forward in the manuscript are overall well supported by the data and as such I do not believe that further experiments are necessarily needed to uphold these key claims. Also, the methodology is satisfactorily reported, and statistics are robust, although two-way Anova like used in Fig 1 seems appropriate for Fig 2 and 3*
We thank the reviewer for his/her positive assessment of our work. We have checked the statistical tests used for all our measures. For Figure 2 and 3 (now 3 and 4) we test for only one factor (PUFA treatment or not) so we ran ordinary one-way ANOVA using Graphpad Prism.
That said, I suggest that the fixed cell internalization experiments (Fig 2 and 3), which relate the effect on the D2R to B2AR and transferrin are revised. This is important since this is relevant to judge whether the effect is a general or a selective molecular mechanism since this is the one of the three assay which this comparison relies on. Alternatively, I suggest omitting this data and include the B2AR in the Live DERET assay and both B2AR and TfR in the ppH assay. Specifically, my concerns with the fixed cell internalization are: • The analysis is based on counting the number of endosomes, which is not necessarily equivalent to the number of receptors internalized
The number of puncta, as well as their fluorescence, is reported by the analysis program (written in Matlab2021 and available upon request). We chose to show number of puncta because they reflect more directly the number of labelled endosomes (in Figures 3 and 4). As shown in the figure below, we found slight but significant differences between groups for FLAG-D2R (88.6 % and 87.6 % of average fluorescence in DHA and DPA treated cells compared to control cells), (panel A), and no differences for FLAG-β2AR (panel B). We find a significant decrease in puncta fluorescence for transferrin uptake in cells incubated with DHA (but not DPA) relative to control cells (panel C). However, because we did not detect differences in the number of puncta or in the frequency and amplitude of endocytic vesicle creation events (see below), we still conclude that enrichment with exogenous PUFAs does not affect clathrin mediated endocytosis.
In conclusion, the most robust measure of endocytosis for this assay is the number of detected puncta per cell rather than their fluorescence.
- The analysis relies on fully effective stripping of the surface pool of receptors - i.e clustered surface receptors not stripped by the protocol will be assessed as internalized. It is often very difficult to obtain full efficiency of the Flag-tag stripping and this is somewhat expression dependent. • The protocol for the constitutive and agonist induced internalization is different and yet shown on the same absolute graph. Although I take it the microscope gain setting are unaltered between the constitutive and agonist induced internalization I don't believe the quantification can be directly related. This is confusing at the very least. More critically however, the membrane signal from the non-stripped condition of constitutive internalization will likely fully shield internalized receptors in the Rab4 membrane proximal recycling pathway leading to under-estimation of the in the constitutive endocytosis. I believe this methodological limitation underlies the massive relative difference in the constitutive endocytosis between panel 2A,B and 2C,D. For comparison, by a quantitative dual color FACS endocytosis assay, we have previously demonstrated the ligand endocytosis a ~4 fold increased over constitutive (in concert with Fig 2A,B here) (Schmidt et al 20XX). Importantly, high relative variability by this methodology could well shield an actual effect of incorporation of FFAs on the constitutive endocytosis. We thank the reviewer for pointing this difference in the protocol. As a matter of fact, we have not used acid stripping in all the conditions used for the uptake assays (Figures 3 and 4). We apologize for the confusion and we have clarified this point in the Methods section. In early experiments we compared conditions with or without stripping but we concluded from these experiments that indeed, the stripping was not complete. Moreover, we noticed early on that many cells treated with DHA or DPA did not have any detectable cluster (13 cells out of 58 quantified cells treated with DHA after addition of QPL, 12/56 cells treated with DPA, 0/68 for cells treated with vehicle). Stripping the antibody would have made these cells undetectable, biasing the analysis. Therefore, to make our results more consistent we decided to use non-stripping conditions. To detect endosomes specifically, we used a segmentation tool developed earlier (see Rosendale et al.* 2019). This tool is based on wavelet transforms which recognizes dot-like structures. In addition, we excluded from the cell mask the labelled plasma membrane by a mask erosion.
We agree the design of experiments was not aimed at comparing the effect of PUFA treatment on low levels of constitutive D2R endocytosis. This would require more sensitive assays and be addressed in subsequent studies.
'Optional' Also, it would be informative to see the ppH Beta-arrestin experiments with the B2AR to assess, whether the putative discrepancy between D2R and B2AR is upstream or downstream of the blunted Beta-arrestin recruitment. To the same point, it would be very informative to assess how the incorporation of the free fatty acids affect receptor signalling, which would also help relate the effect of incorporation of the FFA's in the phospholipids to previous experiment using short term incubation with FFA's
We have now performed live imaging experiments in HEK293 cells expressing SEP-β2AR, GRK2 and βarr2-mCherry and stimulated with isoproterenol (Figure EV6). We show that the clustering of SEP-β2AR, of βarr2-mCherry, as well as endocytosis, are not affected by treatments with DHA or DPA. In this study, we focused on the early trafficking steps of D2R internalization. It will be interesting in a future study to address its consequences on G protein dependent and independent signaling. Moreover, and for good measure, we performed experiments to assess TfR-SEP endocytosis with the ppH assay. Again, we found no difference between cells treated or not with PUFAs (Figure EV5)
*References overall seem appropriate although Schmidt et al would be relevant for reference of the constitutive vs agonist induced endocytosis of D2R and B2AR. *
We have now cited Schmidt et al. 2020 doi 10.1111/bcpt.13274 in the discussion with the following sentences: "D2R also shows constitutive endocytosis (Schmidt et al, 2020) which may be modulated by PUFAs although we did not detect any significant difference in our measures (see Figure 3) which were aimed at detecting high levels of internalization induced by agonists. Further work will be required to specifically examine the effect of PUFAs on constitutive GPCR internalization."
Overall, the figures are well composed and convey the messages fairly well. Specific point that would strengthen the rigor include: • Chosing actual representative pictures of the quantitative data in Fig 2 and 3 (e.g. hard to see 25 endocytic events in Fig 2A constitutive endo, EtOH)
We apologize for the confusion. We employ a normalization procedure to account for cell size. In addition, all numbers have been normalized to the condition stimulated with agonist with no PUFA treatment). In fact, we detect in unstimulated cells very few puncta (on average 0.6, range 0-5) compared to 27.3 clusters (range 2-87) in cells stimulated with QPL.
- Showing actual p values for the statistical comparisons* For easier reading, we have kept the stars convention for the figures but added two tables with all statistical tests and the p values for both main figures and EV figures.
Moreover, for ease of reading the figures (without consulting the legend repeatedly) it would be very helpful to headline individual panel with what the experiments assesses. Figure 1a and 1b for example can't be distinguished at all before reading the figure legend. Also, y-axis could be more informative on what I measured rather than just giving the unit.
We have added titles to panels (in particular for Figure 2A,B which correspond to former Figure 1A,B) and we have given new titles to Y axes to make them clearer. We hope that the reading of our figures will now be easier.
Finally, the figure presentation and description of S1 is very hard to follow. I cannot really make out what is assessed in the different panels.
We have changed substantially Figure EV1 (now Figure 1) with new presentation of data: all 4 conditions (control, treated with DHA, DPA or BA) systematically presented in the same graph, and clearer titles for the parameter displayed on the Y axes. We hope that this figure is now easier to follow.
Significance
*The strength of the manuscript is the use and validation of incorporation of FFA's in the plasma membrane, which more closely mimics the physiological situation than brief application of FFAs as often done. Is addition, the blunted recruitment of beta-arrestin as assessed by the ppH protocol is quite intriguing mechanistically. The limitation are the relative narrow focus on the D2 receptor (and not multiple GPCRs) that does not really speak to as or assess the physiological, pathophysiological or therapeutic role of the observations (except from referring the relation between FFAs and disease). Also, despite the putative role of Beta-arrestin recruitment in the process, the actual causation in the process is not clear. This shortcoming is underscored by the putative effect on the constitutive internalization described above.
My specific expertise for assessing the paper is within general trafficking processes (including the trafficking methodology applied), trafficking of GPCRs and function of the dopamine system including the role of D2 receptors.*
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Reviewer #2 (Evidence, reproducibility and clarity (Required)):
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The only conclusion that I was able to understand from the study was that enrichment of cell membranes with polyunsaturated fatty acids specifically inhibited agonist-induced internalization of D2 receptors. However, I think that the experiments used to conclude that PUFAs do not alter D2R clustering but reduce the recruitment of β-arrestin2 and D2R endocytosis need some clarification (i.e. data depicted in Fig. 2-5). This lack of clarity might be due to the fact I am not familiar enough with the employed technologies or to the unclear writing style of the paper. There was an overuse of acronyms, initialisms and abbreviations, which are difficult to understand for researchers outside of the specific lipid field. I think that the manuscript should be written in a way to be legible also for researchers not working in the immediate filed.
The paper was not written in a manner that a general audience of cell biologists or those interested in GPCR biology could understand and judge. It is indeed interesting that polyunsaturated fatty acids specifically inhibit D2R internalization in HEK293 cells, and it could be significant. But, it is difficult to judge the significance of the observation without more in vivo data.
I would suggest the following. Remove all acronyms and abbreviations. Significantly, expand the Materials and Methods section, either in the manuscript or in the Supplemental section. I suggest clearly explaining each construct used, and the function of each module in the construct, with diagrams. In addition, provide a comprehensive step by step description of each experimental protocol, providing the reader with the rationale for each step in the protocol with explanatory diagrams. The authors should also more clearly explain the rationale and logic that was utilized to make the conclusions that they did from the depicted observations. Only then can a broader audience determine if the authors' conclusions are justified.
We thank the reviewer for his/her comments. Indeed, our main message was that two types of PUFAs (DHA and DPA) specifically alter D2R endocytosis by reducing the recruitment of β-arrestin2 without changing D2R clustering at the plasma membrane. We are sorry that our writing was not clear enough. We also found out that in the last steps of the submission to Review Commons, the first paragraph of the Discussion was inadvertently erased. This made our main conclusions, summarized in this first paragraph, less clear. We have now put back this important paragraph. Moreover, we have extensively rewritten the manuscript thriving to make it as clear as possible to a large audience. We have reduced the use of acronyms to keep only the most used ones [e.g. PUFA (used 99 times), DHA (37 times), GPCR (34 times), D2R (126 times), GRK (17 times)] and made them consistent throughout the manuscript. Following the reviewer's suggestion, we have also added a scheme of the steps following D2R activation by agonist leading to its internalization (Figure EV3).
We understand that the reviewer implies by "in vivo data" results obtained in the brain of animals. As written in the Introduction and in the Discussion, the current work follows up on a recently published manuscripts by a subset of the authors, namely (i) Ducrocq et al. 2020 (doi 10.1016/j.cmet.2020.02.012) in which we show that deficits in motivation in animals deprived in ω3-PUFAs can be restored specifically by conditional expression of a fatty acid desaturase from c. elegans (FAT1) that allows restoring PUFA levels specifically in D2R-expressing striatal projection neurons (which mediate the so-called indirect pathway), and (ii) Jobin et al. 2023 (doi: 10.1038/s41380-022-01928-6) which combines in cellulo (HEK 293 cells) and in vivo data to show that PUFAs affects the ligand binding of the dopamine D2 receptor and its signaling in a lipid context that reflects patient lipid profiles regarding poly-unsaturation levels.
Reviewer #2 (Significance (Required)):
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In summary, I will reiterate that the reported experiments need to be much better explained to make the study understandable to a broader audience and for that audience to determine whether the conclusions are justified.
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Reviewer #3 (Evidence, reproducibility and clarity (Required)):
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Summary:
The authors investigate the role of lipid polyunsaturation in endocytic uptake of the dopamine D2 receptor (D2R). To modulate the degree of unsaturation in live cell plasma membranes, the authors incubate cell lines with pure fatty acid that is metabolized and incorporated into the cellular membranes. To quantify the internalization of D2R in these live cells, the authors utilized quantitative fluorescence assays such as DERET and endosome analysis to determine the degree and rate of D2R internalization in the presence of two model agonists - dopamine and quinpirole. The authors conclude that when the PUFA content of the plasma membrane is increased (i.e., via ω3 or ω6 fatty acids), both the quantity and rate of D2R internalization decrease substantially. The authors confirmed that these phenomena are specific to D2R as caveolar endocytosis and clathrin-mediated endocytosis were unaffected when these same experimental techniques were utilized for β2 adrenergic receptor and transferrin. Additionally, the authors conclude that the clustering ability of D2R is unaffected by lipid unsaturation but that the ability of D2R clusters to interact with β-arrestin2 is inhibited in the presence of excess PUFA. Based on these findings, the authors propose several hypothetical mechanisms for lipid-D2R interactions on the plasma membrane, which will likely be the scope of future work.
Overall, this is a highly thorough and rigorous body of work that convincingly illustrates the connection between PUFA levels and D2R activity. However, I do not agree with the authors' conclusions pertaining to how their results should be interpreted in the context of fatty acid-related disorders. Additionally, this manuscript could benefit from some reorganization which would present the work more clearly. Please see the comments below.
We thank the reviewer for the positive appreciation of our work, qualified as a "thorough and rigorous body of work that convincingly illustrates the connection between PUFA levels and D2R activity". We will address the specific points raised by the reviewer with our answers below.
Comments:
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- A recurring motivation for this study that is brought up by the authors is that dietary deficiency of ω3 fatty acids is tied to D2R dysfunction. This would indicate that PUFA reduction in the plasma membrane results in D2R dysfunction. However, the experiments emphasized in this manuscript investigate the condition where PUFA content is INCREASED in the plasma membrane and D2R function is compromised. It seems inappropriate for the authors to cite dietary deficiency of ω3 as a motivation when they experimentally test a condition that is tied to ω3 surplus.* Regarding the general comment of the reviewer, we agree that direct conclusion cannot be drawn on the etiology of psychiatric disorders by looking at the effect of membrane fatty acid levels on D2R in HEK 293 cells. Nevertheless, we mention in the Introduction the intriguing occurrence of low PUFA levels in psychiatric disorders as starting point to look at D2R as an important target for psychoactive drugs prescribed for these disorders. In the Discussion, we propose that manipulating fatty acid levels might potentiate the efficacy of D2R ligands used as treatments. We felt raising these aspects was not putting too much emphasis on psychiatric disorders. However, in accordance with the reviewer's comment, we toned down these descriptions in the revised manuscript.
The goal of increasing the levels of fatty acids at the membrane in HEK 293, the most widely used cellular system to study GPCR trafficking, was to try to emulate the levels of lipids in brain cells. Indeed, the levels of PUFAs in our culture conditions are much lower (~8 %, Figure 1B) than in brain extracts (~30 %). Therefore, the "control" condition in HEK 293 cells would correspond to PUFA deficiency while after our enrichment protocol these levels are closer to those found in brain cells. Our results could therefore be interpreted as endocytosis of D2R being augmented under membrane PUFA decrease. Importantly, increased receptor internalization often correlates with decreased signaling. Therefore, membrane PUFA enrichment in our conditions would rather potentiate D2R signaling.
- Following up on the first comment, the authors' results seem to indicate that excess ω3's are detrimental to D2R function. This result would be at odds with the conventional view that ω3's are essential and that excessive ω3 may not be harmful. The authors should rationalize their findings in the context of what is known about excess dietary ω3.*
The Reviewer is right that the conventional view is that excessive ω3 PUFA may not be harmful. However, this rather applies to dietary consumption, which might have limited effect to brain fatty acid contents since their accretion is highly regulated. Moreover, the majority of studies looking at ω3 supplementation have been performed in young adults and the effects on the developing brain - as it might be happening in pathological conditions in which D2R is involved - remain poorly understood. Furthermore, as mentioned above, blunted internalization of D2R under membrane PUFA enrichment is not an indication of "detrimental" to D2R function. Nor do we argue that membrane enrichment corresponds to excess PUFAs.
- I would argue that the control experiments with saturated fatty acids (i.e., Behenic Acid in figure 1), represent a scenario mimicking ω3 deficiency as the enrichment of Behenic Acid causes an overall reduction in PUFAs (Figure EV1C - an increase in SFA must correspond to a decrease in PUFA). These Behenic acid results are the only experiments presented by the authors that mimic a scenario resembling ω3 deficiency and the results show that the D2R internalization is unaffected (Figure 1G-H). Therefore, I would further argue that if anything, the authors results suggest that ω3 deficiency is NOT correlated to D2R internalization. Again, the authors must rationalize these findings in the context of what is known about dietary intake of ω3's.*
The Reviewer must refer to the fact that nutrients rich in SFAs are usually poor in PUFAs and vice-versa. Based on our lipidomic analysis, we now present in Figure 1B the effect of treatments (DHA, DPA, BA) on the levels of PUFAs (Figure 1B) and saturated fatty acids (Figure 1C). In cells treated with behenic acid (BA), PUFA levels are not significantly changed relative to control, untreated cells, while saturated fatty acid levels are increased. BA was used here to determine whether the effects observed with PUFAs was related to the enrichment in unsaturations or due to carbon chain length (C22). It is not the case because BA treatment, unlike DHA or DPA treatment, does not affect D2R endocytosis (Figure 2G,H).
- It's not clear why the authors decided to include an ω6 fatty acid in this study. The authors built up a detailed rationale for investigating ω3's as they are dietarily essential and tied to disease when deficient. To my knowledge, ω6's are considered much less beneficial than ω3's in a dietary sense. The inclusion of an ω6 almost seems coerced as the ω6-related results don't provide any interesting additional insights. It would benefit the manuscript if the authors provided some additional discussion explaining why ω6's are being investigated in addition to ω3's. *
We agree that we could have made the rationale clearer. The goal in comparing ω3-DHA and ω6-DPA was to assess whether the position of the first unsaturation (n-3 vs n-6), with the same carbon chain length (C22) might differentially impact D2R endocytosis.
- In Figure EV1D, the AHA and DPA percentages each increase by ~6%. The corresponding Figure EV1B indicates that the overall PUFA% in the plasma membrane also increases by 6%. This makes sense as the total change in PUFA content is consistent with the amount of AHA or DPA being internalized to cells. However, this consistency was not observed with BA and SFAs. In Figure EV1E, the BA percentage increases only ~1% while the total SFA percentage in Figure EV1C increases by ~6%. How can something undergoing a 1% change (relative to total lipid content) result in a 6% overall change in SFA content?*
The reviewer is correct: the level of SFAs is increased by 5.2% (34.5 % of total FAs in control cells to 39.7 % in BA treated cells), more than the increase in BA alone (1.18% from 0.35 % to 1.53 %). A close look at our lipidomics data showed that many of the 10 saturated fatty acids quantified are enhanced. In particular, the two most abundant ones, palmitic acid (16:0) and stearic acid (18:0) are increased, from 21.37 % to 22.28 % and 8.47 % to 11.17%, respectively. The reasons for these apparent discrepancies may involve lipid metabolic pathways which convert the rare and long BA into more common and shorter SFAs to preserve lipid contents and thus membrane properties.
- In Figure 4, the discussion of kinetics does not make sense. How exactly are kinetics being monitored in this figure? (Recruitment kinetics are discussed in panels D and G)*
We wanted to convey the impression that the time to reach the peak βarr2-mCherry recruitment was shorter in PUFA-treated cells than in control cells. However, after analyzing the kinetics in individual cells, we did not find a statistically significant difference in the time to maximum fluorescence. Therefore, we removed this reference to the kinetics of recruitment.
We now write: " However, treatment with DHA or DPA significantly decreased peak βarr2-mCherry fluorescence (Figure 5F-G).."
- In Figure 5, What is the purpose of panel D? Would it be more helpful to include additional, overlaid "cumulative N" plots for scenarios in which PUFAs were enriched? This would work well in conjunction with panel F.*
The purpose of this panel is to show the kinetics of increase in the frequency of endocytic vesicle formation upon agonist addition, and the decrease in frequency when the agonist is removed. We have now added examples of cells treated with DHA and DPA of similar surface for direct comparison with control (EtOH) cells.
- For the readers who are new to this area or unfamiliar with the assays used, Figure 1 is not intuitive and initially difficult to interpret. It would greatly benefit the flow of the manuscript if Figures EV1A-C and EV2A were included in the main text and "Normalized R" was clearly defined in the main text, prior to discussion of Figure 1.*
We have now transferred Figure EV1 as Figure 1. We have adapted the scheme of the DERET assay and its legend (now in Figure EV1A) to make it clearer. We did not put in Figure 2 because this figure is already very big. We have changed "Normalized R" to "Ratio 620/520) (% max)" to be clearer and more consistent with the scheme.
Reviewer #3 (Significance (Required)):
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General assessment: The work, for the most part, is rigorous and scientifically sound. The authors utilize impressive, quantitative assays to expand our understanding of protein-lipid interactions. However, the authors need to improve their discussion of the actual physiological conditions that correspond to their experimental results.
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Advance: This work may fill a gap in our understanding of disorders related to the dopamine D2 receptor. However, some of the results may be at odds with what is currently known/understood about dietary ω3 fatty acids.
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Audience: This work will be of broad interest to researchers in the biophysics field, with particular emphasis on researchers who study protein and membrane biophysics. This work will also be of interest to researchers who study membrane molecular biology.
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Reviewer Expertise: quantitative fluorescence spectroscopy and microscopy; membrane biophysics; protein-lipid interactions
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Referee #3
Evidence, reproducibility and clarity
Summary:
The authors investigate the role of lipid polyunsaturation in endocytic uptake of the dopamine D2 receptor (D2R). To modulate the degree of unsaturation in live cell plasma membranes, the authors incubate cell lines with pure fatty acid that is metabolized and incorporated into the cellular membranes. To quantify the internalization of D2R in these live cells, the authors utilized quantitative fluorescence assays such as DERET and endosome analysis to determine the degree and rate of D2R internalization in the presence of two model agonists - dopamine and quinpirole. The authors conclude that when the PUFA content of the plasma membrane is increased (i.e., via ω3 or ω6 fatty acids), both the quantity and rate of D2R internalization decrease substantially. The authors confirmed that these phenomena are specific to D2R as caveolar endocytosis and clathrin-mediated endocytosis were unaffected when these same experimental techniques were utilized for β2 adrenergic receptor and transferrin. Additionally, the authors conclude that the clustering ability of D2R is unaffected by lipid unsaturation but that the ability of D2R clusters to interact with β-arrestin2 is inhibited in the presence of excess PUFA. Based on these findings, the authors propose several hypothetical mechanisms for lipid-D2R interactions on the plasma membrane, which will likely be the scope of future work.
Overall, this is a highly thorough and rigorous body of work that convincingly illustrates the connection between PUFA levels and D2R activity. However, I do not agree with the authors' conclusions pertaining to how their results should be interpreted in the context of fatty acid-related disorders. Additionally, this manuscript could benefit from some reorganization which would present the work more clearly. Please see the comments below.
Comments:
- A recurring motivation for this study that is brought up by the authors is that dietary deficiency of ω3 fatty acids is tied to D2R dysfunction. This would indicate that PUFA reduction in the plasma membrane results in D2R dysfunction. However, the experiments emphasized in this manuscript investigate the condition where PUFA content is INCREASED in the plasma membrane and D2R function is compromised. It seems inappropriate for the authors to cite dietary deficiency of ω3 as a motivation when they experimentally test a condition that is tied to ω3 surplus.
- Following up on the first comment, the authors' results seem to indicate that excess ω3's are detrimental to D2R function. This result would be at odds with the conventional view that ω3's are essential and that excessive ω3 may not be harmful. The authors should rationalize their findings in the context of what is known about excess dietary ω3.
- I would argue that the control experiments with saturated fatty acids (i.e., Behenic Acid in figure 1), represent a scenario mimicking ω3 deficiency as the enrichment of Behenic Acid causes an overall reduction in PUFAs (Figure EV1C - an increase in SFA must correspond to a decrease in PUFA). These Behenic acid results are the only experiments presented by the authors that mimic a scenario resembling ω3 deficiency and the results show that the D2R internalization is unaffected (Figure 1G-H). Therefore, I would further argue that if anything, the authors results suggest that ω3 deficiency is NOT correlated to D2R internalization. Again, the authors must rationalize these findings in the context of what is known about dietary intake of ω3's.
- It's not clear why the authors decided to include an ω6 fatty acid in this study. The authors built up a detailed rationale for investigating ω3's as they are dietarily essential and tied to disease when deficient. To my knowledge, ω6's are considered much less beneficial than ω3's in a dietary sense. The inclusion of an ω6 almost seems coerced as the ω6-related results don't provide any interesting additional insights. It would benefit the manuscript if the authors provided some additional discussion explaining why ω6's are being investigated in addition to ω3's.
- In Figure EV1D, the AHA and DPA percentages each increase by ~6%. The corresponding Figure EV1B indicates that the overall PUFA% in the plasma membrane also increases by 6%. This makes sense as the total change in PUFA content is consistent with the amount of AHA or DPA being internalized to cells. However, this consistency was not observed with BA and SFAs. In Figure EV1E, the BA percentage increases only ~1% while the total SFA percentage in Figure EV1C increases by ~6%. How can something undergoing a 1% change (relative to total lipid content) result in a 6% overall change in SFA content?
- In Figure 4, the discussion of kinetics does not make sense. How exactly are kinetics being monitored in this figure? (Recruitment kinetics are discussed in panels D and G)
- In Figure 5, What is the purpose of panel D? Would it be more helpful to include additional, overlaid "cumulative N" plots for scenarios in which PUFAs were enriched? This would work well in conjunction with panel F.
- For the readers who are new to this area or unfamiliar with the assays used, Figure 1 is not intuitive and initially difficult to interpret. It would greatly benefit the flow of the manuscript if Figures EV1A-C and EV2A were included in the main text and "Normalized R" was clearly defined in the main text, prior to discussion of Figure 1.
Significance
General assessment: The work, for the most part, is rigorous and scientifically sound. The authors utilize impressive, quantitative assays to expand our understanding of protein-lipid interactions. However, the authors need to improve their discussion of the actual physiological conditions that correspond to their experimental results.
Advance: This work may fill a gap in our understanding of disorders related to the dopamine D2 receptor. However, some of the results may be at odds with what is currently known/understood about dietary ω3 fatty acids.
Audience: This work will be of broad interest to researchers in the biophysics field, with particular emphasis on researchers who study protein and membrane biophysics. This work will also be of interest to researchers who study membrane molecular biology.
Reviewer Expertise: quantitative fluorescence spectroscopy and microscopy; membrane biophysics; protein-lipid interactions
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Referee #2
Evidence, reproducibility and clarity
The only conclusion that I was able to understand from the study was that enrichment of cell membranes with polyunsaturated fatty acids specifically inhibited agonist-induced internalization of D2 receptors. However, I think that the experiments used to conclude that PUFAs do not alter D2R clustering but reduce the recruitment of β-arrestin2 and D2R endocytosis need some clarification (i.e. data depicted in Fig. 2-5). This lack of clarity might be due to the fact I am not familiar enough with the employed technologies or to the unclear writing style of the paper . There was an overuse of acronyms, initialisms and abbreviations, which are difficult to understand for researchers outside of the specific lipid field. I think that the manuscript should be written in a way to be legible also for researchers not working in the immediate filed.
The paper was not written in a manner that a general audience of cell biologists or those interested in GPCR biology could understand and judge. It is indeed interesting that polyunsaturated fatty acids specifically inhibit D2R internalization in HEK293 cells, and it could be significant. But, it is difficult to judge the significance of the observation without more in vivo data.
I would suggest the following. Remove all acronyms and abbreviations. Significantly, expand the Materials and Methods section, either in the manuscript or in the Supplemental section. I suggest clearly explaining each construct used, and the function of each module in the construct, with diagrams. In addition, provide a comprehensive step by step description of each experimental protocol, providing the reader with the rationale for each step in the protocol with explanatory diagrams. The authors should also more clearly explain the rationale and logic that was utilized to make the conclusions that they did from the depicted observations. Only then can a broader audience determine if the authors' conclusions are justified.
Significance
In summary, I will reiterate that the reported experiments need to be much better explained to make the study understandable to a broader audience and for that audience to determine whether the conclusions are justified.
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Referee #1
Evidence, reproducibility and clarity
The manuscript, using different live and fixed cell trafficking assays, demonstrates that incorporation of poly-unsaturated, but not saturated, free fatty acids in the membrane phospholipids reduce agonist induced internalization of the D2 dopamine receptor but not the adrenergic beta2 receptors or the transferrin receptor. Pulsed pH (ppH) live microscopy further demonstrated that the reduced internalization by incorporation of free fatty acid was accompanied by a blunted recruitment of Beta-arrestin for the D2R.
I believe said claims put forward in the manuscript are overall well supported by the data and as such I do not believe that further experiments are necessarily needed to uphold these key claims. Also, the methodology is satisfactorily reported, and statistics are robust, although two-way Anova like used in Fig 1 seems appropriate for Fig 2 and 3
That said, I suggest that the fixed cell internalization experiments (Fig 2 and 3), which relate the effect on the D2R to B2AR and transferrin are revised. This is important since this is relevant to judge whether the effect is a general or a selective molecular mechanism since this is the one of the three assay which this comparison relies on. Alternatively, I suggest omitting this data and include the B2AR in the Live DERET assay and both B2AR and TfR in the ppH assay. Specifically, my concerns with the fixed cell internalization are:
- The analysis is based on counting the number of endosomes, which is not necessarily equivalent to the number of receptors internalized
- The analysis relies on fully effective stripping of the surface pool of receptors - i.e clustered surface receptors not stripped by the protocol will be assessed as internalized. It is often very difficult to obtain full efficiency of the Flag-tag stripping and this is somewhat expression dependent.
- The protocol for the constitutive and agonist induced internalization is different and yet shown on the same absolute graph. Although I take it the microscope gain setting are unaltered between the constitutive and agonist induced internalization I don't believe the quantification can be directly related. This is confusing at the very least. More critically however, the membrane signal from the non-stripped condition of constitutive internalization will likely fully shield internalized receptors in the Rab4 membrane proximal recycling pathway leading to under-estimation of the in the constitutive endocytosis. I believe this methodological limitation underlies the massive relative difference in the constitutive endocytosis between panel 2A,B and 2C,D. For comparison, by a quantitative dual color FACS endocytosis assay, we have previously demonstrated the ligand endocytosis a ~4 fold increased over constitutive (in concert with Fig 2A,B here) (Schmidt et al 20XX). Importantly, high relative variability by this methodology could well shield an actual effect of incorporation of FFAs on the constitutive endocytosis.
'Optional' Also, it would be informative to see the ppH Beta-arrestin experiments with the B2AR to assess, whether the putative discrepancy between D2R and B2AR is upstream or downstream of the blunted Beta-arrestin recruitment. To the same point, it would be very informative to assess how the incorporation of the free fatty acids affect receptor signalling, which would also help relate the effect of incorporation of the FFA's in the phospholipids to previous experiment using short term incubation with FFA's
References overall seem appropriate although Schmidt et al would be relevant for reference of the constitutive vs agonist induced endocytosis of D2R and B2AR. Overall, the figures are well composed and convey the messages fairly well. Specific point that would strengthen the rigor include:
- Chosing actual representative pictures of the qunatiative data in Fig 2 and 3 (e.g. har to see 25 endocytic events in Fig 2A constitutive endo, EtOH)
- Showing actual p values for the statistical comparisions
Moreover, for ease of reading the figures (without consulting the legend repeatedly) it would be very helpful to headline individual panel with what the experiments assesses. Figure 1a and 1b for example can't be distinguished at all before reading the figure legend. Also, y-axis could be more informative on what I measured rather than just giving the unit.
Finally, the figure presentation and description of S1 is very hard to follow. I cannot really make out what is assessed in the different panels.
Significance
The strength of the manuscript is the use and validation of incorporation of FFA's in the plasma membrane, which more closely mimics the physiological situation than brief application of FFAs as often done. Is addition, the blunted recruitment of beta-arrestin as assessed by the ppH protocol is quite intriguing mechanistically. The limitation are the relative narrow focus on the D2 receptor (and not multiple GPCRs) that does not really speak to as or assess the physiological, pathophysiological or therapeutic role of the observations (except from referring the relation between FFAs and disease). Also, despite the putative role of Beta-arrestin recruitment in the process, the actual causation in the process is not clear. This shortcoming is underscored by the putative effect on the constitutive internalization described above.
My specific expertise for assessing the paper is within general trafficking processes (including the trafficking methodology applied), trafficking of GPCRs and function of the dopamine system including the role of D2 receptors.
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Referee #3
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:
- 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.
- 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.
- 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?
- 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. 5. How, exactly, is the unit of flux converted to mmol g-1 h-1? 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)? 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:
- 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. 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. 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). 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). 6. Line 85, it might be difficult to interpret "RNAP fluxes" as the flux of rRNA synthesis without reading the subsequent text. 7. Typo in line 102-103. "...protein fluxes 𝒘" → "...protein synthesis fluxes 𝒘". 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"). 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. 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. 12. The citation in line 142 is not accurate. It should be (Bremer and Dennis, 1996). 13. Lines 192-193: "six" different growth media, not five. 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. 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.
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Reply to the reviewers
General Statements
We thank all three reviewers for their time and care in reviewing our manuscript, in particular Reviewer 3 for providing a detailed critique that was very useful for planning revisions. We are grateful that all three reviewers indicate that the new genome resources presented in this work are of high-quality and address an existing knowledge gap. We are also grateful for general assessments that the manuscript is 'well-written', and the analyses 'well performed' and 'thorough'.
We acknowledge Reviewer 3’s legitimate criticism that the assembly and annotation data is not already publicly available and would like to assure the reviewing team that we have been pressing NCBI to progress the submission status since before the preprint was submitted. We regret the delay but hope that we can resolve this issue promptly. Furthermore, as some additional fields in the REAT genome annotation are lost during the NCBI submission process, we will ensure that comprehensive annotation files are also added to Zenodo.
Reviewer 3 also made the general comment that 'the manuscript could greatly benefit from merging the result and discussion sections' and we would naturally be happy to make this adjustment if the journal in question uses that format.
Description of the planned revisions
• We will follow suggestions by Reviewer 3 to improve clarity of two figures:
Figure S9: Please use a more appropriate colour palette. It is difficult to know the copy number based on the colour gradient.
Figure 5: Consider changing panel B for a similar version of Fig S12. I think it gives a cleaner and more general perspective of the presence of starship elements.
• We will address the choice of LOESS versus linear regression for investigating the relationship between candidate secreted effector protein (CSEP) density and transposable element (TE) density, as queried by Reviewer 3:
Lines 140-144: LOESS smoothing functions are based on local regressions and usually find correlations when there are very weak associations. The authors have to justify the use of this model versus a simpler and more straightforward linear regression. My suspicion is that the latter would fail to find an association. Also, there is no significance of Kendall's Tau estimate (p-value).
We agree with the reviewer, that as we did not find an association with the more sensitive LOESS, we expect that linear regression would also not find an association, supporting our current conclusions. We will add this negative result into the text.
• We will check for other features associated with the distribution of CSEPs, as queried by Reviewer 3:
Lines 157-163: Was there any other feature associated with the CSEP enrichment? GC content? Repetitive content? Centromere likely localisation?
• We will integrate TE variation into the PERMANOVA lifestyle testing, as suggested by Reviewer 3:
Line 186: Why not to test the variation content of TEs as a factor for the PERMANOVA?
In reviewing this suggestion, we also spotted an error in our data plotting code, and the PERMANOVA lifestyle result for all genes will be corrected from 17% to 15% in Fig. 4a. Correcting this error does not impact our ultimate results or interpretation.
• To complement the current graphical-based assessment of approximate data normality, we will include additional tests (Shapiro-Wilk for sample sizes
Line 743: Q-Q plots are not a formal statistical test for normality.
• One of the main critiques from Reviewer 3 was that, although we already acknowledged low sample sizes being a limitation of this work, the manuscript could benefit from reframing with greater consideration of this factor. They also highlighted a few specific places in the text that could be rephrased in consideration of this:
Line 267: "Multiple strains" can be misleading about the magnitude.
Lines 305-307: The fact that there is significant copy number variation between the two GtA strains suggests that the variation in the GtA lineage has not been fully captured and that there may be an unsampled substructure. Although the authors acknowledge the need for pangenomic references, they should recognize this limitation in the sample size of their own study, especially when expressing its size as "multiple strains" (line 267).
Lines 314-317: Again, the sample size is still very small and likely not representative. It suggests UNSAMPLED substructure even for the UK populations.
Line 164 (and whole section): I would invite the authors to cautiously revisit the use of the terms "core", "soft core". The sample size is very low, as they themselves acknowledge, and probably not representative of the diversity of Gaeumannomyces.
We intend to edit the text to address this, including removal of both text and figure references to ‘soft-core’ genes, as we agree the term is likely not meaningful in this case, and removing it has no bearing on the results or interpretation.
Description of the revisions that have already been incorporated in the transferred manuscript
• We have amended the text in a number of places for clarity/fluency as suggested by Reviewer 3:
ii) There need to be an explicit conclusion about the differences between pathogenic Gt and non-pathogenic Gh. Somehow, this is not entirely clear and is probably only a matter of rephrasing.
Please see new lines 477-478: ‘Regarding differences between pathogenic Gt and non-pathogenic Gh, we found that Gh has a larger overall genome size and greater number of genes.’
Lines 309-314: The message seems a bit out of context in the paragraph.
This is valid, these lines have now been removed.
Lines 392-395: The idea that crop pathogenic fungi are under pressure that favours heterothallism does not take into account the multiple cases of successful pathogenic clonal lineages in which sexual reproduction is absent. This paragraph seems very speculative to me. Please rephrase it.
Our intention here was the exact reverse, that crop pathogens are under pressure to favour homothallism (as Reviewer 3 points out, anecdotally this often seems to play out in nature). We have rephrased lines 386-390 to hopefully make our stance more explicit: 'Together, this could suggest a selective pressure towards homothallism for crop fungal pathogens, and a switch from heterothallism in Gh to homothallism in Gt and Ga may, therefore, have been a key innovation underlying lifestyle divergence between non-pathogenic Gh and pathogenic Gt and Ga.'
Lines 463-464: Please refer to the analyses when discussing the genetic divergence.
We have rephrased this sentence to make our intended point clearer, please see new lines 459-461: ‘If we compare Ga and Gt in terms of synteny, genome size and gene content, the magnitude of differences does not appear to be more pronounced than those between GtA and GtB.’
• We have also fixed the following typographic errors highlighted by Reviewer 3:
Line 399: You mean, Fig 4C?
Line 722: You missed "trimAI"
Lines 723-727: Missing citations for "AMAS" and RAxML-NG, "AHDR" and "OrthoFinder"
• We have added genome-wide RIP estimates to Supplementary Table S1 as requested by Reviewer 3:
Lines 416-422: Please provide the data related to the genome-wide estimates of RIP.
• We have added a note clarifying that differences in overall genome size between lineages are not fully explained by differences in gene copy-number (lines 406-408: 'We should note that the total length of HCN genes was not sufficiently large to account for the overall greater genome size of GtB compared to GtA (Supplemental Table S1).') in response to a comment from Reviewer 3:
Line 396: The difference in duplicated genes raises the question of whether there are differences in overall genome size between lineages and, if so, whether they can be explained by the presence of genes.
• We have made an alteration to the author order and added equal second-author contributions.
Description of analyses that authors prefer not to carry out
• In response to our analysis regarding the absence of TE-effector compartmentalisation in this system, Reviewer 1 requested additional analyses:
While TE enrichment is typically associated with accessory compartments, it is not a defining feature. To bolster the authors' claim, it is essential to demonstrate that there is no bias in the ratio of conserved and non-conserved genes across the genomes.
We believe that there are two slightly different compartmentalisation concepts being somewhat conflated here – (1) the idea of compartments where TEs and virulence proteins such as effectors are significantly colocalised in comparison with the rest of the genome, and (2) the idea of compartments containing gene content that is not shared in all strains (i.e. accessory). The two may overlap – as Reviewer 2 states, accessory compartments may also be enriched with TEs – but not necessarily. We specifically address the first concept in our text, and we appreciate Reviewer 3’s response on this subject:
There is a clear answer for the compartmentalisation question. The authors favour the idea of "one-compartment" with compelling analyses.
We believe that the second concept of accessory compartments is shown to be irrelevant in this case from our GENESPACE results (see Fig. 2), which demonstrate that gene content is conserved, broadly syntenic even, across strains, with no clear evidence of accessory compartments or chromosomes regarding gene content. We have already acknowledged that other mechanisms of compartmentalisation beyond TE-effector colocalisation may be at play (as seen from our exploration of effector distributions biased towards telomeres, see section from line 156: ‘Although CSEPs were not broadly colocalised with TEs, we did observe that they appeared to be non-randomly distributed in some pseudochromosomes (Fig. 3a)…’).
• Reviewer 1 questioned the statement that higher level of genome-wide RIP is consistent with lower levels of gene duplication:
L422: Is the highest RIP rate in GtA consistent with its low levels of gene duplication? Does this suggest that duplicated sequences in GtA are no longer recognizable due to RIP mutations? This seems counterintuitive, as RIP is primarily triggered by gene duplication.
Our understanding is that, while RIP can directly mutate coding regions, it predominantly acts on duplicated sequences within repetitive regions such as TEs (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02060-w), which has a knock-on effect of reducing TE-mediated gene duplication. In Neurospora crassa, where RIP was first discovered and thus the model species for much of our understanding of the process, a low number of gene duplicates has been linked to the activity of RIP (https://www.nature.com/articles/nature01554). We therefore believe the current text is reasonable.
• Reviewer 2 stated that experimental validation of gene function is required to make clear links to lifestyle or pathogenicity:
In my eyes, the study has two main limitations. First of all, the research only concerns genomics analyses, and therefore is rather descriptive and observational, and as such does not provide further mechanistic details into the pathogen biology and/or into pathogenesis. This is further enhanced by the lack of clear observations that discriminate particular species/lineages or life styles from others in the study. Some observations are made with respect to variations in candidate secreted effector proteins and biosynthetic gene clusters, but clear links to life style or pathogenicity are missing. To further substantiate such links, lab-based experimental work would be required.
We agree that in an ideal world supportive wet biology gene function experimental evidence would be included. Unfortunately, transformation has not been successfully developed yet in this system (see lines 33-35: ‘There have also been considerable difficulties in producing a reliable transformation system for Gt, preventing gene disruption experiments to elucidate function (Freeman and Ward 2004).’) not for lack of trying – after 18 months of effort using all available transformation techniques and selectable markers neither Gt or Gh was transformable. Undertaking that challenge has proven to be far beyond the scope of this paper, the purpose of which was to generate and analyse high-quality genomic data, a major task in itself. We again appreciate Reviewer 3’s response to this point, agreeing that it is out of scope for this work:
I just want to respectfully disagree with reviewer #2 about the need for more experimental laboratory work, as in my opinion it clearly goes beyond the intention and scope of the submitted work. This could be a limitation that would depend on the chosen journal and its specific format and requirements. Finally, I think it would suffice for the authors to discuss on the lack of in-depth experimental work as part of the limitations of their overall approach.
As per the suggestion by Reviewer 3, we will add text to address the absence of in-depth experimental work within the scope of this study.
• Reviewer 3 suggested we might 'consider including formal population differentiation estimators', however, as they previously highlighted above, our sample sizes are too small to produce reliable population-level statistics.
• Reviewer 3 raised the disparity in the appearance of branches at the root of phylogenetic trees in various figures:
Figure 4a (and Figs S5, S13): The depicted tree has a trichotomy at the basal node. Please correct it so Magnaporthiopsis poae is resolved as an outgroup, as in Fig. S17.
All the trees were rooted with M. poae as the outgroup, and although it may seem counterintuitive, a trifurcation at the root is the correct outcome in the case of rerooting a bifurcating tree, please see this discussion including the developers of both leading phylogeny visualisation tools ggtree and phytools (https://www.biostars.org/p/332030/). Although it is possible to force a bifurcating tree after rooting by positioning the root along an edge, the resulting branch lengths in the tree can be misleading, and so in cases where we wanted to include meaningful branch lengths in the figure (i.e. estimated from DNA substitute rates, in Figures 4a, S5 and S13) we have not circumvented the trifurcation. In Fig S17 meaningful branch lengths have not been included and the tree only represents the topology, resulting in the appearance of bifurcation at the root.
• Reviewer 3 suggested that the discussion on giant Starship TEs resembled more of a review:
Lines 434-451: This section resembles more a review than a discussion of the results of the present work. This also highlights the lack of analysis on the genetic composition and putative function of the identified starship-like elements.
The reviewer has a valid point. However, Starships are a recently discovered and thus underexplored genetic feature that readers from the wider mycology/plant pathology community may not yet be aware of. We believe it is warranted to include some additional exposition to give context for why their discovery here is novel, interesting and unexpected. We are naturally keen to investigate the make-up of the elements we have found in this lineage, however that will require a substantial amount of further work. Analysis of Starships is not trivial, for example the starfish tool is still under development and a limited number of species have been used to train it. How best to compare elements is also an active area of investigation – they are dynamic in their structure and may include genes originating from the host genome or a previous host – and for this reason we believe is out of scope to interrogate alongside the other foundational genomic data presented in this paper.
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Referee #3
Evidence, reproducibility and clarity
Summary:
The manuscript "Evolutionary genomics reveals variation in structure and genetic content implicated in virulence and lifestyle in the genus Gaeumannomyces" by Rowena Hill and collaborators is a thorough, well-planned and designed work. They have described 9 almost complete new assemblages, from their most general characteristics to their genetic content and implications. I am very pleased with the quality and completeness of this work and agree that it provides a very useful resource and framework for further research on this important organism.
The three main motivations of the present study were:
1) Are there genomic signatures distinguishing Gt A/B virulence lineages?;
2) How do gene repertoires differ between pathogenic Gt and non-pathogenic Gh? And, iii) Is there evidence of genome compartmentalisation in Gaeumannomyces?
a) The authors themselves recognise the low number of samples in their work (Lines 453-454) and this limitation hampers the establishment of a clear association between lineage-specific virulence and genomic signatures. I would argue that the present work needs to be reframed factoring this main limitation.
b) There need to be an explicit conclusion about the differences between pathogenic Gt and non-pathogenic Gh. Somehow, this is not entirely clear and is probably only a matter of rephrasing.
c) There is a clear answer for the compartmentalisation question. The authors favour the idea of "one-compartment" with compelling analyses.
Major comments:
The authors have not published the genomic data. Therefore, it is impossible to audit the quality of the assemblies and impedes its reproducibility. It is also bad practice by current scientific standards.
I strongly believe that the manuscript could greatly benefit from merging the result and discussion sections. It would be easier for the reader to follow their entire logic. This is of course something optional and contingent on the journal format.
Minor and specific comments:
RESULTS
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Lines 140-144: LOESS smoothing functions are based on local regressions and usually find correlations when there are very weak associations. The authors have to justify the use of this model versus a simpler and more straightforward linear regression. My suspicion is that the latter would fail to find an association. Also, there is no significance of Kendall's Tau estimate (p-value).
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Lines 157-163: Was there any other feature associated with the CSEP enrichment? GC content? Repetitive content? Centromere likely localisation?
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Line 164 (and whole section): I would invite the authors to cautiously revisit the use of the terms "core", "soft core". The sample size is very low, as they themselves acknowledge, and probably not representative of the diversity of Gaeumannomyces.
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Figure 4a (and Figs S5, S13): The depicted tree has a trichotomy at the basal node. Please correct it so Magnaporthiopsis poae is resolved as an outgroup, as in Fig. S17.
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Line 186: Why not to test the variation content of TEs as a factor for the PERMANOVA?
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Figure S9: Please use a more appropriate colour palette. It is difficult to know the copy number based on the colour gradient.
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Figure 5: Consider changing panel B for a similar version of Fig S12. I think it gives a cleaner and more general perspective of the presence of starship elements.
DISCUSSION
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Line 267: "Multiple strains" can be misleading about the magnitude.
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Lines 305-307: The fact that there is significant copy number variation between the two GtA strains suggests that the variation in the GtA lineage has not been fully captured and that there may be an unsampled substructure. Although the authors acknowledge the need for pangenomic references, they should recognize this limitation in the sample size of their own study, especially when expressing its size as "multiple strains" (line 267).
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Lines 309-314: The message seems a bit out of context in the paragraph.
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Lines 314-317: Again, the sample size is still very small and likely not representative. It suggests UNSAMPLED substructure even for the UK populations.
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Lines 392-395: The idea that crop pathogenic fungi are under pressure that favours heterothallism does not take into account the multiple cases of successful pathogenic clonal lineages in which sexual reproduction is absent. This paragraph seems very speculative to me. Please rephrase it.
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Line 396: The difference in duplicated genes raises the question of whether there are differences in overall genome size between lineages and, if so, whether they can be explained by the presence of genes.
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Line 399: You mean, Fig 4C?
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Lines 416-422: Please provide the data related to the genome-wide estimates of RIP.
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Lines 434-451: This section resembles more a review than a discussion of the results of the present work. This also highlights the lack of analysis on the genetic composition and putative function of the identified starship-like elements.
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Lines 463-464: Please refer to the analyses when discussing the genetic divergence. Consider including formal population differentiation estimators.
METHODS
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Line 722: You missed "trimAI"
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Lines 723-727: Missing citations for "AMAS" and RAxML-NG, "AHDR" and "OrthoFinder" Line 743: Q-Q plots are not a formal statistical test for normality.
Referees cross-commenting
I agree with my peer reviewers and appreciate that we have shared common concerns and suggestions. I also agree with their comments.
I just want to respectfully disagree with reviewer #2 about the need for more experimental laboratory work, as in my opinion it clearly goes beyond the intention and scope of the submitted work. This could be a limitation that would depend on the chosen journal and its specific format and requirements. Finally, I think it would suffice for the authors to discuss on the lack of in-depth experimental work as part of the limitations of their overall approach.
Significance
The work presented by Hill and co-workers contributes to the understanding of the genetic basis of host-pathogen interactions and evolutionary dynamics in the important fungus responsible for wheat "take-all-disease", Gaeumannomyces tritici. By providing 9 new near-complete assemblages, this work will provide a valuable resource for research on this agronomically important organism. This work sets the stage for developing a global pangenome of G. tritici that can expand analyses of its population structure and specific genetic elements that are associated with its virulence.
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Referee #2
Evidence, reproducibility and clarity
In this study, the authors present genome assemblies for nine strains of the genus Gaeumannomyces, including 5 strains that belong to two different virulence lineages of the wheat take-all decline pathogen G. tritici, 2 strains of the antagonist G. hyphopodioides and 2 of the oat take-all decline pathogen G. avenae. The authors assess gene catalogs, CAZyme repertoires, effector catalogs, TE abundance, compartmentalisation and the occurrence of Starship giant transposable elements. Overall, there are no striking differences that discriminate the genomes and that can be linked to differential life styles. Weak correlations were found for some of the different lineages, but no functional analyses have been performed to further solidify such differences.
Significance
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Overall, the study fills a knowledge gap, given that no-few high quality genomes for the soil-borne fungi of the Gaeumannomyces genus are available. The genome assemblies are of high quality, and the work that is presented is mainly solid and robust. The analyses are well performed, sound and informative.
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In my eyes, the study has two main limitations. First of all, the research only concerns genomics analyses, and therefore is rather descriptive and observational, and as such does not provide further mechanistic details into the pathogen biology and/or into pathogenesis. This is further enhanced by the lack of clear observations that discriminate particular species/lineages or life styles from others in the study. Some observations are made with respect to variations in candidate secreted effector proteins and biosynthetic gene clusters, but clear links to life style or pathogenicity are missing. To further substantiate such links, lab-based experimental work would be required.
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Referee #1
Evidence, reproducibility and clarity
The manuscript by Hill et al. presents nearly complete genomes of nine Gaeumannomyces strains, including both phytopathogenic and non-pathogenic (symbiotic) fungi. The manuscript is well-written, and the data it presents are of high quality, offering implications for understanding the evolution and diversification of Magnaporthales fungi, which encompass economically important phytopathogenic species such as Gaeumannomyces graminis and Pyricularia oryzae. I believe that the determination of these nearly complete genomes alone justifies publication. However, I have some concerns as described below.
Major concern:
One potential criticism pertains to whether the authors' assertion that Gaeumannomyces taxa have one-compartment genomes is fully supported by the data. The authors demonstrate in this manuscript that transposable elements (TE) and putative effector genes (CSEPs) are not co-localized in the Gaeumannomyces genomes. However, this evidence may not be robust enough to substantiate their claim. The concept of two- or multi-speed genomes suggests that fungal genomes consist of compartments that differ in the rate of evolution but not necessarily in TE content. While TE enrichment is typically associated with accessory compartments, it is not a defining feature. To bolster the authors' claim, it is essential to demonstrate that there is no bias in the ratio of conserved and non-conserved genes across the genomes.
Minor concern:
L422: Is the highest RIP rate in GtA consistent with its low levels of gene duplication? Does this suggest that duplicated sequences in GtA are no longer recognizable due to RIP mutations? This seems counterintuitive, as RIP is primarily triggered by gene duplication.
In my opinion, the analysis of the genomic differences facilitating parasitic and symbiotic lifestyles seems somewhat weak.
Significance
This manuscript offers new genomic insights into economically important phytopathogenic fungal species, and sheds light on the diversification of parasitic and symbiotic fungi during evolution. While the analyses conducted are mostly appropriate and reasonable, they do not yield particularly surprising findings.
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Reply to the reviewers
Manuscript number: RC- 2023-02122
Corresponding author(s): Andrew Graham Cox and Juan Manuel González-Rosa
- General Statements
We thank the reviewers for taking the time to assess our work and for their considered and constructive comments. We are glad that they appreciate the value of the methodology we have developed. In addressing the points raised by the reviewers, we have significantly strengthened the conclusions reached in our study. Below is a point-by-point response (in regular type, blue) to the specific reviewer comments (in italics, black).
- Point-by-point description of the revisions
Experiment 1: Perform lineage tracing of hepatocytes following cryoinjury.
Reviewer #1 would like us to have a better understanding of the origin of the regenerative hepatocytes following cryoinjury. There are two potential sources of regenerating hepatocytes. In many cases, hepatocytes proliferate giving rise to regenerative hepatocytes. However, during severe injury, the liver can undergo a ductular reaction in which biliary epithelial cells (BECs) can expand and transdifferentiate to give rise to regenerating hepatocytes.
● To address this query we have now used a new transgenic line created in laboratory that can indelibly label hepatocytes for lineage tracing Tg(fabp10a:Tet-ON-Cre). We have crossed this line to floxed reporters (Ubb:Switch) and collect livers at 7 dpci. The healthy parenchyma surrounding the injured area was predominantly labelled in the tracing experiment suggesting that pre-existing hepatocytes are driving the regenerative response.
Experiment 2: Examine BEC and EC proliferation in the ventral and contralateral lobes following cryoinjury.
Reviewers #1 and #2 would like us to better characterise the temporal dynamics of proliferation in BECs and ECs following cryoinjury. Specifically, the reviewers would like to know whether then compensatory hyperplasia in the contralateral lobe also leads to increased BEC and EC proliferation. Moreover, the reviewers would like us to better quantify the extent of EC and BEC proliferation at different stages of regeneration after cryoinjury.
● We have now performed extensive BrdU pulse-chase cryoinjury experiments using Tg(fli1a:nEGFP) zebrafish to visualise ECs. We have also conducted multiplexed immunostaining of the regenerating livers with the BEC marker (Anxa4) in conjunction with immunodetection of proliferation (BrdU and PCNA). These studies outline the kinetics of the regenerative response and provide evidence to support epimorphic regeneration around the site of injury as well as a compensatory hyperplasia on the contralateral lobe.
Experiment 3: Quantification of the temporal dynamics of fibrosis upon cryoinjury.
Reviewer #1 suggested we better characterise the extent of fibrosis in our model.
● We have now performed extensive studies quantifying the extent of collagen deposition at the regenerative margin over the time course (SHAM, 1, 3, 5, and 7 dpci) using immunohistochemical detection.
Experiment 4: Examine the role of Macrophage depletion in liver regeneration.
Reviewer #1 suggested we examine regeneration following cryoinjury in immunodeficient zebrafish in order to understand the role of macrophages in the model.
● To address this question, we have now performed studies involving macrophage depletion, using the well established IP injection of clodronate liposomes. We have now performed cryoinjury comparing untreated and chlodronate-treated Tg(fabp10a:NLSmCherry) or Tg(fabp10a: GreenLantern-H2B) zebrafish and examined the extent of regeneration at 3 and 7 dpci.
Experiment 5: Examine the impact of age and gender on liver regeneration following cryoinjury.
Reviewer #3 wanted to know if the regenerative response to cryoinjury was different depending on age and gender.
● To address this query, we have now performed cryoinjuries on young (4 month) and aged (9 month) males and females in a Tg(fabp10a:NLS-mCherry) or Tg(fabp10a: GreenLantern-H2B) background and examined regeneration at 7 dpci.
Experiment 6: Characterization of the dynamics of Hepatoblasts, Hepatic Stellate Cells, Macrophages and Neutrophils following cryoinjury.
Reviewer #3 suggested that it would be good to have a better cellular characterization of regeneration in the cryoinjury model.
● To address this question, we have now examined distinct cell types over the cryoinjury timecourse including SHAM, 1, 3, 5, and 7 dpci livers to provide a temporal landscape of the cellular response. In addition to BECs and ECs as discussed above, we have also performed immunofluorescence to detect macrophages (mfap4) neutrophils (mpx) during liver regeneration.
Specific Reviewer comments
Reviewer #1
Major points:
Full Revision
1) In this cryoinjury model, the authors found cell proliferation in hepatocytes, BECs, and other cell types near the injury site. The proliferating hepatocytes exclusively provide hepatocytes, and BECs provide BECs, or some transdifferentiation is involved? Like other extreme ablation models, BECs can contribute to some hepatocytes in this model.
We thank the Reviewer #1 for the interesting suggestion. We have addressed this by performing lineage tracing analysis as explained in Experiment 1 (above). For this approach, we have used Tg(fabp10a:Tet-ON-Cre; Ubb:Switch) to indelibly label and trace hepatocytes. These experiments reveal that the new regenerated tissue is derived from pre-existing hepatocytes (see Supplementary Figure 2 Q, R, S, T).
2) In this model, the authors observed the long-range effect of the cryoinjury as they identified increased cell proliferation in the contralateral liver lobes. Is this long-range effect specific to hepatocytes? BECs or endothelial cells also undergo increased cell proliferation in the contralateral lobes?
We thank the Reviewer #1 for this question. We have addressed this query by performing Experiment 2 (above). Briefly, cryoinjuries were performed and markers of proliferating HCs and BECs (PCNA or BrdU stained) were quantified in the ventral and contralateral lobes (see Supplementary Figure 6). The data clearly demonstrates that proliferation is higher at the site of injury, however lower rates of compensatory hyperplasia are still evident on the contralateral lobe. A strong epimorphic hyperplasia and weaker compensatory growth response, has been previously observed in the cardiac cryoinjury model (Pauline Sallin et al. Developmental Biology 2015).
3) This model is a unique liver regeneration model as it induces transient focal fibrosis. Is the fibrosis beneficial for liver regeneration? What happens if you reduce fibrosis pharmacologically? Will it interfere with the rate of regeneration?
We thank the Reviewer #1 for the comments. Although pharmacological interventions of fibrosis are beyond the scope of the current manuscript, we have better quantified the extent of fibrosis in the first week following cryoinjury in Experiment 3 (above; Figure 3I).
4) Do Lcp1+ leucocytes contribute to liver regeneration in this model? In immunodeficiency models such as irf8 mutant, liver regeneration after cryoinjury changed?
We thank the Reviewer #1 for the suggestion of using an immunodeficiency model. We addressed this question by performing Experiment 4 (above). Briefly, we have IP injected clodronate liposomes, which are a well-established method for macrophage depletion, and examined the effect on liver regeneration (Supplementary Figure 5). These extensive experiments showed that macrophage depletion had no significant effect on liver regeneration at 3 and 7 dpci.
5) The CUBIC-clearing procedure is beneficial in the field. The quantitative benefit of the CUBICbased method should be added. Supplement figures 1C and D need scale bars, especially for X Z and Z-Y planes. Can you quantify the Z-plane depth that you can scan with or without CUBIC treatment?
We thank the Reviewer #1 for the input and apologise if we did not present the current data clearly. We have now included the scale bars on the reviewed manuscript in Supplementary Figure 1C, 1D, and 1G. We have quantified the Z-plane depth on our current acquisitions and modified our current panels to make clear the difference in depth (z-stack) that CUBIC-imaging enables during liver acquisitions in Supplementary Figure 1D-I.
6) In the manuscript, the authors measured the injured area after the cryoinjury. But how about the depth of the injury? Does the procedure induce a relatively constant injury depth, or can it not be controlled? The total volume of injured tissue would be more important than the surface injured area.
We thank the Reviewer #1 for the comments. The hepatic cryoinjury approach was developed to injure the liver and avoid deeper tissue lesions to the gastrointestinal tract. Our existing CUBIC data suggests that injury depth remains constant.
Minor points:
7) The sham procedure means exposing the liver by removing the scale and cutting the skin, right? What is the survival rate of the sham procedure? Is the survival rate of sham group significantly lower than cryoinjury-induced group?
The Reviewer #1 is correct about the cryoinjury procedure in SHAM samples. SHAM survival is 95% while the injured animal survival is 92.97% (Figure below; n= 444). This analysis shows no significant difference between the groups (unpaired Student's t-test; p-value: 0.5843)
8) The original RNA-seq data, including FASTQ files, should be deposited to NCBI (Gene Expression Omnibus) or other public databases.
We apologize for not submitting our Bulk RNA-seq data to NCBI GEO during the initial submission. The Bulk RNA-seq data can be found under the accession number GSE245878.
Full Revision
Reviewer #2
Major points:
1) While the authors assayed changes in major cell types during liver regeneration in this model, the selection of varying timepoints for analysis and incomplete quantification for all timepoints precludes detailed comparisons that may lead to mechanistic insights. For example, closure of injury area is assayed at 1,3,7,14 dpci but hepatocyte proliferation is measured at 1,3,5,7, 18, 30 dpi. Fibrosis was only assayed at 5 dpi (assume dpi is the same as dpci). Cholangiocytes and endothelial cells are imaged at 1, 3, 7, 30 dpci but no quantification was provided only a single image. Since most changes are occurring at 1-7 dpci, the authors should at least measure the same timepoints from 1-7 dpci for the different cell types so comparisons can be made and conclusions can be drawn. For example, does hepatocyte proliferation, which seem to peak at 5 dpci, happen before endothelial proliferation, which is measured at 3 and 5 dpci but not measured at 5 dpci?
We thank the Reviewer #2 for the comments regarding temporal dynamics of regeneration. In response we have performed Experiment 2 (above). Briefly, this included examination of BECs and ECs at different time points during regeneration (SHAM, 1, 3, 5, and 7 dpci; Figure 6, Supplementary Figure 6L-P).
2) Fibrosis level seems to be highly variable at 5 dpci, which is the only time point measured. If this level of variability is found across all timepoints then this might not be a good model to study the intersection of fibrosis and regeneration. Since the authors have collected animals at all timepoints, it should be fairly straight forward to carry out collagen staining and quantification across different timepoints without the need of additional fish experiments.
We thank the Reviewer #2 for the comments regarding the fibrotic response. In response we have undertaken Experiment 3 (above). This experiment involves quantifying collagen deposition at the different timepoints (SHAM, 1, 3, 5, and 7 dpci; Figure 3I).
3) The lack of quantification of cholangiocytes and endothelial cells makes it difficult to gauge the reproducibility of this model across different animals and experiments.
We thank the Reviewer #2 for the comments regarding the need to quantify ECs and BECs during regeneration. In response we will undertake Experiment 2 (above). Briefly, this included examination of BECs and ECs at different time points during regeneration (SHAM, 1, 3, and 7 dpci; Figure 6 and Supplementary Figure 7).
4) Transcriptomic data analysis/presentation in Figure 7 can be improved. Cannot read any of the gene labels in Figure 7B. Figure 7H should use at least a few different gene markers from each cell type to approximate cell abundance.
We apologise for the inconvenience and have addressed the issue of legibility. We have increased font size on the volcano plots in Figure 7 and incorporate a new analysis with more markers for each cell type in Figure 7H. In addition, we have included the comparison between Bulk RNA-seq ventral samples and contralateral lobe samples, together with further GOenrichment of the samples in Supplementary Figure 8.
Full Revision
Minor:
5) Is "dpi" the same as "dpci"? Please use the same nomenclature throughout manuscript.
We apologise. Dpi means days post-injury and dpci means days post-cryoinjury. Nomenclature has been corrected in revised version of the manuscript.
6) In the mouse PHx model, hepatocytes reach max proliferation (as measured with Ki67/PCNA staining) at 40-48hrs across different labs and experiments, not at 24rs.
We thank the Reviewer #2, we have changed this reference.
7) Zebrafish references are used when the author is talking about mouse PHx model on page 12.
We thank the Reviewer #2, we have changed this reference 7 and 8 to reference the right papers.
Reviewer #3
Major points:
1) It is not clear whether both male and female fish were used in the analyses and whether there is any gender difference in regeneration responses at cellular and molecular levels. The method mentioned that 4-9 month old fish were used in the study. Was there any difference between young and old fish?
We thank the Reviewer #3 for the comments regarding the need to consider age and gender in regeneration studies. Our experiments have been performed on adult male zebrafish. To examine the impact of age and gender on regeneration we have performed Experiment 5 (above). In brief, we have undertaken cryoinjuries in 4 month or 9 month old females and males in the Tg(fabp10a:NLS-mCherry) or Tg(fabp10a: GreenLantern-H2B) background and examine regeneration at 7 dpci (Supplementary Figure 2 J-N and P. We could not detect a significant difference among any of these comparisons. However, we observed a subtle trend with female adult zebrafish showing smaller insult area compared to adult male zebrafish, both at 3 and 7 dpci (Supplementary Figure 2P).
2) The authors detected increased hepatocyte proliferation following cryoinjury. It will be interesting to investigate if activation of stem cells and transdifferentiation of cholangiocytes also contribute to regeneration in this particular model.
We thank the Reviewer #3 for the comments regarding the need to examine the potential involvement of hepatoblasts and transdifferentiating BECs in regeneration following cryoinjury. We have addressed these aspects with Experiment 6 (above). Briefly, we have performed cryoinjuries in adult zebrafish and utilised Anxa4 staining for detection of BECs at SHAM, 1, 3, 5, and 7 dpci (Figure 6A-F). This analysis showed that the were no detectable signs of transdifferentiation between hepatocytes and cholangiocytes (ie: there were no double positive cells (Anxa+/fabp10a:H2B-GreenLantern+ or fabp10a:H2B-mCherry+). Moreover, we performed lineage tracing experiments and found evidence that pre-existing hepatocytes give rise to the regenerating tissue (Supplementary Figure 2 Q-T). Together, these experiments indicate that hepatocytes are responsible for the regeneration of the liver upon cryoinjury without the necessity of BEC transdifferentiation.
3) It will be important to characterize hepatic stellate cells, macrophages, and neutrophils in this model, given their critical and complex roles in liver regeneration. Transgenic reporter lines marking these cell types are available.
We thank the Reviewer #3 for the comments regarding the need to examine hepatic stellate cells (HSCs), macrophages and neutrophils in regeneration following cryoinjury. We have addressed these aspects with Experiment 6 (above). Briefly, we have studied the temporal dynamics of neutrophils upon cryoinjury by immunofluorescent detection of myeloperoxidase (mpx) (Supplementary Figure 4). We have also explored the role of macrophage depletion in response to cryoinjury by performing clodronate injections. We found no significant changes in liver regeneration following clodronate injections (Supplementary Figure 5). To examine the temporal dynamics of HSCs we attempted to use two approaches, namely imaging transgenic lines labelling HSCs (Tg(BAC-pdgfrb:EGFP) and HCR for HSCs (pdgfrb), but unfortunately we were not able to detect HSCs with these approaches.
4) It is not appropriate to call Fli1a + cells liver sinusoidal cells. As far as I know, there is no specific marker for LSEC in zebrafish. Fli1a transgene labels all vascular cells.
We acknowledge this mistaken nomenclature and have made the necessary amendment to use the term endothelial cells (ECs).
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Referee #3
Evidence, reproducibility and clarity
In this manuscript, Sande-Melon et al described a new model for studying liver regeneration in zebrafish that is induced by cryoinjury. They showed that this model induced hepatocyte proliferation, transient fibrosis and inflammation, and regeneration of the biliary and vascular network. Compared to the other established models, such as partial hepatectomy, drug-induced liver injury, the cryoinjury model is easy to perform, consistent, and involves shorter recovery time. Overall, it is a useful tool that complements existing liver regeneration models. The tissue clearing methodology is highly effective.
Main critiques:
- It is not clear whether both male and female fish were used in the analyses and whether there is any gender difference in regeneration responses at cellular and molecular levels. The method mentioned that 4-9 month old fish were used in the study. Was there any difference between young and old fish?
- The authors detected increased hepatocyte proliferation following cryoinjury. It will be interesting to investigate if activation of stem cells and transdifferentiation of cholangiocytes also contribute to regeneration in this particular model.
- It will be important to characterize hepatic stellate cells, macrophages, and neutrophils in this model, given their critical and complex roles in liver regeneration. Transgenic reporter lines marking these cells types are available.
- It is not appropriate to call Fli1a + cells liver sinusoidal cells. As far as I know, there is no specific marker for LSEC in zebrafish. Fli1a transgene labels all vascular cells.
Significance
In this manuscript, Sande-Melon et al described a new model for studying liver regeneration in zebrafish that is induced by cryoinjury. They showed that this model induced hepatocyte proliferation, transient fibrosis and inflammation, and regeneration of the biliary and vascular network. Compared to the other established models, such as partial hepatectomy, drug-induced liver injury, the cryoinjury model is easy to perform, consistent, and involves shorter recovery time. Overall, it is a useful tool that complements existing liver regeneration models. The tissue clearing methodology is highly effective.
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Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.
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Referee #2
Evidence, reproducibility and clarity
In this manuscript titled "Development of a hepatic cryoinjury model to study liver regeneration" by Sande-Melon et al., the authors developed a novel model to study liver regeneration, namely a cryoinjury model in adult zebrafish. The authors described the methodology in detail and extensively characterized the kinetics of liver regeneration in this model, including hepatocyte necrosis/apoptosis, the proliferation of hepatocytes, cholangiocytes, endothelial cells, and infiltration of leukocytes. Most of the characterization were performed by immunostaining for various cell markers, which the authors corroborated with transcriptomic analysis by bulk RNAseq.
Major comments:
- While the authors assayed changes in major cell types during liver regeneration in this model, the selection of varying timepoints for analysis and incomplete quantification for all timepoints precludes detailed comparisons that may lead to mechanistic insights. For example, closure of injury area is assayed at 1,3,7,14 dpci but hepatocyte proliferation is measured at 1,3,5,7, 18, 30 dpi. Fibrosis was only assayed at 5 dpi (assume dpi is the same as dpci). Cholangiocytes and endothelial cells are imaged at 1, 3, 7, 30 dpci but no quantification was provided only a single image. Since most changes are occurring at 1-7 dpci, the authors should at least measure the same timepoints from 1-7 dpci for the different cell types so comparisons can be made and conclusions can be drawn. For example, does hepatocyte proliferation, which seem to peak at 5 dpci, happen before endothelial proliferation, which is measured at 3 and 5 dpci but not measured at 5 dpci?
- Fibrosis level seems to be highly variable at 5dpci, which is the only time point measured. If this level of variability is found across all timepoints then this might not be a good model to study the intersection of fibrosis and regeneration. Since the authors have collected animals at all timepoints, it should be fairly straight forward to carry out collagen staining and quantification across different timepoints without the need of additional fish experiments.
- The lack of quantification of cholangiocytes and endothelial cells makes it difficult to gauge the reproducibility of this model across different animals and experiments.
- Transcriptomic data analysis/presentation in Figure 7 can be improved. Cannot read any of the gene labels in Figure 7B. Figure 7H should use at least a few different gene markers from each cell type to approximate cell abundance.
- OPTIONAL: Sheets of DAPI staining are observed in Figure 6G'. Is this DNA from necrotic cells? Could they make up a neutrophil extracellular trap (NET)-scaffold like structure that covers/protects the injury site from infection? This is purely speculative but might represent an interesting area of study.
- OPTIONAL: To demonstrate this a useful model that complements existing models of liver regeneration, the authors can try to capitalize on the proposed strength of the model to provide some novel insights into liver regeneration. A notable feature of this model that is missing from the PHx and APAP rodent models is the development of robust fibrosis that rapidly resolves within a short time frame, providing an unique opportunity to investigate the potential crosstalk between fibrosis and regeneration that often co-occur in chronic liver disease patients.
Minor comments:
- Is "dpi" the same as "dpci"? Please use the same nomenclature throughout manuscript
- In the mouse PHx model, hepatocytes reach max proliferation (as measured with Ki67/PCNA staining) at 40-48hrs across different labs and experiments, not at 24rs
- Zebrafish references are used when the author is talking about mouse PHx model on page 12
Significance
Mouse 2/3 partial hepatectomy surgery (PHx) is the most frequently used model to study liver regeneration and much has been learnt from this model. However, mouse PHx involving tying off certain lobes of the liver and the inducing a sterile injury, where hepatocyte proliferation and liver regeneration occurs in the absence of significant inflammation and fibrosis. To understand the full complexity of the liver regeneration response, especially against the backdrop of a necroinflammatory environment that characterize chronic liver disease in patients, alternative models to study liver regeneration have been used such as the rodent APAP model of chemically induced injury. Here, Sande-Melon et al. aims to establish such a liver regeneration model in adult zebrafish that would harness the power of the zebrafish model, such as availability of various transgenic lines that label different cell populations, ease of accessibility to imaging techniques, large N number, and the convenience of working with lower complexity model organisms. While such a zebrafish liver regeneration model will be welcomed by the greater research community interested in studying liver regeneration, this paper in its current forms falls short of demonstrating the robustness and reproducibility of this model that would make it a useful research tool.
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